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  1. 1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Xinchen Wang; [2024]
    Keywords : Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

    Abstract : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. READ MORE

  3. 2. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Khalid El Yaacoub; [2024]
    Keywords : Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

    Abstract : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. READ MORE

  4. 3. Heart rate estimation from wrist-PPG signals in activity by deep learning methods

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Marie-Ange Stefanos; [2023]
    Keywords : Deep Learning; Medical Data; Signal Processing; Heart Rate Estimation; Wrist Photoplethysmography; Djup lärning; Medicinska Data; Signalbehandling; Pulsuppskattning; Handledsfotopletysmograf;

    Abstract : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. READ MORE

  5. 4. Implementations and evaluation of machine learning algorithms on a microcontroller unit for myoelectric prosthesis control

    University essay from Lunds universitet/Avdelningen för Biomedicinsk teknik

    Author : Jonathan Benitez; [2023]
    Keywords : microcontroller; deep learning; artificial neural network; electromyography; myoelectric prosthesis; Technology and Engineering;

    Abstract : Using a microcontroller unit to implement different machine learning algorithms for myoelectric prosthesis control is currently feasible. Still there are hardware and timing constraints that need to be accounted for. READ MORE

  6. 5. Machine Learning model applied to Reactor Dynamics

    University essay from KTH/Fysik

    Author : Dionysios Dimitrios Nikitopoulos; [2023]
    Keywords : Master Thesis; Machine Learning; stability; Energy distribution profiles; Prediction; frequency; decay ratio; Data processing; POLCA-T; Pytorch; testing data; RMSE. ii;

    Abstract : This project’s idea revolved around utilizing the most recent techniques in MachineLearning, Neural Networks, and Data processing to construct a model to be used asa tool to determine stability during core design work. This goal will be achieved bycollecting distribution profiles describing the core state from different steady statesin five burn-up cycles in a reactor to serve as the dataset for training the model. READ MORE