Mohamed F. Abdelkader

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This paper addresses the problem of recognizing human gestures from videos using models that are built from the Riemannian geometry of shape spaces. We represent a human gesture as a temporal sequence of human poses, each characterized by a contour of the associated human silhouette. The shape of a contour is viewed as a point on the shape space of closed(More)
Compressive Sensing (CS) has recently opened the door for efficient algorithms to solve various data gathering problems. Among these problems is sparse events detection in wireless sensor networks. In this problem, it is desirable to reduce the sensing cost by minimizing the number of sensors and the amount of data sent by each sensor. In this paper, we(More)
Visual surveillance systems have gained a lot of interest in the last few years. In this paper, we present a visual surveillance system that is based on the integration of motion detection and visual tracking to achieve better performance. Motion detection is achieved using an algorithm that combines temporal variance with background modeling methods. The(More)
This paper addresses the problem of Car Make and Model recognition as an example of within-category object class recognition. In this problem, it is assumed that the general category of the object is given and the goal is to recognize the object class within the same category. As compared to general object recognition, this problem is more challenging(More)
Spectrum sensing in wideband cognitive radio networks is challenged by several factors such as hidden primary users (PUs), overhead on network resources, and the requirement of high sampling rate. Compressive sensing has been proven effective to elevate some of these problems through efficient sampling and exploiting the underlying sparse structure of the(More)
Spectrum Sensing in wideband cognitive radio networks is considered one of the challenging issues facing opportunistic utilization of the frequency spectrum. Collaborative compressive sensing has been proposed as an effective technique to alleviate some of these challenges through efficient sampling that exploits the underlying sparse structure of the(More)
Compressive Sensing (CS) has been proven effective to elevate some of the problems associated with spectrum sensing in wideband Cognitive Radio (CR) networks through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this chapter, the authors discuss the motivation and challenges of utilizing(More)
Collecting data continuously in Wireless Sensor Networks (WSNs) with limited power and bandwidth is still a challenging issue. Recently, the sparse nature of these data motivated the use of Compressive Sensing (CS) as an efficient data gathering technique. In this paper, several algorithms are proposed to effectively exploit the temporal correlation and the(More)
Improving antenna arrays characteristics such as power consumption, cost and complexity has gained wide attention in the context of cognitive radio networks. One antenna array configuration that offers great potential is the electronically-steerable parasitic array radiator (ESPAR) antenna. In this paper, we propose a transmitter patch ESPAR antenna system(More)
Title of Thesis: Integration and Evaluation of a Video Surveillance System Mohamed F. Abdelkader, Master of Science, 2005 Thesis directed by: Professor Rama Chellappa Electrical and Computer Engineering Department Visual surveillance systems are getting a lot of attention over the last few years, due to a growing need for surveillance applications. In this(More)