Microprocessors 8085 & 8086 and Microcontrollers

Microprocessors 8085 & 8086

Must Learn Topics In Microprocessors

Text Book:
1 Hall Douglas V, “Microprocessors and Interfacing” Programming and Hardware, Tata McGraw-Hill, New Delhi, 2007
Other Specific Book:
2 Gaonkar Ramesh S, “Microprocessor Architecture, Programming and Applications” with 8085
Other Reading 
Jouranls atricles as compulsary readings (specific articles, Complete reference)
3 http://www.pcmag.com/article2/0,2817,1130705,00.asp
4 http://springerlink.com/content/m01270jkp5164571/

Relevant Websites

(Web adress) (only if relevant to the courses) Salient Features
5 http://www.hobbyprojects.com/microprocessor_tutorials/8085_cpu_pin_diagram.html 
pin diagram
6 http://www.hobbyprojects.com/microprocessor_tutorials/8085_mnemonics_opcode_instructions.html
opcode instruction set
7 http://en.wikipedia.org/wiki/Intel_Core_2 core2 processors
8 http://icrontic.com/articles/dual_core dual core processors
9 http://jntuimplab.blogspot.com/2008/02/traffic-light-controlsystem-using-8086.html
traffic light control
10 http://www.eastaughs.fsnet.co.uk/cpu/execution-direct.htm animation on execution of instruction
11 http://www.eastaughs.fsnet.co.uk/cpu/further-pipelining.htm animation on pipelining


General definitions of microcomputers, microprocessors, microcontrollers and digital signal processors. Evolution of microprocessors.

Register structure, ALU, Bus Organization, Timing and Control, Stack structure.
8085 Architecture and its operation.

Signal descriptions and pins of 8085 microprocessor

Memory interfacing.

Programming model, Addressing modes

Instruction set.Arithmetic operations. Logic operations

Machine control and other instruction
Application oriented programming.
 Application oriented programming.
Internal organization of 8086 microprocessor, Signal descriptions and pins of 8086 microprocessor.

Animation on pipeliningweb

Physical memory organization, BIU, EU. Minimum mode 8086 system and timings, Maximum mode 8086 system and timing.

Description of Instructions execution of  instruction
Description of Instructions.
Description of Instructions, Assembly directives
Assembly software programs with algorithms
Assembly software programs with algorithms
Interrupts, Interrupt service routine, Interruptprogramming

Macros, Timings and delays, Interfacing with RAMs, ROMs along with the explanation of timing diagrams

Interfacing with 8254 programmable timer/counter
Interfacing with 8254 programmable timer/counter
Interfacing with 8259 priority interrupt controller
Interfacing with peripheral ICs 8255
Interfacing with peripheral ICs 8255
Interfacing with peripheral IC 8279
Interfacing with LEDs, LCDs
Interfacing with LEDs, LCDs
Interfacing DACs and ADC
Interfacing DACs and ADC
Introduction to 80386, 80486 and PENTIUM, dual core processors
Microprocessor Applications-Stepper motor control, Video demonstration on interfacing with stepper motor
Microprocessor Applications-Temperature control.
Microprocessor Applications- Traffic light control,  Video demonstration on interfacing with traffic light control system

TUTORIALS
(case analysis,problem solving test,role play,business game etc)
Tutorial 1 Bus organization, Timing and control diagrams Problem solving
Tutorial 2 Architecture of 8085 and memory interfacing Problem solving
Tutorial 3 Different addressing modes and register organization Problem solving
Tutorial 4 Programs on 8085 Problem solving
Tutorial 5 Programs on 8085 Problem solving
Tutorial 6 8086 architecture and memory modes Problem solving
Tutorial 7 Interrupt and its related programming Problem solving
Tutorial 8 Programs with algorithm on 8086 Problem solving
Tutorial 9 Interfacing of 8086 with memory Problem solving
Tutorial 10 Application based problem: stepper motor control,
temperature control
Problem solving
Tutorial 11 Application based problem: traffic light control Problem solving
Tutorial 12 Problems on 80286, 80386, 80486 Problem solving

Analog Communication System

Analog Communication System

Text Book:
1 Wayne Tomasi, “ Electronic Communication System Fundamentals through Advance” 5th Edition Pearson Education
Other Specific Book: 
2 Electronics communication system by Kennedy & Davis, 2008
3 Taub & Schilling “Principles of Communication Systems” Tata Mc-Graw Hill, Second Edition
4 Symon Hykens “Analog Communication Systems” John Wiley & Sons 2008
Other Reading 

Sr No Jouranls atricles as compulsary readings (specific articles, Complete reference)
5 http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1052521
6 http://www3.interscience.wiley.com/journal/117946196/grouphome/home.html
7 http://findarticles.com/p/articles/mi_7109/is_7_2/ai_n28318891/ 

