PI: Dr. Sherin Aly
Team members: Dr. Mohamed Hassan, and Dr. Rafael Nunez
Research Area: Artificial Intelligence/ Information Technology
PI: Dr. Mohamed ElShimy
Team members: Dr. Ziad ElSahn
Research Area: Optical Communications
PI: Dr. Mohamed Ismail Nounou
Research Area: ICT for Health
PI: Dr. Mohamed Mohamedin
Team members: Dr. Mohamed Azab, Dr. Ahmed Khalefa, Dr. Mohamed Saad
Research Area: Internet of Things
PI: Dr. Nader Shehata
Funding Agency: Science and Technology Development Fund (STDF)
Duration: 24 Months
Project PI: Prof. Yasser Gaber Dessouky
Project Team: Dr. Hossam ElDin Mostafa, Dr. Ahmed Abou ElFarag, and Eng. Ahmed Gouda
Research area: Power Systems/ Smart Grids
Smart Electrical Grids have acquired nowadays a large interest in the electrical load distribution balancing problem. In case the loads connected to the three phases are unbalanced, load reconnection to other phases is performed manually, which is a tedious and expensive task to perform that requires qualified personnel to perform the task in addition to load disconnection and hence electric supply service interruption.
The proposed solution in this project treats the phase unbalance and the large value of neutral conductor current. Subsequently all over- and/or under-voltage problems are then greatly reduced. In addition to the power switching unit, which is to be designed in order to transfer the load from one specific phase to another, automatically and instantly with no delays, depending on the actual case of loading in all the neighboring loads.
The advantages of the proposed automatic system for smart load balancing problem, include but are not limited to:
- Provide an up-to-date balanced operation of three-phase systems
- Help to continuously avoid unbalance problems and losses.
- Easy to install on any system with almost no significant changes in the distribution system.
- Allow the user to monitor the load current and its phase angle at any time.
- Allow the central computer to monitor all the individual single-phase loads at any time, and to generate an alarm when there exists a fault in any single-phase load.
- Provide a database for the historical values of each load at the central PC.
- Provide remote monitoring of the system status and history using a web interface.
- Power factor correction can be achieved.
Project PI: Dr. Sherif Rabia
Project Team: Dr. Mohamed Hussien, Dr. Amr Yousef, and Eng. Ahmed Tayel
Research area: Intelligent Transportation
Road traffic congestion is a chronic problem that has direct and indirect impacts on everyone in Egypt. It wastes time, money, health and even lives. Exposition to different car emissions increases the risk of fatal diseases such as asthma, lung cancer, cardiovascular issues, and premature death. It also increases the emission of greenhouse gases (GHG) that cause global warming and sea-level rise. Furthermore, traffic jams limit the functionality of emergency vehicles, such as ambulances and fire trucks, to respond quickly to their callers, which in turn causes serious threat to people’s lives and to private and public properties.
In this project, it is proposed to use the existing traffic control infra-structure with anticipated low extra costs to build a hybrid model for optimally controlling the switching time of traffic lights and adapt it to different traffic flow capacities. The model is based on supervised learning and computer vision for vehicle detection and traffic flow estimation; and on queuing theory for providing optimal timings of traffic lights that can reduce both the traffic jams and the waiting time at red traffic lights. Additionally, the proposed technique should be able to detect different emergency vehicles and prioritize their crossings in the presence of traffic congestion. Furthermore, it can help in enforcing traffic laws by automatically detecting and reporting traffic violations.
Project PI: Dr. Nader Shehata
Project Team: Dr. Mohamed Azab, and Eng. Ayman Moussa
Research area: Nanotechnology / wireless sensor network
Project PI: Dr. Nader Shehata
Project Team: Eng. Ayman Moussa, and Eng. Soha Hesham
Research area: Nanotechnology
This project aims to design a multi-purpose optical nano-sensor for detecting the dissolved oxygen and free radicals in water, based on optical fluorescence quenching technique. The detection of dissolved oxygen is important in measuring the water pollution level. The very low or extreme high concentrations of dissolved oxygen can have bad impact on fish resources and other aquatic organisms. Also, oxygen detection is needed in biomedical applications such as surgical operations of tumors removal. The free radicals detection is urgently needed in agriculture for plants’ cell longevity studies and in biomedicine through cancerous cells detection.
In this project, the concentrations of dissolved oxygen and/or free radicals can be detected through measuring the intensity of visible emission under UV or IR excitations. Expected outcomes of this project include:
1- Offering a multi-function optical sensor.
2- More economic, sustainable, and sensitive sensor compared to other sensor types.
3- This sensor can be used in multi-purpose within different applications; water purification, environmental monitoring, industrial process control, agriculture and biomedical applications.
Project PI: Dr. Mohamed Azab
Project Team: Dr. Mohamed Farag, and Dr. Bassem Mokhtar
Research area: Cyber-Physical security