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ECE 401 -

Summer 2020

Official Description

An introduction to signal analysis and processing methods for advanced undergraduates or graduate students in the biological, physical, social, engineering and computer sciences. Signal analysis methods and their capabilities, weaknesses, and artifacts with an emphasis on their practical application. Significant hands-on processing and interpretation of real data using MATLAB. Course Information: 4 undergraduate hours. 4 graduate hours. Credit is not given for both ECE 310 and ECE 401. Prerequisite: MATH 220.

Goals

The goal of the course is to prepare students for a research career that involves the analysis of signals and images correlated with behavioral, linguistic, neuroscientific, and physical scientific phenomena. Students learn what types of information can be extracted from signals and images, why the algorithms work, and how to apply the algorithms in practice through a sequence of linked theoretical and machine problems. Problems address artificial signals, then progress to the analysis of acoustic, bio-electric, and neuro-imaging signals acquired through published research programs. The semester concludes with a final project in which each student presents original creative, documentary, or experimental work.

Topics

* Fundamentals of frequency-domain signal analysis, for students who have not previously taken any signal processing course of any kind.

* Frequency-domain analysis, synthesis, and de-noising of bio-electric, acoustic, and imaging signals.

* Image enhancement to improve the visualization of experimental images, e.g., from biomedical imaging and/or low-light behavioral and environmental research settings.

Detailed Description and Outline

Lecture Topics Contact hours

Continuous and discrete-time signals; period and frequency; Nyquist rate for sinusoids

3

Reverberation and convolution; impulse response; linear and shift-invariant systems

3

Images as 2D signals; 2D convolution; point spread function

3

Review of complex numbers; discrete Fourier series and discrete Fourier transform

3

Frequency-sampled filter design; circular convolution; overlap-add

3

Discrete-time and continuous-time Fourier transforms; FIR filter design; sampling

5

2D Fourier transform and frequency response

3

Z transform; notch filtering

3

Linear predictive filtering and LPC-10 coding of speech

3

Human visual system; representation of color; histogram equalization

3

Overview of image formation: projection-slice theorem, tomography, CT, MRI

3

Cinema audio and video signals

6

In-class quizzes

2

Lecture TOTAL

43

Laboratory Topics Contact hours

Introduction to Matlab

1

Reverberation and convolution

1

2D filtering; simulated motion blur

1

Sine wave speech

1

Frequency-sampled filter design; overlap-add (“tinny speech”)

1

Bandpass filtering (“tinny speech”)

1

Sampling of continouos-time signals; aliasing

1

2D bandpass filtering (“image popout effect”)

1

Notch filter design and implementation: 60Hz denoising, image denoising

1

LPC Coding of speech (“robot speech”)

1

Image processing: pseudo-color, histogram equalization, super-resolution

1

Simulated CT and MR image formation

1

Audio and video signals for a short motion picture

2

Laboratory Contact Hours TOTAL

14

Texts

Signal Processing First, by James H. McClellan, Ronald W. Schafer and Mark A. Yoder, Prentice-Hall, 2003

Course Goals

The goal of the course is to prepare students for a research career that involves the analysis of signals and images correlated with behavioral, linguistic, neuroscientific, and physical scientific phenomena. Students learn what types of information can be extracted from signals and images, why the algorithms work, and how to apply the algorithms in practice through a sequence of linked theoretical and machine problems. Problems address artificial signals, then progress to the analysis of acoustic, bio-electric, and neuro-imaging signals acquired through published research programs. The semester concludes with a final project in which each student presents original creative, documentary, or experimental work.

Instructional Objectives

Week 1: students understand continuous and discrete-time signals; period and frequency; Nyquist rate for sinusoids (1).

Week 2: students understand reverberation and convolution; impulse response; linear and shift-invariant systems, and demonstrate an artificial reverberator (1,2).

Week 3: students understand images as 2D signals; 2D convolution; point spread function. Students are able to apply a model of PSF to predict experimental imaging results from a noisy or low-light imaging experiment (1,6).

Week 4: students understand complex numbers; discrete Fourier series and discrete Fourier transform (1).

Week 5: students understand frequency-sampled filter design; circular convolution; overlap-add, and are able to design a low-pass filter to eliminate high-frequency noise from a speech audio signal (1,6).

Week 7: students understand discrete-time and continuous-time Fourier transforms; FIR filter design; sampling, and are able to compare the results of different filter-design methods for de-noising audio, bio-electric, and image signals (1).

Week 8: students understand 2D Fourier transform and frequency response, and are able to filter direction-dependent noise out of an image (1).

Week 9: students understand Z transform; notch filtering, and are able to filter 60Hz and 0Hz noise out of a bio-electric signal (1).

Week 10: students understand linear predictive filtering and LPC-10 coding of speech, and are able to compute the formant frequencies of a speech signal (1).

Week 11: students understand human visual system; representation of color; histogram equalization, and are able to process images to enhance visibility of desired events (1,2).

Week 12: students understand basics of the projection-slice theorem, tomography, CT, MRI (1).

Week 14: students work as teams to prepare and present a final project demonstrating application of theoretical and practical concepts to original data (3,4,5,6,7).

Last updated

7/20/2018by James Andrew Hutchinson