Relevant Websites

Sr. No. (Web adress) (only if relevant to the courses) Salient Features
8 http://zone.ni.com/devzone/cda/tut/p/id/5831 This portal page for RF / Communications in academia includes technical papers, multimedia
presentations, product information, example code, and more
9 http://cnx.org/content/m18715/latest/ The LabVIEW Modulation Toolkit is an optional add-on to LabVIEW that offers a wide variety of
subVIs to quickly and efficiently implement digital and analog communication systems
10 http://www.tutorvista.com/content/physics/physicsiv/
communication-systems/modulation-types.php
TutorVista’s online tutoring is done using an interactive whiteboard to work problems,
simulations, assessments and other tasks.
11 http://upload.wikimedia.org/wikipedia/commons/9/94/Amfm2.gif
12 http://contact.tm.agilent.com/Agilent/tmo/an-1501/classes/liveAM_popup.html
Interactive demo on AM
13 http://contact.tm.agilent.com/Agilent/tmo/an-1501/classes/liveFM_popup.html 
Interactive demo on FM
14 http://www.mathworks.com/matlabcentral/fileexchange/8732amplitude-modulation
MATLAB code for AM
15 http://www.mathworks.com/matlabcentral/fileexchange/11122frequency-modulation-and-demodulation
MATLAB code for FM modulation and demodulation
16 http://www.mathworks.com/help/comm/ref/fmmod.html Important MATLAB commands for analog passband modulation and demodulation
17 http://www.google.co.in/url?sa=t&source=web&cd=4&ved=0CDwQFjAD&url=http%3A%2F%2Fwww.wepapers.com%2FPapers%2F74668%2FTYPE_OF__SIGNALS_AND_SYSTEMS.ppt&ei=pcweTu-Io7srQeOpviuAg&usg=AFQjCNFXqssGXFTqCkyd287NXjMag
1V0Yw
Types of signals
18 http://www.omega.com/literature/transactions/volume2/analogsignal.html
Analog signal transmission

Analog Communication systems:
AM & FM
Basics of Analog communication: Block diagrams, AM & FM,
Numericals on AM & FM

AM generation &
reception
AM generation, AM modulators, AM receievers

FM receiver, SSB systems, comparison between the performance
of all the systems

Component Frequency

TOPICS
1 Analog vs. Digital Formats
2 ELECTRONIC ACCESS AND BIOMETRICS: SECURITY SYSTEM COMPARISONS
3 Frequency Modulation: Analysis and Simulation
4 Quantum cryptography
5 Analog-Digital Hybrid Modulation
6 Amplitude Modulation: Analysis and Simulation
7 Phase Modulation: Analysis and simulation
8 Low Power Wireless Sensor Network
9 Adaptive Active Phased Array Radars
10 Design of an FM receiver
11 Role of analog communication in daily life
12 Advantages of Digital Communication Systems over Analog Communication Systems
13 Use of internet and electronic communication system
14 Use of modulation techniques in telecom
15 Comparison of different modulation techniques



TUTORIALS ON ANALOG COMMUNICATION SYSTEM
(case analysis,problem solving test,role play,business game etc)
Tutorial 1 Carrier wave, modulation techniques Case analysis
Tutorial 2 Numerical on AM power & AM current Problem solving
Tutorial 3 Frequency modulation, Spectra of FM Problem solving
Tutorial 4 Class test based on homework 1, Phase modulation Test
Tutorial 5 AM generation techniques Problem solving
Tutorial 6 Class test based on homework 2, AM reception Test
Tutorial 7 Problems on image frequency rejection & RF amplifier Problem solving
After Mid-Term
Tutorial 8 FM modulation demodulation Case analysis
Tutorial 9 FM reception techniques Case analysis
Tutorial 10 pre-emphasis and de-emphasis Case analysis
Tutorial 11 SSB transmission , Class test based on homework 3 Test
Tutorial 12 SSB reception Case analysis

Data Structure in C, C++, Java other programming languages

Relative Books
Seymour Lipschutz,Title: Schaum Outline Series,Publishers: Tata McGraw Hill,New Delhi,Year of Publication: 2006

Mark Allen Weises, Data Structures & Algorithmic Analysis in C, Pearson Education
Adam Drozdek, Data Structure & Algorithms in C++. Thomson

Kruse, Data Structures & Program design, Prentice Hall of India, New Delhi

Tenenbaum, Augenstein, & Langsam, Data Structures using C and C++, Prentice Hall of India, New Delhi
Sorenson and Tremblay : An Introduction to Data Structures with Algorithms

Other Reading
Sr No Jouranls atricles as compulsary readings (specific articles, Complete reference)
 Article on An Extensive Examination of Data Structures, http://msdn.microsoft.com/en-us/library/aa289148.aspx
Article on ADT Tool: Learning Data Structures as Visual Abstract Data Types, http://www.actapress.com/PaperInfo.aspx?PaperID=14942&reason=500
Self-adjusting binary search trees, Journal of the ACM, Volume 32 , Issue 3 (July 1985) Pages: 652 - 686, Year of Publication: 1985

