BIOE 223: Physics and Engineering of XRay Computed Tomography (RAD 223)
CT scanning geometries, production of xrays, interactions of xrays with matter, 2D and 3D CT reconstruction, image presentation, image quality performance parameters, system components, image artirfacts, radiation dose. Prerequisites: differential and integral calculus. Knowledge of Fourier transforms (
EE261) recommended.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
CS 232: Digital Image Processing (EE 368)
Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended:
EE261,
EE278.
Terms: Aut

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Wetzstein, G. (PI)
EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)
Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of largescale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.
Terms: not given this year

Units: 34

Grading: Letter or Credit/No Credit
EE 261: The Fourier Transform and Its Applications
The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems. Prerequisites: Math through ODEs, basic linear algebra, Comfort with sums and discrete signals, Fourier series at the level of 102A
Terms: Aut, Spr, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Gill, J. (PI)
;
Osgood, B. (PI)
EE 262: TwoDimensional Imaging
Time and frequency representations, twodimensional auto and crosscorrelation, Fourier spectra, diffraction and antennas, coordinate systems and the Hankel and Abel transforms, line integrals, impulses and sampling, restoration in the presence of noise, reconstruction and tomography, imaging radar. Tomographic reconstruction using projectionslice and layergarm methods. Students create software to form images using these techniques with actual data. Final project consists of design and simulation of an advanced imaging system. Prerequisite:
EE261. Recommended:
EE278,
EE279.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
EE 278: Introduction to Statistical Signal Processing
Review of basic probability and random variables. Random vectors and processes; convergence and limit theorems; IID, independent increment, Markov, and Gaussian random processes; stationary random processes; autocorrelation and power spectral density; mean square error estimation, detection, and linear estimation. Formerly
EE 278B. Prerequisites: EE178 and linear systems and Fourier transforms at the level of
EE102A,B or
EE261.
Terms: Aut, Spr, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
El Gamal, A. (PI)
;
Prabhakar, B. (PI)
EE 355: Imaging Radar and Applications (GEOPHYS 265)
Radar remote sensing, radar image characteristics, viewing geometry, range coding, synthetic aperture processing, correlation, range migration, range/Doppler algorithms, wave domain algorithms, polar algorithm, polarimetric processing, interferometric measurements. Applications: surfafe deformation, polarimetry and target discrimination, topographic mapping surface displacements, velocities of ice fields. Prerequisites:
EE261. Recommended:
EE254,
EE278,
EE279.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Zebker, H. (PI)
;
Yoon, C. (TA)
EE 368: Digital Image Processing (CS 232)
Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended:
EE261,
EE278.
Terms: Aut

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Wetzstein, G. (PI)
EE 398: Image and Video Compression
The principles of source coding for the efficient storage and transmission of still and moving images. Entropy and lossless coding techniques. Runlength coding and fax compression. Arithmetic coding. Ratedistortion limits and quantization. Lossless and lossy predictive coding. Transform coding, JPEG. Subband coding, wavelets, JPEG2000. Motioncompensated coding, MPEG. Students investigate image and video compression algorithms in Matlab or C. Term project. Prerequisites:
EE261,
EE278.
Terms: not given this year

Units: 3

Grading: Letter (ABCD/NP)
GEOPHYS 258: Applied Optimization Laboratory (Geophys 258) (EE 257)
Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of largescale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.
Terms: not given this year

Units: 34

Grading: Letter or Credit/No Credit
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