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Imran A.
@imranahmadquresh
5,0
21
4,2
4,2
95%
Image Processing and Machine Learning Expert
$20 USD/uro
・
Pakistan (5:43 dop.)
・
Pridružen(a) od januar 4, 2016
$20 USD/uro
・
I have made many Image Processing and Machine Learning Projects using Matlab, Python, and R and Embedded Systems Projects using Arduino. My focus is to seek client's trust and long-term relationships. My specialized fields are as follows;
• Image Processing
• Machine Learning
• Embedded Systems
Imran was a delight to work with. Completed the work on time as promised, and was cooperative with all the help for any inquiries and doubts asked. Surely hiring again for any future tasks.
End Technologies is a high technology multi-field engineering concern company, specialized in Research & Development, designing and manufacturing of a top class Science & Engineering equipment. The main purpose of the Company is to give economical solutions with Innovation and commercialization of new ideas. End Technologies currently engaged in fields relating to robotics, image processing, embedded system designing, FPGA`s and other security applications.
jan., 2014 - Prisoten
•
11 let
Izobrazba
International Islamic University
2013 - 2016
•
3 leta
MS Electrical Engineering
Pakistan
2013 - 2016
•
3 leta
Publikacije
Arduino Based Fatigue Level Measurement in Muscular Activity using RMS Technique
2020 International Conference on e-Health and Bioengineering (EHB)
Human fatigue reduces ability to work for some time as a result of abnormal or prolonged workloads. For this problem, an electromyography (EMG) is used to evaluate the physical health of muscles. Each muscle in a human body transmits signals which are produced when a muscle makes different movements. In this paper, forearm muscle is investigated using MyoWare EMG sensor (AT-04-001), an Arduino Uno, keypad and LCD.
Non negative matrix factorization, sparse coding & dictionary learning techniques on fMRI images
Recent Advances in Electrical Engineering (RAEE), 2017 International Symposium on IEEE.
Recent developments in Matrix factorization techniques have led to sparse representation of signals using learned dictionaries. In this research we have applied Alternating Least Square Non-Negative Matrix Factorization (ALSNMF) and Dictionary Learning (DL) technique for sparse representation to simulated and real Functional Magnetic Resonance Images (fMRI) to extract corresponding sources and time courses.
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