Relevant Websites 

Sr. No. (Web adress) (only if relevant to the courses) Salient Features
 http://en.wikipedia.org/wiki/Data_structure Provides with introduction to data structures. It also provides us with the links of various data
structure such as arrays, linked list, queues
 http://en.wikibooks.org/wiki/Data_Structure This link provides with the details of various data structures
 http://cpp.datastructures.net/presentations/home.html This provides us with the various presentations on the data structures
 www.seas.upenn.edu/~swati/ee220s02lec3.ppt This ppt will provides the introduction for finding the complexity of algorithm
http://en.wikipedia.org/wiki/Priority_queue The link provides the relationship between a heap and a priority Queue



Topic
1 1. Multi-dimension Linked Lists on Recursive Algorithm
2. Fault tolerant data structures
3. Parallel Generation of Binary Search Trees
4. Application issues of Fibonacci heaps
5. Implementation issues Max flow min cut algorithm
6. Automatic Transformation of Linked List Data Structures
7. Comparison and non- comparison based sorting algorithms
8. non-recursive algorithm for binary search tree traversal
9. Implementation issues of voronoi diagram
10. Generalized Binary Linked List
11. Parallel complexity of queue versus stack breadth-first search
12. Issues in Travelling salesman problem
13. Different Balanced binary trees
14. Applications issues of Treaps
15. Applications of sparse graphs


List of experiments :

Topic Pedagogical Tools Or Equipment Planned lab Manual
Individual 1 Copy string from one to another Computer,Programming
Language C/C++

Individual 2 Concatenate Two String Computer,Programming
Language C/C++

Individual 3 Arrays traversal,insertion, Deletion Computer,Programming
Language C/C++

Individual 4 Linear & Binary Search Computer,Programming
Language C/C++

Individual 5 Two dimensioanal arrays Computer,Programming
Language C/C++


Individual 6 Pointer Arrays Computer,Programming
Language C/C++

Individual 7 Dynamic Memeory Managemnt Computer,Programming
Language C/C++

Individual 8 Memory Management Functions Computer,Programming
Language C/C++

Individual 9 Linked List Traversal & Searching Computer,Programming
Language C/C++

Individual 10 Linked List Insertion Computer,Programming
Language C/C++

Individual 11 Linked List Deletion Computer,Programming
Language C/C++

Individual 12 Header List Computer,Programming
Language C/C++

Individual 13 Circular List Computer,Programming
Language C/C++

Individual 14 Stack & Queue Operations Computer,Programming
Language C/C++

Individual 15 Deque Computer,Programming
Language C/C++

Individual 16 Priority Queue Computer,Programming
Language C/C++

Individual 17 Recursion Computer,Programming
Language C/C++

Individual 18 Tower of Hanoi Computer,Programming
Language C/C++

Mid Term
Individual 19 Binary Search Tree, traversal and searching Computer,Programming
Language C/C++

Individual 20 Binary Search Tree Insertion and deletion Computer,Programming
Language C/C++

Individual 21 Traversing a Graph Computer,Programming
Language C/C++

Individual 22 Shortest Distance Algorithm Computer,Programming
Language C/C++

Individual 23 Heap and Heap Sort Computer,Programming
Language C/C++

Individual 24 Heap as priority Queue Computer,Programming
Language C/C++



Individual 25 DFS & BFS Computer,Programming
Language C/C++

Individual 26 Hashing Computer,Programming
Language C/C++

Individual 27 Insertion & Selection Sort Computer,Programming
Language C/C++

Individual 28 Merge & Radix Sort Computer,Programming
Language C/C++

Individual 29 Bubble Sort Computer,Programming
Language C/C++

Individual 30 Quick Sort Computer,Programming
Language C/C++

Spill Over
Individual 31 Creating a simple binary tree Computer,Programming
Language C/C++

Individual 32 Searching in a simple binary tree Computer,Programming
Language C/C++

Wireless Sensor Networks

WIRELESS SENSOR 
NETWORKS


Introduction


Sensing & Sensors


Classification and Examples of
Sensors


DATA ACQUISITION & ACTUATION


Wireless Sensor Networks


.A wireless sensor network (WSN) consists of
spatially distributed autonomous sensors to monitor
physical or environmental conditions, such as
temperature, sound, vibration, pressure, motion or
pollutants and to cooperatively pass their data
through the network to a main location. The more
modern networks are bi-directional, enabling also to
control the activity of the sensors.
.The development of wireless sensor networks was
motivated by military applications such as battlefield
surveillance; today such networks are used in many
industrial and consumer applications, such as
industrial process monitoring and control, machine
health monitoring, and so on.



.The WSN is built of "nodes" – from a few to
several hundreds or even thousands, where each
node is connected to one (or sometimes several)
sensors.
.Each such sensor network node has typically
several parts: a radio transceiver with an internal
antenna or connection to an external antenna, a
microcontroller, an electronic circuit for interfacing
with the sensors and an energy source, usually a
battery or an embedded form of energy
harvesting.
.A sensor node might vary in size from that of a
shoebox down to the size of a grain of dust,



Wireless Sensor Networks


WSN


Characteristics of WSN

.Power consumption constrains for nodes using
batteries or energy harvesting
.Ability to cope with node failures
.Mobility of nodes
.Dynamic network topology
.Communication failures
.Heterogeneity of nodes
.Scalability to large scale of deployment
.Ability to withstand harsh environmental
conditions
.Ease of use
.Unattended operation.



