@christouandr7
github

christouandr7

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  • Stars
    star
    4
  • Global Rank 1,611,161 (Top 56 %)
  • Followers 3
  • Following 4
  • Registered about 6 years ago
  • Most used languages
    Java
         50.0 %
    R
         50.0 %
  • Location ๐Ÿ‡ฌ๐Ÿ‡ท Greece
  • Country Total Rank 3,018
  • Country Ranking
    R
         39
    Java
         995

Top repositories

1

uofe_dissertation

Independent Component Analysis (ICA) has received a lot of attention in statistical as well as in biomedical signal processing. It is widely used in blind source separation (BSS) problems, as it is a convenient method to separate signals from different sources, without any prior information about them or the mixing process. In the first part of the dissertation we report a theoretical background of ICA, analyzing what are the preprocessing steps that are needed and how ICA works, and then giving more details on the two algorithms that are compared, fastICA and ProDenICA. In the second part we present experimental results in a simulation environment to see what ICA achieves and what are the merits and drawbacks of the two ICA algorithms while in the third part we consider a real surface Electromyography (sEMG) dataset. sEMG is one type of bioelectrical signals produced by the human body and contain significant information about muscle activity. ICA is applied to sEMG signals in order to recover the original signals originating from each muscle. Besides, a post-ICA method that overcomes the independent component ordering ambiguity is proposed, allowing them to be related to the suitable corresponding muscles. ICA and the post-ICA steps that are described, manage to reach more than 79% accuracy on three hand gesture classification problems. The experimental results in both simulation and sEMG dataset indicate that ICA is an appropriate method for signal recovering and identification of hand gestures using sEMG signals.
R
3
star
2

ntua_dissertation

A project implemented during my dissertation at the National Technical University of Athens. This project was a part of a bigger research project for a Data as a Service (DaaS) Marketplace architecture that was published on April 2020. The aim of my part in this project was to enhance the core functionalities offered by Elasticsearch towards two directions: both the discovery and the ranking. It was developed to extend the search results, capturing semantic relations between different terms, and on the other hand, to revise the Elasticsearch default scoring algorithm used to rank those results. The recommendation system implemented was then compared to Elasticsearch queries, showing an improvement on the percentage of cases where the proposed system returned a more fitting result than the simple Elasticsearch. The dissertation project was implemented in Java using Spring Boot and Elasticsearch.
Java
1
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