Month: October 2020

Spike-Deeptector in conjunction with artifact rejector: A deep-learning based feature extractor for online invasive BCI applications (Thesis-Proposal)

Posted on By Muhammad Saif-Ur-Rehman No Comments on Spike-Deeptector in conjunction with artifact rejector: A deep-learning based feature extractor for online invasive BCI applications (Thesis-Proposal)
teaching

Brain-computer interface (BCI) systems are a rapidly growing technology that controlsexternal devices, e.g. a neuroprosthetic limb, by directly decoding intended movements from the recorded neural activities and bypassing the spinal cord. Decoding neural activity is a two-step process, feature vector extraction and classification/regression. In online BCI applications, non-stationary behavior of neural signals makes the process of … Read More “Spike-Deeptector in conjunction with artifact rejector: A deep-learning based feature extractor for online invasive BCI applications (Thesis-Proposal)” »

Control of a Brushless DC Motor (Thesis-Proposal)

Posted on By ayaz No Comments on Control of a Brushless DC Motor (Thesis-Proposal)
teaching

contact: Muhammad Ayaz Hussain, M. Sc., Tel: +49 208 88254806muhammad.hussain@hs-ruhrwest.de A brushless dc (BLDC) motor drive is characterized by higher efficiency, lower maintenance, and higher cost. In a market driven by profit margins, the appliance industry is reluctant to replace the conventional motor drives with the advanced motor drives (BLDC) due to their higher cost. … Read More “Control of a Brushless DC Motor (Thesis-Proposal)” »

Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)

Posted on By felix No Comments on Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)
teaching

Reinforcement Learning (RL) is a subdomain of machine learning that has developed rapidly in recent years and has become increasingly popular. In reinforcement learning an agent learns from experience using a scalar reward signal, in contrast to learning from examples of labelled data as it is done in supervised machine learning. The agent interacts with … Read More “Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)” »

Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)

Posted on By felix No Comments on Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)
teaching

Reinforcement Learning (RL) is a subdomain of machine learning that has developed rapidly in recent years and has become increasingly popular. In reinforcement learning an agent learns from experience using a scalar reward signal, in contrast to learning from examples of labelled data as it is done in supervised machine learning. The agent interacts with … Read More “Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)” »