Bachelor and Master projects

The Pervasive Systems group continuously investigates new and exciting research areas and there are a lot of opportunities for Masters and Bachelors students to explore new interests or consolidate foundations. Have a look below for some ideas, all the projects can be tailored for either Masters or Bachelors, For more information about the projects and to discuss your own ideas in research at Pervasive Systems, please do get in touch with our Master Project coordinator Alex Chiumento.

You have to fill in the registration form at the start of your project.

Available assignments

The student assignments have been provisionally divided into categories to make reading this page easier. In general, each category represents the application domain in which each research project can be carried out. All projects have a small header at the front specifying if the main work deals with sensing, data science, or networking. Interested students are encouraged to contact prospective supervisors to see how a topic can be adapted.

[S = Sensing,   N = Networking,   D = Data Analytics and Machine Learning] 

Humans and Health

• [DS] Machine learning pipeline for measuring emotions during bicycle rides
• [SND] Wearable system to digitize your body posture to Metaverse
• [DS] FMCW Radar: Stay home and listen to your heart!
• [DS] FMCW Radar: Check your pet's heart rate together with your heart rate!
• [DS] FMCW Radar: How you doing buddy? Track and monitor your pet.
• [SND] Sensor data pre-processing & compression for BLE transmission
• [D] Smartphone's Context Detection using Transformer
• [D] Human/ Animal Activity Recognition Data Analysis
• [SD] Activity Recognition Using Two Radars
• [SD] Gesture/ Posture Recognition Using Spectrogram
• [DN] WiFi vs. Sensors: Face-off between physical sensors and wireless signals in human activity recognition
• [DN] WiPi: Contactless sensing using a Raspberry Pi and NEXMON
• [D] Facial Video-based rPPG Estimation
• [D] DeepFake Manipulation Detection
• [D] Language-guided Audio-Visual Source Separation

The Natural World: animals, plants and biodiversity

• [D] Smart Labeling of Animal Activity Data
• [D] Use Everything you got
• [ND] Enhancing Underground Ad-Hoc Network Performance Under Dynamic Soil Conditions
• [D] IMU Generator with End-To-End Learning for IMU Data Generation from Videos
• [SD] Play with your pet: Analyse your pets activities or vital signs

The theses just below can be performed together with Wageningen University and Research

• [SD] Volume/ Weight estimation using Radar point cloud!
• [SD] Activity recognition using Radar point cloud!
• [SD] Activity recognition using Radar spectrogram!
• [SD] Tracking calves using Radar!
• [SD] Vital sign detection using a thermal camera!
• [SD] Vital sign detection using Radar!
• [SD] Tracking humans/animals using Radar point clouds remotely!

Wireless networks of the future

• [D,S,N] Leave me alone Wi-Fi spy!
• [N] Do Horoscopes Work for Factories? Performance Comparison of Wireless Channel Prediction Algorithms within an Indoor Factory Floor
• [N] Are Horoscopes beneficial? Sensitivity Analysis of Predicting Wireless Channel in multiple environments
• [N] Reliable low-latency networking: many radios, many paths
• [N] Hierarchical and distributed control of wireless networks
• [N] Build a clever Wi-Fi access point
• [ND] Bringing intelligence to Wireless Sensor Nodes
• [DN] Edge AI for a better wireless network
• [DN] One AI to rule them all (the wireless networks)
• [N] Connect: Ad-Hoc LoRa network in extremely lossy environments 
• [ND] Fixing the Wi-Fi in the future?: Predictive Forward Error Correction
• [ND] Finding the Truth: Probabilistic Graphical Models for cross-layer inference
• [SD] Securing your things: Find the anomaly in the IoT
• [SN] CommuniFi: Data transmission during device-free sensing of human activities
• [DN] VariFi: Variable WiFi data rates during device-free sensing
• [D] A Step towards Federated Learning
• [N] MaritimeManet in a Lab
• [N] Unleashing 5G: From Evolution to Implementation
• [N] Resource Management in IEEE 802.11ax based Networks for Spectrum Efficiency and QoS

Localization and Navigation

• [D] Smartphone-Based Indoor Localization using Compressive Transformer
• [D] Transfer Learning for Indoor Localization with Smartphones
• [D] Convolutional Algorithms for Real-Time Data Processing with Time Series on Smartphones for Navigation and Tracking
• [D] Compressed generative adversarial networks for real-time feature extraction for fingerprinting localization with smartphones
• [SD] Neural Inertial Navigation

Smart Mobility and Transport

• [SD] Smart Bikes: Trajectory Prediction with Onboard Sensors
• [SN] E-bell: Enabling bike to bike/car communication
• [N] Broadcast storm migration for V2V communication

Smart infrastructure and predictive maintenance

• [D] Embedded Machine Learning for Real-Time Flow Rate Measurement
• [D] Lightweight image-based key point tracking for real-time bridge monitoring with smartphones

IoT & Security

• [D,N] Comparative Study of Operating Systems for Docker Containers on IoT Devices
• [D,N] Hacking Traffic Lights
• [D] Empowering IoT with K3s: Optimizing Container Orchestration at the Edge

IT-security in mobile ad-hoc networks

• [S, N] Secure mobile ad-hoc routing in MaritimeManet

Construction Sites

• [D] Smartphone AR-Based Data Representation in Real-time for Construction Sites
• [D] Object Detection at Construction Sites
• [S,D] AI-driven Digital Twins for Construction Sites

Theses in Industry

The Pervasive Systems group collaborates very actively with many industry and research partners. Below is a list of interesting industry-lead research topics for the students interested in an experience in the industry sector.

Several Projects on Embedded Machine-Learning with NXP AI-Team Eindhoven 

• [D] Deep learning for fungal identification based on fungal growth images
• [D] Object detection model comparison for detecting sugar beets applied in a mechanical weeder 
• [D] Depth reconstruction by using deep learning stereo matching algorithms
• [D] Estimating the field of view quality of cameras applied in the agricultural sector
• [D] Using AI to create synthetical images of seedlings recorded in greenhouses 
• [D] Deblurring and investigating video data collected by a newly developed innovative drone 
• [D] Identifying the use of different camera lenses of a spectral camera in the agricultural sector
• [D] Develop a sound classifier for autonomous tractors in the agriculture 
• [DN] Attacks against AI/ML-based systems in communication networks @ TNO
• Towards a new process for fieldwork on soil and subsoil with SAXION
• Automation and digitalisation for soil and subsoil fieldwork with SAXION

• [D] Investigating which QR code sizes and materials are most suitable for drone data collection with TRACK32
• [D] Optimization of seedling detection by using instance segmentation with TRACK 32
• [D] Deblurring and investigating video data collected by a newly developed innovative drone with TRACK32
• [DS] Identifying the use of different camera lenses of a spectral camera in the agricultural sector with TRACK32
• [D] Using AI to create synthetical images of seedlings recorded in greenhouses with TRACK32
• [D] Depth reconstruction by using deep learning stereo matching algorithms with TRACK32
• [D] Object detection model comparison for detecting sugar beets applied in a mechanical weeder with TRACK32