Communication in a WSN


Single-hop Vs Multi-hop
communication in sensor
networks


Parameters of sensor nodes

.The cost of sensor nodes is similarly variable,
ranging from hundreds of dollars to a few
pennies, depending on the complexity of the
individual sensor nodes. Size and cost
constraints on sensor nodes result in
corresponding constraints on resources such as
energy, memory, computational speed and
communications bandwidth.
.The topology of the WSNs can vary from a simple
star network to an advanced multi-hop wireless
mesh network.
. The propagation technique between the hops of
the network can be routing or flooding.





 Applications of WSN
.Military,
.Environmental,
.Health (Scanning),
.Space,
.Exploration,
.Vehicular Movement,
.Mechanical stress
levels on attached
objects etc.





 Area Monitoring
.Area monitoring is a common application of
WSNs. In area monitoring, the WSN is deployed
over a region where some phenomenon is to be
monitored. A military example is the use of
sensors to detect enemy intrusion; a civilian
example is the geo-fencing of gas or oil pipelines.
.When the sensors detect the event being
monitored (heat, pressure), the event is reported
to one of the base stations, which then takes
appropriate action (e.g., send a message on the
internet or to a satellite). Similarly, wireless
sensor networks can use a range of sensors to
detect the presence of vehicles ranging from
motorcycles to train cars.



Area Pollution Monitoring

.Wireless sensor networks have been deployed in
several cities (Stockholm, London or Brisbane) to
monitor the concentration of dangerous gases for
citizens.




 Forest fires detection

.A network of Sensor Nodes can be installed in a
forest to control when a fire has started. The
nodes will be equipped with sensors to control
temperature, humidity and gases which are
produced by fire in the trees or vegetation.



Greenhouse monitoring

.Wireless sensor networks are also used to control
the temperature and humidity levels inside
commercial greenhouses. When the temperature
and humidity drops below specific levels, the
greenhouse manager must be notified via e-mail
or cell phone text message, or host systems can
trigger misting systems, open vents, turn on fans,
or control a wide variety of system responses.



Landslide Detection

.A landslide detection system, makes use of a
wireless sensor network to detect the slight
movements of soil and changes in various
parameters that may occur before or during a
landslide. And through the data gathered it may
be possible to know the occurrence of landslides
long before it actually happens.



Industrial Monitoring

 Machine health monitoring

.Wireless sensor networks have been developed
for machinery condition-based maintenance
(CBM)as they offer significant cost savings and
enable new functionalities. In wired systems, the
installation of enough sensors is often limited by
the cost of wiring. Previously inaccessible
locations, rotating machinery, hazardous or
restricted areas, and mobile assets can now be
reached with wireless sensors



Wastewater Monitoring

.Agriculture


 Using wireless sensor networks within the
agricultural industry is increasingly common;
using a wireless network frees the farmer from
the maintenance of wiring in a difficult
environment. Gravity feed water systems can be
monitored using pressure transmitters to monitor
water tank levels, pumps can be controlled using
wireless I/O devices and water use can be
measured and wirelessly transmitted back to a
central control center for billing. Irrigation
automation enables more efficient water use and
reduces waste.



Structural monitoring

.Wireless sensors can be used to monitor the
movement within buildings and infrastructure
such as bridges, flyovers, embankments, tunnels
etc... enabling Engineering practices to monitor
assets remotely with out the need for costly site
visits, as well as having the advantage of daily
data, whereas traditionally this data was collected
weekly or monthly, using physical site visits,
involving either road or rail closure in some
cases. it is also far more accurate than any visual
inspection that would be carried out.

Digital Signal Processing - Topics to learn for an Electronics and communication engineer!

Reading Books: 
1 John G Proakis, Dimtris G Manolakis, Digital Signal Processing Principles, Algorithms and Application, PHI, 4th Edition, 2009
Other Specific Book:
2 Alan V Oppenheim, Ronald W Schafer, John R Back, Discrete Time Signal Processing, PHI, 2nd Edition 2008
3 S.Salivahanan, A.Vallavaraj, Gnanapriya, Digital Signal Processing, McGraw-Hill / TMH, 2007
4 S.K.Mitra, “Digital Signal Processing- A Computer based approach”, Tata McGraw-Hill, 2006, New Delhi.
5 Johny R.Johnson :Introduction to Digital Signal Processing, Prentice Hall, 2006.
6 Avtar singh, S.Srinivasan DSP Implementation using DSP microprocessor with Examples from TMS32C54XX -Thamson / Brooks cole
Publishers, 2008

Other Relative Reading Material
Sr No Jouranls atricles as compulsary readings (specific articles, Complete reference)
7 Nikolaos V. Boulgouris, Juwei Lu, Konstantinos N. Plataniotis, and Arun Ross, “Advanced Signal Processing and Pattern Recognition Methods for Biometrics,”
EURASIP Journal on Advances in Signal Process
8 Xin Cindy Guo and Dimitrios Hatzinakos, “Image Authentication Using Added Signal- Dependent Noise,” Research Letters in Signal Processing, vol. 2007,
Article ID 47549, 5 pages, 2007
9 Giulio Agostini, Maurizio Longari, and Emanuele Pollastri, “Musical Instrument Timbres Classification with Spectral Features,” EURASIP Journal on Applied
Signal Processing, vol. 2003, no. 1, pp. 5-14,
10 Amritpal, Manmohan, “Waveform distortion estimation using multiple power quality events”, Proceedings of ICSTC 2008, Sandiego, USA.

