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Email verificeret.
islamabad,
pakistan
Klokken er nu 4:31 PM her
Tilmeldt januar 4, 2016
2
Anbefalinger
Imran A.
@imranahmadquresh
4,1
4,1
islamabad,
pakistan
95 %
Jobs færdiggjort
98 %
Indenfor budgettet
100 %
Til tiden
18 %
Genansættelsesrate
Image Processing and Machine Learning Expert
Kontakt Imran A. angående dit job
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Portefølje indlæg
Attendance Management System through Image Recognition
OMR Scanner for Counting Marks with Tick Cross in Matlab
Clustering fingerprint Images using K-means in Python
Brain Tumor Segmentation using morphological ops in Matlab
Blind Source Separation of fMRI Images using DL in Matlab
Symbol error rate for 2,4,8-PAM modulation
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Erfaring
Manager R&D
jan. 2014 - Nuværende
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.
Uddannelse
MS Electrical Engineering
(3 år)
Publikationer
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.
Kontakt Imran A. angående dit job
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Verifikationer
Bedste færdigheder
Gennemse lignende freelancere
Electrical Engineers in Pakistan
Electrical Engineers
Matlab and Mathematica Engineers
DSP Engineers
Gennemse lignende fremvisninger
Electrical Engineering
Matlab and Mathematica
Signal Processing
Python
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