UNDERGRADUATE:
————————————-
EE 212 Electronics I

Semiconductor device physics; operation principles of p-n junction diode, field effect transistor, bipolar junction transistor. Diode circuits. Basic single-stage BJT and FET amplifier biasing and small-signal models. Differential amplifiers, current mirrors, operational amplifier circuits. Introduction to circuit analysis with Spice simulator.

EE 331 Signals and Systems

Signals and systems. Linear time-invariant systems. Fourier series representation of periodic signals. The continuous-time Fourier transform. The discrete-time Fourier transform. Time and frequency characterization of signals and systems. Sampling. Communication systems. The Laplace transform. The z-transform.

EE 432 Speech Processing

Speech Production and modeling, short-term processing of speech, linear prediction analysis, cepstral analysis, speech coding and synthesis, speech recognition.

EE 433 Introduction to Digital Signal Processing

Discrete-time signals and systems. The z-transform. Sampling of continuous-time signals. Transform analysis of linear time-invariant systems. Structures for discrete-time systems. Filter design techniques. The discrete Fourier transform. Fourier analysis of signals using the discrete Fourier transform. Discrete Hilbert transforms.

EE 434 Biomedical Signal Processing

Biomedical signals, types and their sources; Sampling and aliasing in biomedical signals; A/D and D/A conversions; Fourier analysis and applications on biomedical signals; Timefrequency domain methods: Wavelet transformation, Wigner-Ville distribution and their applications in biomedical engineering; Filtering: FIR and IIR filters and their biomedical applications: Noise removal, signal compensation, etc.; Interpolation methods and algorithms; Spectral estimation and applications in biomedical engineering; Matched filtering and applications. Independent component analysis and blind source separation: Applications on EEG signal analysis. Nonlinear models for biomedical signals.

GRADUATE:
————————————-
EE 545 Image Processing

Properties and analysis tools for multidimensional signal and systems. Image perception and human visual systems. Stochastic models for image representation. Transform techniques and image data compression. Analysis of video images, motion estimation. Image analysis and computer vision. Image reconstruction from projections.

EE 546 Pattern Recognition

Feature selection: Space transformations; Karhunen-Loeve expansion: Various distance measures. Supervised learning: Discriminant functions, linear and nonlinear training algorithms, statistical parametric and nonparametric methods. Nonsupervised learning: Clustering with known or unknown number of classes. Classification by neural networks.

EE 548 Medical Imaging Systems and Applications

Medical imaging technology, systems, and modalities. Projection radiography: X-Ray systems, digital radiography. Computed tomography (CT): Principles, reconstruction methods, hardware. Magnetic resonance imaging (MRI): Mathematics, spin physics, NMR spectroscopy, fourier transforms, imaging principles. Ultrasound (US): Mathematical principles, echo equation, impulse response, diffraction, lateral and depth resolution, phased array systems, noise removal. Nuclear Medicine: Positron emission tomography (PET), single photon emission computed tomography (SPECT), imaging methods, resolution, 3-D imaging. Medical image storage, archiving and communication systems and formats: PACS, DICOM, TIFF. Image processing applications on medical images: Enhancement, segmentation, registration, compression, etc.