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Kiel University

Faculty of engineering, department of computer science.

Distributed Systems

• Bachelor and Master Theses

Master & Bachelor Theses

Open Master & Bachelor Theses (Last update 31.03.2024)

Below, you see a snapshot of some open thesis topics for bachelor and master students. Typically, we have many more topics, please talk to the team directly to find out more. All topics (or nearly all topics) base on the Distributed Systems and/or Internet of Things courses. If you have not taken both courses, please let us know this when choosing the topics. Also, taking our seminar and project courses is recommended before doing a thesis with us.

For each topic we list a contact person, you find their contact information on the team website. Please don't hesitate to contact them directly when a topic sounds interesting. You will mostly likely know the team members from your courses with us. Also, you are welcome to contact Olaf to have a chat with him about the different topics and how they match to your interests and background. To get a feeling how a thesis with us looks like, please see here for a list of completed thesis . If you plan to propose your own thesis topic or plan to make your thesis with a company, please read this and this first.

We offer you a great work atmosphere, motivated advisors, and a coffee machine (or tea if you prefer). Next to frequent meetings and discussion with your supervisor(s), we each term also organize a series of workshops to guide you throughout the different phases of your thesis, such as design, implementation, evaluation and writing. 

Machine Learning

Seismic event detection & analysis in the sensor plane (b.sc/m.sc, tayyaba ).

A dense sensor network monitors the earth's surface every second to detect events such as earthquakes, volcanos, land sliding, and many more. This data is further used for research purposes, education, and for early warning systems. To capture this data, sensors are deployed underwater, underground, or in rural areas, where there is limited bandwidth. Sending continuous raw data to the cloud or edge devices from the resource constraint sensors, so that they can be analyzed by the complex and computation extensive NN model is costly. We are aiming to bring seismic data analysis to the sensors to filter the metadata from a raw continuous stream of data. For this project, we will only be interested in earthquake detection and analysis. You will be analyzing different off-the-shelf models and techniques to construct a model architecture, and perform a hyperparameter search to get to the optimal model (size wise) with on par detection and analysis accuracy as compared to state-of-the-art models. As NN inference on resource constraint devices can be time-consuming, we will first try to detect earthquake using a non-NN approach with low false negatives and further verify the detection using NN and analyze the data. Language: Python; Tools: Jupyter, nni microsoft; Libraries: Tensorflow, tensorflow lite, keras: End device: nrf52840or similar

• Resource-efficient AI for autonomous underwater data analytics and autonomous underwater robots, in collaboration with Geomar  (M.Sc, Kevin Köser &  Olaf )
• Edge AI: Explore methods for developing Device-agnostic models (B.Sc./M.Sc., Kainat )
• Using different object tracking algorithms to improve object detection efficiency (B.Sc./M.Sc., Momin )

Communication Protocols

Low-latency and adaptive tunnel architecture to control delay-sensitive smart devices (taken) (b.sc./m.sc., birkan / patrick ).

Low-latency live media delivery over QUIC (Taken) (B.Sc./M.Sc., Birkan )

Recent advances such as Media over QUIC or RTP over QUIC made delivering low-latency media over QUIC connections possible. Video encoding creates segments that have dependencies among frame types. Segmented delivery of video using separate streams or datagrams with priorities can lower the overall latency of the system. This thesis aims to explore, evaluate, and extend suggested protocols.

Multipath communication over QUIC with Stream Scheduling (M.Sc., Birkan )

The number of devices with multiple connections (e.g. LTE, 5G, Wi-Fi) is increasing. Recent extensions of QUIC, namely Multipath QUIC, enable users to exploit the available channels to increase total bandwidth, maintain high reliability, and provide low latency depending on the stream scheduler algorithm used. This thesis aims to explore, evaluate, and extend suggested algorithms, possibly using AI-based models.

QUIC-based VPN tunnel to proxy IP packets (Taken) (B.Sc./M.Sc., Birkan )

Internship / master thesis.

•  Internship and/or Master Thesis on embedded AI engineering  with Bosch  (M.Sc, Olaf )

Localization

• Belief-Propagation for Distributed Anchor Calibration in UWB Localization Systems (M.Sc., Patrick )
• UWB Drift Inspection: CFO vs. DS-TWR (M.Sc., Patrick )
• Graph Neural Networks for Localization (M.Sc., Patrick )
• Error estimation of TWR and TDoA with alternative calculation with moving entities and variable response delays (M.Sc., Patrick )

Propose own topic :  Firstly, a thesis topic should address a research question and not "just" implement a product. Furer, As per official requirement, we - as a research group - define a thesis topic. Suggesting your own, custom topic is possible, if you have a strong track record in a specific domain beyond course work such as contributions to an open-source project. And, of course, it should be a good research question and someone of us needs to have the time and interest to supervise it. Thus, the closer the topic is to our ongoing research, the better. To propose your own topic, you should contact us (=Olaf) at least 2, better 3, months before the planned start date, so we can work with you on defining and refining your thesis topic. 

If you want to do your thesis at a company: Firstly, a thesis topic should address a research question and not "just" implement a product (or part of a product) for a company. Further, as per official requirement, we - as a research group - define a thesis topic. Thus, doing a thesis at a company is possible, but not in the way that the company just defines a topic. However, it is possible that we and the company define a topic that addresses a research question together. For this, we strongly suggest that the company actually does research and moreover that the advisor in the company has a PhD and/or experience in advising bachelor and master theses. And, of course, it should be a good research question and someone of us needs to have the time and interest to supervise it. Thus, the closer the topic is to our ongoing research, the better. To do your thesis at a company, you should contact us (=Olaf) at least 2, better 3, months before the planned start date, so we can work with the company and you on defining and refining your thesis topic.

Communication and Distributed Systems

Thesis topics, specific topics.

The research group "Communication and Distributed Systems" releases occasionally specific topics for a BSc or MSc thesis. The currently available ones can be found listed below. If you are interested in undertaking some topic or for any related clarifications please contact the related research assistant.

General Topics

Additionally, we offer topics for Bachelor and Master theses that are generally in line with the group’s ongoing conducted research. The general directions are shown below along with the involved research assistant. For more insight regarding their research interests, you can also consider their  publications . If you want to define a thesis topic please come in contact with them.

Computer Science Thesis Topics

This page provides a comprehensive list of computer science thesis topics , carefully curated to support students in identifying and selecting innovative and relevant areas for their academic research. Whether you are at the beginning of your research journey or are seeking a specific area to explore further, this guide aims to serve as an essential resource. With an expansive array of topics spread across various sub-disciplines of computer science, this list is designed to meet a diverse range of interests and academic needs. From the complexities of artificial intelligence to the intricate designs of web development, each category is equipped with 40 specific topics, offering a breadth of possibilities to inspire your next big thesis project. Explore our guide to find not only a topic that resonates with your academic ambitions but also one that has the potential to contribute significantly to the field of computer science.

1000 Computer Science Thesis Topics and Ideas

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• Internet Of Things (IoT) Thesis Topics

Machine Learning Thesis Topics

Neural networks thesis topics, programming thesis topics, quantum computing thesis topics, robotics thesis topics, software engineering thesis topics, web development thesis topics.