Relevant Websites 

Sr. No. (Web adress) (only if relevant to the courses) Salient Features
11 http://www.dsptutor.freeuk.com/dfilt1.htm Contains Informative material regarding digital filters
12 http://www.mathworks.com/products/signal/de mos.html?
BB=1
Contains animated signal processing demos
13 http://www.ee.ucla.edu/~dsplab/index.html Interactive Digital Signal Processing Laboratory
14 http://www.jhu.edu/signals/ Contains animation related to DSP
15 http://djj.ee.ntu.edu.tw/FRFT.pdf Fractional fourier transform
16 http://www.medialab.ch/ds/vorlesung/LeastSquaresFilter.pdf linear phase filters




 Introduction to Signals and Systems, Classification
of Signals- energy and power,periodic and
aperiodic, even and odd

Advantages of digital signal processing as
compared to analog signal processing


Signal processing applications
real world examples

Discrete time signals-Unit sample, Unit step,Unit
ramp,exponential signals.


Discrete time Systems-block diagram
representations, classification-static vs dynamic,
Time variant vs Time Invariant, linear vs non-
linear,causal and non-causal, stable vs unstable

LTI systems-Techniques for analysis of LTI
systems,resolution of Discrete time signals into
impulses,response of LTI systems to arbitrary inputs

 properties of convolution and interconnection of LTI
systems,causal LTI systems,stability of LTI systems


discrete time systems described by difference
equations

Implementation of Discrete time systems
Correlation of Discrete -Time Signals

fourier series for continuous time periodic and
aperiodic signals,power density spectrum of periodic
signals, energy density spectrum of aperiodic
signals

Frequency analysis of discrete time signals
DTFT,Properties of DTFT
problems on DFT
 linear convolution and circular convolution  animation
problems on convolution
 problems on convolution
FFT, Radix-2, decimation in Time
Decimation in frequency
inverse DFT
Introduction to fractional fourier transform


 The Z-transform, properties
 ROC, convolution using Z-transform using matlab
 inverse z-transform, analysis of LTI systems in z-
domain

Structures for realization of discrete time systems-
structure for FIR systems-direct form structure and
cascade structure
matlab
 frequency sampling structure and lattice structure
structure for IIR systems



 Effects of coefficient quantization

round off effects in filters
 filter design using windows
(Blackman,hamming,hanningKaiser)

 FIR filter design using frequency sampling method
 IIR filter design BLT
IIR filter design by impulse invariance
Optimum approximation of FIR filters
Linear phase filters
revision

problems on fourier transform
problems on Z-transform
problems on FIR filter design
problems on IIR filter design


Tutorial 1 applications of DSP Case analysis
Tutorial 2 operations on sequences Problem solving
Tutorial 3 convolution Problem solving
Tutorial 4 problems on linearity Problem solving
Tutorial 5 fourier transform Problem solving
Tutorial 6 DIT-FFT Problem solving
Tutorial 7 DIF FFT Problem solving
After Mid-Term
Tutorial 8 Z-transform Problem solving
Tutorial 9 basic structure of filters Problem solving
Tutorial 10 FIR filter design Problem solving
Tutorial 11 IIR filter design Problem solving

Infinite Impulse Response IIR FILTER (IIR)

6. IIR FILTER.



AIM: To design and implement IIR (LPF/HPF) filters.

EQUIPMENTS:
Software - MATLAB

Learning Objectives: To make the students familiar with designing concepts of FIR filter
with the use of MATLAB.

THEORY:
The IIR filter can realize both the poles and zeroes of a system because it has a rational
transfer function, described by polynomials in z in both the numerator and the denominator:
The difference equation for such a system is described by the following:
M and N are order of the two polynomials.
bk and ak are the filter coefficients. These filter coefficients are generated using FDS (Filter
Design software or Digital Filter design package).
IIR filters can be expanded as infinite impulse response filters. In designing IIR
filters, cutoff frequencies of the filters should be mentioned. The order of the filter
can be estimated using butter worth polynomial. That’s why the filters are named as
butter worth filters. Filter coefficients can be found and the response can be plotted.

