Research and Teaching Focus Areas:
- Dependable Edge AI
- Fault Injection for Autonomous Systems
- UAV Networks
- Software-Defined Networks
- Machine Learning
- Autonomous Vehicles
- IoT
- 5G and 6G Networks
Proposed Bachelor Projects and Theses under My Supervision:
1. Edge Object Detection Benchmark for Assistive Navigation
Deploy lightweight detectors (e.g., YOLO variants, MobileNet-SSD) on Raspberry Pi 5 and Jetson Orin Nano. Measure accuracy, latency, and power consumption on indoor and outdoor scenes relevant to visually impaired users.
2. Obstacle Detection and Distance Estimation with a Depth Camera
Build a prototype that uses a depth camera (e.g., Intel RealSense or OAK-D) to detect obstacles in the walking path and estimate their distance. Give the user simple audio or vibration warnings.
3. Audio and Haptic Feedback Interface for a Wearable Assistant
Design and compare feedback methods for the user: spoken messages, spatial audio cues, and vibration patterns. Evaluate clarity and reaction time in a small user study with blindfolded participants.
4. Scene Description with Cloud Vision-Language Models
Connect a wearable camera to a cloud VLM (e.g., Qwen2.5-VL) to generate short scene descriptions. Evaluate response time, description quality, and how to keep descriptions short and useful.
5. Edge–Cloud Offloading Decision Prototype
Implement a simple rule-based or learned module that decides whether a task runs locally or in the cloud, based on network quality, battery level, and task urgency. This is a first step toward Adaptive Intelligence Partitioning.
6. Text Recognition (OCR) for Everyday Objects on Edge Devices
Run an OCR pipeline on an edge device to read signs, labels, and documents aloud. Compare on-device and cloud OCR in accuracy and speed.
7. Model Compression for Wearable Perception
Apply quantization and pruning to a perception model and measure the trade-off between model size, accuracy, and inference time on embedded hardware.
8. Dataset Collection and Annotation for Assistive Scenarios
Record and annotate a small dataset of typical scenes for visually impaired users (crosswalks, stairs, doors, bus stops). Define annotation guidelines and evaluate an existing detector on it.
9. Energy Profiling of a Wearable AI Device
Measure the power consumption of the camera, the processing, and the wireless transmission on the prototype. Estimate battery life under different usage scenarios.
10. Robustness Testing of Perception Models under Real-World Conditions
Test how detection performance drops under low light, motion blur, rain, or camera faults, using image corruptions and simple fault injection.
Faezeh Pasandideh received her B.Sc. in Computer Engineering in 2012 and her M.Sc. in Computer Science in 2015. In February 2024, she successfully completed her Ph.D. through a dual doctoral program between the Federal University of Rio Grande do Sul (UFRGS), Brazil, and Hochschule Hamm-Lippstadt (HSHL), Germany.
She has over 10 years of experience in research and teaching across multiple countries.
- Sep. 2015 – Nov. 2019: Lecturer and Research Assistant, Iran. Courses taught: Databases, Algorithms, Programming, Artificial Intelligence, Software Development.
- Jan. 2018 – Feb. 2020: Research Assistant, Nasim Telecom, Iran.
- Mar. 2021 – Sep. 2022: Research Assistant in Cloud Computing and Machine Learning, Unisinos in partnership with Dell Technologies, Brazil.
- Since Sep. 2022: Research Associate (Wissenschaftliche Mitarbeiterin) and Lecturer (LfbA) at Hochschule Hamm-Lippstadt, within the EU Horizon Europe project EdgeAI-Trust, focusing on dependable edge AI inference, fault injection for autonomous systems, and embedded systems.
Courses taught: Systems Engineering, Embedded Systems, Electrical Engineering 1 & 3, Control Engineering, Computer Science 2, Prototyping Lab, Microcontroller Programming.
Publications:
2026
- F. Pasandideh, A. Rettberg, LLM-Generated Fault Scenarios for Evaluating Perception-Driven Lane Following in Autonomous Edge Systems, arXiv preprint arXiv:2604.07362, 2026.
