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@SaikaS
Flag of Canada Calgary, Canada
Member since January 18, 2020
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SaikaS

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I'm an experienced Python and C++ developer. My areas of expertise : - Desktop software development - Pandas - Scikit-Learn - visualizing and analyzing Data - Excel / CSV / XML / JSON - PostgreSQL - Linux I am a results-oriented person, who is hard working, can learn fast, and values honesty and integrity.
$15 USD/hr
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Experience

Software Engineer

Jan 2015

1. Hands-on programming experience using C/C++, Python 2. Experience working with PostgreSQL 3. Expert Level SQL, PL/pgSQL, including stored procedures, functions, triggers, and views. 4. Experience building applications using the Qt framework 5. Experience working with Linux, Windows 6. Excellent communication and interpersonal skills 7. Experience with Scrum/Agile development practices

QA Engineer

Jan 2012 - Dec 2014 (2 years)

- Planned, designed and executed test plans for a variety of products, test equipment and test platform - Analyzed the functional specifications and designed test cases for product's new features and functionalities - Configured necessary hardware and operating environments as needed to complete assigned testing. - Executed regression, performance and stability testing during the various operational phases before a product release.

Education

M.Sc in Electrical Engineering from University of Calgary

2004 - 2006 (2 years)

Publications

Motion Detection of a Real Beacon using Estimator Correlator

A novel technique to detect the motion of a beacon, i.e., moving station, using a single base station (BS) is demonstrated. The beacon is controlled by a free running crystal oscillator with a random offset error in the range of 2 ppm. The detection principle is based on the estimator correlator (EC) which accounts for the correlation properties of the beacon signal. The real data is collected from a network based wireless location system in a multipath environment.

Event Detection by Spatio-Temporal Indexing of Video Clips

With the continuous recording of video data in current surveillance systems, it is almost impossible to quickly identify frames where events of interest did occur within a camera scene. This paper presents the concept of Spatio-temporal Indexing, which is a novel video indexing and retrieval technique that can be used for event detection. The proposed technique was tested on hours of video recordings. The results obtained provide 99.9% accuracy for event detection and retrieval.

Certifications

  • US English Level 1
    80%

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