PROGRAM:
% IIR filters LPF & HPF
clc;clear all;close all;
disp('enter the IIR filter design specifications');
rp=input('enter the passband ripple');
rs=input('enter the stopband ripple');
wp=input('enter the passband freq');
ws=input('enter the stopband freq');
fs=input('enter the sampling freq');
w1=2*wp/fs;w2=2*ws/fs;
[n,wn]=buttord(w1,w2,rp,rs,'s');
c=input('enter choice of filter 1. LPF 2. HPF \n ');
if(c==1)
disp('Frequency response of IIR LPF is:');


[b,a]=butter(n,wn,'low','s');
end
if(c==2)
disp('Frequency response of IIR HPF is:');
[b,a]=butter(n,wn,'high','s');
end
w=0:.01:pi;
[h,om]=freqs(b,a,w);
m=20*log10(abs(h));
an=angle(h);
figure,subplot(2,1,1);plot(om/pi,m);
title('magnitude response of IIR filter is:');
xlabel('(a) Normalized freq. -->');
ylabel('Gain in dB-->');
subplot(2,1,2);plot(om/pi,an);
title('phase response of IIR filter is:');
xlabel('(b) Normalized freq. -->');
ylabel('Phase in radians-->');

Finite Impulse Response FIR FILTER (FIR)

5. FIR FILTER.


AIM: To verify FIR filters.

EQUIPMENTS:
Constructor –MATLAB Software

Learning Objectives: To make the students familiar with designing concepts of FIR filter
with the use of MATLAB.

THEORY:
A Finite Impulse Response (FIR) filter is a discrete linear time-invariant system whose
output is based on the weighted summation of a finite number of past inputs. An FIR transversal
filter structure can be obtained directly from the equation for discrete-time convolution.
In this equation, x(k) and y(n) represent the input to and output from the filter at time n.
h(n-k) is the transversal filter coefficients at time n. These coefficients are generated by using
FDS (Filter Design Software or Digital filter design package).
FIR – filter is a finite impulse response filter. Order of the filter should be specified.
Infinite response is truncated to get finite impulse response. placing a window of finite length
does this. Types of windows available are Rectangular, Barlett, Hamming, Hanning, Blackmann
window etc. This FIR filter is an all zero filter.

PROGRAM:
%fir filt design window techniques
clc;
clear all;
close all;
rp=input('enter passband ripple');
rs=input('enter the stopband ripple');
fp=input('enter passband freq');
fs=input('enter stopband freq');
f=input('enter sampling freq ');
wp=2*fp/f;
ws=2*fs/f;
num=-20*log10(sqrt(rp*rs))-13;
dem=14.6*(fs-fp)/f;
n=ceil(num/dem);
n1=n+1;
if(rem(n,2)~=0)
n1=n;
n=n-1;
end
c=input('enter your choice of window function 1. rectangular 2. triangular 3.kaiser: \n ');
if(c==1)
y=rectwin(n1);
disp('Rectangular window filter response');
end
if (c==2)
y=triang(n1);
disp('Triangular window filter response');
end
if(c==3)
y=kaiser(n1);
disp('kaiser window filter response');
end

%LPF
b=fir1(n,wp,y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,1);plot(o/pi,m);
title('LPF');
ylabel('Gain in dB-->');
xlabel('(a) Normalized frequency-->');
%HPF

b=fir1(n,wp,'high',y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,2);plot(o/pi,m);
title('HPF');
ylabel('Gain in dB-->');
xlabel('(b) Normalized frequency-->');
%BPF

wn=[wp ws];
b=fir1(n,wn,y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,3);plot(o/pi,m);
title('BPF');
ylabel('Gain in dB-->');
xlabel('(c) Normalized frequency-->');
%BSF
b=fir1(n,wn,'stop',y);
[h,o]=freqz(b,1,256);
m=20*log10(abs(h));
subplot(2,2,4);plot(o/pi,m);
title('BSF');
ylabel('Gain in dB-->');
xlabel('(d) Normalized frequency-->')

INVERSE Z TRANSFORM

4. INVERSE Z TRANSFORM



AIM: To develop a program for Computing Inverse Z-Transform

EQUIPMENTS: MATLAB 7.5

Learning Objectives: To make the students familiar with concept of inverse Z-transform
with the use of MATLAB.

THEORY:
Description: In mathematics and signal processing, the Z-transform converts a discrete
time-domain signal, which is a sequence of real or complex numbers, into a complex frequency-
domain representation. The Z-transform, like many other integral transforms, can be defined as
either a one-sided or two-sided transform.
The bilateral or two-sided Z-transform of a discrete-time signal x[n] is the function X(z)
defined as
.
Alternatively, in cases where x[n] is defined only for n = 0, the single-sided or unilateral
Z-transform is defined as
Description: X(z) = \mathcal{Z}\{x[n]\} = \sum_{n=-\infty}^{\infty} x[n] z^{-n} \
Description: X(z) = \mathcal{Z}\{x[n]\} = \sum_{n=0}^{\infty} x[n] z^{-n} \


In signal processing, this definition is used when the signal is causal.
Rational Z-transform to partial fraction form:
Consider the transfer function in the rational form i-e;
18z3
G(z)= ------------------
18z3+3z2-4z-1
We can evaluate the partial fraction form of the above system using matlab command. The
partial fraction form be,
G(z)= 0.36__ + __0.24__ + _0.4____
1 – 0.5z-1 1+0.33 z-1 (1+0.33 z-1)
Matlab command that converts rational z-transform in to partial fraction form is
‘residuez’.
If you want to see the poles and zeros in a zplane. This function displays the poles and zeros
of discrete-time systems. Use the under given matlab command
zplane(b,a)

ALGORITHM:


1. Write the poles and zeros of the input sequence.
2. Returned vector R contains the residues, Column vector contains P contains the pole
locations. And row vector contains the direct terms.



