Eslam G. Profile

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Liittynyt huhtikuuta 30, 2020

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Eslam G.

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Computer Vision | Embedded System | Android

- Graduated from the Electronics & Communications department at Faculty of Engineering Alexandria University with grade (excellent with honor). - I have +4 years of experience in the field of Robotics, Computer Vision, Python, Embedded Systems, and Android Development. - Currently, I am a top writer on Quora. I write answers to questions about Programming, Embedded Systems, Deep Learning, and Computer Vision. I have garnered over 175k answer views and over 600 answer upvotes. - Specifically, I have extensive experience with the following technologies : ------------------------------------------------------------------------------------------------------------------------ • Deep Learning. • Python. • Keras. • TensorFlow. • Microcontrollers (ATmega 16/32, Tiva C, and 8051). • Arduino. • C. • Java. • MATLAB. • Android. • Jetson Nano board. • Bluetooth module. • WiFi module (NodeMCU esp 8266). • Sensors (Ultrasonic, IR, Proximity). - Computer Vision Research Intern at Faculty of Engineering, Alexandria University. - Completed Deep Learning Specialization on Coursera. - Completed TensorFlow in Practice Specialization on Coursera. - Completed Android Nanodegree on Udacity. - Completed OpenVINO foundational course in the Intel IoT Edge AI Scholarship.

Katso kaikki Data Entry Clerks paikassa Egypt

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Portfolio

General Signal Generator on MATLAB

Representing mathematics function on MATLAB

7263629

the touchless 3D tracking that works on capacitive sensing theory and by our hand we can change the capacitance of three plates like as (X, Y, Z). the three plates are positively charged and my hand is supposed to be ground so if my hand is very close to any plate, the capacitance of this plate will increase because the capacitance is reversely proportional to the distance between my hand and the plate corresponding to this equation:
C= (ε A)/d
So we can control the robot by this touchless 3D tracking by using this relationship for example if my hand moves forward and the capacitance of the plate that faces my hand increases, the robot will move forward.
And if my hand returns back and the capacitance of the plate that faces my hand decreases, the robot will move back and so on.

Arduino: Control robot using touchless 3D tracking system

Video Link: https://www.youtube.com/watch?v=AvxhHDXSPcU&t=1s

BumpDetector App is an android application used to notify the driver about road bumps.

BumpDetetor App

Bump Detection

6552116

Video Link: https://www.youtube.com/watch?v=JbVhkPpdi0s
A number of deep learning methods have been developed and demonstrated for face detection.

The MTCNN is popular because it achieved then state-of-the-art results on a range of benchmark datasets, and because it is capable of also recognizing other facial features such as eyes and mouth, called landmark detection.

The network uses a cascade structure with three networks; first the image is rescaled to a range of different sizes (called an image pyramid), then the first model (Proposal Network or P-Net) proposes candidate facial regions, the second model (Refine Network or R-Net) filters the bounding boxes, and the third model (Output Network or O-Net) proposes facial landmarks.

Face Detection using Deep Learning

General Signal Generator on MATLAB

Representing mathematics function on MATLAB

7263629

Arduino: Control robot using touchless 3D tracking system

BumpDetetor App

Bump Detection

6552116

Face Detection using Deep Learning

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Quora

kesäk. 2019 - Voimassa

I write answers to questions on Quora about Programming, Embedded Systems, Deep Learning, and Computer Vision. I have garnered over 175k answer views and over 600 answer upvotes. One of the Most Viewed Writers in 10 topics such as computer vision, object detection, CNN, etc. Contributor on Spaces: - Machine Learning. - Applied Data Science.

Faculty of Engineering, Alexandria University, Egypt

heinäk. 2019 - syysk. 2019 (2 kuukautta, 1 )

A research intern in computer vision. Course: CS231n: Convolutional Neural Networks for Visual Recognition by Stanford University Presentation: Give a presentation on 5 papers of object detection from KITTI benchmark. Project: Police Car vs Normal Car Classifier. Accuracy = 92%. Supervised by Dr. Marwan Torki, Associate Professor at Faculty of Engineering, Alexandria University.