- F. Pasandideh, M. Azarafza, A. Rettberg, Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection, arXiv preprint arXiv:2604.09631, 2026.
- F. Pasandideh, VisionFault-350K: A Large-Scale Fault Injection Dataset for Robotic Vision Systems, Zenodo Dataset, 2026.
- M. Azarafza, M. Nayyeri, F. Pasandideh, S. Staab, A. Rettberg, Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning, ICLR 2026 Workshop on Logical Reasoning of Large Language Models, 2026.
- F. Pasandideh, M. Azarafza, A. Rettberg, Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection, Preprint, Mar. 2026.
2025
- F. Pasandideh, A. Najafzadeh, J. P. J. da Costa, E. Pignaton de Freitas, Providing an Energy Efficient UAV BS Positioning Mechanism to Improve Wireless Connectivity, Ad Hoc Networks, Feb. 2025.
2023
- P. H. M. Pereira, F. Pasandideh, M. Basso, J. P. J. da Costa, E. Pignaton de Freitas, Design and Deployment of an Efficient Communication Service for Multi-UAV Systems, Conference Paper, Jun. 2023.
- F. Pasandideh, F. E. Rodriguez Cesen, P. H. M. Pereira, C. E. Rothenberg, E. Pignaton de Freitas, An Improved Particle Swarm Optimization Algorithm for UAV Base Station Placement, Wireless Personal Communications, Mar. 2023.
- F. Pasandideh, J. P. J. da Costa, R. Kunst, N. Islam, W. Hardjawana, E. Pignaton de Freitas, A Systematic Literature Review of Flying Ad Hoc Networks: State-of-the-Art, Challenges, and Perspectives, Journal of Field Robotics, Jan. 2023.
2022
- F. Pasandideh, J. P. J. da Costa, R. Kunst, N. Islam, W. Hardjawana, E. Pignaton de Freitas, A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies, Remote Sensing, 14(18), 4459, 2022.
- F. Pasandideh, T. Dapper, A. S. Silva, E. Pignaton de Freitas, Topology Management for Flying Ad Hoc Networks Based on Particle Swarm Optimization and Software-Defined Networking, Wireless Networks, 28(1), 257–272, 2022.
2021
- N. Islam, M. M. Rashid, F. Pasandideh, B. Ray, S. Moore, R. Kadel, A Review of Applications and Communication Technologies for IoT and UAV Based Sustainable Smart Farming, Sustainability, 13(4), 1821, 2021.
- AA. Rezaee, F. Pasandideh, An Optimized Service Differentiated Congestion Management Protocol for Delay Constrained Traffic in Healthcare WSNs, Journal of Communication Engineering, 9(1), 126–153, 2021.
- J. Tavakoli, N. Moghim, A. Leila, F. Pasandideh, A Fuzzy Based Energy Efficient Clustering Routing Protocol in Underwater Sensor Networks, Journal of Communication Engineering, 9(1), 154–167, 2021.
2020
- N. Islam, B. Ray, F. Pasandideh, IoT Based Smart Farming: Are the LPWAN Technologies Suitable for Remote Communication?, IEEE International Conference on Smart Internet of Things (SmartIoT), 2020.
2018
- F. Pasandideh, AA. Rezaee, A Fuzzy Congestion Control Protocol Based on Active Queue Management in Wireless Sensor Networks with Medical Applications, Wireless Personal Communications, 98(1), 815–842, 2018.
- F. Pasandideh, AA. Rezaee, A Fuzzy Priority Based Congestion Control Scheme in Wireless Body Area Networks, International Journal of Wireless and Mobile Computing, 14(1), 1–15, 2018.
- AA. Rezaee, F. Pasandideh, Z. I. Ghodoosi, Introduce OPNET Modeler (Buch, auf Farsi), Imam Reza International University Press, Mashhad, Iran, 2018.