printer friendly pageEE 101B: Circuits II
Second of two-course sequence. MOS large-signal and small-signal models. MOS amplifier design including DC bias, small signal performance, multistage amplifiers, frequency response, and feedback. Prerequisite:
EE101A,
EE102A.
Terms: Spr
| Units: 4
| UG Reqs: WAY-SMA, GER:DB-EngrAppSci
Instructors:
Murmann, B. (PI)
EE 102A: Signal Processing and Linear Systems I
Concepts and tools for continuous- and discrete-time signal and system analysis with applications in signal processing, communications, and control. Mathematical representation of signals and systems. Linearity and time invariance. System impulse and step responses. System frequency response. Frequency-domain representations: Fourier series and Fourier transforms. Filtering and signal distortion. Time/frequency sampling and interpolation. Continuous-discrete-time signal conversion and quantization. Discrete-time signal processing. Prerequisite:
MATH 53 or
ENGR 155A.
Terms: Win, Sum
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
Instructors:
Pauly, J. (PI)
;
Shi, X. (PI)
EE 152: Green Electronics
Many green technologies including hybrid cars, photovoltaic energy systems, efficient power supplies, and energy-conserving control systems have at their heart intelligent, high-power electronics. This course examines this technology and uses green-tech examples to teach the engineering principles of modeling, optimization, analysis, simulation, and design. Topics include power converter topologies, periodic steady-state analysis, control, motors and drives, photovol-taic systems, and design of magnetic components. The course involves a hands-on laboratory and a substantial final project. Required:
EE101B,
EE102A,
EE108A. Recommended: ENGR40 or
EE122A.
Terms: Aut
| Units: 4
Instructors:
Dally, B. (PI)
EE 264: Digital Signal Processing
The fundamentals of digital signal processing techniques and their applications. Topics include review of two sided Z-transform, linear time invariant discrete-time systems, and sampling theory; A/D and D/A conversion, rate conversion, and oversampling techniques for ADC and DAC; filter design; quantization in digital filter implementation; discrete Fourier analysis; and parametric signal modeling. Prerequisite: EE102A and
EE102B . Recommended:
EE261,
EE278B.
Terms: Aut, Spr, Sum
| Units: 3
Instructors:
Park, D. (PI)
;
Schafer, R. (PI)
EE 278B: 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. Prerequisites:
EE178/278A and linear systems and Fourier transforms at the level of
EE102A,B or
EE261.
Terms: Aut, Win, Sum
| Units: 3
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 multi-dimensional Fourier transforms, and image quality metrics. It will then study projection imaging systems (projection X-Ray), backprojection based systems (CT, PET, and SPECT), systems that use beam forming (ultrasound), and systems that use Fourier encoding (MRI). Prerequisites:
EE102A,
EE102B
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