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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

Prof. Mohamed El-Helw is the Director of the Centre for Informatics Science (CIS). 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, Prof. El-Helw had been working as a 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.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. El-Helw received a B.Sc. in Computer Science from the American University in Cairo, an M.Sc. in Computer Science from the University of Hull, UK, and a 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

Scale-adaptive object tracking with diverse ensembles

Tracking by detection techniques have recently been gaining increased attention in visual object tracking due to their promising results in applications such as robotics, surveillance, traffic monitoring, to name a few. These techniques often employ semi-supervised appearance model where a set of samples are continuously extracted around the object to train a discriminant classifier between the

Artificial Intelligence

Real-time vehicle detection and tracking using haar-like features and compressive tracking

This paper presents a real-time vision framework that detects and tracks vehicles from stationary camera. It can be used to calculate statistical information such as average traffic speed and flow as well as in surveillance tasks. The framework consists of three main stages. Vehicles are first detected using Haar-like features. In the second phase, an adaptive appearance-based model is built to

Software and Communications
Mechanical Design

A time series classification approach for motion analysis using ensembles in Ubiquitous healthcare systems

Human motion analysis is a vital research area for healthcare systems. The increasing need for automated activity analysis inspired the design of low cost wireless sensors that can capture information under free living conditions. Body and Visual Sensor Networks can easily record human behavior within a home environment. In this paper we propose a multiple classifier system that uses time series

Artificial Intelligence
Healthcare

ITS navigation and live timetables for the blind based on RFID robotic localization algorithms and ZigBee broadcasting

This paper tries to alleviate some challenges facing blind and visually impaired people in public transportation systems by providing them with in-station navigation information and real-time schedule information. Novel system architecture for the Intelligent Transportation Systems (ITS) navigation for blind and visually impaired people based on recent Radio Frequency Identification (RFID)

Software and Communications
Mechanical Design

The visual object tracking VOT2013 challenge results

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different

Artificial Intelligence

Robust real-time tracking with diverse ensembles and random projections

Tracking by detection techniques have recently been gaining popularity and showing promising results. They use samples classified in previous frames to detect an object in a new frame. However, because they rely on self updating, such techniques are prone to object drift. Multiple classifier systems can be used to improve the detection over that of a single classifier. However, such techniques can

Artificial Intelligence

Motion history of skeletal volumes and temporal change in bounding volume fusion 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 3D imaging gained a lot of interest nowadays. In this paper we propose a novel view-independent action recognition

Artificial Intelligence
Software and Communications

Enhanced target tracking in UAV imagery with P-N learning and structural constraints

This paper presents improved automatic moving target detection and tracking framework that is suitable for UAV imagery. The framework is comprised of motion compensation phase to detect moving targets from a moving camera, target state estimation with Kalman filter, and overlap-rate-based data association. Finally, P-N learning is used to maintain target appearance by utilizing novel structural

Artificial Intelligence

Tracking ground targets from a UAV using new P-N constraints

This paper presents improved automatic moving target detection and tracking framework that is suitable for UAV imagery. The framework is comprised of motion compensation phase to detect moving targets from a moving camera, target state estimation with Kalman filter, and overlap-rate-based data association. Finally, P-N learning is used to maintain target appearance by utilizing novel structural

Artificial Intelligence
Software and Communications
Research Tracks
  • Ubiquitous systems
  • 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