• Ethical Implications of AI in Decision-Making Processes
• The Role of AI in Personalized Medicine: Opportunities and Challenges
• Advances in AI-Driven Predictive Analytics in Retail
• AI in Autonomous Vehicles: Safety, Regulation, and Technology Integration
• Natural Language Processing: Improving Human-Machine Interaction
• The Future of AI in Cybersecurity: Threats and Defenses
• Machine Learning Algorithms for Real-Time Data Processing
• AI and the Internet of Things: Transforming Smart Home Technology
• The Impact of Deep Learning on Image Recognition Technologies
• Reinforcement Learning: Applications in Robotics and Automation
• AI in Finance: Algorithmic Trading and Risk Assessment
• Bias and Fairness in AI: Addressing Socio-Technical Challenges
• The Evolution of AI in Education: Customized Learning Experiences
• AI for Environmental Conservation: Tracking and Predictive Analysis
• The Role of Artificial Neural Networks in Weather Forecasting
• AI in Agriculture: Predictive Analytics for Crop and Soil Management
• Emotional Recognition AI: Implications for Mental Health Assessments
• AI in Space Exploration: Autonomous Rovers and Mission Planning
• Enhancing User Experience with AI in Video Games
• AI-Powered Virtual Assistants: Trends, Effectiveness, and User Trust
• The Integration of AI in Traditional Industries: Case Studies
• Generative AI Models in Art and Creativity
• AI in LegalTech: Document Analysis and Litigation Prediction
• Healthcare Diagnostics: AI Applications in Radiology and Pathology
• AI and Blockchain: Enhancing Security in Decentralized Systems
• Ethics of AI in Surveillance: Privacy vs. Security
• AI in E-commerce: Personalization Engines and Customer Behavior Analysis
• The Future of AI in Telecommunications: Network Optimization and Service Delivery
• AI in Manufacturing: Predictive Maintenance and Quality Control
• Challenges of AI in Elderly Care: Ethical Considerations and Technological Solutions
• The Role of AI in Public Safety and Emergency Response
• AI for Content Creation: Impact on Media and Journalism
• AI-Driven Algorithms for Efficient Energy Management
• The Role of AI in Cultural Heritage Preservation
• AI and the Future of Public Transport: Optimization and Management
• Enhancing Sports Performance with AI-Based Analytics
• AI in Human Resources: Automating Recruitment and Employee Management
• Real-Time Translation AI: Breaking Language Barriers
• AI in Mental Health: Tools for Monitoring and Therapy Assistance
• The Future of AI Governance: Regulation and Standardization
• AR in Medical Training and Surgery Simulation
• The Impact of Augmented Reality in Retail: Enhancing Consumer Experience
• Augmented Reality for Enhanced Navigation Systems
• AR Applications in Maintenance and Repair in Industrial Settings
• The Role of AR in Enhancing Online Education
• Augmented Reality in Cultural Heritage: Interactive Visitor Experiences
• Developing AR Tools for Improved Sports Coaching and Training
• Privacy and Security Challenges in Augmented Reality Applications
• The Future of AR in Advertising: Engagement and Measurement
• User Interface Design for AR: Principles and Best Practices
• AR in Automotive Industry: Enhancing Driving Experience and Safety
• Augmented Reality for Emergency Response Training
• AR and IoT: Converging Technologies for Smart Environments
• Enhancing Physical Rehabilitation with AR Applications
• The Role of AR in Enhancing Public Safety and Awareness
• Augmented Reality in Fashion: Virtual Fitting and Personalized Shopping
• AR for Environmental Education: Interactive and Immersive Learning
• The Use of AR in Building and Architecture Planning
• AR in the Entertainment Industry: Games and Live Events
• Implementing AR in Museums and Art Galleries for Interactive Learning
• Augmented Reality for Real Estate: Virtual Tours and Property Visualization
• AR in Consumer Electronics: Integration in Smart Devices
• The Development of AR Applications for Children’s Education
• AR for Enhancing User Engagement in Social Media Platforms
• The Application of AR in Field Service Management
• Augmented Reality for Disaster Management and Risk Assessment
• Challenges of Content Creation for Augmented Reality
• Future Trends in AR Hardware: Wearables and Beyond
• Legal and Ethical Considerations of Augmented Reality Technology
• AR in Space Exploration: Tools for Simulation and Training
• Interactive Shopping Experiences with AR: The Future of Retail
• AR in Wildlife Conservation: Educational Tools and Awareness
• The Impact of AR on the Publishing Industry: Interactive Books and Magazines
• Augmented Reality and Its Role in Automotive Manufacturing
• AR for Job Training: Bridging the Skill Gap in Various Industries
• The Role of AR in Therapy: New Frontiers in Mental Health Treatment
• The Future of Augmented Reality in Sports Broadcasting
• AR as a Tool for Enhancing Public Art Installations
• Augmented Reality in the Tourism Industry: Personalized Travel Experiences
• The Use of AR in Security Training: Realistic and Safe Simulations
• The Role of Big Data in Improving Healthcare Outcomes
• Big Data and Its Impact on Consumer Behavior Analysis
• Privacy Concerns in Big Data: Ethical and Legal Implications
• The Application of Big Data in Predictive Maintenance for Manufacturing
• Real-Time Big Data Processing: Tools and Techniques
• Big Data in Financial Services: Fraud Detection and Risk Management
• The Evolution of Big Data Technologies: From Hadoop to Spark
• Big Data Visualization: Techniques for Effective Communication of Insights
• The Integration of Big Data and Artificial Intelligence
• Big Data in Smart Cities: Applications in Traffic Management and Energy Use
• Enhancing Supply Chain Efficiency with Big Data Analytics
• Big Data in Sports Analytics: Improving Team Performance and Fan Engagement
• The Role of Big Data in Environmental Monitoring and Sustainability
• Big Data and Social Media: Analyzing Sentiments and Trends
• Scalability Challenges in Big Data Systems
• The Future of Big Data in Retail: Personalization and Customer Experience
• Big Data in Education: Customized Learning Paths and Student Performance Analysis
• Privacy-Preserving Techniques in Big Data
• Big Data in Public Health: Epidemiology and Disease Surveillance
• The Impact of Big Data on Insurance: Tailored Policies and Pricing
• Edge Computing in Big Data: Processing at the Source
• Big Data and the Internet of Things: Generating Insights from IoT Data
• Cloud-Based Big Data Analytics: Opportunities and Challenges
• Big Data Governance: Policies, Standards, and Management
• The Role of Big Data in Crisis Management and Response
• Machine Learning with Big Data: Building Predictive Models
• Big Data in Agriculture: Precision Farming and Yield Optimization
• The Ethics of Big Data in Research: Consent and Anonymity
• Cross-Domain Big Data Integration: Challenges and Solutions
• Big Data and Cybersecurity: Threat Detection and Prevention Strategies
• Real-Time Streaming Analytics in Big Data
• Big Data in the Media Industry: Content Optimization and Viewer Insights
• The Impact of GDPR on Big Data Practices
• Quantum Computing and Big Data: Future Prospects
• Big Data in E-Commerce: Optimizing Logistics and Inventory Management
• Big Data Talent: Education and Skill Development for Data Scientists
• The Role of Big Data in Political Campaigns and Voting Behavior Analysis
• Big Data and Mental Health: Analyzing Patterns for Better Interventions
• Big Data in Genomics and Personalized Medicine
• The Future of Big Data in Autonomous Driving Technologies
• The Role of Bioinformatics in Personalized Medicine
• Next-Generation Sequencing Data Analysis: Challenges and Opportunities
• Bioinformatics and the Study of Genetic Diseases
• Computational Models for Understanding Protein Structure and Function
• Bioinformatics in Drug Discovery and Development
• The Impact of Big Data on Bioinformatics: Data Management and Analysis
• Machine Learning Applications in Bioinformatics
• Bioinformatics Approaches for Cancer Genomics
• The Development of Bioinformatics Tools for Metagenomics Analysis
• Ethical Considerations in Bioinformatics: Data Sharing and Privacy
• The Role of Bioinformatics in Agricultural Biotechnology
• Bioinformatics and Viral Evolution: Tracking Pathogens and Outbreaks
• The Integration of Bioinformatics and Systems Biology
• Bioinformatics in Neuroscience: Mapping the Brain
• The Future of Bioinformatics in Non-Invasive Prenatal Testing
• Bioinformatics and the Human Microbiome: Health Implications
• The Application of Artificial Intelligence in Bioinformatics
• Structural Bioinformatics: Computational Techniques for Molecular Modeling
• Comparative Genomics: Insights into Evolution and Function
• Bioinformatics in Immunology: Vaccine Design and Immune Response Analysis
• High-Performance Computing in Bioinformatics
• The Challenge of Proteomics in Bioinformatics
• RNA-Seq Data Analysis and Interpretation
• Cloud Computing Solutions for Bioinformatics Data
• Computational Epigenetics: DNA Methylation and Histone Modification Analysis
• Bioinformatics in Ecology: Biodiversity and Conservation Genetics
• The Role of Bioinformatics in Forensic Analysis
• Mobile Apps and Tools for Bioinformatics Research
• Bioinformatics and Public Health: Epidemiological Studies
• The Use of Bioinformatics in Clinical Diagnostics
• Genetic Algorithms in Bioinformatics
• Bioinformatics for Aging Research: Understanding the Mechanisms of Aging
• Data Visualization Techniques in Bioinformatics
• Bioinformatics and the Development of Therapeutic Antibodies
• The Role of Bioinformatics in Stem Cell Research
• Bioinformatics and Cardiovascular Diseases: Genomic Insights
• The Impact of Machine Learning on Functional Genomics in Bioinformatics
• Bioinformatics in Dental Research: Genetic Links to Oral Diseases
• The Future of CRISPR Technology and Bioinformatics
• Bioinformatics and Nutrition: Genomic Insights into Diet and Health
• Blockchain for Enhancing Cybersecurity in Various Industries
• The Impact of Blockchain on Supply Chain Transparency
• Blockchain in Healthcare: Patient Data Management and Security
• The Application of Blockchain in Voting Systems
• Blockchain and Smart Contracts: Legal Implications and Applications
• Cryptocurrencies: Market Trends and the Future of Digital Finance
• Blockchain in Real Estate: Improving Property and Land Registration
• The Role of Blockchain in Managing Digital Identities
• Blockchain for Intellectual Property Management
• Energy Sector Innovations: Blockchain for Renewable Energy Distribution
• Blockchain and the Future of Public Sector Operations
• The Impact of Blockchain on Cross-Border Payments
• Blockchain for Non-Fungible Tokens (NFTs): Applications in Art and Media
• Privacy Issues in Blockchain Applications
• Blockchain in the Automotive Industry: Supply Chain and Beyond
• Decentralized Finance (DeFi): Opportunities and Challenges
• The Role of Blockchain in Combating Counterfeiting and Fraud
• Blockchain for Sustainable Environmental Practices
• The Integration of Artificial Intelligence with Blockchain
• Blockchain Education: Curriculum Development and Training Needs
• Blockchain in the Music Industry: Rights Management and Revenue Distribution
• The Challenges of Blockchain Scalability and Performance Optimization
• The Future of Blockchain in the Telecommunications Industry
• Blockchain and Consumer Data Privacy: A New Paradigm
• Blockchain for Disaster Recovery and Business Continuity
• Blockchain in the Charity and Non-Profit Sectors
• Quantum Resistance in Blockchain: Preparing for the Quantum Era
• Blockchain and Its Impact on Traditional Banking and Financial Institutions
• Legal and Regulatory Challenges Facing Blockchain Technology
• Blockchain for Improved Logistics and Freight Management
• The Role of Blockchain in the Evolution of the Internet of Things (IoT)
• Blockchain and the Future of Gaming: Transparency and Fair Play
• Blockchain for Academic Credentials Verification
• The Application of Blockchain in the Insurance Industry
• Blockchain and the Future of Content Creation and Distribution
• Blockchain for Enhancing Data Integrity in Scientific Research
• The Impact of Blockchain on Human Resources: Employee Verification and Salary Payments
• Blockchain and the Future of Retail: Customer Loyalty Programs and Inventory Management
• Blockchain and Industrial Automation: Trust and Efficiency
• Blockchain for Digital Marketing: Transparency and Consumer Engagement
• Multi-Cloud Strategies: Optimization and Security Challenges
• Advances in Cloud Computing Architectures for Scalable Applications
• Edge Computing: Extending the Reach of Cloud Services
• Cloud Security: Novel Approaches to Data Encryption and Threat Mitigation
• The Impact of Serverless Computing on Software Development Lifecycle
• Cloud Computing and Sustainability: Energy-Efficient Data Centers
• Cloud Service Models: Comparative Analysis of IaaS, PaaS, and SaaS
• Cloud Migration Strategies: Best Practices and Common Pitfalls
• The Role of Cloud Computing in Big Data Analytics
• Implementing AI and Machine Learning Workloads on Cloud Platforms
• Hybrid Cloud Environments: Management Tools and Techniques
• Cloud Computing in Healthcare: Compliance, Security, and Use Cases
• Cost-Effective Cloud Solutions for Small and Medium Enterprises (SMEs)
• The Evolution of Cloud Storage Solutions: Trends and Technologies
• Cloud-Based Disaster Recovery Solutions: Design and Reliability
• Blockchain in Cloud Services: Enhancing Transparency and Trust
• Cloud Networking: Managing Connectivity and Traffic in Cloud Environments
• Cloud Governance: Managing Compliance and Operational Risks
• The Future of Cloud Computing: Quantum Computing Integration
• Performance Benchmarking of Cloud Services Across Different Providers
• Privacy Preservation in Cloud Environments
• Cloud Computing in Education: Virtual Classrooms and Learning Management Systems
• Automation in Cloud Deployments: Tools and Strategies
• Cloud Auditing and Monitoring Techniques
• Mobile Cloud Computing: Challenges and Future Trends
• The Role of Cloud Computing in Digital Media Production and Distribution
• Security Risks in Multi-Tenancy Cloud Environments
• Cloud Computing for Scientific Research: Enabling Complex Simulations
• The Impact of 5G on Cloud Computing Services
• Federated Clouds: Building Collaborative Cloud Environments
• Managing Software Dependencies in Cloud Applications
• The Economics of Cloud Computing: Cost Models and Pricing Strategies
• Cloud Computing in Government: Security Protocols and Citizen Services
• Cloud Access Security Brokers (CASBs): Security Enforcement Points
• DevOps in the Cloud: Strategies for Continuous Integration and Deployment
• Predictive Analytics in Cloud Computing
• The Role of Cloud Computing in IoT Deployment
• Implementing Robust Cybersecurity Measures in Cloud Architecture
• Cloud Computing in the Financial Sector: Handling Sensitive Data
• Future Trends in Cloud Computing: The Role of AI in Cloud Optimization
• Advances in Microprocessor Design and Architecture
• FPGA-Based Design: Innovations and Applications
• The Role of Embedded Systems in Consumer Electronics
• Quantum Computing: Hardware Development and Challenges
• High-Performance Computing (HPC) and Parallel Processing
• Design and Analysis of Computer Networks
• Cyber-Physical Systems: Design, Analysis, and Security
• The Impact of Nanotechnology on Computer Hardware
• Wireless Sensor Networks: Design and Optimization
• Cryptographic Hardware: Implementations and Security Evaluations
• Machine Learning Techniques for Hardware Optimization
• Hardware for Artificial Intelligence: GPUs vs. TPUs
• Energy-Efficient Hardware Designs for Sustainable Computing
• Security Aspects of Mobile and Ubiquitous Computing
• Advanced Algorithms for Computer-Aided Design (CAD) of VLSI
• Signal Processing in Communication Systems
• The Development of Wearable Computing Devices
• Computer Hardware Testing: Techniques and Tools
• The Role of Hardware in Network Security
• The Evolution of Interface Designs in Consumer Electronics
• Biometric Systems: Hardware and Software Integration
• The Integration of IoT Devices in Smart Environments
• Electronic Design Automation (EDA) Tools and Methodologies
• Robotics: Hardware Design and Control Systems
• Hardware Accelerators for Deep Learning Applications
• Developments in Non-Volatile Memory Technologies
• The Future of Computer Hardware in the Era of Quantum Computing
• Hardware Solutions for Data Storage and Retrieval
• Power Management Techniques in Embedded Systems
• Challenges in Designing Multi-Core Processors
• System on Chip (SoC) Design Trends and Challenges
• The Role of Computer Engineering in Aerospace Technology
• Real-Time Systems: Design and Implementation Challenges
• Hardware Support for Virtualization Technology
• Advances in Computer Graphics Hardware
• The Impact of 5G Technology on Mobile Computing Hardware
• Environmental Impact Assessment of Computer Hardware Production
• Security Vulnerabilities in Modern Microprocessors
• Computer Hardware Innovations in the Automotive Industry
• The Role of Computer Engineering in Medical Device Technology
• Deep Learning Approaches to Object Recognition
• Real-Time Image Processing for Autonomous Vehicles
• Computer Vision in Robotic Surgery: Techniques and Challenges
• Facial Recognition Technology: Innovations and Privacy Concerns
• Machine Vision in Industrial Automation and Quality Control
• 3D Reconstruction Techniques in Computer Vision
• Enhancing Sports Analytics with Computer Vision
• Augmented Reality: Integrating Computer Vision for Immersive Experiences
• Computer Vision for Environmental Monitoring
• Thermal Imaging and Its Applications in Computer Vision
• Computer Vision in Retail: Customer Behavior and Store Layout Optimization
• Motion Detection and Tracking in Security Systems
• The Role of Computer Vision in Content Moderation on Social Media
• Gesture Recognition: Methods and Applications
• Computer Vision in Agriculture: Pest Detection and Crop Analysis
• Advances in Medical Imaging: Machine Learning and Computer Vision
• Scene Understanding and Contextual Inference in Images
• The Development of Vision-Based Autonomous Drones
• Optical Character Recognition (OCR): Latest Techniques and Applications
• The Impact of Computer Vision on Virtual Reality Experiences
• Biometrics: Enhancing Security Systems with Computer Vision
• Computer Vision for Wildlife Conservation: Species Recognition and Behavior Analysis
• Underwater Image Processing: Challenges and Techniques
• Video Surveillance: The Evolution of Algorithmic Approaches
• Advanced Driver-Assistance Systems (ADAS): Leveraging Computer Vision
• Computational Photography: Enhancing Image Capture Techniques
• The Integration of AI in Computer Vision: Ethical and Technical Considerations
• Computer Vision in the Gaming Industry: From Design to Interaction
• The Future of Computer Vision in Smart Cities
• Pattern Recognition in Historical Document Analysis
• The Role of Computer Vision in the Manufacturing of Customized Products
• Enhancing Accessibility with Computer Vision: Tools for the Visually Impaired
• The Use of Computer Vision in Behavioral Research
• Predictive Analytics with Computer Vision in Sports
• Image Synthesis with Generative Adversarial Networks (GANs)
• The Use of Computer Vision in Remote Sensing
• Real-Time Video Analytics for Public Safety
• The Role of Computer Vision in Telemedicine
• Computer Vision and the Internet of Things (IoT): A Synergistic Approach
• Future Trends in Computer Vision: Quantum Computing and Beyond
• Advances in Cryptography: Post-Quantum Cryptosystems
• Artificial Intelligence in Cybersecurity: Threat Detection and Response
• Blockchain for Enhanced Security in Distributed Networks
• The Impact of IoT on Cybersecurity: Vulnerabilities and Solutions
• Cybersecurity in Cloud Computing: Best Practices and Tools
• Ethical Hacking: Techniques and Ethical Implications
• The Role of Human Factors in Cybersecurity Breaches
• Privacy-preserving Technologies in an Age of Surveillance
• The Evolution of Ransomware Attacks and Defense Strategies
• Secure Software Development: Integrating Security in DevOps (DevSecOps)
• Cybersecurity in Critical Infrastructure: Challenges and Innovations
• The Future of Biometric Security Systems
• Cyber Warfare: State-sponsored Attacks and Defense Mechanisms
• The Role of Cybersecurity in Protecting Digital Identities
• Social Engineering Attacks: Prevention and Countermeasures
• Mobile Security: Protecting Against Malware and Exploits
• Wireless Network Security: Protocols and Practices
• Data Breaches: Analysis, Consequences, and Mitigation
• The Ethics of Cybersecurity: Balancing Privacy and Security
• Regulatory Compliance and Cybersecurity: GDPR and Beyond
• The Impact of 5G Technology on Cybersecurity
• The Role of Machine Learning in Cyber Threat Intelligence
• Cybersecurity in Automotive Systems: Challenges in a Connected Environment
• The Use of Virtual Reality for Cybersecurity Training and Simulation
