EE 367: Computational Imaging and Display (CS 448I)
Spawned by rapid advances in optical fabrication and digital processing power, a new generation of imaging technology is emerging: computational cameras at the convergence of applied mathematics, optics, and highperformance computing. Similar trends are observed for modern displays pushing the boundaries of resolution, contrast, 3D capabilities, and immersive experiences through the codesign of optics, electronics, and computation. This course serves as an introduction to the emerging field of computational imaging and displays. Students will learn to master bits and photons.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wetzstein, G. (PI)
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: Win

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Girod, B. (PI)
EE 369B: Medical Imaging Systems II
Imaging internal structures within the body using magnetic resonance studied from a systems viewpoint. Analysis of magnetic resonance imaging systems including physics, Fourier properties of image formation, effects of system imperfections, image contrast, and noise. Prerequisite:
EE 261
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nishimura, D. (PI)
EE 373A: Adaptive Signal Processing
Learning algorithms for adaptive digital filters. Selfoptimization. Wiener filter theory. Quadratic performance functions, their eigenvectors and eigenvalues. Speed of convergence. Asymptotic performance versus convergence rate. Applications of adaptive filters to statistical prediction, process modeling, adaptive noise canceling, adaptive antenna arrays, adaptive inverse control, and equalization and echo canceling in modems. Artificial neural networks. Cognitive memory/human and machine. Natural and artificial synapses. Hebbian learning. The HebbianLMS algorithm. Theoretical and experimental research projects in adaptive filter theory, communications, audio systems, and neural networks. Biomedical research projects, supervised jointly by EE and Medical School faculty. Recommended:
EE263,
EE264,
EE278.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Widrow, B. (PI)
EE 376A: Information Theory (STATS 376A)
The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178 or
STATS 116, or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Weissman, T. (PI)
EE 376B: Network Information Theory (STATS 376B)
Network information theory deals with the fundamental limits on information flow in networks and the optimal coding schemes that achieve these limits. It aims to extend Shannon's pointtopoint information theory and the FordFulkerson maxflow mincut theorem to networks with multiple sources and destinations. The course presents the basic results and tools in the field in a simple and unified manner. Topics covered include: multiple access channels, broadcast channels, interference channels, channels with state, distributed source coding, multiple description coding, network coding, relay channels, interactive communication, and noisy network coding. Prerequisites:
EE376A.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
El Gamal, A. (PI)
EE 377: Information Theory and Statistics (STATS 311)
Information theoretic techniques in probability and statistics. Fano, Assouad,nand Le Cam methods for optimality guarantees in estimation. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). Approximation of (Bayes) optimalnprocedures, surrogate risks, fdivergences. Penalized estimators and minimumndescription length. Online game playing, gambling, noregret learning. Prerequisites:
EE 376A (or equivalent) or
STATS 300A.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Duchi, J. (PI)
EE 378B: Inference, Estimation, and Information Processing
Techniques and models for signal, data and information processing, with emphasis on incomplete data, nonordered index sets and robust lowcomplexity methods. Linear models; regularization and shrinkage; dimensionality reduction; streaming algorithms; sketching; clustering, search in high dimension; lowrank models; principal component analysis.nnApplications include: positioning from pairwise distances; distributed sensing; measurement/traffic monitoring in networks; finding communities/clusters in networks; recommendation systems; inverse problems. Prerequisites: EE278 and EE263 or equivalent. Recommended but not required:
EE378A
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Montanari, A. (PI)
EE 379: Digital Communication
Modulation: linear, differential and orthogonal methods; signal spaces; power spectra; bandwidth requirements. Detection: maximum likelihood and maximum a posteriori probability principles; sufficient statistics; correlation and matchedfilter receivers; coherent, differentially coherent and noncoherent methods; error probabilities; comparison of modulation and detection methods. Intersymbol interference: singlecarrier channel model; Nyquist requirement; whitened matched filter; maximum likelihood sequence detection; Viterbi algorithm; linear equalization; decisionfeedback equalization. Multicarrier modulation: orthogonal frequencydivision multiplexing; capacity of parallel Gaussian channels; comparison of single and multicarrier techniques. Prerequisite: EE102B and
EE278 (or equivalents). EE279 is helpful but not required.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Kahn, J. (PI)
EE 380: Colloquium on Computer Systems
Live presentations of current research in the design, implementation, analysis, and applications of computer systems. Topics range over a wide range and are different every quarter. Topics may include fundamental science, mathematics, cryptography, device physics, integrated circuits, computer architecture, programming, programming languages, optimization, applications, simulation, graphics, social implications, venture capital, patent and copyright law, networks, computer security, and other topics of related to computer systems. May be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Allison, D. (PI)
;
Freeman, A. (PI)
Filter Results: