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BSEE and
MSEE Degrees I entered
the Electrical Engineering program at the prestigious Shiraz University,
Shiraz, Iran, in 1990 following my ranking
I received
my engineering degree in 1994 with the citation Highest Honors for an
outstanding grade-point average and completion of an
intellectually-challenging curriculum. I joined the graduate program in
the same institution and received my Master's degree in the summer of
1997. My thesis was written in English and was titled ``An Adaptive
Approach to Digital Filter Bank Design''. For this thesis, I received the
maximum numerical mark possible (20 out of 20) and the citation of Highest
Distinction for completion of original research of exceptional quality. PhD Degree Upon finishing my Master's degree, I applied to and received admission
offers (with full financial support) from several During my PhD research, I developed a fundamental statistical theory for information processing in multirate linear systems. The main theme of the theory to infer sample values or spectral properties of a high-resolution signal low-resolution observations. It also provides a means for quantifying the amount of statistical information gained through each observation, as well as a precise mathematical definition for optimal multi-resolution signal decomposition. The main theory consists of four interconnected sub-theories as described below. -
**Multirate Statistical Inference:**uses the Maximum Entropy principle to estimate the spectrum of a high-resolution random signal given statistical properties of low-resolution observations. -
**Information Theory of Multirate Systems:**provides a measure for the quantity of information gained about a non-observable high-resolution signal through low-resolution statistical measurements. -
**Multirate Signal Estimation:**takes sample values of given low-resolution observations and estimates sample values of the non-observable high-resolution signal under measurement. -
**Algebraic Theory of Optimal Multirate Systems:**provides a rigorous framework for comparing different multi-resolution measurement systems in terms of scalability. Furthermore, this theory clarifies and generalizes several existing notions of optimality for multirate filter banks.
Post Doctoral Fellowship Upon completing my PhD degree, I joined the very young and talented University of Toronto Professor Parham Aarabi in his then newly established Artificial Perception Laboratory (APL). In my capacity as a Post-Doctoral Fellow in APL, I applied the theory I had developed to several novel signal processing applications. These applications included fusing the data recorded by a low-resolution microphone array, time delay of arrival (TDOA) estimation between multirate signals, phase-based speech enhancement in microphone arrays, and sensor validation. I also became interested in sensor networks and the problem of merging statistical information in a distributed network of sensors. While working on this problem, I developed a very general class of algorithms for distributed signal processing in sensor networks. The results have been published in several papers and in Chapter 38 of the Embedded Systems Handbook published by CRC Press in 2005. A complete account of my research contributions to the fields of multirate signal processing and sensor networks will be presented in more detailed form in my book ``Multirate Statistical Signal Processing'' which is due to be published by Springer-Verlag in 2006.
Teaching I believe that the purpose of teaching is more than just communicating
facts and developing basic skills. The goal of teaching is to develop
students' ability to make critical judgments of ideas and approaches. In
serving this goal, I have found that one must constantly challenge the
students and force them to take a firm stance for their beliefs. I taught two undergraduate courses at the University of Toronto: -
MIE346: "Analog and Digital Electronics for Mechatronics'' in 2001 -
ECE231: "Introductory Electronics'' in 2003 and 2004.
I was the sole instructor for the first course. I coordinated the
lectures, labs and tutorial sessions with the help of four teaching
assistants. The second course was a multi-section one with 3 instructors,
21 teaching/lab assistants and more than 400 students. I am very pleased
to say that at the end, I received one of the highest teaching evaluations
of any instructor/professor in the entire Department. Thanks to this
success, the Department offered me a lectureship position for the 2004
Spring term as well.
Research Interests I am interested in developing distributed information processing algorithms for sensor network, RFID, and biometric identification applications. This is an ambitious goal demanding a multidisciplinary research program and a merger among several established disciplines such as digital communications, signal processing and network computing.
A network of sensors can yield a rich multi-modal stream of sensory data. Furthermore, it can be highly scalable, cost effective and robust with respect to failure of individual nodes. However, realizing the full potential of large distributed sensor systems requires fundamental advances in the theory of distributed data fusion. For instance, the network must support both high- and low-end data transmission and processing, while allowing for interpretation, modelling, and fusion of sensed information. A primary goal will be the development of signal processing algorithms designed expressly for sensor network applications. More specifically, I will focus on the following three fundamental problems: scalable fusion algorithms for sensor networks, multi-resolution transmission of sensory information in large networks, and localization and self-calibration of sensor nodes.
Radio Frequency Identification (RFID) is an emerging identification method that relies on storing and remotely retrieving data using devices called RFID tags. An RFID tag is a small object that can be attached to or incorporated into a product, animal, or person. RFID tags are passive devices containing a silicon chip and antennas which enable them to receive and respond to radio-frequency queries from an RFID reader. RFID tags require no battery, and thus no maintenance. In view of this, they have the potential to be used as environmental sensors on an unprecedented scale. However, there are many engineering challenges that must be addressed before RFID sensing becomes economically viable. I am very interested in signal processing aspects of RFID sensing. Sample research topics include accurate tag localization, simultaneous reading of multiple tags, and data fusion in distributed reader/tag networks.
A biometric is a physiological or behavioral characteristic of a human being that can distinguish one person from another. Examples include fingerprint, iris scan, face geometry, etc. A biometric
As biometric | |||

- 㯰yright Omid Jahromi 2004 -

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