• Advanced Persistent Threats (APT): Detection and Response
• Cybersecurity for Smart Cities: Challenges and Solutions
• Deep Learning Applications in Malware Detection
• The Role of Cybersecurity in Healthcare: Protecting Patient Data
• Supply Chain Cybersecurity: Identifying Risks and Solutions
• Endpoint Security: Trends, Challenges, and Future Directions
• Forensic Techniques in Cybersecurity: Tracking and Analyzing Cyber Crimes
• The Influence of International Law on Cyber Operations
• Protecting Financial Institutions from Cyber Frauds and Attacks
• Quantum Computing and Its Implications for Cybersecurity
• Cybersecurity and Remote Work: Emerging Threats and Strategies
• IoT Security in Industrial Applications
• Cyber Insurance: Risk Assessment and Management
• Security Challenges in Edge Computing Environments
• Anomaly Detection in Network Security Using AI Techniques
• Securing the Software Supply Chain in Application Development
• Big Data Analytics: Techniques and Applications in Real-time
• Machine Learning Algorithms for Predictive Analytics
• Data Science in Healthcare: Improving Patient Outcomes with Predictive Models
• The Role of Data Science in Financial Market Predictions
• Natural Language Processing: Emerging Trends and Applications
• Data Visualization Tools and Techniques for Enhanced Business Intelligence
• Ethics in Data Science: Privacy, Fairness, and Transparency
• The Use of Data Science in Environmental Science for Sustainability Studies
• The Impact of Data Science on Social Media Marketing Strategies
• Data Mining Techniques for Detecting Patterns in Large Datasets
• AI and Data Science: Synergies and Future Prospects
• Reinforcement Learning: Applications and Challenges in Data Science
• The Role of Data Science in E-commerce Personalization
• Predictive Maintenance in Manufacturing Through Data Science
• The Evolution of Recommendation Systems in Streaming Services
• Real-time Data Processing with Stream Analytics
• Deep Learning for Image and Video Analysis
• Data Governance in Big Data Analytics
• Text Analytics and Sentiment Analysis for Customer Feedback
• Fraud Detection in Banking and Insurance Using Data Science
• The Integration of IoT Data in Data Science Models
• The Future of Data Science in Quantum Computing
• Data Science for Public Health: Epidemic Outbreak Prediction
• Sports Analytics: Performance Improvement and Injury Prevention
• Data Science in Retail: Inventory Management and Customer Journey Analysis
• Data Science in Smart Cities: Traffic and Urban Planning
• The Use of Blockchain in Data Security and Integrity
• Geospatial Analysis for Environmental Monitoring
• Time Series Analysis in Economic Forecasting
• Data Science in Education: Analyzing Trends and Student Performance
• Predictive Policing: Data Science in Law Enforcement
• Data Science in Agriculture: Yield Prediction and Soil Health
• Computational Social Science: Analyzing Societal Trends
• Data Science in Energy Sector: Consumption and Optimization
• Personalization Technologies in Healthcare Through Data Science
• The Role of Data Science in Content Creation and Media
• Anomaly Detection in Network Security Using Data Science Techniques
• The Future of Autonomous Vehicles: Data Science-Driven Innovations
• Multimodal Data Fusion Techniques in Data Science
• Scalability Challenges in Data Science Projects
• The Role of Digital Transformation in Business Model Innovation
• The Impact of Digital Technologies on Customer Experience
• Digital Transformation in the Banking Sector: Trends and Challenges
• The Use of AI and Robotics in Digital Transformation of Manufacturing
• Digital Transformation in Healthcare: Telemedicine and Beyond
• The Influence of Big Data on Decision-Making Processes in Corporations
• Blockchain as a Driver for Transparency in Digital Transformation
• The Role of IoT in Enhancing Operational Efficiency in Industries
• Digital Marketing Strategies: SEO, Content, and Social Media
• The Integration of Cyber-Physical Systems in Industrial Automation
• Digital Transformation in Education: Virtual Learning Environments
• Smart Cities: The Role of Digital Technologies in Urban Planning
• Digital Transformation in the Retail Sector: E-commerce Evolution
• The Future of Work: Impact of Digital Transformation on Workplaces
• Cybersecurity Challenges in a Digitally Transformed World
• Mobile Technologies and Their Impact on Digital Transformation
• The Role of Digital Twin Technology in Industry 4.0
• Digital Transformation in the Public Sector: E-Government Services
• Data Privacy and Security in the Age of Digital Transformation
• Digital Transformation in the Energy Sector: Smart Grids and Renewable Energy
• The Use of Augmented Reality in Training and Development
• The Role of Virtual Reality in Real Estate and Architecture
• Digital Transformation and Sustainability: Reducing Environmental Footprint
• The Role of Digital Transformation in Supply Chain Optimization
• Digital Transformation in Agriculture: IoT and Smart Farming
• The Impact of 5G on Digital Transformation Initiatives
• The Influence of Digital Transformation on Media and Entertainment
• Digital Transformation in Insurance: Telematics and Risk Assessment
• The Role of AI in Enhancing Customer Service Operations
• The Future of Digital Transformation: Trends and Predictions
• Digital Transformation and Corporate Governance
• The Role of Leadership in Driving Digital Transformation
• Digital Transformation in Non-Profit Organizations: Challenges and Benefits
• The Economic Implications of Digital Transformation
• The Cultural Impact of Digital Transformation on Organizations
• Digital Transformation in Transportation: Logistics and Fleet Management
• User Experience (UX) Design in Digital Transformation
• The Role of Digital Transformation in Crisis Management
• Digital Transformation and Human Resource Management
• Implementing Change Management in Digital Transformation Projects
• Scalability Challenges in Distributed Systems: Solutions and Strategies
• Blockchain Technology: Enhancing Security and Transparency in Distributed Networks
• The Role of Edge Computing in Distributed Systems
• Designing Fault-Tolerant Systems in Distributed Networks
• The Impact of 5G Technology on Distributed Network Architectures
• Machine Learning Algorithms for Network Traffic Analysis
• Load Balancing Techniques in Distributed Computing
• The Use of Distributed Ledger Technology Beyond Cryptocurrencies
• Network Function Virtualization (NFV) and Its Impact on Service Providers
• The Evolution of Software-Defined Networking (SDN) in Enterprise Environments
• Implementing Robust Cybersecurity Measures in Distributed Systems
• Quantum Computing: Implications for Network Security in Distributed Systems
• Peer-to-Peer Network Protocols and Their Applications
• The Internet of Things (IoT): Network Challenges and Communication Protocols
• Real-Time Data Processing in Distributed Sensor Networks
• The Role of Artificial Intelligence in Optimizing Network Operations
• Privacy and Data Protection Strategies in Distributed Systems
• The Future of Distributed Computing in Cloud Environments
• Energy Efficiency in Distributed Network Systems
• Wireless Mesh Networks: Design, Challenges, and Applications
• Multi-Access Edge Computing (MEC): Use Cases and Deployment Challenges
• Consensus Algorithms in Distributed Systems: From Blockchain to New Applications
• The Use of Containers and Microservices in Building Scalable Applications
• Network Slicing for 5G: Opportunities and Challenges
• The Role of Distributed Systems in Big Data Analytics
• Managing Data Consistency in Distributed Databases
• The Impact of Distributed Systems on Digital Transformation Strategies
• Augmented Reality over Distributed Networks: Performance and Scalability Issues
• The Application of Distributed Systems in Smart Grid Technology
• Developing Distributed Applications Using Serverless Architectures
• The Challenges of Implementing IPv6 in Distributed Networks
• Distributed Systems for Disaster Recovery: Design and Implementation
• The Use of Virtual Reality in Distributed Network Environments
• Security Protocols for Ad Hoc Networks in Emergency Situations
• The Role of Distributed Networks in Enhancing Mobile Broadband Services
• Next-Generation Protocols for Enhanced Network Reliability and Performance
• The Application of Blockchain in Securing Distributed IoT Networks
• Dynamic Resource Allocation Strategies in Distributed Systems
• The Integration of Distributed Systems with Existing IT Infrastructure
• The Future of Autonomous Systems in Distributed Networking
• The Integration of GIS with Remote Sensing for Environmental Monitoring
• GIS in Urban Planning: Techniques for Sustainable Development
• The Role of GIS in Disaster Management and Response Strategies
• Real-Time GIS Applications in Traffic Management and Route Planning
• The Use of GIS in Water Resource Management
• GIS and Public Health: Tracking Epidemics and Healthcare Access
• Advances in 3D GIS: Technologies and Applications
• GIS in Agricultural Management: Precision Farming Techniques
• The Impact of GIS on Biodiversity Conservation Efforts
• Spatial Data Analysis for Crime Pattern Detection and Prevention
• GIS in Renewable Energy: Site Selection and Resource Management
• The Role of GIS in Historical Research and Archaeology
• GIS and Machine Learning: Integrating Spatial Analysis with Predictive Models
• Cloud Computing and GIS: Enhancing Accessibility and Data Processing
• The Application of GIS in Managing Public Transportation Systems
• GIS in Real Estate: Market Analysis and Property Valuation
• The Use of GIS for Environmental Impact Assessments
• Mobile GIS Applications: Development and Usage Trends
• GIS and Its Role in Smart City Initiatives
• Privacy Issues in the Use of Geographic Information Systems
• GIS in Forest Management: Monitoring and Conservation Strategies
• The Impact of GIS on Tourism: Enhancing Visitor Experiences through Technology
• GIS in the Insurance Industry: Risk Assessment and Policy Design
• The Development of Participatory GIS (PGIS) for Community Engagement
• GIS in Coastal Management: Addressing Erosion and Flood Risks
• Geospatial Analytics in Retail: Optimizing Location and Consumer Insights
• GIS for Wildlife Tracking and Habitat Analysis
• The Use of GIS in Climate Change Studies
• GIS and Social Media: Analyzing Spatial Trends from User Data
• The Future of GIS: Augmented Reality and Virtual Reality Applications
• GIS in Education: Tools for Teaching Geographic Concepts
• The Role of GIS in Land Use Planning and Zoning
• GIS for Emergency Medical Services: Optimizing Response Times
• Open Source GIS Software: Development and Community Contributions
• GIS and the Internet of Things (IoT): Converging Technologies for Advanced Monitoring
• GIS for Mineral Exploration: Techniques and Applications
• The Role of GIS in Municipal Management and Services
• GIS and Drone Technology: A Synergy for Precision Mapping
• Spatial Statistics in GIS: Techniques for Advanced Data Analysis
• Future Trends in GIS: The Integration of AI for Smarter Solutions
• The Evolution of User Interface (UI) Design: From Desktop to Mobile and Beyond
• The Role of HCI in Enhancing Accessibility for Disabled Users
• Virtual Reality (VR) and Augmented Reality (AR) in HCI: New Dimensions of Interaction
• The Impact of HCI on User Experience (UX) in Software Applications
• Cognitive Aspects of HCI: Understanding User Perception and Behavior
• HCI and the Internet of Things (IoT): Designing Interactive Smart Devices
• The Use of Biometrics in HCI: Security and Usability Concerns
• HCI in Educational Technologies: Enhancing Learning through Interaction
• Emotional Recognition and Its Application in HCI
• The Role of HCI in Wearable Technology: Design and Functionality
• Advanced Techniques in Voice User Interfaces (VUIs)
• The Impact of HCI on Social Media Interaction Patterns
• HCI in Healthcare: Designing User-Friendly Medical Devices and Software
• HCI and Gaming: Enhancing Player Engagement and Experience
• The Use of HCI in Robotic Systems: Improving Human-Robot Interaction
• The Influence of HCI on E-commerce: Optimizing User Journeys and Conversions
• HCI in Smart Homes: Interaction Design for Automated Environments
• Multimodal Interaction: Integrating Touch, Voice, and Gesture in HCI
• HCI and Aging: Designing Technology for Older Adults
• The Role of HCI in Virtual Teams: Tools and Strategies for Collaboration
• User-Centered Design: HCI Strategies for Developing User-Focused Software
• HCI Research Methodologies: Experimental Design and User Studies
• The Application of HCI Principles in the Design of Public Kiosks
• The Future of HCI: Integrating Artificial Intelligence for Smarter Interfaces
• HCI in Transportation: Designing User Interfaces for Autonomous Vehicles
• Privacy and Ethics in HCI: Addressing User Data Security
• HCI and Environmental Sustainability: Promoting Eco-Friendly Behaviors
• Adaptive Interfaces: HCI Design for Personalized User Experiences
• The Role of HCI in Content Creation: Tools for Artists and Designers
• HCI for Crisis Management: Designing Systems for Emergency Use
• The Use of HCI in Sports Technology: Enhancing Training and Performance
• The Evolution of Haptic Feedback in HCI
• HCI and Cultural Differences: Designing for Global User Bases
• The Impact of HCI on Digital Marketing: Creating Engaging User Interactions
• HCI in Financial Services: Improving User Interfaces for Banking Apps
• The Role of HCI in Enhancing User Trust in Technology
• HCI for Public Safety: User Interfaces for Security Systems
• The Application of HCI in the Film and Television Industry
• HCI and the Future of Work: Designing Interfaces for Remote Collaboration
• Innovations in HCI: Exploring New Interaction Technologies and Their Applications
• Deep Learning Techniques for Advanced Image Segmentation
• Real-Time Image Processing for Autonomous Driving Systems
• Image Enhancement Algorithms for Underwater Imaging
• Super-Resolution Imaging: Techniques and Applications
• The Role of Image Processing in Remote Sensing and Satellite Imagery Analysis
• Machine Learning Models for Medical Image Diagnosis
• The Impact of AI on Photographic Restoration and Enhancement
• Image Processing in Security Systems: Facial Recognition and Motion Detection
• Advanced Algorithms for Image Noise Reduction
• 3D Image Reconstruction Techniques in Tomography
• Image Processing for Agricultural Monitoring: Crop Disease Detection and Yield Prediction
• Techniques for Panoramic Image Stitching
• Video Image Processing: Real-Time Streaming and Data Compression
• The Application of Image Processing in Printing Technology
• Color Image Processing: Theory and Practical Applications
• The Use of Image Processing in Biometrics Identification
• Computational Photography: Image Processing Techniques in Smartphone Cameras
• Image Processing for Augmented Reality: Real-time Object Overlay
• The Development of Image Processing Algorithms for Traffic Control Systems
• Pattern Recognition and Analysis in Forensic Imaging
• Adaptive Filtering Techniques in Image Processing
• Image Processing in Retail: Customer Tracking and Behavior Analysis
• The Role of Image Processing in Cultural Heritage Preservation
• Image Segmentation Techniques for Cancer Detection in Medical Imaging
• High Dynamic Range (HDR) Imaging: Algorithms and Display Techniques
• Image Classification with Deep Convolutional Neural Networks
• The Evolution of Edge Detection Algorithms in Image Processing
• Image Processing for Wildlife Monitoring: Species Recognition and Behavior Analysis
• Application of Wavelet Transforms in Image Compression
• Image Processing in Sports: Enhancing Broadcasts and Performance Analysis
• Optical Character Recognition (OCR) Improvements in Document Scanning
• Multi-Spectral Imaging for Environmental and Earth Studies
• Image Processing for Space Exploration: Analysis of Planetary Images
• Real-Time Image Processing for Event Surveillance
• The Influence of Quantum Computing on Image Processing Speed and Security
• Machine Vision in Manufacturing: Defect Detection and Quality Control
• Image Processing in Neurology: Visualizing Brain Functions
• Photogrammetry and Image Processing in Geology: 3D Terrain Mapping
• Advanced Techniques in Image Watermarking for Copyright Protection
• The Future of Image Processing: Integrating AI for Automated Editing
• The Evolution of Enterprise Resource Planning (ERP) Systems in the Digital Age
• Information Systems for Managing Distributed Workforces
• The Role of Information Systems in Enhancing Supply Chain Management
• Cybersecurity Measures in Information Systems
• The Impact of Big Data on Decision Support Systems
• Blockchain Technology for Information System Security
• The Development of Sustainable IT Infrastructure in Information Systems
• The Use of AI in Information Systems for Business Intelligence
• Information Systems in Healthcare: Improving Patient Care and Data Management
• The Influence of IoT on Information Systems Architecture
• Mobile Information Systems: Development and Usability Challenges
• The Role of Geographic Information Systems (GIS) in Urban Planning
• Social Media Analytics: Tools and Techniques in Information Systems
• Information Systems in Education: Enhancing Learning and Administration
• Cloud Computing Integration into Corporate Information Systems
• Information Systems Audit: Practices and Challenges
• User Interface Design and User Experience in Information Systems
• Privacy and Data Protection in Information Systems
• The Future of Quantum Computing in Information Systems
• The Role of Information Systems in Environmental Management
• Implementing Effective Knowledge Management Systems
• The Adoption of Virtual Reality in Information Systems
• The Challenges of Implementing ERP Systems in Multinational Corporations
• Information Systems for Real-Time Business Analytics
• The Impact of 5G Technology on Mobile Information Systems
• Ethical Issues in the Management of Information Systems
• Information Systems in Retail: Enhancing Customer Experience and Management
• The Role of Information Systems in Non-Profit Organizations
• Development of Decision Support Systems for Strategic Planning
• Information Systems in the Banking Sector: Enhancing Financial Services
• Risk Management in Information Systems
• The Integration of Artificial Neural Networks in Information Systems
• Information Systems and Corporate Governance
• Information Systems for Disaster Response and Management
• The Role of Information Systems in Sports Management
• Information Systems for Public Health Surveillance
• The Future of Information Systems: Trends and Predictions
• Information Systems in the Film and Media Industry
• Business Process Reengineering through Information Systems
• Implementing Customer Relationship Management (CRM) Systems in E-commerce
• Emerging Trends in Artificial Intelligence and Machine Learning
• The Future of Cloud Services and Technology
• Cybersecurity: Current Threats and Future Defenses
• The Role of Information Technology in Sustainable Energy Solutions
• Internet of Things (IoT): From Smart Homes to Smart Cities
• Blockchain and Its Impact on Information Technology
• The Use of Big Data Analytics in Predictive Modeling
• Virtual Reality (VR) and Augmented Reality (AR): The Next Frontier in IT
• The Challenges of Digital Transformation in Traditional Businesses
• Wearable Technology: Health Monitoring and Beyond
• 5G Technology: Implementation and Impacts on IT
• Biometrics Technology: Uses and Privacy Concerns
• The Role of IT in Global Health Initiatives
• Ethical Considerations in the Development of Autonomous Systems
• Data Privacy in the Age of Information Overload
• The Evolution of Software Development Methodologies
• Quantum Computing: The Next Revolution in IT
• IT Governance: Best Practices and Standards
• The Integration of AI in Customer Service Technology
• IT in Manufacturing: Industrial Automation and Robotics
• The Future of E-commerce: Technology and Trends
• Mobile Computing: Innovations and Challenges
• Information Technology in Education: Tools and Trends
• IT Project Management: Approaches and Tools
• The Role of IT in Media and Entertainment
• The Impact of Digital Marketing Technologies on Business Strategies
• IT in Logistics and Supply Chain Management
• The Development and Future of Autonomous Vehicles
• IT in the Insurance Sector: Enhancing Efficiency and Customer Engagement
• The Role of IT in Environmental Conservation
• Smart Grid Technology: IT at the Intersection of Energy Management
• Telemedicine: The Impact of IT on Healthcare Delivery
• IT in the Agricultural Sector: Innovations and Impact
• Cyber-Physical Systems: IT in the Integration of Physical and Digital Worlds
• The Influence of Social Media Platforms on IT Development
• Data Centers: Evolution, Technologies, and Sustainability
• IT in Public Administration: Improving Services and Transparency
• The Role of IT in Sports Analytics
• Information Technology in Retail: Enhancing the Shopping Experience
• The Future of IT: Integrating Ethical AI Systems

