Käyttäjän alperblm profiilikuva
Maan Turkey lippu Bursa, Turkey
Jäsen alkaen 3. elokuuta 2018
0 Suositukset


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I am a graduate of electronic engineering and I am a phd student. I have 4 years of experience. I can do your jobs in embedded systems, pcb design, internet of things , biomedical electronics, IoT, Machine Learning. Some of the projects I have undertaken; Some deep learning projects Some machine learning Motor imagery signal classificatin with Deep learning (EEG) 8-channel Portable EEG Measurement Device (graduate thesis) Embedded Web Server (undergraduate thesis) Smart street lighting control card Smart home system prototype Encrypted card pass system Wifi-controlled smart socket Led lighting control card
$15 USD/hr
1 arvostelu
  • 100%Suoritetut työtehtävät
  • 100%Budjetin mukaisesti
  • 100%Ajallaan
  • N/AUudelleenpalkkaamisaste


Viimeaikaiset arvostelut

  • käyttäjän Pritpal S. kuva Arduino sketch rectification ₹1500.90 INR

    “excellent quality of work, knows what he is doing.. a one stop shop for Arduino coding.. if he bids on the project completing it is no problem for him.. very professional, polite, great value for money..would hire him again without fail..cheers”


Electronics Engineer

Jan 2015 - Aug 2018 (3 years)

LED Lighting control cards design


Electronics Engineering

2010 - 2015 (5 years)

Electrical and Electronics Engineering (MSc)

2016 - 2018 (2 years)

Electrical and Electronics Engineering (PhD)

2018 - 2018 (1 month)


8 Channel Mobile EEG Measurement Device

Msc thesis.

Classification of Motor Imagery Signals by Convolutional Neural Network for BCI Applications

With BBA applications, it is possible to control machines using only mental activities, especially for patients with limited mobility. Motor imagery signals (MIS) which are formed as a result of the imagination of moving a limb. In this study, it is aimed to classify MIS signals with Convolutional Neural Network by using BCI-IV 2b dataset for this purpose. As a result, higher (%75,7) performance was obtained with lower number of parameters compared to similar previous studies.

Customized Integrated Circuit ADS1299 Evaluation of the Performance for EEG Measurement


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