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, so contact us for more information and also if you have your own proposal for a project that fits Pervasive Systems.

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. The interested students are encouraged to contact the protective supervisors to see how a topic can be adapted.

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

Humans and Health

• [DS] Analysis of stress levels in cyclists
• [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

The Natural World: animals, plants and biodiversity

• [D] Species Localization - GeolifeCLEF
• [DS] SPY-miCE [SPYCE] – An IoT smart sensor-trap station to monitor rodent      
• [D] Frog Counting Tool - EY Challenge
• [D] Overground vs. treadmill: where is my horse running? Context detection based on IMU data
• [D] Intelligent nest box camera to monitor birds and biodiversity
• [D] Luistervinq 2 – Activity recognition in nature using sound and AI
• [D] Smart Labeling of Animal Activity Data
• [D] Use Everything you got
• [ND] Enhancing Underground Ad-Hoc Network Performance Under Dynamic Soil Conditions
• [SD] No hoof, no horse: Image processing for hoof shape monitoring 
• [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

Wireless networks of the future

• [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] Mobile Ad-hoc Routing in MaritimeManet
• [N] Design and implementation of an improved MaritimeManet node 
• [N] Revitalizing the wireless network infrastructure for MaritimeManet
• [N] Distributed Neighbourhood Discovery in MaritimeManet
• [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
• [N] Bringing Control to Wi-Fi: Dynamic Network Slicing in your Home Router
• [N] ML-enabled Channel Assignment and Power Control in Wi-Fi Networks
• [N] User Mobility Management in Software-Defined Wi-Fi Networks

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] Am I indoors or outdoors?: Modeling location of smartphones

Logistics and transport

• [D] Federated Learning in Smart-Bikes
• [SD] Road Safety in Smart-Bikes
• [D] A tool for the systematic review of multimodal transport and logistics simulation techniques
• [D] Building a simulation tool based on Cloud-Fog-Edge Computing in multi-modal logistics
• [D] Open IoT data-based visualization tool for transport and logistics movements
• Cycling-related Insights for Stakeholders
• [SD] Bike-Lane Quality Assessment
• [SD] Bike unturned: Estimating the tilt during turns
• [SD] Fast lane: Estimating the speed during turns
• [S] Connected bike: Retrieving and analysing data from the on-board sensors in Smartphones

Smart infrastructure and predictive maintenance

• [D] DumpingMapper: Illegal dumping detection from high spatial resolution satellite imagery
• [D] Using Artificial Intelligence in Real Estate: Can we understand the condition of a building from above?
• [D] From point cloud to CAD model
• [D] Embedded Machine Learning for Real-Time Flow Rate Measurement
• [D] Lightweight image-based key point tracking for real-time bridge monitoring with smartphones
• [D] Automatic detection and estimation of the area of buildings & pools
• [D] Smartphone AR-based data representation in real-time
• [D] Smartphone image processing in real-time
• [D] Augmented Reality for defect detection 

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.

• [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

Group Assignments

MaritimeManet – Mobile Ad-hoc Networking at Sea