Vincent Amuso Headshot

Vincent Amuso

Senior Lecturer
Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

585-475-2488
Office Location

Vincent Amuso

Senior Lecturer
Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

Education

BS, Western New England University; MS, Syracuse University; Ph.D., Rensselaer Polytechnic Institute

Bio

Dr. Vincent J. Amuso received his B.S. in Electrical Engineering from Western New England College, his M.S. in Electrical Engineering from Syracuse University, and his Ph.D. in Electrical Engineering from the Rensselaer Polytechnic Institute. His Ph.D. research used classical signal processing techniques married with stochastic modeling and neural networks, to successfully develop an algorithm used to separate mixed speech. The three main components of the algorithm include multiple layer neural networks, maximum likelihood deconvolution and Bayesian inference networks.

Dr. Amuso was employed by the Rome Air Development Center as an electronics engineer in the Advanced Concepts and Analysis Branch. His responsibilities included the investigation of the feasibility of state-of-the-art concepts in the area of radar signal processing. He was employed by the General Electric Co. as an Electrical/Systems engineer where his responsibilities included the design of hardware for airborne and ground-based radar and infrared systems. As a systems engineer he was responsible for the application and implementation of a multiple model Extended Kalman Filter (EKF) in modified spherical coordinates to determine the time to intercept of a target by using angle only measurements. He was also responsible for the design and development of a missile launch detection algorithm, the design and development of a discrimination algorithm used to identify helicopters by a ground-based infrared detection system, and the modeling and simulation of infrared atmospheric and target phenomenology. Dr. Amuso was employed as professor at Mohawk Valley Community College. The Sensis Corporation employed him as a senior radar systems engineer where his responsibilities included system requirement derivation, analysis and flow down, and the generation of software and hardware requirements specifications. He also had responsible for the development and implementation of an offline signal and track processor (MATLAB based) used to analyze collected coherent radar data. The signal processing functionality included waveform design, pulse compression, digital demodulation, moving target indicator (MTI) processing, Doppler processing, multiple time around processing and monopulse processing. The track processing functionality included clutter and weather map generation, same scan and multi-scan correlation. He was also responsible for the development and implementation of a Bayesian Inference Network technique used to reduce false alarm returns caused by anomalous propagation and turbulent clutter.

Dr. Amuso was the head of the Electrical Engineering Department from 2006 through 2009. He now teaches courses in the areas of signal processing and communications systems. He also delivers courses in the areas of neural networks and evolutionary computation techniques applied to radar and acoustic systems. He performs research in the design and analysis of deep Ground Penetrating Radar (GPR) systems as well as three-dimensional SAR target modeling. He also performs research in the area of speech signal processing. Dr. Amuso is actively working both as a researcher and consultant in the area of Waveform Diversity & Design and Ultra Narrowband RF Tomography with the Air Force Research Laboratory. He is one of the founding co-chairman of the first International Waveform Diversity & Design conference (2004), and has co-authored and co-edited the seminal book in the area of Waveform Diversity & Design. Dr. Amuso has authored or co-authored several papers in the areas of multi-mission waveform design and deep GPR target modeling.

Recent Publications
  • Wicks, Mokole, Blunt, Schneible & Amuso, Principles of Waveform Diversity and Design, SCITECH Publishing, Raleigh, NC, 2010.
585-475-2488

Currently Teaching

EEEE-602
3 Credits
In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters.
EEEE-789
3 Credits
Topics and subject areas that are not regularly offered are provided under this course. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
EEEE-679
3 Credits
A study of the various techniques for the design of filters to meet the given specifications. The emphasis is on the design of active filters using op amps. The following topics are discussed in detail: Review of transfer functions, Bode diagrams and the analysis of op amp circuits; ideal filter characteristics, approximations to the ideal filter using Butterworth, Chebyshev and Bessel-Thompson polynomials; standard filter stages; magnitude and frequency scaling; low-pass filter design; design of high-pass, band-pass and band-reject filters; passive ladder filter network design; frequency dependent negative resistance networks; switched capacitor filters.
EEEE-579
3 Credits
A study of the various techniques for the design of filters to meet the given specifications. The emphasis is on the design of active filters using op amps. The following topics are discussed in detail: Review of transfer functions, Bode diagrams and the analysis of op amp circuits; ideal filter characteristics, approximations to the ideal filter using Butterworth, Chebyshev and Bessel-Thompson polynomials; standard filter stages; magnitude and frequency scaling; low-pass filter design; design of high-pass, band-pass and band-reject filters; passive ladder filter network design; frequency dependent negative resistance networks; switched capacitor filters.
EEEE-499
0 Credits
One semester of paid work experience in electrical engineering.
EEEE-768
3 Credits
An introduction to the fundamental concepts of adaptive systems; open and closed loop adaptive systems; adaptive linear combiner; performance function and minimization; decorrelation of error and input signal. Adaptation algorithms such as steepest descent, LMS and LMS/Newton algorithm. Noise and misadjustments. Applications will include system identification, deconvolution and equalization, adaptive arrays and multipath communication channels.
EEEE-484
3 Credits
Introduction to Communication Systems provides the basics of the formation, transmission and reception of information over communication channels. Spectral density and correlation descriptions for deterministic and stationary random signals. Amplitude and angle modulation methods (e.g. AM and FM) for continuous signals. Carrier detection and synchronization. Phase-locked loop and its application. Introduction to digital communication. Binary ASK, FSK and PSK. Noise effects. Optimum detection: matched filters, maximum-likelihood reception. Computer simulation.
EEEE-353
4 Credits
Linear Systems provides the foundations of continuous and discrete signal and system analysis and modeling. Topics include a description of continuous linear systems via differential equations, a description of discrete systems via difference equations, input-output relationship of continuous and discrete linear systems, the continuous time convolution integral, the discrete time convolution sum, application of convolution principles to system response calculations, exponential and trigonometric forms of Fourier series and their properties, Fourier transforms including energy spectrum and energy spectral density. Sampling of continuous time signals and the sampling theorem, the Laplace, Z and DTFT. The solution of differential equations and circuit analysis problems using Laplace transforms, transfer functions of physical systems, block diagram algebra and transfer function realization is also covered. A comprehensive study of the z transform and its inverse, which includes system transfer function concepts, system frequency response and its interpretation, and the relationship of the z transform to the Fourier and Laplace transform is also covered. Finally, an introduction to the design of digital filters, which includes filter block diagrams for Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters is introduced.