Prof. Moeness Amin
Prof. Moeness Amin, Villanova University, USA
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Biography:
Dr. Moeness Amin, received his Ph.D. degree in 1984 from University of Colorado, in Electrical Engineering. In 1984, Dr. Amin joined the University of Colorado, Denver as a Visiting Assistant Professor. He has been on the Faculty of the Department of Electrical and Computer Engineering at Villanova University since 1985. In 2002, he became the Director of the Center for Advanced Communications, College of Engineering.
Dr. Amin is the Recipient of the 2009 Individual Technical Achievement Award from the European Association of Signal Processing, and the Recipient of the 2010 NATO Scientific Achievement Award. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), 2001; Fellow of the International Society of Optical Engineering, 2007; and a Fellow of the Institute of Engineering and Technology (IET), 2010. Dr. Amin is a Recipient of the IEEE Third Millennium Medal, 2000; Recipient of the Chief of Naval Research Challenge Award, 2010; Distinguished Lecturer of the IEEE Signal Processing Society, 2003-2004;Chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts; Recipient Villanova University Outstanding Faculty Research Award, 1997; and the Recipient of the IEEE Philadelphia Section Award, 1997. He is a member of IEEE, SPIE, EURASIP, ION, Eta Kappa Nu, Sigma Xi, and Phi Kappa Phi.
Dr. Amin has over 550 journal and conference publications in the areas of Wireless Communications, Time-Frequency Analysis, Smart Antennas, Waveform Design and Diversity, Interference Cancellation in Broadband Communication Platforms, Anti-Jam GPS, Target Localization and Tracking, Direction Finding, Channel Diversity and Equalization, Ultrasound Imaging and Radar Signal Processing. He is a recipient of seven best paper awards.
Dr. Amin currently serves on the Editorial Board of the IEEE Signal Processing Magazine. He also serves on the Editorial Board of the EURASIP Signal Processing Journal. He was a Plenary Speaker at ICASSP 2010. Dr. Amin was the Special Session Co-Chair of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing. He was the Technical Program Chair of the 2nd IEEE International Symposium on Signal Processing and Information Technology, 2002. Dr. Amin was the General and Organization Chair of the IEEE Workshop on Statistical Signal and Array Processing, 2000. He was the General and Organization Chair of the IEEE International Symposium on Time-Frequency and Time-Scale Analysis, 1994. He was an Associate Editor of the IEEE Transactions on Signal Processing during 1996-1998. He was a member of the IEEE Signal Processing Society Technical Committee on Signal Processing for Communications during 1998-2002. He was a Member of the IEEE Signal Processing Society Technical Committee on Statistical Signal and Array Processing during 1995-1997. He has given several keynote and plenary talks, and served as a Session Chair in several technical meetings. He organized seven Workshops and Seminars for the Franklin Institute Medal Award Program and the IEEE Philadelphia Section.
Dr. Amin is a Guest Editor of the November-2013 Special Issue of the IEEE Signal Processing Magazine on Time-Frequency Analysis and Applications. He was the Guest Editor of the Journal of Franklin Institute September-08 Special Issue on Advances in Indoor Radar Imaging; a Guest Editor of the IEEE Transactions on Geoscience and Remote Sensing May-09 Special Issue on Remote Sensing of Building Interior; and a Guest Editor of the ET Signal Processing December-09 Special Issue on Time-Frequency Approach to Radar Detection, Imaging, and Classification.
Title: COMPRESSIVE URBAN SENSING
Abstract:
Urban sensing is an area that involves Imaging of building interiors. This is enabled by radar that uses electromagnetic (EM) waves for various purposes, including determining the building layout, discerning the building intent and nature of activities, locating and tracking the occupants, and even identifying and classifying inanimate objects of interest within the building. These capabilities are highly desirable for law enforcement, fire and rescue, and emergency relief, and military operations. High fidelity sensing and imaging can allow a police force to obtain an accurate description of building interior in a hostage crisis, or allow firefighters to locate people, which can be very critical, when trapped inside a burning structure.
Towards the objective of providing timely actionable intelligence in urban environments, the emerging compressive sensing (CS) techniques have recently been shown to yield reduced cost, simplified hardware, and efficient sensing operations that allow super-resolution imaging of sparse behind-the-wall scenes. Compressive sensing is a very effective technique for scene reconstruction from a relatively small number of data samples without compromising the imaging quality. In general, the minimum number of data samples or sampling rate that is required for scene image formation is governed by the Nyquist theorem. However, when the scene is sparse, compressed sensing provides very efficient sampling, thereby significantly decreasing the required volume of data collected.
Compressive Sensing for Urban Radars, or Compressive Urban Sensing (CUS), is an area of research and development which investigates the radar performance within the context of compressive sensing and with a focus on urban applications. CUS examines the effect of using significantly reduced data measurements in time, space and frequency on 2D and 3D imaging quality, strong EM reflections from exterior and interior walls, target ghosts, and moving target detection and tracking. In this respect, CUS is a hybrid between the two areas of compressive sensing and urban sensing. In essence, it enables reliable imaging of indoor targets using a very small percentage of the entire data volume.
In this talk, the theory of compressive sensing will be put in context for radar, in general, and in particular for the urban environment. We will explain how CS can achieve different radar sensing goals and objectives, and how it compares with the use of full data volumes. Different radar specifications and configurations will be used. In particular, we will address CS for urban radars towards achieving (a) Imaging through walls; (b) Detection of behind the wall targets; (c) Mitigation of wall clutter; and (d) Exploitation of multipath. All of the above issues will be examined using data generated at the Radar Imaging Lab, Villanova University.
