14:332:346 Digital Signal Processing
14:332:346 – Digital Signal Processing (3)
Introduction to digital signal processing, sampling and quantization, A/D and D/A converters, discrete time systems, convolution, ztransforms, transfer functions, digital filter realizations, fast Fourier transforms, analog & digital filter design, digital audio applications.
14:332:345 (and 347)  Linear Systems and Signals (and Lab). C or MATLAB programming experience is required.
14:332:348 (mandatory associated DSP lab)
1. Complex numbers and trigonometry
2. Differential and integral calculus
3. Linear timeinvariant systems
4. Convolution and transfer functions
5. Laplace transforms and ztransforms
6. Difference equations
S. K. Mitra, Digital Signal Processing, 4th ed., McGrawHill, 2011, or equivalent.
S.J. Orfanidis, Introduction to Signal Processing, PrenticeHall, 1996, and available freely online from: http://www.ece.rutgers.edu/~orfanidi/intro2sp/
MatLab: Student Version, Current Edition, The MathWorks, Inc.
To introduce the basic principles, methods, and applications of digital signal processing, emphasizing its algorithmic, computational, and programming aspects.
A student who successfully fulfills the course requirements will have demonstrated:
1. Understanding of the two key DSP concepts of sampling and quantization, and the practical issues involved in sampling, aliasing, and analog reconstruction of signals, and in choosing and defining specifications for antialiasing prefilters and antiimage postfilters.
2. Understanding of the quantization process and some practical implementations of A/D and D/A converters such as the conversion algorithm for bipolar two's complement successive approximation converters.
3. Understanding of basic discretetime systems concepts, such as linearity, timeinvariance, impulse response, convolution, FIR and IIR filters, causality, stability, ztransforms, transfer functions, frequency response, time constants, transient and steadystate response.
4. Understanding of how to implement digital filters in software and hardware, using block processing methods based on convolution, or realtime samplebysample processing methods based on block diagram realizations that are implemented with linear or circular delayline buffers.
5. Ability to translate a filter’s transfer function into blockdiagram realizations, such as direct, canonical, transposed, and cascade forms. And conversely, the ability to start with a given block diagram, determine its transfer function, and translate it into a realtime processing algorithm implementable in software or hardware.
6. Understanding of various digital filter design methods meeting prescribed specifications, such as pole/zero placement or bilinear transformation methods, and appreciating design tradeoffs between the specifications and filter order, time constant, and pole locations.
7. Understanding of the discrete Fourier transform and the fast Fourier transform and their use in spectral analysis, data compression, and fast convolution. Understanding of the tradeoffs between frequency resolution and signal duration and the use of windows for reducing frequency leakage. Ability to perform short FFTs by hand.
 Prerequisite Quiz (ABET): 3%
 Homeworks (3% each): 12%
 Random attendance: 10%
 Midterm 1: 20%
 Midterm 2: 20%
 Final Exam (cumulative): 35%
N = none S = Supportive H = highly related
Outcome 
Level 
Proficiency assessed by 
(a) an ability to apply knowledge of Mathematics, science, and engineering 
H 
HW Problems, Exams 
(b) an ability to design and conduct experiments and interpret data 
N 

(c) an ability to design a system, component or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability 
S 
Digital filter design examples meeting prescribed specifications 
(d) an ability to function as part of a multidisciplinary team 
N 

(e) an ability to identify, formulate, and solve ECE problems 
H 
HW Problems, Exams 
(f) an understanding of professional and ethical responsibility 
N 

(g) an ability to communicate in written and oral form 
S 
HW Problems, exams 
(h) the broad education necessary to understand the impact of electrical and computer engineering solutions in a global, economic, environmental, and societal context 
N 

(i) a recognition of the need for, and an ability to engage in lifelong learning 
S 
Homework, emphasized during lectures 
(j) a knowledge of contemporary issues 
N 

(k) an ability to use the techniques, skills, and modern engineering tools necessary for electrical and computer engineering practice 
H 
HW Problems, Exams 
Basic disciplines in Electrical Engineering 
H 
HW Problems, Exams 
Depth in Electrical Engineering 
S 
HW Problems, Exams 
Basic disciplines in Computer Engineering 
H 
Programming DSP algorithms in C, MATLAB, and assembly language for DSP chips 
Depth in Computer Engineering 
S 
Software and hardware programming 
Laboratory equipment and software tools 
H 
Analog Devices DSP2181 digital signal processor. Programming in C, MATLAB, and DSP software development environment 
Variety of instruction formats 
S 
Lecture, office hour discussions 
Lecture 
Topic 
Exams 
13 
Signal and Signal Processing 

4 
Closed book 
PREREQUISITE QUIZ (3%) 
57 
DiscreteTime Signals and Systems 

8 
First Recitation 

911 
DiscreteTime Fourier Transform 

12 
Second Recitation 
HW1 due 
13 
Closed book 
MIDTERM 1 (20%) 
14,15 
Digital Processing of ContinuousTime Signals 

16,17 
FiniteLength Discrete Transform 

18 
Third Recitation 
HW2 due 
1921 
zTransform 

22 
Fourth Recitation 
HW3 due 
23 
Closed book 
MIDTERM 2 (20%) 
24, 25 
LTI DiscreteTime Systems in the Transform Domain 

26, 27 
Digital Filter Structure 

28 
Fifth Recitation 
HW4 due 
Closed book 
FINAL EXAM (35%) 
DSP algorithm programming in C, MATLAB, and Assembly Language.
14:332:348 Digital Signal Processing Laboratory (mandatory)
HW problems in designing digital filters using various techniques. In conjunction with 332:348, designing and programming realtime audio signal processing algorithms on DSP hardware.
1. Homework, 2.MATLAB programming, 3.Testing (Quizzes, Exams)
(a) Collegelevel Mathematics and Basic Sciences: 0.5 credit hours
(b) Engineering Topics (Science and/or Design): 2.5 credit hours
(c) General Education: 0.0 credit hours
Total credits: 3