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Resource Optimal Neural Networks for Safety-critical Real-time Systems
(2020-07-10)
Deep neural networks consume an excessive amount of hardware resources, making
them difficult to deploy to real-time systems. Previous work in the field of network
compression lack the explicit hardware feedback necessary ...
Analyzing the override strategy for collision avoidance functions
(2022-10-14)
The automotive industry has been shifting towards leveraging intelligent software
solutions to ensure safety and ease of use. However, ensuring safety during execution
heavily depends on how the human user interacts with ...
CNN-LSTM architecture for predicting hazardous driving situations
(2023-10-05)
This study aims to investigate how a CNN-LSTM model can be used together with recorded vehicle data from trucks and external weather data in order to predict a hazardous driving situation. The dataset consists of ...
Predicting Stock Prices using Transformers
(2024-10-16)
This thesis explores how a Transformer-based architecture can enhance stock market
prediction accuracy, using Mean Squared Error (MSE) as the measure. It also
investigates if focusing on the last 15 minutes of trading ...
Investigating Text-only and Multimodal Retrieval Augmented Generation frameworks for Visual Question Answering - A study on the impact of modality and parameter optimization
(2024-10-16)
Verifying the correctness of product assembly processes in a manufacturing setting
is a crucial part of ensuring the quality of products. Automating this procedure
can help improve both the security and the efficiency ...
Tracking the Rubik’s Cube - Using point trackers for semi-automatic video annotation
(2024-10-16)
The need for extensive manual labor in annotating video datasets causes a scarcity of
them. Without video datasets of good quality and sufficient diversity, the research
and development of machine learning algorithms in ...