Internet of Things (IoT) Thesis Topics

• Enhancing IoT Security: Strategies for Safeguarding Connected Devices
• IoT in Smart Cities: Infrastructure and Data Management Challenges
• The Application of IoT in Precision Agriculture: Maximizing Efficiency and Yield
• IoT and Healthcare: Opportunities for Remote Monitoring and Patient Care
• Energy Efficiency in IoT: Techniques for Reducing Power Consumption in Devices
• The Role of IoT in Supply Chain Management and Logistics
• Real-Time Data Processing Using Edge Computing in IoT Networks
• Privacy Concerns and Data Protection in IoT Systems
• The Integration of IoT with Blockchain for Enhanced Security and Transparency
• IoT in Environmental Monitoring: Systems for Air Quality and Water Safety
• Predictive Maintenance in Industrial IoT: Strategies and Benefits
• IoT in Retail: Enhancing Customer Experience through Smart Technology
• The Development of Standard Protocols for IoT Communication
• IoT in Smart Homes: Automation and Security Systems
• The Role of IoT in Disaster Management: Early Warning Systems and Response Coordination
• Machine Learning Techniques for IoT Data Analytics
• IoT in Automotive: The Future of Connected and Autonomous Vehicles
• The Impact of 5G on IoT: Enhancements in Speed and Connectivity
• IoT Device Lifecycle Management: From Creation to Decommissioning
• IoT in Public Safety: Applications for Emergency Response and Crime Prevention
• The Ethics of IoT: Balancing Innovation with Consumer Rights
• IoT and the Future of Work: Automation and Labor Market Shifts
• Designing User-Friendly Interfaces for IoT Applications
• IoT in the Energy Sector: Smart Grids and Renewable Energy Integration
• Quantum Computing and IoT: Potential Impacts and Applications
• The Role of AI in Enhancing IoT Solutions
• IoT for Elderly Care: Technologies for Health and Mobility Assistance
• IoT in Education: Enhancing Classroom Experiences and Learning Outcomes
• Challenges in Scaling IoT Infrastructure for Global Coverage
• The Economic Impact of IoT: Industry Transformations and New Business Models
• IoT and Tourism: Enhancing Visitor Experiences through Connected Technologies
• Data Fusion Techniques in IoT: Integrating Diverse Data Sources
• IoT in Aquaculture: Monitoring and Managing Aquatic Environments
• Wireless Technologies for IoT: Comparing LoRa, Zigbee, and NB-IoT
• IoT and Intellectual Property: Navigating the Legal Landscape
• IoT in Sports: Enhancing Training and Audience Engagement
• Building Resilient IoT Systems against Cyber Attacks
• IoT for Waste Management: Innovations and System Implementations
• IoT in Agriculture: Drones and Sensors for Crop Monitoring
• The Role of IoT in Cultural Heritage Preservation: Monitoring and Maintenance
• Advanced Algorithms for Supervised and Unsupervised Learning
• Machine Learning in Genomics: Predicting Disease Propensity and Treatment Outcomes
• The Use of Neural Networks in Image Recognition and Analysis
• Reinforcement Learning: Applications in Robotics and Autonomous Systems
• The Role of Machine Learning in Natural Language Processing and Linguistic Analysis
• Deep Learning for Predictive Analytics in Business and Finance
• Machine Learning for Cybersecurity: Detection of Anomalies and Malware
• Ethical Considerations in Machine Learning: Bias and Fairness
• The Integration of Machine Learning with IoT for Smart Device Management
• Transfer Learning: Techniques and Applications in New Domains
• The Application of Machine Learning in Environmental Science
• Machine Learning in Healthcare: Diagnosing Conditions from Medical Images
• The Use of Machine Learning in Algorithmic Trading and Stock Market Analysis
• Machine Learning in Social Media: Sentiment Analysis and Trend Prediction
• Quantum Machine Learning: Merging Quantum Computing with AI
• Feature Engineering and Selection in Machine Learning
• Machine Learning for Enhancing User Experience in Mobile Applications
• The Impact of Machine Learning on Digital Marketing Strategies
• Machine Learning for Energy Consumption Forecasting and Optimization
• The Role of Machine Learning in Enhancing Network Security Protocols
• Scalability and Efficiency of Machine Learning Algorithms
• Machine Learning in Drug Discovery and Pharmaceutical Research
• The Application of Machine Learning in Sports Analytics
• Machine Learning for Real-Time Decision-Making in Autonomous Vehicles
• The Use of Machine Learning in Predicting Geographical and Meteorological Events
• Machine Learning for Educational Data Mining and Learning Analytics
• The Role of Machine Learning in Audio Signal Processing
• Predictive Maintenance in Manufacturing Through Machine Learning
• Machine Learning and Its Implications for Privacy and Surveillance
• The Application of Machine Learning in Augmented Reality Systems
• Deep Learning Techniques in Medical Diagnosis: Challenges and Opportunities
• The Use of Machine Learning in Video Game Development
• Machine Learning for Fraud Detection in Financial Services
• The Role of Machine Learning in Agricultural Optimization and Management
• The Impact of Machine Learning on Content Personalization and Recommendation Systems
• Machine Learning in Legal Tech: Document Analysis and Case Prediction
• Adaptive Learning Systems: Tailoring Education Through Machine Learning
• Machine Learning in Space Exploration: Analyzing Data from Space Missions
• Machine Learning for Public Sector Applications: Improving Services and Efficiency
• The Future of Machine Learning: Integrating Explainable AI
• Innovations in Convolutional Neural Networks for Image and Video Analysis
• Recurrent Neural Networks: Applications in Sequence Prediction and Analysis
• The Role of Neural Networks in Predicting Financial Market Trends
• Deep Neural Networks for Enhanced Speech Recognition Systems
• Neural Networks in Medical Imaging: From Detection to Diagnosis
• Generative Adversarial Networks (GANs): Applications in Art and Media
• The Use of Neural Networks in Autonomous Driving Technologies
• Neural Networks for Real-Time Language Translation
• The Application of Neural Networks in Robotics: Sensory Data and Movement Control
• Neural Network Optimization Techniques: Overcoming Overfitting and Underfitting
• The Integration of Neural Networks with Blockchain for Data Security
• Neural Networks in Climate Modeling and Weather Forecasting
• The Use of Neural Networks in Enhancing Internet of Things (IoT) Devices
• Graph Neural Networks: Applications in Social Network Analysis and Beyond
• The Impact of Neural Networks on Augmented Reality Experiences
• Neural Networks for Anomaly Detection in Network Security
• The Application of Neural Networks in Bioinformatics and Genomic Data Analysis
• Capsule Neural Networks: Improving the Robustness and Interpretability of Deep Learning
• The Role of Neural Networks in Consumer Behavior Analysis
• Neural Networks in Energy Sector: Forecasting and Optimization
• The Evolution of Neural Network Architectures for Efficient Learning
• The Use of Neural Networks in Sentiment Analysis: Techniques and Challenges
• Deep Reinforcement Learning: Strategies for Advanced Decision-Making Systems
• Neural Networks for Precision Medicine: Tailoring Treatments to Individual Genetic Profiles
• The Use of Neural Networks in Virtual Assistants: Enhancing Natural Language Understanding
• The Impact of Neural Networks on Pharmaceutical Research
• Neural Networks for Supply Chain Management: Prediction and Automation
• The Application of Neural Networks in E-commerce: Personalization and Recommendation Systems
• Neural Networks for Facial Recognition: Advances and Ethical Considerations
• The Role of Neural Networks in Educational Technologies
• The Use of Neural Networks in Predicting Economic Trends
• Neural Networks in Sports: Analyzing Performance and Strategy
• The Impact of Neural Networks on Digital Security Systems
• Neural Networks for Real-Time Video Surveillance Analysis
• The Integration of Neural Networks in Edge Computing Devices
• Neural Networks for Industrial Automation: Improving Efficiency and Accuracy
• The Future of Neural Networks: Towards More General AI Applications
• Neural Networks in Art and Design: Creating New Forms of Expression
• The Role of Neural Networks in Enhancing Public Health Initiatives
• The Future of Neural Networks: Challenges in Scalability and Generalization
• The Evolution of Programming Paradigms: Functional vs. Object-Oriented Programming
• Advances in Compiler Design and Optimization Techniques
• The Impact of Programming Languages on Software Security
• Developing Programming Languages for Quantum Computing
• Machine Learning in Automated Code Generation and Optimization
• The Role of Programming in Developing Scalable Cloud Applications
• The Future of Web Development: New Frameworks and Technologies
• Cross-Platform Development: Best Practices in Mobile App Programming
• The Influence of Programming Techniques on Big Data Analytics
• Real-Time Systems Programming: Challenges and Solutions
• The Integration of Programming with Blockchain Technology
• Programming for IoT: Languages and Tools for Device Communication
• Secure Coding Practices: Preventing Cyber Attacks through Software Design
• The Role of Programming in Data Visualization and User Interface Design
• Advances in Game Programming: Graphics, AI, and Network Play
• The Impact of Programming on Digital Media and Content Creation
• Programming Languages for Robotics: Trends and Future Directions
• The Use of Artificial Intelligence in Enhancing Programming Productivity
• Programming for Augmented and Virtual Reality: New Challenges and Techniques
• Ethical Considerations in Programming: Bias, Fairness, and Transparency
• The Future of Programming Education: Interactive and Adaptive Learning Models
• Programming for Wearable Technology: Special Considerations and Challenges
• The Evolution of Programming in Financial Technology
• Functional Programming in Enterprise Applications
• Memory Management Techniques in Programming: From Garbage Collection to Manual Control
• The Role of Open Source Programming in Accelerating Innovation
• The Impact of Programming on Network Security and Cryptography
• Developing Accessible Software: Programming for Users with Disabilities
• Programming Language Theories: New Models and Approaches
• The Challenges of Legacy Code: Strategies for Modernization and Integration
• Energy-Efficient Programming: Optimizing Code for Green Computing
• Multithreading and Concurrency: Advanced Programming Techniques
• The Impact of Programming on Computational Biology and Bioinformatics
• The Role of Scripting Languages in Automating System Administration
• Programming and the Future of Quantum Resistant Cryptography
• Code Review and Quality Assurance: Techniques and Tools
• Adaptive and Predictive Programming for Dynamic Environments
• The Role of Programming in Enhancing E-commerce Technology
• Programming for Cyber-Physical Systems: Bridging the Gap Between Digital and Physical
• The Influence of Programming Languages on Computational Efficiency and Performance
• Quantum Algorithms: Development and Applications Beyond Shor’s and Grover’s Algorithms
• The Role of Quantum Computing in Solving Complex Biological Problems
• Quantum Cryptography: New Paradigms for Secure Communication
• Error Correction Techniques in Quantum Computing
• Quantum Computing and Its Impact on Artificial Intelligence
• The Integration of Classical and Quantum Computing: Hybrid Models
• Quantum Machine Learning: Theoretical Foundations and Practical Applications
• Quantum Computing Hardware: Advances in Qubit Technology
• The Application of Quantum Computing in Financial Modeling and Risk Assessment
• Quantum Networking: Establishing Secure Quantum Communication Channels
• The Future of Drug Discovery: Applications of Quantum Computing
• Quantum Computing in Cryptanalysis: Threats to Current Cryptography Standards
• Simulation of Quantum Systems for Material Science
• Quantum Computing for Optimization Problems in Logistics and Manufacturing
• Theoretical Limits of Quantum Computing: Understanding Quantum Complexity
• Quantum Computing and the Future of Search Algorithms
• The Role of Quantum Computing in Climate Science and Environmental Modeling
• Quantum Annealing vs. Universal Quantum Computing: Comparative Studies
• Implementing Quantum Algorithms in Quantum Programming Languages
• The Impact of Quantum Computing on Public Key Cryptography
• Quantum Entanglement: Experiments and Applications in Quantum Networks
• Scalability Challenges in Quantum Processors
• The Ethics and Policy Implications of Quantum Computing
• Quantum Computing in Space Exploration and Astrophysics
• The Role of Quantum Computing in Developing Next-Generation AI Systems
• Quantum Computing in the Energy Sector: Applications in Smart Grids and Nuclear Fusion
• Noise and Decoherence in Quantum Computers: Overcoming Practical Challenges
• Quantum Computing for Predicting Economic Market Trends
• Quantum Sensors: Enhancing Precision in Measurement and Imaging
• The Future of Quantum Computing Education and Workforce Development
• Quantum Computing in Cybersecurity: Preparing for a Post-Quantum World
• Quantum Computing and the Internet of Things: Potential Intersections
• Practical Quantum Computing: From Theory to Real-World Applications
• Quantum Supremacy: Milestones and Future Goals
• The Role of Quantum Computing in Genetics and Genomics
• Quantum Computing for Material Discovery and Design
• The Challenges of Quantum Programming Languages and Environments
• Quantum Computing in Art and Creative Industries
• The Global Race for Quantum Computing Supremacy: Technological and Political Aspects
• Quantum Computing and Its Implications for Software Engineering
• Advances in Humanoid Robotics: New Developments and Challenges
• Robotics in Healthcare: From Surgery to Rehabilitation
• The Integration of AI in Robotics: Enhanced Autonomy and Learning Capabilities
• Swarm Robotics: Coordination Strategies and Applications
• The Use of Robotics in Hazardous Environments: Deep Sea and Space Exploration
• Soft Robotics: Materials, Design, and Applications
• Robotics in Agriculture: Automation of Farming and Harvesting Processes
• The Role of Robotics in Manufacturing: Increased Efficiency and Flexibility
• Ethical Considerations in the Deployment of Robots in Human Environments
• Autonomous Vehicles: Technological Advances and Regulatory Challenges
• Robotic Assistants for the Elderly and Disabled: Improving Quality of Life
• The Use of Robotics in Education: Teaching Science, Technology, Engineering, and Math (STEM)
• Robotics and Computer Vision: Enhancing Perception and Decision Making
• The Impact of Robotics on Employment and the Workforce
• The Development of Robotic Systems for Environmental Monitoring and Conservation
• Machine Learning Techniques for Robotic Perception and Navigation
• Advances in Robotic Surgery: Precision and Outcomes
• Human-Robot Interaction: Building Trust and Cooperation
• Robotics in Retail: Automated Warehousing and Customer Service
• Energy-Efficient Robots: Design and Utilization
• Robotics in Construction: Automation and Safety Improvements
• The Role of Robotics in Disaster Response and Recovery Operations
• The Application of Robotics in Art and Creative Industries
• Robotics and the Future of Personal Transportation
• Ethical AI in Robotics: Ensuring Safe and Fair Decision-Making
• The Use of Robotics in Logistics: Drones and Autonomous Delivery Vehicles
• Robotics in the Food Industry: From Production to Service
• The Integration of IoT with Robotics for Enhanced Connectivity
• Wearable Robotics: Exoskeletons for Rehabilitation and Enhanced Mobility
• The Impact of Robotics on Privacy and Security
• Robotic Pet Companions: Social Robots and Their Psychological Effects
• Robotics for Planetary Exploration and Colonization
• Underwater Robotics: Innovations in Oceanography and Marine Biology
• Advances in Robotics Programming Languages and Tools
• The Role of Robotics in Minimizing Human Exposure to Contaminants and Pathogens
• Collaborative Robots (Cobots): Working Alongside Humans in Shared Spaces
• The Use of Robotics in Entertainment and Sports
• Robotics and Machine Ethics: Programming Moral Decision-Making
• The Future of Military Robotics: Opportunities and Challenges
• Sustainable Robotics: Reducing the Environmental Impact of Robotic Systems
• Agile Methodologies: Evolution and Future Trends
• DevOps Practices: Improving Software Delivery and Lifecycle Management
• The Impact of Microservices Architecture on Software Development
• Containerization Technologies: Docker, Kubernetes, and Beyond
• Software Quality Assurance: Modern Techniques and Tools
• The Role of Artificial Intelligence in Automated Software Testing
• Blockchain Applications in Software Development and Security
• The Integration of Continuous Integration and Continuous Deployment (CI/CD) in Software Projects
• Cybersecurity in Software Engineering: Best Practices for Secure Coding
• Low-Code and No-Code Development: Implications for Professional Software Development
• The Future of Software Engineering Education
• Software Sustainability: Developing Green Software and Reducing Carbon Footprints
• The Role of Software Engineering in Healthcare: Telemedicine and Patient Data Management
• Privacy by Design: Incorporating Privacy Features at the Development Stage
• The Impact of Quantum Computing on Software Engineering
• Software Engineering for Augmented and Virtual Reality: Challenges and Innovations
• Cloud-Native Applications: Design, Development, and Deployment
• Software Project Management: Agile vs. Traditional Approaches
• Open Source Software: Community Engagement and Project Sustainability
• The Evolution of Graphical User Interfaces in Application Development
• The Challenges of Integrating IoT Devices into Software Systems
• Ethical Issues in Software Engineering: Bias, Accountability, and Regulation
• Software Engineering for Autonomous Vehicles: Safety and Regulatory Considerations
• Big Data Analytics in Software Development: Enhancing Decision-Making Processes
• The Future of Mobile App Development: Trends and Technologies
• The Role of Software Engineering in Artificial Intelligence: Frameworks and Algorithms
• Performance Optimization in Software Applications
• Adaptive Software Development: Responding to Changing User Needs
• Software Engineering in Financial Services: Compliance and Security Challenges
• User Experience (UX) Design in Software Engineering
• The Role of Software Engineering in Smart Cities: Infrastructure and Services
• The Impact of 5G on Software Development and Deployment
• Real-Time Systems in Software Engineering: Design and Implementation Challenges
• Cross-Platform Development Challenges: Ensuring Consistency and Performance
• Software Testing Automation: Tools and Trends
• The Integration of Cyber-Physical Systems in Software Engineering
• Software Engineering in the Entertainment Industry: Game Development and Beyond
• The Application of Machine Learning in Predicting Software Bugs
• The Role of Software Engineering in Cybersecurity Defense Strategies
• Accessibility in Software Engineering: Creating Inclusive and Usable Software
• Progressive Web Apps (PWAs): Advantages and Implementation Challenges
• The Future of Web Accessibility: Standards and Practices
• Single-Page Applications (SPAs) vs. Multi-Page Applications (MPAs): Performance and Usability
• The Impact of Serverless Computing on Web Development
• The Evolution of CSS for Modern Web Design
• Security Best Practices in Web Development: Defending Against XSS and CSRF Attacks
• The Role of Web Development in Enhancing E-commerce User Experience
• The Use of Artificial Intelligence in Web Personalization and User Engagement
• The Future of Web APIs: Standards, Security, and Scalability
• Responsive Web Design: Techniques and Trends
• JavaScript Frameworks: Vue.js, React.js, and Angular – A Comparative Analysis
• Web Development for IoT: Interfaces and Connectivity Solutions
• The Impact of 5G on Web Development and User Experiences
• The Use of Blockchain Technology in Web Development for Enhanced Security
• Web Development in the Cloud: Using AWS, Azure, and Google Cloud
• Content Management Systems (CMS): Trends and Future Developments
• The Application of Web Development in Virtual and Augmented Reality
• The Importance of Web Performance Optimization: Tools and Techniques
• Sustainable Web Design: Practices for Reducing Energy Consumption
• The Role of Web Development in Digital Marketing: SEO and Social Media Integration
• Headless CMS: Benefits and Challenges for Developers and Content Creators
• The Future of Web Typography: Design, Accessibility, and Performance
• Web Development and Data Protection: Complying with GDPR and Other Regulations
• Real-Time Web Communication: Technologies like WebSockets and WebRTC
• Front-End Development Tools: Efficiency and Innovation in Workflow
• The Challenges of Migrating Legacy Systems to Modern Web Architectures
• Microfrontends Architecture: Designing Scalable and Decoupled Web Applications
• The Impact of Cryptocurrencies on Web Payment Systems
• User-Centered Design in Web Development: Methods for Engaging Users
• The Role of Web Development in Business Intelligence: Dashboards and Reporting Tools
• Web Development for Mobile Platforms: Optimization and Best Practices
• The Evolution of E-commerce Platforms: From Web to Mobile Commerce
• Web Security in E-commerce: Protecting Transactions and User Data
• Dynamic Web Content: Server-Side vs. Client-Side Rendering
• The Future of Full Stack Development: Trends and Skills
• Web Design Psychology: How Design Influences User Behavior
• The Role of Web Development in the Non-Profit Sector: Fundraising and Community Engagement
• The Integration of AI Chatbots in Web Development
• The Use of Motion UI in Web Design: Enhancing Aesthetics and User Interaction
• The Future of Web Development: Predictions and Emerging Technologies

We trust that this comprehensive list of computer science thesis topics will serve as a valuable starting point for your research endeavors. With 1000 unique and carefully selected topics distributed across 25 key areas of computer science, students are equipped to tackle complex questions and contribute meaningful advancements to the field. As you proceed to select your thesis topic, consider not only your personal interests and career goals but also the potential impact of your research. We encourage you to explore these topics thoroughly and choose one that will not only challenge you but also push the boundaries of technology and innovation.

The Range of Computer Science Thesis Topics

Computer science stands as a dynamic and ever-evolving field that continuously reshapes how we interact with the world. At its core, the discipline encompasses not just the study of algorithms and computation, but a broad spectrum of practical and theoretical knowledge areas that drive innovation in various sectors. This article aims to explore the rich landscape of computer science thesis topics, offering students and researchers a glimpse into the potential areas of study that not only challenge the intellect but also contribute significantly to technological progress. As we delve into the current issues, recent trends, and future directions of computer science, it becomes evident that the possibilities for research are both vast and diverse. Whether you are intrigued by the complexities of artificial intelligence, the robust architecture of networks and systems, or the innovative approaches in cybersecurity, computer science offers a fertile ground for developing thesis topics that are as impactful as they are intellectually stimulating.

Current Issues in Computer Science

One of the prominent current issues in computer science revolves around data security and privacy. As digital transformation accelerates across industries, the massive influx of data generated poses significant challenges in terms of its protection and ethical use. Cybersecurity threats have become more sophisticated, with data breaches and cyber-attacks causing major concerns for organizations worldwide. This ongoing battle demands continuous improvements in security protocols and the development of robust cybersecurity measures. Computer science thesis topics in this area can explore new cryptographic methods, intrusion detection systems, and secure communication protocols to fortify digital defenses. Research could also delve into the ethical implications of data collection and use, proposing frameworks that ensure privacy while still leveraging data for innovation.

Another critical issue facing the field of computer science is the ethical development and deployment of artificial intelligence (AI) systems. As AI technologies become more integrated into daily life and critical infrastructure, concerns about bias, fairness, and accountability in AI systems have intensified. Thesis topics could focus on developing algorithms that address these ethical concerns, including techniques for reducing bias in machine learning models and methods for increasing transparency and explainability in AI decisions. This research is crucial for ensuring that AI technologies promote fairness and do not perpetuate or exacerbate existing societal inequalities.

Furthermore, the rapid pace of technological change presents a challenge in terms of sustainability and environmental impact. The energy consumption of large data centers, the carbon footprint of producing and disposing of electronic waste, and the broader effects of high-tech innovations on the environment are significant concerns within computer science. Thesis research in this domain could focus on creating more energy-efficient computing methods, developing algorithms that reduce power consumption, or innovating recycling technologies that address the issue of e-waste. This research not only contributes to the field of computer science but also plays a crucial role in ensuring that technological advancement does not come at an unsustainable cost to the environment.

These current issues highlight the dynamic nature of computer science and its direct impact on society. Addressing these challenges through focused research and innovative thesis topics not only advances the field but also contributes to resolving some of the most pressing problems facing our global community today.

Recent Trends in Computer Science

In recent years, computer science has witnessed significant advancements in the integration of artificial intelligence (AI) and machine learning (ML) across various sectors, marking one of the most exciting trends in the field. These technologies are not just reshaping traditional industries but are also at the forefront of driving innovations in areas like healthcare, finance, and autonomous systems. Thesis topics within this trend could explore the development of advanced ML algorithms that enhance predictive analytics, improve automated decision-making, or refine natural language processing capabilities. Additionally, AI’s role in ethical decision-making and its societal impacts offers a rich vein of inquiry for research, focusing on mitigating biases and ensuring that AI systems operate transparently and justly.

Another prominent trend in computer science is the rapid growth of blockchain technology beyond its initial application in cryptocurrencies. Blockchain is proving its potential in creating more secure, decentralized, and transparent networks for a variety of applications, from enhancing supply chain logistics to revolutionizing digital identity verification processes. Computer science thesis topics could investigate novel uses of blockchain for ensuring data integrity in digital transactions, enhancing cybersecurity measures, or even developing new frameworks for blockchain integration into existing technological infrastructures. The exploration of blockchain’s scalability, speed, and energy consumption also presents critical research opportunities that are timely and relevant.

Furthermore, the expansion of the Internet of Things (IoT) continues to be a significant trend, with more devices becoming connected every day, leading to increasingly smart environments. This proliferation poses unique challenges and opportunities for computer science research, particularly in terms of scalability, security, and new data management strategies. Thesis topics might focus on optimizing network protocols to handle the massive influx of data from IoT devices, developing solutions to safeguard against IoT-specific security vulnerabilities, or innovative applications of IoT in urban planning, smart homes, or healthcare. Research in this area is crucial for advancing the efficiency and functionality of IoT systems and for ensuring they can be safely and effectively integrated into modern life.

These recent trends underscore the vibrant and ever-evolving nature of computer science, reflecting its capacity to influence and transform an array of sectors through technological innovation. The continual emergence of new research topics within these trends not only enriches the academic discipline but also provides substantial benefits to society by addressing practical challenges and enhancing the capabilities of technology in everyday life.

Future Directions in Computer Science

As we look toward the future, one of the most anticipated areas in computer science is the advancement of quantum computing. This emerging technology promises to revolutionize problem-solving in fields that require immense computational power, such as cryptography, drug discovery, and complex system modeling. Quantum computing has the potential to process tasks at speeds unachievable by classical computers, offering breakthroughs in materials science and encryption methods. Computer science thesis topics might explore the theoretical underpinnings of quantum algorithms, the development of quantum-resistant cryptographic systems, or practical applications of quantum computing in industry-specific scenarios. Research in this area not only contributes to the foundational knowledge of quantum mechanics but also paves the way for its integration into mainstream computing, marking a significant leap forward in computational capabilities.

Another promising direction in computer science is the advancement of autonomous systems, particularly in robotics and vehicle automation. The future of autonomous technologies hinges on improving their safety, reliability, and decision-making processes under uncertain conditions. Thesis topics could focus on the enhancement of machine perception through computer vision and sensor fusion, the development of more sophisticated AI-driven decision frameworks, or ethical considerations in the deployment of autonomous systems. As these technologies become increasingly prevalent, research will play a crucial role in addressing the societal and technical challenges they present, ensuring their beneficial integration into daily life and industry operations.

Additionally, the ongoing expansion of artificial intelligence applications poses significant future directions for research, especially in the realm of AI ethics and policy. As AI systems become more capable and widespread, their impact on privacy, employment, and societal norms continues to grow. Future thesis topics might delve into the development of guidelines and frameworks for responsible AI, studies on the impact of AI on workforce dynamics, or innovations in transparent and fair AI systems. This research is vital for guiding the ethical evolution of AI technologies, ensuring they enhance societal well-being without diminishing human dignity or autonomy.