PROGRAM CODE:
%program to perform Inverse Z-Transform
b=[1,0.4*sqrt(2)];
a=[1,-0.8*sqrt(2),0.64];
[R,P,C]=residuez(b,a);
R
P
C
Zplane(b,a);

Discrete Fourier Transform & Inverse Discrete Transform (DFT AND IDFT)

3. Discrete Fourier Transform & Inverse Discrete Transform (DFT AND IDFT)



AIM: To develop a program for Computing DFT and IDFT in MATLAB

REQUIREMENTS: MATLAB 7.5

Learning Objectives: To make the students familiar with concept of DFT and IDFT with
the use of MATLAB.

THEORY: The discrete Fourier transform (DFT) X[k] of a finite-length sequence x[n] can be
easily computed in MATLAB using the function fft. There are two versions of this function.
fft(x) computes the DFT X[k] of the sequence x[n] where the length of X[k] is the same as that of
x[n]. fft(x,L) computes the L-point DFT of a sequence x[n] of length N where L = N. If L > N,
x[n] is zero-padded with L-N trailing zero-valued samples before the DFT is computed. The
inverse discrete Fourier transform (IDFT) x[n] of a DFT sequence X[k] can likewise be computed
using the function ifft, which also has two versions.

ALGORITHM (For DFT):
1 Enter the input Sequence ,x having length=4
2 Set the range of k according to the length of x.
3 Computing DFT, store the value in X(k).
4 Plotting the DFT of given Sequence,store in X(k).

PROGRAM CODE:
% Program to perform Discrete Fourier Transform:
clc;
clear all;
close all hidden;
x=input('The given i/p sequence is x(n): ');
subplot(2,2,[1,2]), stem(x);
title('i/p sequencce x(n)is:');
xlabel('---->n');
ylabel('---->x(n)');grid;
N=length(x);
for k=1:N
X(k)=0;
for n=1:N
X(k)=X(k)+x(n).*exp(-j.*2.*pi.*(n-1).*(k-1)./N);
end
end
disp('The DFT of the i/p sequence x(n) is X(n):')
p=0:(N-1);
subplot(2,2,[3,4]), stem(p,abs(X));
title('The DFT of the i/p sequence x(n) is X(n):');
xlabel('---->n');
 ylabel('---->X(n)');grid;
disp(X);

ALGORITHM (For IDFT):

1 Enter the input Sequence, x having length=4
2 Set the range of k according to the length of x.
3 Computing IDFT, store the value in X(k).
4 Plotting the IDFT of given Sequence, store in X(k).

% Program to perform Inverse Discrete Fourier Transform:
clc;
clear all;
close all hidden;
X=input('The given i/p sequence is X(n): ');
subplot(2,2,[1,2]), stem(X);
title('i/p sequencce X(n)is:');
xlabel('---->n');
ylabel('---->X(n)');grid;
N=length(X);
for n=1:N
x(n)=0;
for k=1:N
x(n)=x(n)+X(k).*exp(j.*2.*pi.*(n-1).*(k-1)./N);
x(n)=x(n)./N;
end
end
disp('The IDFT of the i/p sequence X(n) is x(n):')
p=0:(N-1);
subplot(2,2,[3,4]), stem(p,abs(x));
title('The IDFT of the i/p sequence X(n) is x(n):');
xlabel('---->n');
ylabel('---->x(n)');grid;
disp(x);

CIRCULAR CONVOLUTION

2. CIRCULAR CONVOLUTION



AIM: To verify Circular Convolution.

EQUIPMENTS:
Software - MATLAB 7.5

Learning Objectives: To make the students familiar with concept of circular convolution
with the help of MATLAB.

THEORY:
Circular convolution is another way of finding the convolution sum of two input signals.
It resembles the linear convolution, except that the sample values of one of the input signals is
folded and right shifted before the convolution sum is found. Also note that circular convolution
could also be found by taking the DFT of the two input signals and finding the product of the
two frequency domain signals. The Inverse DFT of the product would give the output of the
signal in the time domain which is the circular convolution output. The two input signals could
have been of varying sample lengths. But we take the DFT of higher point, which ever signals
levels to. For eg. If one of the signal is of length 256 and the other spans 51 samples, then we
could only take 256 point DFT. So the output of IDFT would be containing 256 samples instead
of 306 samples, which follows N1+N2 – 1 where N1 & N2 are the lengths 256 and 51
respectively of the two inputs. Thus the output which should have been 306 samples long is
fitted into 256 samples. The 256 points end up being a distorted version of the correct signal.
This process is called circular convolution.

