Dr. Sohail A. Dianat
at Rochester Institute of Technology
SPIE Involvement:
Author | Editor | Instructor
Publications (24)

SPIE Journal Paper | 8 May 2015
JMI Vol. 2 Issue 02
KEYWORDS: Image segmentation, 3D modeling, Magnetic resonance imaging, 3D image processing, Computed tomography, Algorithm development, Barium, 3D acquisition, Detection and tracking algorithms, Brain

SPIE Journal Paper | 26 March 2015
JEI Vol. 24 Issue 02
KEYWORDS: Image segmentation, Imaging systems, Detection and tracking algorithms, Image filtering, Cameras, Optical character recognition, Image processing, Optical filters, Computing systems, Sensors

SPIE Journal Paper | 17 July 2012
JBO Vol. 17 Issue 7
KEYWORDS: Independent component analysis, Video, Beam propagation method, RGB color model, Oximeters, Signal processing, Communication engineering, Heart, Detection and tracking algorithms, Photoplethysmography

Proceedings Article | 2 February 2012
Proc. SPIE. 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
KEYWORDS: Neck, Medicine, Image processing, Diagnostics, Image registration, Medical imaging, Solids, Computed tomography, Binary data, 3D image processing

Proceedings Article | 28 April 2010
Proc. SPIE. 7706, Wireless Sensing, Localization, and Processing V
KEYWORDS: Signal to noise ratio, Data modeling, Modulation, Error analysis, Interference (communication), Computer simulations, Monte Carlo methods, Convolution, Electrical engineering, Data communications

Showing 5 of 24 publications
Proceedings Volume Editor (17)

SPIE Conference Volume | 10 June 2014

SPIE Conference Volume | 6 June 2013

SPIE Conference Volume | 8 June 2012

SPIE Conference Volume | 11 May 2011

SPIE Conference Volume | 25 April 2010

Showing 5 of 17 publications
Conference Committee Involvement (20)
Video Surveillance and Transportation Imaging Applications 2015
10 February 2015 | San Francisco, California, United States
Wireless Sensing, Localization, and Processing IX
7 May 2014 | Baltimore, Maryland, United States
Video Surveillance and Transportation Imaging Applications 2014
3 February 2014 | San Francisco, California, United States
Wireless Sensing, Localization, and Processing VIII
1 May 2013 | Baltimore, Maryland, United States
Video Surveillance and Transportation Imaging Applications
4 February 2013 | Burlingame, California, United States
Showing 5 of 20 Conference Committees
Course Instructor
SC197: Fundamentals of Digital Signal/Image Processing
This course covers: fundamental concepts of digital signal processing (DSP) systems such as analog to digital converter (A/D), aliasing, scalar and vector quantization, and coding; point spread function (PSF) of imaging systems; modulation transfer function (MTF); circularly symmetric imaging systems; techniques used for analyzing DSP based systems such as convolution, frequency response, MTF measurement, etc.; Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) algorithms, fast convolution and correlation; design of 1-D and 2-D digital FIR filters; sampling rate conversion; spectral estimation; adaptive signal processing with applications to deconvolution, system identification, and noise removal; adaptive arrays and beamforming; signal detection and estimation techniques; software for simulating some of the component blocks will be provided by the instructor.
SC161: Wavelet Transforms: Theory and Applications
The course deals with theory and application of continuous and discrete wavelet transforms. Topics covered include: time-frequency resolution, orthogonal and biorthogonal discrete wavelet transforms, multiresolution analysis, wavelet packets, wavelet design, implementation methods, image compression techniques and standards (JPEG2000), pattern recognition, watermarking for secure multimedia information dissemination and wavelet multitone modulation for high-speed internet access using ADSL technology.
SC949: Linear and Nonlinear Principal Components Analysis with Applications in Sensing and Processing
This course is designed to give the audience an understanding of linear and nonlinear Principal Components Analysis (PCA). Principal Components Analysis is a useful statistical technique to find patterns in data of high dimension and reduce the data dimensionality. It is a nonparametric approach to extract relevant information from high dimension raw data sets. The course provides basic methods and algorithms to compute linear and nonlinear PCA from large sets of data. Participants will learn specific topics in detail, including data normalization, relevant statistics, data covariance matrix, overview of statistical signal processing, eigenvalue/eigenvector decomposition, linear PCA and its limitations, nonlinear PCA, feature vector extraction, and implementation of linear and nonlinear PCA. Application examples to be discussed include data compression in one dimensional (1-d), 2-d and 3-d space, remote sensing, face recognition, data processing for direction finding, image indexing and retrieval, source localization and signal detection in communication and radar systems.
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