These future directions in computer science not only highlight the field’s potential for substantial technological advancements but also underscore the importance of thoughtful consideration of their broader implications. By exploring these areas in depth, computer science research can lead the way in not just technological innovation, but also in shaping a future where technology and ethics coexist harmoniously for the betterment of society.

In conclusion, the field of computer science is not only foundational to the technological advancements that characterize the modern age but also crucial in solving some of the most pressing challenges of our time. The potential thesis topics discussed in this article reflect a mere fraction of the opportunities that lie in the realms of theory, application, and innovation within this expansive field. As emerging technologies such as quantum computing, artificial intelligence, and blockchain continue to evolve, they open new avenues for research that could potentially redefine existing paradigms. For students embarking on their thesis journey, it is essential to choose a topic that not only aligns with their academic passions but also contributes to the ongoing expansion of computer science knowledge. By pushing the boundaries of what is known and exploring uncharted territories, students can leave a lasting impact on the field and pave the way for future technological breakthroughs. As we look forward, it’s clear that computer science will continue to be a key driver of change, making it an exciting and rewarding area for academic and professional growth.

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A Research Review of Distributed Computing System

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• First Online: 23 August 2018
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• Wang Xingang 16  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 752))

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With the rapid development of Internet technology, global data are growing at an exponential rate, and human computing has undergone profound changes. The early stand-alone mode has not met people’s needs, and the network-based collaborative distributed gained more and more attention and favor. Distributed computing system is one of the hottest Internet research directions in the era of big data era. It has the characteristics of high efficiency, high capacity, dynamic processing, and so on. It shows great application value in the commercial field and scientific research field of society. Based on the development and present situation of distributed computing system, this paper reviews and summarizes two popular key technologies: grid technology and cloud computing technology and expounds the differences between the two. At the same time, the design principles and system performance of Hadoop, Storm, Spark, and other typical distributed computing platforms are compared and analyzed in detail, which is of theoretical significance.

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Xingang, W. (2019). A Research Review of Distributed Computing System. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_42

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Distributed Systems and Parallel Computing

No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.

From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model .

Recent Publications

Some of our teams.

Algorithms & optimization

Graph mining

Network infrastructure

System performance

We're always looking for more talented, passionate people.

• School of Engineering and Applied Sciences
• UB Directory
• Department of Computer Science and Engineering >
• Research >
• Research Areas >

Distributed Systems and Networks

Research areas.

Word cloud describing CSE research areas.  The relative relative word sizes represent the number of faculty working in each area.  Photo credit: Christian Miller

Research in networking and distributed systems focuses on enabling communication of and orchestrating coordination of a large number of computing nodes.

Research Ranking

UB's institutional reputation in the field of computer science has improved dramatically over the last decade.  By the most valid measure, our national ranking has risen from 50th to 29th .

The Computing Research Association (CRA) is a leading computer science advocacy organization whose mission is to unite industry, academia, and government.  The CRA recommends CSRankings: Computer Science Rankings as the best institutional ranking agency, preferring it over the traditional standard, the US News and World Report Best Graduate Schools report.

The CRA supports the CSRankings report because its evaluative criteria meet the ' GOTO ' standard:

Good data .  Data have been cleaned and curated.

Open .  Data available, regarding attributes measured, at least for verification.  

Transparent .  Process and methodologies are entirely transparent.

Objective .  Based on measurable attributes.

For more details, see Department Rankings , by H.V. Jagadish .

According to CSRankings (2008-2018) , UB's 10-year computer science institutional ranking is #50 in the nation, tied with the University of Central Florida and the University of North Carolina .

According to CSRankings (2015-2018) , UB's three-year computer science institutional ranking is #34 in the nation, making our peer institution the University of Virginia .

According to CSRankings (2017-2018) , UB's one-year computer science institutional ranking is #29 in the nation, putting us in company with Harvard , Johns Hopkins , Ohio State , and Penn State .

Research Highlights

An article on PhysOrg reports UB has received a $584,469 grant from the National Science Foundation to create a tool designed to work with the existing computing infrastructure to boost data transfer speeds by more than 10 times, and quotes Tevfik Kosar , associate professor of computer science.

Ken Regan develops algorithms that detect cheating in chess games.  His software compares a player's moves to a database of the player's typical gameplay, then makes an assessment of the statistical likelihood of cheating.  Dr. Regan frequently consults at international chess matches.

Professor and Chair Chunming Qiao leads Instrument for Connected and Autonomous Vehicle Evaluation and Experimentation (iCAVE2) —a multidisciplinary academic-industrial partnership that's helping to make self-driving cars safer, cleaner, and more efficient.

Wenyao Xu leads an NSF-funded program that detects 3D printing data security vulnerabilities by using smart phones to analyze electromagnetic and acoustic waves.  Kui Ren and Chi Zhou are co-authors.

Proposed software solution could extend battery life, reduce energy consumption.

Karthik Dantu owns the vision component of the RoboBee Initiative , led by the National Science Foundation and Harvard University.  The "eyes" that Dr. Dantu is integrating are laser-powered sensors that enable the mechanical bees to orient themselves in space.

Wenyao Xu created AutoDietary — software that tracks the unique sounds produced by food as people chew it.  AutoDietary, placed near the throat by a necklace delivery system developed at China's Northeastern University, helps users measure their caloric intake.

Recognitions

Deborah Chung and Venu Govindaraju will receive the UB President’s Medal,   recognizing extraordinary service to the university.

Jinjun Xiong, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering, has been elevated to fellow in the Institute of Electrical and Electronics Engineers. 

Awards acknowledge and provide system-wide recognition for consistently superior professional achievement and the ongoing pursuit of excellence.

Atri Rudra has been named the first Katherine Johnson Chair in Artificial Intelligence, which was established by a generous gift from computer science alums D. Sivakumar, PhD ’96, and Uma Mahadevan, PhD ’98.

Siwei Lyu, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering, is one of the world’s leading experts on deepfakes.

Design and control of distributed computing systems (operating systems and database systems). Topics include principles of naming and location, atomicity, resource sharing, concurrency control and other synchronization, deadlock detection and avoidance, security, distributed data access and control, integration of operating systems and computer networks, distributed systems design, consistency control, and fault tolerance.

Note: Will not be offered through CEE due to low enrollment.

This course will be available in the Continuing Engineering Education program.--> A more detailed course description prepared for the CEE program is available, as is a course preview briefing containing more detailed information on requirements and expectations. The course outline is given below.

To provide additional support the CEE program, Professor Clifton will be available during office hours through H.323/T.120 desktop videoconferencing (e.g., SunForum , Microsoft NetMeeting .) Please send email if you wish to make use of this, or you might try opening an H.323 connection to blitz.cs.purdue.edu.

More course information may be available in WebCT ( direct link ).

Please add yourself to the course mailing list. Send mail to [email protected] containing the line:

add your email to cs603

Feel free to send things to the course mailing list if you feel it is appropriate. An example might be a pointer to a particularly helpful on-line manual describing an API used in one of the projects.

Course Methodology

The course will be taught through lectures, with class participation expected and encouraged. There will be frequent reading assignments to supplement the lectures.

For now, Professor Clifton will not have regular office hours. Feel free to drop by anytime, or send email with some suggested times to schedule an appointment. You can also try H.323/T.120 desktop videoconferencing (e.g., SunForum , Microsoft NetMeeting .) You can try opening an H.323 connection to blitz.cs.purdue.edu - send email if there is no response.

Prerequisites

The official requirement is CS 503 (Operating systems), with CS 542 (Distributed Database systems) recommended. The practical requirement is a solid undergraduate background in computer science including some database and operating systems theory, and substantial programming experience. If you don't have 503, but feel you have sufficient background, please send me an explanation of why you feel you are prepared, along with a number/times for me to call and discuss approving your registration.

The following is recommended (it will be a useful reference for much of the lab work in the course):

Internetworking with TCP/IP Vol.III: Client-Server Programming and Applications, D. E. Comer and D. Stevens, Prentice Hall, (choose appropriate version for your favorite platform), 0-13-032071-4

The following have been recommended in the past, and may provided useful background reading. However, none are required.

Distributed Systems, 1993 Sape Mullender Prentice Hall 0-201-62427-3 Distributed Algorithms, 1997 Nancy Lynch Morgan Kaufmann 1-55860-348-4 Distributed Operating Systems, 1995 Tanenbaum Prentice Hall 0-13-219908-4

Evaluation/Grading:

Evaluation will be a subjective process, however it will be based primarily on your understanding of the material as evidenced in:

• Midterm Exam (25%)
• Final Exam (35%)
• Projects (4-5) (40%)

Exams will be open note / open book. To avoid a disparity between resources available to different students, electronic aids are not permitted. (If everyone has a notebook with wireless connection and all agree they want to use them in the exams, I could relax this.)

I will evaluate projects on a five point scale:

A substantial portion of your education in this course will come through performing programming projects: building components of a distributed system. Some examples of what projects might involve are:

• Building a server capable of handling multiple simultaneous TCP/IP connections using the Socket API. The server would be trivial (e.g., calculate the square of the input and return the result after a five second delay), the key effort would be the API.
• Implement an application that connects to a (provided) CORBA server.
• Implement a clock synchronization protocol.

My current expectation is that all projects will be done individually, as it is probable that some of the CEE students will not be collocated with other students in the course.

Note on Network Access : If you will be doing your project work for the course at a site that is behind a firewall, let me know as soon as possible. Some of the projects will involve connecting to an on-campus server, and if that will involve a firewall on your end I need to know so I can ensure that the ports used are not blocked.

Policy on Intellectual Honesty

Please read the above link to the policy written by Professor Spafford . This will be followed unless I provide written documentation of exceptions.

Late work will be penalized except in case of documented emergency (e.g., medical emergency), or by prior arrangement if doing the work in advance is impossible due to fault of the instructor (e.g., you are going to a conference and ask to start the project early, but I don't have it ready yet.)

The penalty for late work is 1 point (of the possible 5) if turned in after the deadline, and one additional point for each week late.

Syllabus (numbers correspond to week):

Project start/due dates are tentative!

• Course overview , Components of a distributed system
• Message Passing
• Stream-oriented communications
• Remote Procedure Call
• Remote Method Invocation
• DCE RPC ( reading )
• Java RMI ( reading )
• SOAP (Reading: SOAP 1.1 spec , XML Protocol Working Group , Apache SOAP )
• Active Directory ( reading )
• What is clock synchronization? Leslie Lamport, " Time, clocks, and the ordering of events in a distributed system ", Communications of the ACM 21(7) (July 1978).
• Possibility and impossibility Lundelius, J. and Lynch, N., " An Upper and Lower Bound for Clock Synchronization ," Information and Control, Vol. 62, Nos. 2/3, pp. 190-204, 1984. Danny Dolev, Joe Halpern, and H. Raymond Strong, " On the possibility and impossibility of achieving clock synchronization ", Journal of Computer and System Sciences 32(3) 230-250. April 1986. Michael J. Fischer, Nancy A. Lynch, and Michael Merritt, " Easy impossibility proofs for distributed consensus problems " Proceedings of the fourth annual symposium on Principles of distributed computing 1985 , Minaki, Ontario, Canada.
• Practical solution: NTP ( Reading )

Other Reading: Leslie Lamport and P. M. Melliar-Smith, " Synchronizing clocks in the presence of faults " Journal of the ACM 32(1) (January 1985). Jennifer Lundelius and Nancy Lynch, " A new fault-tolerant algorithm for clock synchronization , Proceedings of the third annual ACM symposium on Principles of distributed computing 1984 , Vancouver, British Columbia, Canada.

• Overview : Global State, Mutual Exclusion Leslie Lamport, `` The Mutual Exclusion Problem '', Journal of the ACM 33(2) (April 1986). Read Part II section 2 - the rest is optional. Leslie Lamport, `` 1983 Invited address: Solved problems, unsolved problems and non-problems in concurrency , Proceedings of the third annual ACM symposium on Principles of distributed computing , 1984, Vancouver, British Columbia, Canada. Optional - Global State: K. Mani Chandy and Leslie Lamport, `` Distributed Snapshots: Determining Global States of Distributed Sytems '', ACM Transactions on Computer Systems 3(1) (February 1985) 63-75.
• Fault Tolerant Solutions Michael J. Fischer, Nancy A. Lynch, James E. Burns and Allan Borodin, `` Distributed FIFO allocation of identical resources using small shared space '' ACM Transactions on Programming Languages and Systems 11(1) (1989) pp. 90-114.
• Multiple resources Requirements Please don't check these out - others may want to read them. Dijkstra, E. `` Hierarchical Ordering of Sequential Processes '', ACTA Informatica 1 (1971), 115-138. M. Rabin and D. Lehmann, ``On the Advantages of Free Choice: A Symmetric and Fully Distributed Solution to the Dining Philosophers Problem'', Proceedings of the 8th Symposium on Principles of Programming Languagues (1981) pp. 133-138.
• 2-Phase Commit
• Formal Models for failure and recovery
• 3-Phase Commit
• Basics Reading: Philip A. Bernstein, Vassos Hadzilacos, Nathan Goodman, Concurrency Control and Recovery in Database Systems , Chapter 8: Replicated Data , Addison Wesley, 1987.
• Example: Replication in Oracle
• Advanced Techniques: Quasi-Copies Reading: Rafael Alonso, Daniel Barbará , and Hector Garcia-Molina, `` Data caching issues in an information retrieval system '', ACM Transactions on Database Systems (TODS) 15(3), September 1990.
• Mid-Semester Review March 8, in class: Midterm on material from weeks 1-7. Please advise if this is a problem. -->
• Threads vs. Processes, Code migration basics
• Mobile Agents
• Mobile Agents example: D'Agents Reading: D'Agents web site , position paper.
• Distributed Object systems: CORBA ( OMG ) Reading: CORBA Overview from The Common Object Request Broker: Architecture and Specification , OMG group , 2001. CORBA Security Service ( reading ). Third project due April 3 , fourth project starts.
• DCOM Reading: DCOM vs. .NET
• Distributed Coordination: Jini . Further reading: Jan Newmarch's Guide to JINI Technologies .
• Failure models . Reading: Dr. Flaviu Cristian , Understanding Fault-Tolerant Distributed Systems , Communications of the ACM 34(2) February 1991.
• Fault Tolerance Reading: Felix C. Gärtner, Fundamentals of Fault-Tolerant Distributed Computing in Asynchronous Environments ACM Computing Surveys 31(1), March 1999.
• Reliable communication
• Recovery Optional reading: Richard Golding and Elizabeth Borowsky, Fault-Tolerant Replication Management in Large-Scale Distributed Storage Systems , in Proceedings of the 18th IEEE Symposium on Reliable Distributed Systems 18-21 October, 1999, Lausanne, Switzerland. Hector Garcia-Molina, Christos A. Polyzois and Robert B. Hagmann, Two Epoch Algorithms for Disaster Recovery , in Proceedings of the 1990 conference on Very Large Data Bases , Brisbane, Australia, August 13-16 1990.

Final exam Thursday, May 2, 2002 from 1:00pm to 3:00pm in RHPH 164.

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Dissertations / Theses on the topic 'Heterogeneous distributed computing systems'

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Raman, Pirabhu. "GEMS Gossip-Enabled Monitoring Service for heterogeneous distributed systems /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000598.

Hernandez, Jesus Israel. "Reactive scheduling of DAG applications on heterogeneous and dynamic distributed computing systems." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2336.

Ramesh, Vasanth Kumar. "A game theoretic framework for dynamic task scheduling in distributed heterogeneous computing systems." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001115.

Al-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.

Zhan, Zhiyuan. "Meeting Data Sharing Needs of Heterogeneous Distributed Users." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14598.

Rahman, Hasibur. "Distributed Intelligence-Assisted Autonomic Context-Information Management : A context-based approach to handling vast amounts of heterogeneous IoT data." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-149513.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 7: Submitted.

Hwang, Jae Woong. "OODSF : an object-oriented data specification framework in a heterogeneous computing environment /." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-172449/.

Villebonnet, Violaine. "Scheduling and Dynamic Provisioning for Energy Proportional Heterogeneous Infrastructures." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEN057/document.

Cancela, Paulo Filipe Neves Bento. "Orchestration of heterogeneous middleware services and its application to a comand and control platform." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/1970.

Banino-Rokkones, Cyril. "Algorithmic and Scheduling Techniques for Heterogeneous and Distributed Computing." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1462.

The computing and communication resources of high performance computing systems are becoming heterogeneous, are exhibiting performance fluctuations and are failing in an unforeseeable manner. The Master-Slave (MS) paradigm, that decomposes the computational load into independent tasks, is well-suited for operating in these environments due to its loose synchronization requirements. The application tasks can be computed in any order, by any slave, and can be resubmitted in case of slave failures. Although, the MS paradigm naturally adapts to dynamic and unreliable environments, it nevertheless suffers from a lack of scalability.

This thesis providesmodels, techniques and scheduling strategies that improve the scalability and performance of MS applications. In particular, we claim that deploying multiple masters may be necessary to achieve scalable performance. We address the problem of finding the most profitable locations on a heterogeneous Grid for hosting a given number of master processes, such that the total task throughput of the system is maximized. Further, we provide distributed scheduling strategies that better adapt to system load fluctuations than traditional MS techniques. Our strategies are especially efficient when communication is expensive compared to computation (which constitutes the difficult case).

Furthermore, this thesis investigates also the suitability ofMS scheduling techniques for the parallelization of stencil code applications. These applications are usually parallelized with domain decompositionmethods, that are highly scalable, but rather impractical for dealing with heterogeneous, dynamic and unreliable environments. Our experimental results with two scientific applications show that traditional MS tasking techniques can successfully be applied to stencil code applications when the master is used to control the parallel execution. If the master is used as a data access point, then deploying multiple masters becomes necessary to achieve scalable performance.

Branco, Kalinka Regina Lucas Jaquie Castelo. ""Índices de carga e desempenho em ambientes paralelos/distribuídos - modelagem e métricas"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18052005-163302/.

Lee, Young Choon. "Problem-centric scheduling for heterogeneous computing systems." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/9321.

Rizvanovic, Larisa. "Resource Management Framework for Distributed Heterogeneous Systems." Licentiate thesis, Västerås : School of Innovation, Design and Engineering [Akademin för innovation, design och teknik], Mälardalen University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-585.

Dlodlo, Nomusa. "Heterogeneous distributed systems and user interface issues." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361536.

Eriksson, Lennart. "ROBUST COMMUNICATION IN HETEROGENEOUS AND DISTRIBUTED SYSTEMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35828.

