Index

Predictive Maintenance for Electrical Panels: Hotspot Forecasting and Anomaly Detection Using Thermal Data

This thesis proposes the development of a machine learning-based predictive maintenance
system for electrical panels using thermal data. The system will address the limitations of periodic manual
inspections and enable the detection of anomalies in the operation of electrical devices. By leveraging real-
time thermal data and applying machine learning techniques, the solution aims to enhance the
sustainability and efficiency of maintenance processes, especially in environments like airports where
baggage handling systems (BHS) are critical. This project proposes the use of a 32×32 thermopile sensor
array to collect continuous thermal data and apply machine learning models to predict potential failures
before they occur.

he thermal dataset is provided by Siemens Logistics GmbH.

Problem Statement: Manual thermographic inspections in electrical panels have several limitations,
including the inability to coincide with peak operating times and the reliance on operator expertise to
interpret infrared images. Furthermore, the inability to continuously monitor and analyze the thermal
behavior of electrical panels leads to missed opportunities for early intervention and predictive
maintenance.

Expected Outcomes: The primary outcome of this thesis will be a system that predicts when anomalies
are likely to occur within electrical panels. This will result in fewer manual inspections, minimized
downtime by predicting failures, and recommend inspections for timely maintenance.

Normalization of Sensor and Smartphone Gait Signals of Parkinson’s Disease Patients Using Deep Learning

Signal-Specific Fault Detection in Controller Area Network using Deep Learning

Online Retrieval Augmented Generation for Accurate Medical Question Answering

Motion Detection and Motion Artifact Mitigation in Dual-Energy Computed Tomography

Improving Time-Resolved CT Imaging through Non-Local Spatio-Temporal Denoising

LLM-Centric Framework for Ontology-Driven SPARQL Query Generation in RAG for DICOM Databases

Enhancing small-sized video object detection through temporal information and synthetic data

Optimization of CT Image Volume in Dedicated Breast CT with Circle-Spiral Trajectory

This master’s thesis will focus on optimizing image reconstruction methods for dedicated breast CT scans using a circle-spiral trajectory. The aim is to improve post-reconstruction image quality and mitigate artifacts arising from the uneven distribution of information between the top and bottom regions, where the circular trajectory contributes more data than the helical section. The research will explore techniques to balance these differences and enhance overall image fidelity.

Diffusion Model-Based 3D CT Reconstruction for Arbitrary Trajectories

This master’s thesis will develop a novel approach using diffusion models for 3D CT reconstruction. The process will begin with denoising a highly blurred and noisy initial reconstruction from FBP or SIRT, aiming to enhance the quality of reconstructions obtained from a limited number of projection data on arbitrary trajectories. This research will focus on optimizing the denoising process to improve the output, advancing CT imaging capabilities with limited input data.