# ECE 310

## ECE 310 - Digital Signal Processing

### Spring 2023

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Digital Signal ProcessingECE310CS70980LCD3 -    Naresh R Shanbhag
Digital Signal ProcessingECE310D56913LEC30900 - 0950 M W F  1015 Electrical & Computer Eng Bldg Zhi-Pei Liang
Chuanyi Zhang
Shilan He
Will Cai
Ziyang Xu
Digital Signal ProcessingECE310E65517LEC31200 - 1250 M W F  1013 Electrical & Computer Eng Bldg Thomas Moon
Chuanyi Zhang
Shilan He
Will Cai
Ziyang Xu
Digital Signal ProcessingECE310G56914LEC31500 - 1550 M W F  1015 Electrical & Computer Eng Bldg Corey Ethan Snyder
Chuanyi Zhang
Shilan He
Will Cai
Ziyang Xu
Digital Signal ProcessingECE310ZJ175255LEC3 -    Zhi-Pei Liang

### Official Description

Introduction to discrete-time systems and discrete-time signal processing with an emphasis on causal systems; discrete-time linear systems, difference equations, z-transforms, discrete convolution, stability, discrete-time Fourier transforms, analog-to-digital and digital-to-analog conversion, digital filter design, discrete Fourier transforms, fast Fourier transforms, spectral analysis, and applications of digital signal processing. Course Information: Credit is not given towards graduation for both ECE 310 and ECE 401. Prerequisite: ECE 210.

### Subject Area

• Signal Processing

### Description

Introduction to discrete-time systems and discrete-time signal processing: discrete-time linear systems, difference equations, z-transform, discrete convolution, stability, discrete-time Fourier transform, analog-to-digital and digital-to-analog conversion, interpolation and decimation, digital filter design, discrete Fourier transform, fast Fourier transform, spectral analysis, applications of digital signal processing.

### Goals

Introduce fundamentals of discrete-time linear systems and digital signal processing. Emphasizes theory but also includes design and applications.

### Topics

• Overview of digital signal processing
• Discrete-time linear shift-invariant systems
• Difference equations
• Complex numbers and functions of a complex variable
• Convolution and impulse response
• z-transform
• Transfer functions and block diagrams
• Convolution via z-transform
• Stability
• Discrete-time Fourier transform (DTFT)
• Digital frequency and frequency response
• Sampling
• Analog frequency response of a digital processor
• A/D and D/A conversion
• Interpolation and oversampling A/D, D/A
• FIR and IIR filter structures
• Generalized linear phase
• FIR filter design
• IIR filter design
• Discrete Fourier transform (DFT)
• Spectral analysis
• Fast Fourier transform (FFT)
• Applications to speech, audio/music, medical imaging, communications, etc.

### Detailed Description and Outline

Discrete-time linear shift-invariant systems (3 hrs)

Complex numbers and functions of a complex variable (2 hrs)

z-transform (3 hrs)

Convolution and impulse response (3 hrs)

Transfer function and block diagrams (2 hrs)

Discrete-time Fourier transform (DTFT) (4 hrs)

Digital frequency and frequency response (2 hrs)

A/D and D/A conversion (4 hrs)

Multirate systems (3 hrs)

FIR and IIR filter design (6 hrs)

Discrete Fourier transform (DFT) (3 hrs)

Spectral analysis (3 hrs)

Fast Fourier transform (FFT) (3 hrs)

Applications to speech, medical imaging, communications, etc. (4 hrs)

### Computer Usage

Students can access the ECE 310 course web site to obtain course documents, homework, and solutions. In the optional companion laboratory course, ECE311 Students use Labview and Matlab to complete several assignments involving design and implementation.

### Topical Prerequisites

Analog signal processing (circuit analysis, differential equations, convolution, Fourier and Laplace transforms) ---

ECE 210 or consent of instructor

### Texts

D. C. Munson, Jr., and A.C. Singer ECE 310 Course Notes, 2013.

D. G. Manolakis and V. K. Ingle, Applied Digital Signal Processing: Theory and Practice, Cambridge Univ. Press, 2011

### ABET Category

Engineering topics: 100%

### Course Goals

To introduce fundamentals of discrete-time linear systems and digital signal processing. Emphasizes theory but also includes design and applications.

### Instructional Objectives

A student completing this course should, at a minimum, be able to:

1. Determine whether systems are linear or nonlinear, causal or noncausal, shift-invariant, or shift varying. (1)

2. Model systems with difference equations and compute their solutions. (1)

3. Apply the z-transform as a tool in system modeling and analysis. (1, 6)

4. Visualize and compute discrete-time convolution. (1)

5. Apply the z-transform as a tool in system modeling and analysis, and understand the related abstract concepts of function of a complex variable and region of convergence. (1, 6)

6. Calculate impulse response and convolution using the concept of transfer function. (1)

7. Draw block diagrams of common digital filters, including those using complex arithmetic. (2)

8. Determine whether a system is stable or unstable and demonstrate an understanding of the abstract concept of stability. (1)

9. Demonstrate an understanding of the discrete-time Fourier transform and the concept of digital frequency. (1)

10. Choose the sampling rate for a digital system and understand the effects of aliasing. (1, 2)

11. Compute the analog frequency response of a digital system. (1)

12. Demonstrate familiarity with actual A/D and D/A circuits. (1, 2)

13. Mathematically analyze decimation and interpolation and their effects on oversampling A/Ds and D/As. (1, 6)

14. Decompose a high-order transfer function into a realization composed of second-order building blocks. (1, 2)

15. Design FIR filters using the window design method. (1, 2)

16. Design FIR filters using the frequency sampling method. (1, 2)

17. Design IIR filters using the bilinear transformation. (1, 2)

18. Demonstrate an understanding of the DFT and its use in spectral analysis and frequency sampling filter design. (1, 2, 6)

19. Demonstrate an understanding of the FFT and its use in fast convolution. (1, 2)

20. Explain how digital signal processing is used in applications. (6)

5/15/2019
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