Dramlitsch, Thomas. "Distributed computations in a dynamic, heterogeneous Grid environment." Phd thesis, Universität Potsdam, 2002. http://opus.kobv.de/ubp/volltexte/2005/79/.

Kim, Song Hun. "Distributed Reconfigurable Simulation for Communication Systems." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/29700.

Shan, Meijuan. "Distributed object-oriented parallel computing on heterogeneous workstation clusters using Java." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq43403.pdf.

Jeffreys, Steven. "Uniform Access to Signal Data in a Distributed Heterogeneous Computing Environment." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/611956.

Bridges, Christopher P. "Agent computing platform for distributed satellite systems." Thesis, University of Surrey, 2009. http://epubs.surrey.ac.uk/770399/.

張立新 and Lap-sun Cheung. "Load balancing in distributed object computing systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31224179.

PINA, FELIPE FREIXO. "UTILIZATION OF DHT IN DISTRIBUTED COMPUTING SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19132@1.

Stößer, Jochen. "Market-based scheduling in distributed computing systems." [S.l. : s.n.], 2009. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000010437.

Cheung, Lap-sun. "Load balancing in distributed object computing systems." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2329428.

Bhasker, Bharat. "Query processing in heterogeneous distributed database management systems." Diss., Virginia Tech, 1992. http://hdl.handle.net/10919/39437.

Hines, Kenneth J. "Coordination-centric debugging for heterogeneous distributed embedded systems /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6914.

Grewe, Dominik. "Mapping parallel programs to heterogeneous multi-core systems." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8852.

Panagiotidis, Alexandros [Verfasser], and Thomas [Akademischer Betreuer] Ertl. "Visualization challenges in distributed heterogeneous computing environments / Alexandros Panagiotidis ; Betreuer: Thomas Ertl." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2016. http://d-nb.info/1118368401/34.

Simons, Christof. "Context aware applications in mobile distributed systems /." Aachen : Shaker, 2008. http://d-nb.info/987900757/04.

Olson, Chandra. "Jini an investigation in distributed computing /." [Florida] : State University System of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7122/chandra.PDF.

Obrovac, Marko. "Chemical Computing for Distributed Systems: Algorithms and Implementation." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00925257.

Gerami, Majid. "Coding, Computing, and Communication in Distributed Storage Systems." Doctoral thesis, KTH, Kommunikationsteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193887.

Pages 153-168 are removed due to copyright reasons.

QC 20161012

Worek, William J. "Matching Genetic Sequences in Distributed Adaptive Computing Systems." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34374.

Gelado, Fernández Isaac. "On the programmability of heterogeneous massively-parallel computing systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/6031.

Daga, Mayank. "Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32535.

Adurti, Devi Abhiseshu, and Mohit Battu. "Optimization of Heterogeneous Parallel Computing Systems using Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21834.

Athauda, Rukshan Indika. "Integration and querying of heterogeneous, autonomous, distributed database systems." FIU Digital Commons, 2000. http://digitalcommons.fiu.edu/etd/1332.

Simons, Christof. "Context aware applications in mobile distributed systems." Aachen Shaker, 2007. http://d-nb.info/987900757/04.

Wong, Ying-ying. "Process migration for distributed Java computing." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43085386.

Dey, Akon Samir. "Cherry Garcia: Transactions across Heterogeneous Data Stores." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14212.

Torres-Rojas, Francisco Jose. "Efficient time representation in distributed systems." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/8301.

Wong, Ying-ying, and 王瑩瑩. "Process migration for distributed Java computing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43085386.

Zhou, Wanlei, and mikewood@deakin edu au. "Building reliable distributed systems." Deakin University. School of Computing and Mathematics, 2001. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.160921.

Diamos, Gregory Frederick. "Harmony: an execution model for heterogeneous systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42874.

Mellor, Paul Vincent. "An adaptation of Modula-2 for distributed computing systems." Thesis, University of Hull, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327802.

Sriram, M. G. "Stochastic analysis of load imbalance in distributed computing systems /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487862399451975.

Crellin, Kenneth Thomas. "Network time : synchronisation in real time distributed computing systems." Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/17933.

Nachin, Mergen. "Scaling RFID positioning systems using distributed and split computing." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/129111.

al-Safadi, Yasser Haycam. "Distributed computing environment for standards based multimedia healthcare systems." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187400.

Reid, Jason Frederick. "Enhancing security in distributed systems with trusted computing hardware." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16379/1/Jason_Reid_Thesis.pdf.

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Reliability of Trust Management Systems in Cloud Computing

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

Guest Editorial: Special Section on Parallel and Distributed Computing Techniques for Non-Von Neumann Technologies

Asynchronous rpc interface in distributed computing system, developing an efficient secure query processing algorithm on encrypted databases using data compression.

Abstract Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

Deep distributed computing to reconstruct extremely large lineage trees

Distributed computing and artificial intelligence, volume 2: special sessions 18th international conference, software engineering, artificial intelligence, networking and parallel/distributed computing, chinese keyword extraction model with distributed computing, on allocation of systematic blocks in coded distributed computing, export citation format, share document.

CS595: Hot Topics in Distributed Systems: Data-Intensive Computing

Quarter: Fall 2010 Lecture Time: Monday/Wednesday, 1:50PM - 3:15PM Lecture Location: Stuart Building 106 Office Hours Time: Wednesday, 3:15PM - 4:15PM Office Hours Location: Stuart Building 237D Professor: Dr. Ioan Raicu ([email protected] )

The support for Data Intensive Computing is critical to advancing modern science as storage systems have experienced an increasing gap between its capacity and its bandwidth by more than 10-fold over the last decade. There is an emerging need for advanced techniques to manipulate, visualize and interpret large datasets. Building large scale distributed systems that support data-intensive computing involves challenges at multiple levels, from the network (e.g., transport, routing) to the algorithmic (e.g., data distribution, resource management) and even the social (e.g., incentives). This course is a tour through various research topics in distributed systems, covering topics in cluster computing, grid computing, supercomputing, and cloud computing. We will explore solutions and learn design principles for building large network-based computational systems to support data intensive computing. Our readings and discussions will help us identify research problems and understand methods and general approaches to design, implement, and evaluate distributed systems to support data intensive computing. Topics include resource management (e.g. discovery, allocation, compute models, data models, data locality, virtualization, monitoring, provenance), programming models, application models, and system characterization. Our discussions will often be grounded in the context of deployed distributed systems, such as the TeraGrid, Amazon EC2 and S3, various top supercomputers (e.g. IBM BlueGene/P, Sun Constellation, Cray XT5), and various software/programming platforms (e.g. Google's MapReduce, Hadoop, Dryad, Sphere/Sector, Swift/Falkon, and Parrot/Chirp). The course involves lectures, outside invited speakers, discussions of research papers, and a major project (including both a written report and an oral presentation).

Lecture topics:

Last modified: July 07, 2011

General Thesis Information

On this page you find all the information you need if you are interested in writing a Bachelor’s or Master’s thesis at DOS. Please read the content of this page carefully before inquiring about a topic.

Who can write a thesis with us and what are the prerequisites?

In general, any student with an interest and some experience in the group’s research areas is welcome to write a Bachelor’s or Master’s thesis with us. To ensure the successful completion of a thesis, we see the following requirements:

• Completion of at least a seminar or a project or a bachelor thesis at our chair.
• Good programming skills in one or more of the following languages: Java, Scala, Python, C, and Go. The specific necessary programming language typically depends on the particular research topic.
• Familiarity with common tools such as version control tools, IDEs, and LaTeX.
• Motivation to work scientifically and basic academic writing skills.
• Ability to work independently.

How do you get a topic?

Topics are offered by our individual group members. You can find a couple of thesis announcements on our website, but since not all ideas make their way there, feel invited to also make yourself familiar with our current research areas (e.g. take a look at the current projects and recent publications) and then contact the respective researchers directly. Please, do not write to the entire group. In any case, please provide the following information so that we get a first idea which topics might fit you well:

• Areas of interest for the thesis
• Your academic background (e.g. list of completed modules, transcript of records)
• A CV containing experiences in relevant topics from projects or employments
• What is the general process of writing a thesis?

Once you have met with one of our researchers and have found a topic that is interesting to you, we ask you to write an exposé of three to four pages in which you summarize the problem motivation, the related work and your idea for addressing the problem as well as an approximate timeline for your thesis project. Understand the exposé as a first version of the introduction section of your thesis later. Furthermore, it is often also a good reminder of how hard writing can be and helps to not forget the bigger picture in the beginning of the thesis project. Once you have received feedback on the proposal and you and the supervising researcher both agree with the exposé, you can register the thesis with the examination office.

During the thesis project, the supervising researcher will be available to provide guidance and feedback. At the end, we expect at least 30 pages for a Bachelor’s thesis and at least 60 pages for a Master’s thesis (single column, with a separate title page, table of contents, list of references). There is no official template at TU, so feel free to use any template that meets our requirements.

Please also take a look at the Study and Examination Regulations for your program, which contain further information about duration, language, and other conditions for the successful completion of a thesis.

Available topics

In the following you find currently available theses at the Bachelor’s or Master’s level. However, most of the topics are scalable and can be either expanded or narrowed down after consultation with the staff member.

If you are interested in one of the topics described below or would like to propose a related topic, please contact the respective person.

We strongly welcome individual modifications of the described topics and are open for additional suggestions. Please contact lehre ∂ dos.tu-berlin.de for any questions and suggestions.

• TrustLLM – Trustworthiness and characteristics of LLMs
• Resource Configuration for Graph Algorithms in Distributed Dataflow Systems
• Adaptive Memory Management for Scientific Workflow Tasks
• Context-Aware Performance Modeling for Resource Management of Distributed Dataflows
• Root cause analysis of errors to increase IT reliability
• Carbon-aware federated learning
• PDF Parsing Algorithm
• Finetuning of Large Language Models (LLM)
• LLM Routing (Query Complexity Estimation)
• Continual learning for Open Set Recognition with Deep Generative Models
• Dynamically Adjusting Cluster Resource Allocations for Batch Data Processing
• Resource Management for Adaptive Memory Requirements of Scientific Workflows
• Advancing Log Anomaly Detection and Root Cause Analysis for Enhanced IT Reliability in AI Operations (AIOps)

Ongoing Theses

• A Distributed Scheduler for Offloaded Real-Time Task in Self-Organized Wireless Networks [M] Kalin Iliev advised by Ilja Behnke since 2023-10
• Towards Predicting Runtimes of Distributed Batch Data Processing via Lightweight Profiling [B] Alaa Alhaidar advised by Jonathan Will since 2023-12
• Vergleichende Analyse der vertikalen und horizontalen Skalierung für Spark-Datenverarbeitungs-Workloads [B] Dogukan Canatan advised by Jonathan Will since 2024-02
• Comparative Evaluation of Profiling-based Cluster Resource Allocation Approaches for Batch Data Processing [B] Anton Liudchyk advised by Jonathan Will since 2024-04
• Improving Dynamic Memory Prediction for Scientific Workflows [B] Sven Hoferichter advised by Jonathan Bader since 2024-01
• Dynamic Memory Allocation for Large Scale Scientific Workflows in Kubernetes [M] Julian Marcel Tochman-Szewc advised by Jonathan Bader since 2023-12
• Improving Renewable Energy Utilization in Data Centers Through Probabilistic Computation Offloading [M] Gesche Gräfe advised by Philipp Wiesner since 2024-02
• Optimizing Renewable Energy Integration in Data Centers Through Co-Simulation [B] Julius Irion advised by Philipp Wiesner since 2024-03
• Enabling Federated Learning to Interact with Energy Systems [M] Ovidiu Tatar advised by Philipp Wiesner since 2024-03
• Avoiding Load Peaks in Testing-as-a-Service Applications [B] Mikolaj Cankudis advised by Philipp Wiesner since 2024-04
• Enabling Federated Learning to Interact with Energy Systems [B] Paul Kilian advised by Philipp Wiesner since 2024-04
• Adaptive Anomaly Detection in LogData: Investigating the Role of Predicted Anomalies in Continuous Learning [M] Oscar Heimrecht advised by Thorsten Wittkopp since 2024-02
• Efficiency of Different Retraining Strategies for Machine Learning Models for Log Anomaly Detection [B] Jonas Möller advised by Thorsten Wittkopp since 2023-10
• Automation of Kubernetes-Based Experiment Setups for Distributed Dataflow Systems [B] Julian Nic Hahn advised by Dominik Scheinert since 2024-02
• On the Feasibility of Lightweight Profiling for Performance Estimation of Distributed Data Processing Workloads in the Cloud [B] William Anton Knöpp advised by Dominik Scheinert since 2024-02
• Evaluation of Batch Workload Characterization Techniques for Performance Modeling of Distributed Data Processing Systems [M] Alexander Guttenberger advised by Dominik Scheinert since 2024-01
• Efficient Resource Allocation for Distributed Dataflows using Contextual Performance Modeling [M] Marvin Kronsbein advised by Dominik Scheinert since 2024-03
• Insights into Distributed System Failures: Location Anomalies in Log Data [B] Anis Ben Saada advised by Thorsten Wittkopp since 2024-05
• Bidding-Based Distributed Scheduling for Offloaded Real-Time Tasks [B] Jan Läpple advised by Ilja Behnke since 2024-04
• Finetunig Named Entity Recognition Models on Domain Specific Datasets [B] Frédéric Ndjiki-Nya advised by Thorsten Wittkopp since 2024-05
• Ansätze zur Gewährleitstung der Sicherheit von Patientendaten in IoMT Systemen [M] Daniel Yermakov advised by Ilja Behnke since 2024-05

• Affective Interactive Systems
• Immersive Multimedia Systems
• Quality of Experience (QoE) & User Experience (UX)
• Media Production, Consumption and Sharing
• Digital publishing
• Web technologies at W3C
• Publications
• CWIPC SXR Dataset
• CWI Point Cloud Quality Metrics
• CEAP-360VR Dataset
• 6-DoF Nav Dataset
• QoE Testbed
• Bio-Signal Data Processing and Visualisation
• Invisible XML

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Master's Thesis Topics 2021

Publication date: 2020-09-23

The Distributed & Interactive Systems group at CWI has new open positions for motivated students who would like to work on their Master’s thesis as an internship in the group. Topics include smart textiles , activity recognition , physiological sensing , virtual reality , point clouds , Internet of things and web technologies . Keep reading for more information about research topics, requirements and contact information.

One-shot Interaction for Emotion Recognition using Physiological Signals

Contact: Tianyi Zhang ( [email protected] ), Abdallah El Ali ( [email protected] ), Pablo Cesar ( [email protected] )

Traditional approaches of emotion recognition rely either on custom models trained in situ, or general models pre-trained on existing datasets. The first kind of methods need users to annotate their emotions in situ. Since the data is collected in-situ as well, accuracy tends to be quite high while the burden of annotation to the user is also high. The second kind of methods build the recognition system based on the information learned from other datasets, which leads to lower annotation burden to the user. However, since there is no data and annotation specifically for one individual user, the recognition accuracy on that user tends to be less accurate. To overcome these challenges, this work will develop an emotion recognition system aiming to provide high classification accuracy, while minimizing the annotation burden of users. The system will use self-supervised and one-shot learning to first automatically learn the data representation in situ and only require users to input their emotions (annotation) when labels are needed to fine-tune the algorithm for better accuracy. The self-supervised learning algorithm will automatically determine the annotation frequency to make a tradeoff between annotation burden and recognition accuracy. The work of this project includes hardware and software implementation of emotion recognition using self-supervised learning techniques. The work also includes experiments to evaluate the user burden of annotation and the recognition accuracy compared with other methods (i.e., systems trained in situ and pre-trained on other datasets).

• Knowledge about machine learning, especially about one shot learning and self-supervised learning
• Knowledge about time series signal processing
• Good programming skills in python, Java (for Android)
• Deep learning
• Human computer interaction

Literature:

• CHI 2020: “Automated Class Discovery and One-Shot Interactions for Acoustic Activity Recognition” https://dl.acm.org/doi/10.1145/3313831.337687
• Sarkar P, Etemad A. Self-supervised learning for ecg-based emotion recognition. InICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 May 4 (pp. 3217-3221). IEEE.
• Banville H, Moffat G, Albuquerque I, Engemann DA, Hyvärinen A, Gramfort A. Self-supervised representation learning from electroencephalography signals. In2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) 2019 Oct 13 (pp. 1-6). IEEE.
• Li Fei-Fei, Rob Fergus, and Pietro Perona. 2006. Oneshot learning of object categories. IEEE transactions on pattern analysis and machine intelligence. IEEE, 594- 611.
• Oriol Vinyals et al. 2016. Matching networks for one shot learning. Advances in neural information processing systems (NIPS). 3630-3638
• Saeed A, Ozcelebi T, Lukkien J. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2019 Jun 21;3(2):1-30.

Posed vs. Spontaneous Smile Dataset using Face, Muscle, and Physiological Sensing

Contact: Gerard Pons [email protected] , Abdallah El Ali ( [email protected] ), Pablo Cesar ( [email protected] )

Facial expressions play an important role in daily social life and communication between people. It is a non-verbal way for people to show their emotions and intentions. A special type of facial expressions are micro-expressions. Micro-expressions occur when people try to hide their true feelings and emotions. This can be deliberate concealment (suppression) or unconscious concealment (repression). The recognition of micro-expressions is useful for different applications such as security, interrogations and clinical diagnosis. It is hard for people to recognize micro-expressions because of the short duration and low intensity. Therefore in the last years research about the automatic recognition of micro-expressions is getting more attention. Most of these works use RGB videos and images for automatic recognition. This project asks: How to collect a spontaneous micro-smile dataset that contains thermal videos, RGB videos and EMG signals? How well does micro-smile detection from thermal videos compare with other modalities (e.g.RGB, EMG, EDA, HR)? The main objective of this work is creating a new spontaneous micro-smile dataset with synchronised thermal videos, RGB videos and EMG signals, as well as baseline performance measures. This is a joint project with Monica Perusquia-Hernandez at NTT (Japan).

