This course teaches the basic signal processing principles necessary to understand many modern hightech systems, with a particular view on audio & visual data compression techniques. In the class, starting from the basic definitions of a discretetime signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail.
Name  Another Details 

DSPLecture 0 

DSPLecture 1 

DSPLecture 2 
Analog and Digital Data

DSPLecture 3 
DiscreteTime Signals and Systems Basic Sequences and Operations DiscreteTime Systems Memoryless System Linear; TimeInvariant; Causal; stable Systems

DSPLecture 4 
Sampling & Aliasing 
HomeWork01 

DSPLecture 5 
Quantization and Encoding 
DSPLecture 6 
The Discrete Fourier Transform 
DSPLecture 7 
The Fast Fourier Transform 
DSPLecture 8 
DiscreteTime Fourier Transform 
HomeWork02 
Sampling and Quantization HW 
DSPLecture 9 
Z Transform

DSPLecture 10 
Transform Analysis of LTI 
DSPLecture 11 
Structures for DiscreteTime Systems 
DSP Project 
EE3240MATLAB miniProject 
DTFT&DFTSolved exercises 
Fourier Analysis of Discrete Time SignalsProblem Solutions 
HomeWork03 
DTFT and FFT HW 
EE3240  Digital Signal Processing  
Units  Hours  
Theoretical  Practical  
1  3  1 
This course teaches the basic signal processing principles necessary to understand many modern hightech systems, with a particular view on audio & visual data compression techniques. In the class, starting from the basic definitions of a discretetime signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail.
Prerequisites for this course: EE3010
Sanjit K. Mitra,
Dr. Medien Zeghid
Outcome  Proficiency Assessment 

1 . Determine if a discrete time system is linear, timeinvariant, causal, and memoryless, determine asymptotic, marginal and BIBO stability of systems given in frequency domain.  Quizzes, Assignments,Test 1,Final Exam 
2 . Perform Fourier transform and inverse Fourier transform transforms using the definitions, Tables of Standard Transforms and Properties.  Quizzes, Assignments,Test 1,Final Exam 
3 . Perform Z and inverse Z using tables, Partial Fraction Expansion, and power series expansion.  Quizzes, Assignments, Test 2, Final Exam 
4 . Be able to design FIR and IIR filters by hand to meet specific magnitude and phase requirements.  Quizzes, Assignments, Test 2, Final Exam 
5 . Use computers and MATLAB to create, analyze and process signals, and to simulate and analyze systems sound and image synthesis and analysis, to plot and interpret magnitude and phase of LTI system frequency responses.  Project 
Week  Description  Reading  

12 


34 
Nyquist sampling theorem; reconstruction of a continuoustime signal from its discretetime samples; interpolation and decimation. 

58 
Fourier transform and inverse Fourier transform; signal representation using Fourier transform; linear difference equations and their solutions. 

9 
ztransform; and inverse ztransform 

1011 
LTI DiscreteTime Systems in the Transform domain: Transfer Function Classification Based on Magnitude Characteristics and Phase 

1213 
Finite impulse response (FIR) and infinite impulse response (IIR) networks. 

1415 
IIR filter design using analog prototypes, and transforms from continuoustime to discretetime 
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