PROGRAM:
%circular convolution program:
clc;
clear all;
close all;
disp('circular convolution program');
x=input('enter i/p sequence x(n):');
a=length(x);
disp(a);
h=input('enter i/p sequence h(n):');
b=length(h);
disp(b);
subplot(2,2,1), stem(x);
title('i/p sequence x(n)is:');
xlabel('---->n');
ylabel('---->x(n)');grid;
subplot(2,2,2), stem(h);
title('i/p sequence h(n)is:');
xlabel('---->n');
ylabel('---->h(n)');grid minor;
disp('circular convolution of x(n) & h(n) is y(n):');
if(a>b)
n=a;


 else
n=b;
end
if(a-b~=0)
if(a>b)
h=[h,zeros(1,a-b)];
else
x=[x,zeros(1,b-a)];
end
end
disp(x);
disp(h);
y=zeros(1,n);
for i=1:n
y(i)=0;
k=i;
for j=1:n
y(i)=y(i)+(x(j)*h(k));
if k==1
k=n+1;
end
k=k-1;
end
end
subplot(2,2,[3,4]),stem(y);
title('circular convolution of x(n) & h(n) is:');
xlabel('---->n');
ylabel('---->y(n)');grid;
disp(y);

LINEAR CONVOLUTION AND CORRELATION

1. LINEAR CONVOLUTION AND CORRELATION



AIM: To verify Linear Convolution.

EQUIPMENTS: Software -- MATLAB 7.5

Learning Objectives: To make the students familiar with concept of discrete convolution
and correlation with the use of MATLAB.

THEORY: Convolution is a formal mathematical operation, just as multiplication, addition, and
integration. Addition takes two numbers and produces a third number, while convolution takes
two signals and produces a third signal. Convolution is used in the mathematics of many fields,
such as probability and statistics. In linear systems, convolution is used to describe the
relationship between three signals of interest: the input signal, the impulse response, and the
output signal.
In this equation, x1(k), x2(n-k) and y(n) represent the input to and output from the system
at time n. Here we could see that one of the input is shifted in time by a value every time it is
multiplied with the other input signal. Linear Convolution is quite often used as a method of
implementing filters of various types.

ALGORITHM:

1. Enter the input Sequence ,x having length=4
2. Enter the Impulse Sequence, h having length=4
3. Performing the Convolution, store the value in y
4. Plotting the Input Sequence.
5. Plotting the Impulse Sequence.
6. Plotting the Output Sequence.



PROGRAM:
%linear convolution program:
clc;
clear all;
close all;
disp('linear convolution program');
x=input('enter i/p x(n):');
m=length(x);
disp(m);
h=input('enter i/p h(n):');
n=length(h);
disp(n);
x=[x,zeros(1,n)];


 subplot(2,2,1), stem(x);
title('i/p sequence x(n)is:');
xlabel('---->n');
ylabel('---->x(n)');grid;
h=[h,zeros(1,m)];
subplot(2,2,2), stem(h);
title('i/p sequence h(n)is:');
xlabel('---->n');
ylabel('---->h(n)');grid;
disp('convolution of x(n) & h(n) is y(n):');
y=zeros(1,m+n-1);
for i=1:m+n-1
y(i)=0;
for j=1:m+n-1
if(j
y(i)=y(i)+x(j)*h(i-j+1);
end
end
end
subplot(2,2,[3,4]),stem(y);
title('convolution of x(n) & h(n) is :');
xlabel('---->n');
ylabel('---->y(n)');grid;
disp(y);



%program for discrete Correlation
x=[1 2 3 4];
y=[2 3 4 5];
z=xcorr(x,y);
stem(z);
subplot(2,2,1),stem(x)
title(‘input sequence 1’)
subplot(2,2,2),stem(y)
title(‘input sequence 2’)
subplot(2,2,3),stem(z)
title(‘output sequence’)

ALGORITHM:
1 Enter the input Sequence ,x having length=4
2 Enter the Impulse Sequence, y having length=4
3 Performing the Correlation, store the value in y
4 Plotting the Output Sequence ,store in z.

Top 10 Electronics Experiment in Mid- Engineering.

Top 10 Electronics Experiment in Mid- Engineering.

1.
To develop program for linear convolution and correlation using MATLAB.
2.
To develop a program for computing circular convolution Using MATLAB.
3.
To develop a program for computing DFT and IDFT using MATLAB.
4.
To develop a program for computing inverse Z-transform using MATLAB.
5.
To develop a program for designing FIR Filter in MATLAB.
6.
To develop a program for designing IIR Filters in MATLAB.
7.
To generate a FM Signal and measure Depth of modulation.
8.
To obtain Amplitude modulated envelope and determine depth of modulation
9.
To study envelope detector for demodulation of AM signal and observe diagonal peak clipping effect.
10.
Design Hartley oscillator and determine lowest and highest frequency it can generate.
11.
Design and observe waveforms of colpitt’s oscillator, compare its characteristics with Hartley oscillator.
12.
Design RC phase shift oscillator and determine lowest and highest frequency it can generate.