Index

Sign Language Recognition Using Transformer and Comparison with Traditional Techniques

This thesis is about creating a system to recognize sign language using transformer networks and
comparing it with older methods. The aim is to build a system that is both effective and accurate by
using transformer models, which are good at handling sequences of data, to understand and interpret
sign language. The study will include collecting data, preparing it, training models, evaluating them, and
comparing the results with traditional methods like CNNs.

The main idea of this thesis is to use transformer networks for recognizing sign language. Unlike
traditional models that process data step-by-step, transformers can handle entire sequences at once,
which improves understanding and accuracy. The system will use different types of data (e.g., video) to
be more robust and accurate. This research will compare transformers with traditional methods like
CNNs to show the benefits and possible improvements of transformers in sign language recognition.

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.