Chapter 7
582
Filter Design Techniques
TABLE 7.4 AVERAGE NUMBER OF REQUIRED
MULTIPLICATIONS PER OUTPUT SAMPLE FOR
EACH OF THE DESIGNED FILTERS.
Filter design
Direct form
Symmetric
Polyphase
Butterworth
Chebyshev I
Chebyshev II
Elliptic
Kaiser
ParksMcClell

Chapter 4
Problems
237
bandwidth of the signal, then aliasing distortion occurs and the original signal cannot
be reconstructed by bandlimited interpolation.
The ability to represent signals by sampling permits the discrete-time processing of
continuous-t

Chapter 5
Problems
341
of such systems and the associated eigenvalues correspond to the system function or
frequency response.
A particularly important class of LTI systems is that characterized by linear constantcoefcient difference equations. Systems ch

Chapter 8
684
The Discrete Fourier Transform
identical to one period of the DFS coefcients. Because of the importance of this underlying periodicity, we rst examined the properties of DFS representations and then
interpreted those properties in terms of n

Chapter 2
70
Discrete-Time Signals and Systems
That is, for a zero-mean white-noise input, the cross-correlation between input and
output of a linear system is proportional to the impulse response of the system. Similarly,
the power spectrum of a white-no

Chapter 6
464
Structures for Discrete-Time Systems
IIR and FIR discrete-time systems. These included the direct form I, direct form II, cascade form, parallel form, lattice form, and transposed version of all the basic forms. We
showed that these forms ar

ELL319 Digital Signal Processing
Assignment 1: Range and Velocity Estimation of a Moving Target
Neelkanth Kundu (2014EE10520) Nilesh Kumar Jha (2014EE10523)
Objective: To study and implement Correlation Processing and End point detection for calculating
t

Chapter 3
138
The z -Transform
technique of inverse transformation based on the partial fraction expansion of X(z). We
also discussed other techniques for inverse transformation, such as the use of tabulated
power series expansions and long division.
An i

ELL-319 Digital Signal Processing
Assignment-2 Pitch Detection
Neelkanth Kundu (2014EE10520), Nilesh Kumar Jha (2014EE10523)
AIM:
To implement two algorithms to find the pitch of a sound signal: One algorithm in time domain and
other in frequency domain.

ELL319: Digital Signal Processing
Assignment 1
Due date: 2nd November 2016
18th October 2016
Range and Velocity Estimation of a Moving Target
Radar systems operate by transmitting electromagnetic waves, most commonly of microwave frequency, toward
an obje

ELL319 Digital Signal Processing Lab
Experiment 1: Familiarization with Code Composer Studio (CCS)
Aim:
The overall aim is to get comfortable with the Code Composer Studio, learn to run and verify code
given to you, and understand about the architecture o

ELL319: Digital Signal Processing
Instructor: Seshan Srirangarajan
Updates: Please visit the course page on Moodle for further information.
Lecture Timing:
Day/Time: Mon, Wed 11:00 - 11:50 am and Thu 12:00-12:50 pm (Slot H)
Room: LH-310 (Lecture Hall Comp

ELL319 Digital Signal Processing Lab
Experiment 5: Real-time filtering on the DSK
(2 turns)
Aim:
The aim of this experiment is to write simple and efficient code for real-time filtering.
Goal:
At the end of the experiment, you should be familiar with the

ELL319 Digital Signal Processing Lab
Experiment 3: PN Sequence Generation
Theory: Pseudo-Noise (PN) sequences are commonly used to generate noise that is approximately "white".
It has applications in scrambling, cryptography, and spread-spectrum communica

ELL319 Digital Signal Processing Lab
Experiment 4: FIR Filter Implementation
(2 turns)
Aim:
The aim of this experiment is to familiarize you with the various possible instructions that can be used for
FIR filtering and the C code that can be used.
Goals:

ELL319 Digital Signal Processing
Experiment 2: Number Representation, Addressing modes, and Instructions
Aim:
The aim of this part of the experiment is to familiarize you with the number representations, and
the addressing modes possible in C5510. You are