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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

AiroDiag: A sophisticated tool that diagnoses and updates vehicles software over air

This paper introduces a novel method for diagnosing embedded systems and updating embedded software installed on the electronics control units of vehicles through the Internet using client and server units. It also presents the communication protocols between the vehicle and the manufacturer for instant fault diagnosis and software update while ensuring security for both parties. AiroDiag ensures

Artificial Intelligence
Software and Communications

Light-ECV: An intelligent lightweight framework for embedded computer vision applications

Recent developments in embedded systems open up a new realm of computer vision applications in surveillance and healthcare delivery, etc. However, such applications require high computational resources, which is challenging for embedded devices that are characterized by constrained processing power and limited memory capacity. In this paper we present a novel intelligent framework that enables

Distributed component-based framework for Unmanned Air Vehicle systems

Unmanned Air Vehicles (UAVs) are gaining increased importance in a variety of applications, both military and civilian, due to their ability to carry out critical missions with reduced costs and minimal risks to human life. However, a UAV system is essentially a complex distributed system involving multiple heterogeneous software and hardware modules. The seamless integration of such components is

Robust autonomous visual detection and tracking of moving targets in UAV imagery

The use of Unmanned Aerial Vehicles (UAVs) for reconnaissance and surveillance applications has been steadily growing over the past few years. The operations of such largely autonomous systems rely primarily on the automatic detection and tracking of targets of interest. This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time and is

Artificial Intelligence

On-board multiple target detection and tracking on camera-equipped aerial vehicles

This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time with enhanced accuracy and is suitable for UAV imagery. The framework is deployed for on-board processing and tested over datasets collected by our UAV system. The framework is based on image feature processing and projective geometry and is carried out on the following stages

Artificial Intelligence

EEG spectral analysis for attention state assessment: Graphical versus classical classification techniques

Advances in Brain-computer Interface (BCI) technology have opened the door to assisting millions of people worldwide with disabilities. In this work, we focus on assessing brain attention state that could be used to selectively run an application on a hand-held device. We examine different classification techniques to assess brain attention state. Spectral analysis of the recorded EEG activity was

Artificial Intelligence

Remote prognosis, diagnosis and maintenance for automotive architecture based on least squares support vector machine and multiple classifiers

Software issues related to automotive controls account for an increasingly large percentage of the overall vehicles recalled. To alleviate this problem, vehicle diagnosis and maintenance systems are increasingly being performed remotely, that is while the vehicle is being driven without need for factory recall and there is strong consumer interest in Remote Diagnosis and Maintenance (RD&M) systems

Artificial Intelligence
Software and Communications

Motion history of skeletal volumes for human action recognition

Human action recognition is an important area of research in computer vision. Its applications include surveillance systems, patient monitoring, human-computer interaction, just to name a few. Numerous techniques have been developed to solve this problem in 2D and 3D spaces. However most of the existing techniques are view-dependent. In this paper we propose a novel view-independent action

Artificial Intelligence
Software and Communications

A semi supervised learning-based method for adaptive shadow detection

In vision-based systems, cast shadow detection is one of the key problems that must be alleviated in order to achieve robust segmentation of moving objects. Most methods for shadow detection require significant human input and they work in static settings. This paper proposes a novel approach for adaptive shadow detection by using semi-supervised learning which is a technique that has been widely

Artificial Intelligence
Software and Communications
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