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Prof. Mohamed El-Helw

Associate Dean for PG Programs & Director of Center for Informatics (CIS)

Faculty Office Ext.

1755

Faculty Building

UB1

Office Number

205

Biography

Dr. Mohamed El-Helw; CIS director. He joined Nile University as an Assistant Professor in 2008 where he led the Ubiquitous and Visual Computing Group (UbiComp) at the Centre for Informatics Science (CIS). Prior to moving to NU, Dr. ElHelw had been working as post-doctoral researcher at the Department of Computing and the Institute of Biomedical Engineering, Imperial College London where he carried out work on the use of image-based modeling and rendering techniques for medical simulation, understanding visual perception and the development of wireless body sensor networks. His research interests are focused on ubiquitous systems, computer vision, 3D computer graphics, deep neural networks, and scientific computing. He has a proven research and development track record in the above areas with more than 70 refereed publications and major research grants of more than EGP 25 million.

Dr. ElHelw received B.Sc. in Computer Science from the American University in Cairo, M.Sc. in Computer Science from the University of Hull, UK, and Ph.D. in Computer Science from Imperial College London, University of London in 2006. He also holds a Diploma in Visual Information Processing (DIC) from Imperial College London. He is a full Professor and a Senior Member of the IEEE society

Achievements

1) Mohamed El-Helw received the Cairo Innovates Award 2014 for Innovation from the Academy for Scientific Research and Innovation (ASRT).

2) Best paper award in the International Conference on Pervasive Computing Technologies for Healthcare held in London, UK, 2009.
3) Certificate of Recognition, Microsoft Research, 2010.
4) 3rd place winner of the International AMD OpenCL Innovation Challenge Competition 2011.
5) Winner of the 2014 Cairo Innovate Award.
6) Creator and leader of the Ubiquitous and Visual Computing Group (UbiComp).

Recent Publications

NU-Net: Deep residual wide field of view convolutional neural network for semantic segmentation

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the

Artificial Intelligence

A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization

In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera. The framework exploits the abundance of satellite imagery, along with established computer vision and deep learning methods, to locate the UAV in a satellite imagery map. It utilizes the contextual information extracted from the scene to attain increased geolocalization

Artificial Intelligence
Software and Communications

Optical character recognition using deep recurrent attention model

We address the problem of recognizing multi-digit numbers in optical character images. Classical approaches to solve this problem include separate localization, segmentation and recognition steps. In this paper, an integrated approach to multi-digit recognition from raw pixels to ultimate multi class labeling is proposed by using recurrent attention model based on a spatial transformer model

Artificial Intelligence

Convolutional Neural Network-Based Deep Urban Signatures with Application to Drone Localization

Most commercial Small Unmanned Aerial Vehicles (SUAVs) rely solely on Global Navigation Satellite Systems (GNSSs) - such as GPS and GLONASS - to perform localization tasks during the execution of autonomous navigation activities. Despite being fast and accurate, satellite-based navigation systems have typical vulnerabilities and pitfalls in urban settings that may prevent successful drone

Artificial Intelligence
Software and Communications

Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms

New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more

Artificial Intelligence
Software and Communications

On Board Evaluation System for Advanced Driver Assistance Systems

The evaluation of Advanced Driver Assistance Systems (ADAS including driver assistance and active safety) has increasing interest from authorities, industry and academia. AsPeCSS active safety project concludes that good results in a laboratory test for active safety system design does not necessarily equate to an effective system in real traffic conditions. Moreover, many ADAS assessment projects

Artificial Intelligence

Real-time scale-adaptive compressive tracking using two classification stages

In this paper, we describe a method for Scale-Adaptive visual tracking using compressive sensing. Instead of using scale-invariant-features to estimate the object size every few frames, we use the compressed features at different scale then perform a second stage of classification to detect the best-fit scale. We describe the proposed mechanism of how we implement the Bayesian Classifier used in

Artificial Intelligence
Software and Communications
Mechanical Design

Traffisense: A smart integrated visual sensing system for traffic monitoring

Intelligent camera systems provide an effective solution for road traffic monitoring with traffic stream characteristics, such as volumes and densities, continuously computed and relayed to control stations. However, developing a functional vision-based traffic monitoring system is a complex task that entails the creation of appropriate visual sensing platforms with on-board visual analytics

Artificial Intelligence
Circuit Theory and Applications
Software and Communications

Robust scale-invariant object tracking

Tracking by detection methods are becoming increasingly popular in recent years. They use samples classified in previous frames to detect object in a new frame. These methods have shown successful results. However, due to the self updating nature of this approach, tracking by detection methods usually suffer from object drift. Inaccurately detected samples are added to the training set which

Artificial Intelligence
Research Tracks
  • Machine Learning and Pattern Recognition
  • Computer Vision
  • Computer Graphics and Visualization
Projects
1
Research Project

AgriSem: Semantic Web Technologies for Agricultural Data Interoperability

Objective/Contributions: The amount and types of raw data generated within the agriculture domain are dramatically growing. However, these raw data in themselves are meaningless and isolated, and therefore may offer little value to the farmer. The Agricultural Research Center (ARC)and the Central Lab for Agricultural Expert Systems (CLAES) was established to enhance the productivity of knowledge
a
Research Project

Rice Plant Disease Detection and Diagnosis Using Deep Convolutional Neural Networks and Hyperspectral Imaging

Objective/Contributions: One of the main challenges of early detection of key rice blast disease is that it can be misclassified as the brown spot disease by less experienced agriculture extension officers (as both are fungal diseases and have similar appearances in their early stages) which can lead to wrong treatment. Given the current scarcity of experienced extension officers in the country
3
Research Project

Smart Agricultural Clinic: Egyptian Farmer Electronic Platform for the Future

Objective/Contributions: Smart agricultural clinic (SAC) aims to: 1) Provide an integrated end-to-end digital system to effectively deliver personalized agriculture extension and veterinary services, including best cultivation, fertilization and breeding practices, to farmers and animal producers through the use of mobile/handheld devices. 2) Use advanced computer vision and deep learning
3
Research Project

Subsidies Mobile Wallet (SMW) and Its Applications to Fertilizer Distribution

Objective/Contributions: The subsidy is a strategic service in Emerging countries like Egypt; it makes available essential items to poor people at discounted prices, as they are unable to purchase such items or services at their market price. The subsidy is always a hot topic that floats every year with the preparation of any annual government budget in Egypt. Subsidy remains a major burden for
Research Project

TraffiSense-Pro

Objective/Contributions: Prolonged daily periods of road traffic congestion waste time, and money, and degrade both the environment and our quality of life. In Egypt, the problem is significant with severe traffic delays and high accident rates leading to devastating effects on economic growth and challenging any progression towards sustainable development. Conventional traffic management