• Machine learning
• Computer vision
• Time series signal processing
• CHI 2020: “The invisible potential of facial electromyography: A comparison of EMG and computer vision when distinguishing posed from spontaneous smiles.” https://dl.acm.org/citation.cfm?id=3300379
• ICCV 2011: Recognizing spontaneous facial micro- expressions. https://ieeexplore.ieee.org/abstract/document/6126401
• JNB 2009: “All smiles are not created equal: Morphology and timing of smiles perceived as amused, polite, and embarrassed/nervous” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2701206/
• TENCON 2004: “Facial expression recognition through pattern analysis of facial muscle movements utilizing electromyogram sensors.” https://ieeexplore.ieee.org/document/1414843/
• IEEE Trans. Affective Computing 2018: “SAMM: A spontaneous micro-facial movement dataset.” https://ieeexplore.ieee.org/document/7492264
• ECCV 2012: “Are you really smiling at me? spontaneous versus posed enjoyment smiles.” https://link.springer.com/chapter/10.1007/978-3-642-33712-3_38
• ICSP 2016: “Spontaneous facial micro-expression detection based on deep learning.” https://ieeexplore.ieee.org/document/7878004
• IEEE Trans. Affective Computing 2018: “Cas(me)2 : A database for spontaneous macro- expression and micro-expression spotting and recognition” https://ieeexplore.ieee.org/document/7820164

Developing and Comparing Novel Emotion Elicitation Methods

Contact: Abdallah El Ali ( [email protected] ), Pablo Cesar ( [email protected] )

Affect is a fundamental aspect of internal and external human behavior and processes. While much research has been done on eliciting emotions, it remains a challenge what is the most effective method(s) for inducing emotions, and under which context. In this project, you will design and build novel multi sensory elicitation techniques: these can be visual, auditory, haptic, smell, taste, or heat. You can focus on one or more, with the goal of providing an unobtrusive and enjoyable experience that can elicit different emotion states.

• Hardware prototyping (e.g. Arduino)
• Experiment design (controlled, field)
• Interest in human computer interaction
• Front Psychol. 2014: “How does this make you feel? A comparison of four affect induction procedures” https://www.ncbi.nlm.nih.gov/pubmed/25071659
• Front Psychol. 2016: “Emotion Elicitation: A Comparison of Pictures and Films” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756121/
• AutoUI EA 2018: “A Comparison of Emotion Elicitation Methods for Affective Driving Studies” https://dl.acm.org/citation.cfm?id=3265945
• ICEEPSY 2012: “Using mood induction procedures in psychological research” https://www.sciencedirect.com/science/article/pii/S1877042812053669
• CHI EA 2016: “Touch, Taste, & Smell User Interfaces: The Future of Multisensory HCI” https://dl.acm.org/doi/10.1145/2851581.2856462
• Interactions 2016: “SENSING THE FUTURE OF HCI: TOUCH, TASTE, AND SMELL USER INTERFACES” https://interactions.acm.org/archive/view/september-october-2016/sensing-the-future-of-hci
• CHI 2017: “Essence: Olfactory Interfaces for Unconscious Influence of Mood and Cognitive Performance” https://dl.acm.org/doi/abs/10.1145/3025453.3026004

Emotion Annotation across Smell, Taste, and/or Temperature

Our annotations are discrete, but our sensations are continuous. In this project, we aim to investigate multi-sensory emotion annotation techniques by actuating our senses. In this project, you will focus on one of these sensations, or perhaps a combination, and investigate different techniques of describing felt sensations. You will need to be comfortable building multi-sensory actuators, whether haptic, olfactory, or taste-based interfaces.

• Electronics & hardware prototyping (e.g. Arduino)
• GUI development (mobile or desktop)
• Experiment design (controlled)
• Controlled user studies
• IJHCS 2013: “AffectButton: A method for reliable and valid affective self-report” https://www.sciencedirect.com/science/article/pii/S1071581913000220
• IEEE Transactions on Affective Computing 2017: “Continuous, real-time emotion annotation: A novel joystick-based analysis framework” https://ieeexplore.ieee.org/document/8105870
• ISS 2017: “OSpace: Towards a Systematic Exploration of Olfactory Interaction Spaces” https://dl.acm.org/doi/10.1145/3132272.3134121
• CHI 2020: “ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect.” https://dl.acm.org/doi/abs/10.1145/3313831.3376682
• CHI “2020: RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth Labels.” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020.

Design and Development of a Steering Wheel for Emotion Annotation

Contact: Abdallah El Ali ( [email protected] ), Pablo Cesar ( [email protected] ), Kaspar Jansen (Design Engineering - TU Delft)

Emotion recognition has moved away from the desktop, and on to the road, whether in automated or non-automated vehicles. This requires collecting precise ground truth labels in such settings, that do not pose driver distraction (whether the primary task is driving or situation monitoring in the case of automated driving). This project asks: How can drivers enter their mood while driving? This is a joint project with the Design Engineering department at TU Delft. It will require prototyping emotion input techniques on the steering wheel, and evaluating them in a desktop-based driving simulator study to ensure high usability of the wheel concept and high quality of the collected annotations.

• Sensors and input techniques
• Design engineering methods
• Graphics Interface 2007: “Eyes on the road, hands on the wheel: thumb-based interaction techniques for input on steering wheels” https://dl.acm.org/citation.cfm?id=1268535
• CHI 2011: “Gestural interaction on the steering wheel: reducing the visual demand” https://dl.acm.org/citation.cfm?id=1979010
• CHI 2013: “The wheels are turning: content rotation on steering wheel displays” https://dl.acm.org/citation.cfm?id=2466238
• UIST 2017: “Reinventing the Wheel: Transforming Steering Wheel Systems for Autonomous Vehicles” https://dl.acm.org/citation.cfm?id=3126655
• AutomotiveUI 2012:“Exploring the back of the steering wheel: text input with hands on the wheel and eyes on the road” https://dl.acm.org/citation.cfm?id=2390275
• AutomotiveUI 2012: “Multimodal interaction in the car: combining speech and gestures on the steering wheel” https://dl.acm.org/citation.cfm?id=2390282
• AutomotiveUI 2014: “The Steering Wheel as a Touch Interface: Using Thumb-Based Gesture Interfaces as Control Inputs While Driving” https://dl.acm.org/citation.cfm?id=2667299
• AutomotiveUI 2017: “Force-enabled Touch Input on the Steering Wheel: An Elicitation Study” https://dl.acm.org/citation.cfm?id=3131740

Exploring Emotion-reactive Wearables using Physiological Sensing

Wearable biotech fashion is becoming a recent trend, however we still know very little on what the best means of visualizing such biometric data. This is a project to explore the intersection between fashion, aesthetics, and wearable biotech sensors. The project should result in a series of emotion-reactive wearable prototypes, and ideally evaluated in the field. This can be a joint project with the Design Engineering department at TU Delft.

• Textile fabrication methods
• Multimodal output
• Smart Textiles and Wearable Technology – A study of smart textiles in fashion and clothing http://www.diva-portal.org/smash/get/diva2:884011/FULLTEXT01.pdf%20adresinden%2010.04.2017
• IEEE Information Visualisation 2010: Wearing Emotions: Physical Representation and Visualization of Human Emotions Using Wearable Technologies https://ieeexplore.ieee.org/document/5571298/
• TEXTILE 2005: Electronic Textiles: Wearable Computers, Reactive Fashion, and Soft Computation https://www.tandfonline.com/doi/abs/10.2752/147597505778052639
• CHI EA 2017: LightingHair Slice: Situated Personal Wearable Fashion Interaction System https://dl.acm.org/citation.cfm?id=3053093
• Tangible Apps Bracelet: Designing Modular Wrist-Worn Digital Jewellery for Multiple Purposes https://dl.acm.org/citation.cfm?doid=2901790.2901838
• CSCW 2017: Can Biosignals be Expressive?: How Visualizations Affect Impression Formation from Shared Brain Activity https://dl.acm.org/citation.cfm?id=3134706
• CHI 2017: HeartChat: Heart Rate Augmented Mobile Messaging to Support Empathy and Awareness https://dl.acm.org/citation.cfm?id=3025758
• CHI 2018: AlterWear: Battery-Free Wearable Displays for Opportunistic Interactions https://dl.acm.org/citation.cfm?id=3173574.3173794
• TEI 2018: HairI ¨ O: Human Hair as Interactive Material https://dl.acm.org/citation.cfm?id=3173232

ThermalWear II: Exploring Wearable Thermal Displays to Augment Human-Machine Interactions with Affect

Thermal stimulation is an intrinsic aspect of sensory and perceptual experience, and is tied with several experience facets, including cognitive, emotional, and social phenomena. The capability of thermal stimuli to evoke emotions has been demonstrated in isolation, or to augment media. This project will build on our prior work (see CHI 2020 paper “ThermalWear” below), and extended our ThermalWear prototype in one of several ways: (a) developing a user calibration model (b) multi-site actuation (c) dialogue systems (d) multimodal feedback. The project should result in tangible prototypes, and evaluated in a controlled study or in the field. This can be a joint project with the Design Engineering department at TU Delft.

• Fabrication
• Thermal and/or multimodal output
• CHI 2015: “In the Heat of the Moment: Subjective Interpretations of Thermal Feedback During Interaction” https://dl.acm.org/citation.cfm?id=2702123.2702219
• CHI 2016: “Hot Under the Collar: Mapping Thermal Feedback to Dimensional Models of Emotion” https://dl.acm.org/citation.cfm?id=2858036.2858205
• ICMI 2011: “Emotional responses to thermal stimuli” https://dl.acm.org/citation.cfm?id=2070513
• HAID 2013 “Cold or Hot? How Thermal Stimuli Are Related to Human Emotional System?” https://link.springer.com/chapter/10.1007/978-3-642-41068-0_3
• CHI 2017: “The Heat is On: A Temperature Display for Conveying Affective Feedback” https://dl.acm.org/citation.cfm?id=3025844
• MobileHCI ‘12: “Thermal icons: evaluating structured thermal feedback for mobile interaction” https://dl.acm.org/citation.cfm?id=2371621

FaceEmotionInput: Using Facial Expressions for Mood Input on Smartphones

Tracking and recognizing emotions has moved away from the desktop, and on to mobile and ubiquitous settings. We now can enter how we feel on mobile apps, and even on smartwatches. However, entering our moods is cumbersome. In this project, you will devise an unobtrusive, low(or no)-burden self-report collection strategy. The idea is to have a passive sensing method, that does not require active annotation from the participant. You will build on our own prior work (e.g., Face2Emoji, RCEA), and develop a facial expression-based input method. This can be evaluated remotely, or in controlled, user studies.

• Android development
• (Basic) machine learning and computer vision
• Quantitative research methods
• Sensors 2018: “A brief review of facial emotion recognition based on visual information.” https://www.mdpi.com/1424-8220/18/2/401
• IJMLC 2020: “A survey on analysis of human faces and facial expressions datasets.” https://link.springer.com/article/10.1007/s13042-019-00995-6
• CHI 2017: “Face2emoji: Using facial emotional expressions to filter emojis.” https://dl.acm.org/doi/pdf/10.1145/3027063.3053086
• IEEE TAFFC 2017: “Continuous, real- time emotion annotation: A novel joystick-based Analysis framework. https://ieeexplore.ieee.org/document/8105870
• CHI 2020: “RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth Labels.” https://dl.acm.org/doi/abs/10.1145/3313831.3376808

CakeVR: Designing and Evaluating Interaction Techniques for Making Cakes in Virtual Reality (VR)

Contact: Jie Li ( [email protected] ), Pablo Cesar ( [email protected] )

We have developed a social VR tool for pastry chefs and clients to remotely co-design cakes (Mei, 2020), including using natural gestures to manipulate the cake size, decorating the cakes with pre-designed cake components (e.g., cream flowers, fruits), and showing an instant 3D visualization of the co-design outcome. However, many interaction techniques in the current prototype need to be improved. For example, how to precisely position a decoration on the virtual cake, how to perform mid-air sketch stably, how to calculate the dimension of the cake, and how to duplicate and group decorations.

In this project, you will identify the necessary interactions for collaborative creative tasks such as making cakes, and design and implement the interaction techniques for a smooth cake co-design experience.

This project will require knowledge of Unity. It will involve building 3D interaction models and running controlled studies to collect (and later analyze) data about user experiences of the developed interaction techniques.

• Programming, prototyping (C#/Unity)
• Running user studies
• Quantitative and qualitative analysis methods
• Scott W Greenwald, Alexander Kulik, André Kunert, Stephan Beck, Bernd Fröhlich, Sue Cobb, Sarah and others. 2017. Technology and applications for collaborative learning in virtual reality. Philadelphia, PA: International Society of the Learning Sciences.
• Jan Gugenheimer, Evgeny Stemasov, Julian Frommel, and Enrico Rukzio. 2017. Sharevr: Enabling co-located experiences for virtual reality between hmd and non-hmd users. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 4021–4033.
• Hikaru Ibayashi, Yuta Sugiura, Daisuke Sakamoto, Natsuki Miyata, Mitsunori Tada, Takashi Okuma, Takeshi Kurata, Masaaki Mochimaru, and Takeo Igarashi. 2015. Dollhouse VR: a multi-view, multi-user collaborative design workspace with VR technology. In SIGGRAPH Asia 2015 Emerging Technologies. ACM, 8.
• Yanni Mei (2020). Design a social VR tool for the remote co-design of customized cakes. Master’s Thesis. Delft University of Technology. http://resolver.tudelft.nl/uuid:78a1147b-e97b-418f-a5e6-3ce944df4f49

Designing for Serendipitous Social Encounters in Online Museum Exhibits

Contact: Alina Striner ( [email protected] ), Pablo Cesar ( [email protected] )

Interactive exhibits in museum spaces create opportunities for serendipitous social encounters. These social encounters act as icebreakers in academic settings, allowing students and researchers to form connections with peers in a relaxed informal setting. COVID-19 has limited in-person social encounters, making it difficult for people to form such connections in the physical world.

How should we redesign interactive museum exhibits in the virtual world for serendipitous encounters? This project will redesign an interactive museum exhibit to emulate serendipitous in-person encounters, considering aspects of storytelling through 3D space design. This project will require knowledge of unity and 3D environments. The project will involve building a prototype of an interactive museum exhibit, and will involve running controlled studies to collect (and later analyze) data about user experiences in these environments.

• Experience with Unity
• VR/AR development
• Interest in human-computer interaction
• Karin Ryding. 2020. The Silent Conversation: Designing for Introspection and Social Play in Art Museums. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–10.
• Karen Johanne Kortbek and Kaj Grønbæk. 2008. Communicating art through interactive technology: new approaches for interaction design in art museums. In Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges. 229–238.
• Astrid Bin, Christina Bui, Benjamin Genchel, KaushalSali, Brian Magerko, and Jason Freeman.[n.d.]. From Museum The browser: Translating a music-driven exhibit from physical space to a web app. ([n. d.]).
• Areti Damala, Ian Ruthven, and Eva Hornecker. 2019. The MUSETECH model: A comprehensive evaluation framework for museum technology. Journal on Computing and Cultural Heritage (JOCCH) 12, 1 (2019), 1–22
• Jordan Graves and Brian Magerko. 2020. Community garden: designing for connectedness in online museum exhibits. In Proceedings of the 2020 ACM. Interaction Design and Children Conference: Extended Abstracts. 268–271.

Continuous Evaluation of Quality of Experience in Virtual Reality

Contact: Irene Viola ( [email protected] ), Abdallah El Ali ( [email protected] ), Pablo Cesar ( [email protected] )

Recent advances in 3D acquisition and rendering technologies, such as low-cost sensors and cross reality (XR) devices, as well as commodity hardware with sufficient computational power, have led to a renewed interest in photo-realistic immersive virtual reality experiences, such as 360-degree videos. Such experiences are characterized by having more degrees of freedom with respect with traditional media, as the user can freely navigate and does not visualize the entire content at the same time. This may lead to different perceived quality across different parts of the 360-degree video, if they are not encoded using the same parameters. Users are generally asked to give an opinion on the overall quality of experience after they are done visualizing it, thus averaging across the variations they have seen.

The question we want to answer is: can we continuously capture the perceived visual quality of an immersive content, and how does it relate to the final user judgement? How does the continuous annotation task affect the VR experience? The project requires knowledge in Unity to develop a continuous annotation system for VR. Alternative methods for continuous annotation will be designed and tested through user studies to understand the benefits and drawbacks.

• Good Unity programming skills
• Image and video processing basics
• Design skills
• Mario Graf, Christian Timmerer, and Christopher Mueller. 2017. Towards Bandwidth Efficient Adaptive Streaming of Omnidirectional Video over HTTP: Design, Implementation, and Evaluation. In Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17). Association for Computing Machinery, New York, NY, USA, 261–271. DOI:https://doi.org/10.1145/3083187.3084016
• BT, RECOMMENDATION ITU-R. “Methodology for the subjective assessment of the quality of television pictures.” International Telecommunication Union (2002).
• Xue, T., Ghosh, S., Ding, G., El Ali, A., & Cesar, P. (2020, April). Designing Real-time, Continuous Emotion Annotation Techniques for 360° VR Videos. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-9).

Deep Learning-based Methods for Point Cloud Compression

Contact: Irene Viola ( [email protected] ), Pablo Cesar ( [email protected] )

Point cloud representation has seen a surge of popularity in recent years, thanks to its capability to reproduce volumetric scenes in immersive scenarios. A point cloud is a collection of unordered points in 3D space. Attributes such as color and normal information are commonly associated with each point. As no connectivity information among the points needs to be stored, they result in faster rendering, thus making them suitable for real-time systems. New compression solutions for streaming of point cloud contents have been proposed and are currently being standardized by the MPEG standardization body. In the last years, several deep learning-based solutions have emerged to perform compression of point cloud contents. However, they have largely focused on encoding of the geometrical structure, and few have been proposed to tackle attribute encoding. Extending such methods to dynamic (i.e., video) sequences adds the additional complexity of considering temporal redundancy.

In this project, you will design a deep learning-based method for compression of dynamic point cloud sequences. After selecting suitable contents for training and testing, the proposed method will be compared to the state of the art to assess its performance and coding efficiency.

• Good knowledge of deep learning methods and frameworks (TensorFlow, PyTorch)
• Image and Video Processing basics
• Computer graphics
• S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. Cesar, et.al. Emerging MPEG Standards for Point Cloud Compression. IEEE Journal on Emerging and Selected Topics in Circuits and Systems (IEEE JETCAS), 9(1) : pp. 133-148, 2019.
• Quach, M., Valenzise, G., & Dufaux, F. (2020). Improved deep point cloud geometry compression. arXiv preprint arXiv:2006.09043.
• Guarda, A. F., Rodrigues, N. M., & Pereira, F. (2020, July). Deep Learning-Based Point Cloud Geometry Coding: RD Control Through Implicit and Explicit Quantization. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). IEEE.
• Alexiou, E., Tung, K., & Ebrahimi, T. (2020, August). Towards neural network approaches for point cloud compression. In Applications of Digital Image Processing XLIII (Vol. 11510, p. 1151008). International Society for Optics and Photonics.

