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Implementation and Evaluation of an Online, AI-based Pedestrian Detector for LiDAR Point Clouds
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- Frameworks such as OpenPCDet enable training and evaluation of neural networks for object detection in point clouds, using public data sets. Within this Master thesis, the aforementioned framework will be adapted and re-structured to process a live recording of a running LiDAR sensor to detect pedestrians online. The developed system should be tested in a variety of scenarios to systematically evaluate the strengths and weaknesses of the detector.
- Requires knowledge of C++ and Python and experience with Linux operating systems.
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| Improving and Adapting Resource-constrained Cybersecurity Algorithms on Wireless Sensor Networks |
- The algorithms we designed for securing Controller Area Network (CAN) Buses against cyber attacks will be transferred and refined for use in Wireless Sensor Networks (WSNs). This adaptation is motivated by the critical observation that both CAN and WSN platforms are fundamentally characterized by severe resource limitations, making the application of conventional cybersecurity protocols impractical and inefficient.
- Requires knowledge of wireless communication, cybersecurity (authentication, encryption, etc.), MATLAB/Simulink and C or Python.
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| Wi-Fi-Based Identity Recognition Using CSI-Linux Firmware |
- The goal of this thesis is to explore the use of the Linux 802.11n CSI Tool for collecting Channel State Information (CSI) and using it to recognize individuals based on their Wi-Fi signals. The student will set up the firmware on compatible Intel 5300 Wi-Fi chip, record data from different people, and train a deep learning model for identity classification. The main idea is to see how well this setup performs and whether it can provide cleaner or more stable CSI compared to what we usually get from other setups.
- Requires knowledge of Python, wireless communication or signal processing, deep learning frameworks (PyTorch or TensorFlow), Linux environments and hardware setup.
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| Impact of Transmitter–Receiver Distance on Wi-Fi CSI-Based Sensing Performance |
- This thesis investigates how the distance between Wi-Fi transmitter and receiver affects the quality and stability of Channel State Information (CSI) for classifying an individual’s identity. The student will record CSI data at different distances under the same experimental conditions and analyze how signal strength, noise, and classification accuracy change. After identifying an optimal distance range, additional experiments will be performed on different days to check the consistency of the results. The goal is to better understand how device placement influences CSI-based sensing performance and reliability.
- Requires knowledge of Python, wireless communication or signal processing and machine learning, interest in experimental setup and data-driven analysis.
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| Wi-Fi CSI for Room Occupancy and Crowd Size Estimation |
- This thesis focuses on using Wi-Fi Channel State Information (CSI) to estimate how many people are present in a room. The idea is to analyze the changes in wireless signals caused by human presence and movement, and to see if these patterns can be used to detect and classify occupancy levels. The student will collect CSI data in different scenarios, process the signals, and experiment with models that can distinguish between empty and occupied environments or estimate small group sizes. The goal is to understand how reliable CSI-based sensing can be for simple, privacy-friendly occupancy detection.
- Requires knowledge of Python, wireless communication or signal processing, data analysis or machine learning, interest in experimental setup and data-driven analysis.
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| Development of a Real-Time LiDAR Health Monitoring System with GUI for Railway Applications |
- LiDAR sensors are used for obstacle detection and environment monitoring in railway systems. Reliable sensor operation is essential for safe system performance. However, sensor failures, data loss, or communication problems can reduce detection accuracy and create safety risks. This thesis focuses on developing a real-time monitoring system with a graphical user interface (GUI) to supervise LiDAR performance and detect abnormal behavior.
- The work includes:
- monitoring LiDAR frame rate and data flow
- detecting missing or corrupted point cloud frames
- analyzing point cloud density and stability
- identifying communication loss or delay
- generating a clear health status (Healthy / Warning / Error)
- visualizing sensor condition through an intuitive GUI
- validating the system using recorded and live LiDAR data
- Requires basic knowledge of Linux, C++, and ROS 2,Qt (GUI).
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| Brightness Correction in Live Camera Data using Dynamic Regions of Interest |
- Unstable lighting conditions require reactive camera control mechanisms, leveraging exposure times, sensor gain and image post-processing to increase visibility of objects in a scene to humans.
- Work with different metrics on how to evaluate object visibility.
- Program and experiment with industrial cameras.
- Research and develop a method to dynamically select a ROI which is used to evaluate. brightness of "important" regions.
- Requires basic knowledge of Linux (basic) and C++ (pthreads, openCV, etc.).
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