CME 263: Introduction to Linear Dynamical Systems (EE 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: Linear algebra and matrices as in EE103 or
MATH104; ordinary differential equations and Laplace transforms as in EE102B or
CME 102.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
...
more instructors for CME 263 »
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Zhou, Z. (TA)
EE 102B: Signal Processing and Linear Systems II
Continuation of
EE 102A. Concepts and tools for continuous and discretetime signal and system analysis with applications in communications, signal processing and control. Analog and digital modulation and demodulation. Sampling, reconstruction, decimation and interpolation. Finite impulse response filter design. Discrete Fourier transforms, applications in convolution and spectral analysis. Laplace transforms, applications in circuits and feedback control. Z transforms, applications in infinite impulse response filter design. Prerequisite:
EE 102A.
Terms: Spr

Units: 4

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Goldsmith, A. (PI)
;
Dean, J. (TA)
EE 169: Introduction to Bioimaging
Bioimaging is important for both clinical medicine, and medical research. This course will provide a introduction to several of the major imaging modalities, using a signal processing perspective. The course will start with an introduction to multidimensional Fourier transforms, and image quality metrics. It will then study projection imaging systems (projection XRay), backprojection based systems (CT, PET, and SPECT), systems that use beam forming (ultrasound), and systems that use Fourier encoding (MRI). Prerequisites:
EE102A,
EE102B
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nishimura, D. (PI)
;
Behzadian, N. (TA)
EE 263: Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: Linear algebra and matrices as in EE103 or
MATH104; ordinary differential equations and Laplace transforms as in EE102B or
CME 102.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
...
more instructors for EE 263 »
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Zhou, Z. (TA)
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: not given this year

Units: 3

Grading: Letter or Credit/No Credit
EE 392E: VLSI Signal Processing
DSP architecture design. Study of circuit and architecture techniques in energyareaperformance space, design methodology based on a dataflow graph model that leads to hardware implementation. We explore automated wordlength reduction, direct and recursive filters, timefrequency analysis and other examples. The project focuses on architecture exploration for selected DSP algorithms. Useful for algorithm designers who consider hardware constraints and for circuit designers who prototype DSP algorithms in hardware. Prerequisites: EE102B and
EE108A; Recommended: EE264 and
EE271.
Terms: not given this year, last offered Autumn 2015

Units: 3

Grading: Letter or Credit/No Credit
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