Objective Metrics for Point Cloud Quality Assessment

Volumetric data captured by state of the art capture devices, in its most primitive form, consists of a collection of points called a point cloud (PC). A point cloud consists of a set of individual 3D points. Each point, in addition to having a 3D (x, y, z) position, i.e., spatial attribute, may also contain a number of other attributes such as color, reflectance, surface normal, etc. There are no spatial connections or ordering relations specified among the individual points. When a PC signal is processed, for example undergoing lossy compression to reduce its size, it is critical to be able to quantify how well the processed signal is approximating the original one, as in the perception of the end user, which is the human being who will visualize the signal. The goal of this project is to develop a new algorithm (i.e. objective full-reference quality metric) to evaluate the perceptual fidelity of a processed PC with respect to its original version. A framework implementing the objective metrics currently available in literature to assess PC visual quality, and comparing the performance to the proposed method will also be developed. Subjective feedback on the visual quality of the signals will be collected from users to serve as ground-truth.

• Good (Matlab, Python, or C++) programming skills
• E. Torlig; E. Alexiou; T. Fonseca; R. de Queiroz; T. Ebrahimi, A novel methodology for quality assessment of voxelized point clouds”, 2018. SPIE Optical Engineering + Applications
• E. Alexiou; T. Ebrahimi, “Benchmarking of objective quality metrics for colorless point clouds”, 2018 Picture Coding Symposium
• Meynet, G., Nehmé, Y., Digne, J., & Lavoué, G. (2020, May). PCQM: A Full-Reference Quality Metric for Colored 3D Point Clouds. In 12th International Conference on Quality of Multimedia Experience (QoMEX 2020).
• Viola, I., Subramanyam, S., & César, P. (2020). A color-based objective quality metric for point cloud contents. In 12th International Conference on Quality of Multimedia Experience (QoMEX 2020).

Group leader: Pablo Cesar

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Data-Driven Computing and Networking Solution for Securing Cyber-Physical Systems

In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.

RNCP: A RESILIENT NETWORKING AND COMPUTING PARADIGM FOR NASA SPACE EXPLORATION

National Aeronautics and Space Administration

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Socio-economic inequalities drive antimicrobial resistance risks. Here's how – and how to curb it

The effects of antimicrobial resistance are not spread equally across society. Image:  Unsplash/Towfiqu barbhuiya

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Gunnar ljungqvist, victoria saint.

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved .chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} with our crowdsourced digital platform to deliver impact at scale

• Antimicrobial resistance is one of the biggest global challenges of our time. It threatens public health and requires urgent action.
• The driving forces of antimicrobial resistance are unequally distributed across society, with vulnerable populations at the sharp end of associated health and economic risks and burdens.
• International and national antimicrobial resistance policies must consider social and economic factors to maximize their effectiveness and minimize health inequalities.

Antimicrobial resistance (also known as AMR) is estimated to be directly responsible for 1.27 million deaths in 2019 alone , equivalent to approximately 3,500 people each day. This number is estimated to reach 10 million per annum by 2050 without substantial action.

The economic consequences of antimicrobial resistance for individuals, health systems and society are also considerable. Individuals who contract antimicrobial-resistant infections are at risk of mortality, treatment delays, long-term disability, lost income and debt or poverty from high health costs, with suffering and socio-economic impacts for families and communities.

Antimicrobial resistance increases financial pressure on health systems as it leads to more hospitalizations, longer stays, more expensive diagnosis and treatment and reduced ability to provide treatments such as chemotherapy and surgical care safely.

It can also negatively impact national economies, which may have fewer and less productive workers and populations spending more on health care. If infections cannot be prevented and treated, trade and agriculture can also be negatively affected, with increased death and illness among farmed animals.

However, the consequences of antimicrobial resistance are not equally distributed across society.

Have you read?

Antimicrobial resistance: avoiding antibiotic overconsumption with the right data, it's a bigger killer than hiv/aids and malaria. here's what we can do to beat antimicrobial resistance, who does antimicrobial resistance affect.

Social, cultural and biological factors mean that women are more likely than men to experience occupational exposure to antimicrobial resistance and to be prescribed antimicrobials for several infections.

The risk of its development is heightened in populations living in urban and overcrowded environments with limited access to clean water, sanitation, and hygiene (WASH) infrastructure, as is the case in many low- and middle-income countries. Populations with limited access to formal healthcare can experience increased inappropriate antibiotic use due to poorly regulated access. Many countries also experience persistent shortages of essential antimicrobials, leaving them infected for longer periods and given less targeted antimicrobials.

Conditions in conflict-affected areas make infections easier to spread, with populations who are forcibly displaced also facing fragile healthcare systems, supply of essential antimicrobials and access to WASH infrastructure. Implementing effective policies to combat antimicrobial resistance is also challenging in contexts with political instability, limited rule of law and higher levels of corruption .

How environmental factors impact resistant infections

Climate change is a significant driver of antimicrobial resistance as rising ambient temperatures increase the proliferation of bacteria while also contributing to extreme weather events that can disrupt healthcare services, displace communities and reduce access to sanitation.

The use of antimicrobials on animals, together with poor measures to prevent and control infections, can further drive antimicrobial resistance. Drug-resistant pathogens can be passed between animals and humans in occupational settings and through food contamination.

Pollution is also a major driver of antimicrobial resistance in the environment – for example, through waste from the pharmaceutical and healthcare industries, heavy metals from industrial and agricultural processes, particle pollution in the air and plastic waste in our water systems.

Policies must go beyond ‘drugs and bugs’

Until recently, policymakers and academics have primarily focused on the medical and microbiological factors that drive antimicrobial resistance. There has been insufficient focus on the relationship between the social and economic factors described here and the emergence of antimicrobial-resistant infections.

A new report published by the European Observatory on Health Systems and Policies, distilling findings from research supported by the World Economic Forum and the Novo Nordisk Foundation, sheds light on key socio-economic drivers and impacts of antimicrobial resistance and what this will mean for policy and research.

It helps explain how these factors can impact our health and economies and the effectiveness of measures to tackle antimicrobial resistance at the individual, health system and societal levels.

The policy report argues that four socio-economic elements must be built into the design of antimicrobial resistance policy and policy implementation, monitoring and evaluation (Figure 1):

• Governance and leadership that are mindful of the socio-economic drivers and impacts of antimicrobial resistance are central to co-ordinate action across different sectors.
• Action focused on people and that fosters equity and encourages policies responsive to individuals’ needs.
• Multi-sectorality recognizes antimicrobial resistance policy as a cross-cutting issue with involvement from various government departments and levels.
• Best evidence-informed policies help ensure that they look beyond the biomedical and value research from different disciplines and approaches to tackle the socio-economic drivers and impacts of antimicrobial resistance.

The report describes how these overarching principles can be incorporated into four core antimicrobial resistance policy areas.

• Stewardship relates to the responsible use of pre-existing and new antimicrobials, in humans and animals. However, many people, especially in low- and middle-income countries still die from a lack of or delayed access to antimicrobials. A balanced approach is therefore needed to promote rational use of antimicrobials while not inadvertently creating barriers for populations in situations of vulnerability.
• Prevention highlights the need for more investment in programmes that prevent and control infections in community and healthcare settings and biosecurity measures in animal health settings. Policymakers must also not neglect the global need for equitable access to WASH infrastructure.
• Access relates to ensuring equitable access to essential health services, antimicrobials, and diagnostics. This requires reducing barriers to accessing healthcare, changing procurement policy and strengthening the supply of antimicrobials in poorer and richer countries.
• Innovation in antimicrobial resistance health technologies seeks to improve prevention, diagnosis and treatment of infections. Stimulating research and development requires investment and incentivization but in ways that match the needs and operational contexts of lower resource settings, including access and affordability.

Many policymakers are currently revisiting their policy priorities related to antimicrobial resistance. At the international level, the UN General Assembly is holding a high-level meeting , which is expected to result in a political declaration on global antimicrobial resistance priorities and commitments. Simultaneously, many countries are revising their national action plans on antimicrobial resistance.

Policymakers and other stakeholders must leverage this critical window of opportunity to move beyond a “drugs and bugs” approach to antimicrobial resistance towards a more holistic, people-centred, equity-oriented approach that tackles key social and economic drivers and impacts of antimicrobial resistance.

Learn more by registering for the European Observatory webinar series “From evidence to practice in AMR prevention and control” on 21 May and 23 May 2024.

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OPINION article

This article is part of the research topic.

Emerging Technologies for the Construction of Renewable Energy-Dominated Power System

Opinions on Hosting Capacity Evaluation of Distribution Network with Zonotope Power Flexibility Aggregation Provisionally Accepted

• 1 Hunan University, China
• 2 Shenzhen Polytechnic, China
• 3 Shandong University, China

The final, formatted version of the article will be published soon.

The installation of solar photovoltaic (PV) systems has been stimulated by governmental incentive mechanisms and the continual reduction in technology costs in recent years [1]. However, with the substantial integration of distributed PV systems at high penetration levels [2], reverse power flow in the distribution network has been observed, thus triggering issues such as voltage violations and reverse overloads [3]- [4]. Therefore, the evaluation of maximum PV hosting capacity of the distribution network can assist distribution network planners in making decisions regarding PV generation [5]. The current evaluation method considering safe operation constraints is traditional planning method of optimal power flow [6] and random scenario simulation method [7] which can ensure the randomness of PV configuration.To enhance the PV hosting capacity, strategies such as reactive power control [8], voltage control using OLTC [9], energy storage technologies [10], and network reconfiguration [11] are continuously proposed.With the increasing proliferation of distributed resources, a promising approach to enhancement is also presented by power aggregation [12]- [13] and proactive control of diversified flexibility resources along feeders. Therefore, this research aims to provide insightful viewpoints and discussions on the assessment method of the maximum PV hosting capacity of the distribution network based on the aggregation of diversified flexibility resources.The main contributions of this work can be twofold as listed: (1) A highly constrained zonotope aggregation model for diversified flexibility resources is proposed, and a two-stage adaptive robust framework is employed to innerly approximate the projection region of the high-dimensional original space of diversified flexibility resources; (2)A PV hosting capacity evaluation method with flexibility space boundaries is presented to accommodate distributed PV by maximizing the net load during peak PV output on the load side. Due to diversified flexibility resources' small scale, dispersion, and large number, coordinating their control is highly challenging [14]. Aggregating flexibility resources on feeders can fully utilize the potential flexibility, reduce invocation difficulty, and lower computational complexity. Specifically, the process of flexibility aggregation can be described as the projection of the power feasible domain of all flexibility resources onto the total power feasible domain of feeders [14]- [16]. Based on the acquisition of the power feasible domain of all flexibility resources on feeders, upon observation of the strong constraints imposed by the network of the distribution network when the aggregation scale is large [13], the high-dimensional precise original space of flexibility resources is constituted.The analytical form of computing its dimensionality reduction projection onto the precise power flexibility space of the feeder is highly challenging; thus, most studies are focused on approximation methods [14].Firstly, the power-adjustable range of individual flexibility resources, including energy storage devices, electric vehicles, and HVAC-like energy storage devices, is described through a virtual energy storage model in this paper [17]. Given the discrete scheduling decision cycle with N scheduling points and a time interval of t and a quantity of M flexibility resources, we consider flx , it P and flx, it E representing the power and energy of individual flexibility resources within the scheduling intervalWhere, it P respectively represent the upper and lower limits of the power of flexibility resource i during time period t; it E respectively represent the upper and lower limits of the energy; denotes the operational feasible region of flexibility resource i , denotes the operational feasible region of the whole flexibility resource, which can be described as the convex polytope characterized by the aforementioned set of M constraints.The linear method outlined in [18] is employed to derive the network power flow model, whereby the magnitudes of node voltage v, branch current i, and feeder line aggregated active power pagg, can be expressed as the following linear expressions: (5)Where D,d,F,f,H,h, J and j are the system parameters. It is necessary to ensure that node voltages and branch currents are not exceeded, as follows:(6)Where and represent the upper and lower limits of node voltages respectively, and and denote the upper and lower limits of branch currents respectively. The convex polytope formed by equation ( 2) is intersected by equation ( 6)'s constraints, resulting in irregular polytopes, while the highdimensional strong constraint primal space Z of flexibility resources is formed by Equations ( 1)- (6)..The proposed model, approximation method and PV hosting capacity evaluation Then, the feeder power flexibility space P, representing the dimensionality reduction projection of the high-dimensional constrained space Z, is approximately obtained using the zonotope U as shown in Figure 1(a). For the N-dimensional zonotope [12], its representation can be established with the central point c, a specific generator matrix G, and a scaling factor . Ng denotes the number of generator vectors. The directions along which the zonotope can be extended are described by the generator matrix . The extension range along each generator vector direction is determined by the scaling factor, as follows:agg agg max max The advantage of zonotope projection approximation over ellipsoidal projection [19] and cuboidal projection [20] lies in its generator vectors i g and Ni g which can respectively depict the power and energy constraints of flexibility resources. Therefore, the operationally feasible region of flexibility resources aligns more closely with the characteristics of the zonotope shape. The feeder power flexibility space obtained after dimensionality reduction projection becomes more intricate and challenging to obtain. The inner approximation requires ensuring that the approximated flexibility space is optimally bounded internally. Simultaneously, it is imperative to ensure that any aggregated power trajectory within the approximated flexibility space can be realized through scheduling without violating operational constraints, thereby guaranteeing the feasibility of the disaggregation [14].In this paper, the power flexibility aggregation and disaggregation problem are formulated as a twostage adaptive robust optimization problem as shown in Figure 1(b), In the first stage, the objective is to determine the optimal approximation space for the aggregated feeder power. In the second stage, the objective is to ensure the feasibility of disaggregation.The construction of any S normal vectors, is performed. The diameter of the zonotope U in the direction of the normal vectors is calculated by equation ( 9). The problem of determining the diameter of the feeder power flexibility space P in the direction of the normal vectors can be addressed using equations (10) to (11). Thus, the similarity between the approximate and the original region is defined as shown in equation (12). As its value increases, the zonotope's approximation to the power flexibility space of the feeder becomes larger [12]. The introduction of as an uncertain variable acting on scaling factors applied to each generator vector of the zonontope, the uncertain set is denoted as . An uncertain zonotope region is constructed, with its parameter feasible domain as . Therefore, a two-stage adaptive robust power aggregation solution model is established as shown in Equations ( 13)- (16). In the first stage, the zonotope parameters () 、 are decision variables, and the optimal inner approximation region is so the uncertain set ught using equation (13). In the second stage, the power scheduling scheme flx () is the decision variable, ensuring the feasibility of disaggregation. Equations ( 15) and ( 16) represent the linear compact form of the highly constrained space of diversified flexibility resources p flx mentioned earlier. Equation ( 14) represents the projection of the highly constrained space of diversified flexibility resources p flx onto the lower-dimensional space of feeder aggregated power pagg. The solution of this model can be implemented using the column-and-constraint generation algorithm. This process is not further elaborated in this paper [21]. As the penetration rate of PV systems in distribution networks continues to increase, the occurrence of peak PV output not coinciding with peak load power [22] may lead to phenomena such as reverse ig power flow and overvoltage in low-voltage distribution networks [23]- [24]. The increase in node voltages within distribution networks becomes the primary factor limiting the integration of distributed PV systems [25]. Reference [26] ensures magnitudes of each bus are maintained within the safety range due to the load shedding. Therefore, effectively increasing the net load during periods of high PV output on the load side helps mitigate the risk of operational constraints exceeding limits, thereby enhancing the hosting capacity of distributed PV systems [27].In represent the starting and ending times of PV output. During this period, each feeder utilizes the upper boundary of the aggregated power flexibility space, maximizing distributed PV integration [7]. In subsequent periods, the lower boundary is employed to reduce the load on the distribution network, ensuring its stable and safe operationThe PV penetration rate range selected in this paper is 0-300%, with an incremental step size of 10%. Using the Monte Carlo method, the quantity, location, and capacity of PV grid connections are randomly simulated, with the PV grid connection capacity increasing according to the PV penetration rate. The steady-state power flow of the system is then calculated. For each PV penetration rate PV , multiple samples are drawn to compute the total installed PV capacity and maximum voltage of system nodes for each random scenario. These values serve as the abscissa and ordinate to construct a scatter plot of random simulations, depicted in Figure 1(c), where each point represents one simulation result.In the low-voltage distribution network, the voltage per unit value ( t i V ) of each feeder line must satisfy the constraint given by Equation (12), and the scatter plot intersects with the upper voltage constraint of 1.07 per unit at points HC 1 and HC 2 Two lines parallel to the vertical axis are drawn respectively at points HC1 and HC2 to divide the coordinate graph into three regions: A, B, and C. In region A, points represent scenarios where the capacity of PV systems connected to the distribution network is less than HC1. Regardless of the node in the distribution network where PV systems are connected, the system voltage remains within the permissible range of the supply voltage. In region B, points represent scenarios where the capacity of PV systems connected to the distribution network falls between HC1 and HC2. If the selection of PV integration positions and capacity allocation is unreasonable, it may lead to excessively high or even over-limit system voltage levels. In such cases, the distribution network planner must ensure that the PV systems are appropriately allocated. In region C, points represent scenarios where the capacity of PV systems connected to the distribution network exceeds HC2. Regardless of the installation scheme employed, it will lead to over-limit system voltage. In this section, the enhancement effect of PV hosting capacity by aggregated and coordinated diversified flexibility resources is demonstrated through numerical simulations based on the proposed method. IEEE 33-bus distribution network system is employed as a case study for simulation verification. The comparison between results of PV hosting capacity and computational time under different algorithms is shown in Table 1. As shown in Table 1, it can be seen that the proposed method, compared to the random scenario simulation, can increase the PV hosting capacity by over 9.96%. Due to the necessity of considering flexible resource aggregation and proactive control, the computational time is comparatively longer. When the scale of flexible resources is large, aggregation can shorten the computational time compared to distributed scheduling. However, our method show a slight decrease in the PV hosting capacity compared to the demand response enhancement method, which is attributed to the approximate feasible domain of the aggregation solution, leading to certain accuracy errors. In this paper, the flexibility resource power regulation model, feeder power aggregation model, twostage robust aggregation solution method, and PV hosting capacity assessment strategy is elaborately investigated. The key findings are summarized as follows: 1) A highly constrained zonotope aggregation model of diversified flexibility resources is proposed, and a two-stage adaptive robust method is introduced to internally approximate the power flexibility space, ensuring the optimality of aggregation and the feasibility of disaggregation; 2) The aggregation and control of flexibility resource power on the load side can accommodate high peak output from distributed PV, thereby enhancing the PV hosting capacity of the distribution network and simultaneously reducing the computational complexity of dispatch decision-making.

Keywords: Diversified flexibility resources, flexibility space boundaries, Hosting capacity, twostage adaptive robust, zonotope aggregation 1

Received: 25 Mar 2024; Accepted: 13 May 2024.

Copyright: © 2024 Su, Yang, Liu and Mu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Chongming Yang, Shenzhen Polytechnic, Shenzhen, China

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