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$12 USD / hour
Flag of INDIA
thiruvananthapuram, india
$12 USD / hour
It's currently 4:54 AM here
Joined May 4, 2015
0 Recommendations

Swathikiran S.

@swathikirans

5.0 (1 review)
0.4
0.4
100%
100%
$12 USD / hour
Flag of INDIA
thiruvananthapuram, india
$12 USD / hour
100%
Jobs Completed
100%
On Budget
100%
On Time
N/A
Repeat Hire Rate

MATLAB Freelancer

I am an Electronics Engineering graduate who is quite an expert in MATLAB. My areas of expertise includes image processing, pattern recognition and computer vision. I am also well versed in Arduino programming and circuit designs.

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Reviews

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5.0
RM76.00 MYR
He is an excellent person to work. Although he was new on freelancer, he is excellent on the way he talks and also how he does the work. Should hire him without having the doubt.
C Programming
Java
JavaScript
Matlab and Mathematica
JavaFX
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Flag of Ramesh K.
@rameshhelp
9 years ago

Education

Master of Technology(M. Tech.)

University of Kerala, India 2011 - 2013
(2 years)

Bachelor of Technology(B. Tech.)

University of Kerala, India 2007 - 2011
(4 years)

Qualifications

IELTS

British Council
2015
The International English Language Testing System(IELTS), is an international standardised test of English language proficiency for non-native English language speakers. It is jointly managed by Cambridge English Language Assessment, the British Council and IDP Education Pvt Ltd and was established in 1989. The test consists of four sections with each section having a maximum score of 9. The average of the four sections are taken as the overall score. These are the scores obtained by me: 1. Listening: 8.5/9 2. Reading: 9/9 3. Writing: 7/5/9 4. Speaking: 7/5/9 Overall: 8/9

Publications

Sparse distributed localized gradient fused features of objects

Pattern Recognition Journal, Elsevier
The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse feature processing and encoding method, which improved the recognition performance of an automated object recognition system. Randomly distributed localized gradient enhanced features were selected before employing aggregate functions for representation, where we used a modular and hierarchical approach to detect the object feature

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