EXP10:Signal Processing Application

This was a Group Experiment performed by Ninad Chitnis,Saish Desai,Vaibhavi Dichwalkar,Shrinish Donde, Akash Bangera, Ankesh Patil. We choose "Morse Code Generation from a given Analog Signal" as an application.

Problem Definition :
1) Accept Analogy input and convert it into Digital Signal(Binary).
2) Covert Binary to Morse and implement the dots and dashes as 0 and 1 respectively.
    (Dots = 1 = LED ON, Dash = 0 = LED OFF)

Patent Review :International Morse Code Number GeneratorPatent Number :- US4292624
Inventors :- Serp William K.
Publication Date :- 09/29/1981
Application Number :- 05/517813

Summary :- 
Morse code is a code in which letters are represented by combinations of long and short light or sound signals.It transmits text as a series of on-off tones,lights or clicks. The code which is to be transmitted is loaded into preset-able counters.Switch mean is used to connect the  binary level to a specific memory location.The counter thereby sends data sequentially generating Morse Code.It sends one digit at a time.The next lesser significant digit to the address and binary counter are recycled. Switching means are used for the generation of leading zeros and for selectively abbreviating the international Morse code of certain digit comprising the numbers.

Applications:-
It allows physically handicapped person to communicate by the means of tap or blink.
In aviation, pilots use the Morse code to ensure whether the stations are servicable or not by transmitting a short set of identification numbers.

Patent's Link :- https://drive.google.com/file/d/0B3rfIyzoQA19bXYxOEtaS2VPQ3c/view?usp=sharing

IEEE Paper Review:
Automatic Morse Code recognition with adaptive variable ratio threshold prediciton for physically handicapped persons.Authors :- Ching-Hsing Luo
                 Ming-Che Hsien
Publisher :- IEEE
Published in :- Engineering in Medicine and Biology,1999. 21st annual conference and the 1999 annual fall meeting of biomedical engineering society.
Date of Conference :- 1999

Summary:-
Morse code helps physically impaired persons to communicate easily. However they have difficulties in maintaining a stable type. Hence the threshold and prediction method is in demand. In threshold prediction, the Morse code time series with unit time period and ratio are used.Two least mean square predictors are applied to trace the dash and the dot interval and the dot-dash difference and a predicted threshold based on a variable ratio decision rule is used to distinguish between dashes and dots. The same method is applied to identify the character space.

Paper's Link :- https://drive.google.com/file/d/0B3rfIyzoQA19RkFDZGFrWXh2azA/view?usp=sharing

EXP9:Basic Operations on DSP Processor

In theory we study mathematical analysis of various signals and systems. However, it is very difficult to analyse real-time signal theoretically. Therefore we use DSP Processor for real time analysis. DSP processor is capable of carrying out large number of mathematical operations on real time signals. In this experiment we used C2000 board and performed basic operations like addition, subtraction, multiplication, logical operations etc.

EXP8: Digital FIR filter using Frequency Sampling

In this experiment a digital FIR filter is designed using Frequency sampling method.In FSM we subsitute w=2(pi)k/n.5 parameters namely, Attenuation in both pass and stop bands in dB, pass band and stop band frequencies in Hertz and Sampling frequency in Hertz are taken from user. Once we obtain the desired frequency response, Hd[w], we can directly convert them into the frequency domain  using the conventional Discrete Fourier Transform algorithm, H[k]. The final output sequence, h[n] can be obtained by performing the  Inverse Discrete Fourier Transform algorithm. The final output sequence, h[n] is always symmetric about the point of symmetry i.e. N/2 . Discontinuity is observed in phase plot between lobes and also when the spectrum goes out of the range that is from -pi to +pi.

EXP7: Digital FIR Filter using Window Function

In this experiment we used a particular windowing function based upon the attenuation in stop band.There are  different types of windowing functions Rectangular ,Bartlett ,Hamming ,Hanning, and Blackman.The specifications of As,Ap, stop band frequency, passband frequency and sampling frequency are taken from the user.It is observed that as the order of the filter increases the number of lobes in frequency response increase.The attenuation depends upon the type of window used.

EXP6:Chebyshev IIR Filter

The Chebyshev IIR filter was designed in scilab, chebyshev filter contains ripples in pass band and stop band in its magnitude spectrum. The order of the chebyshev filter is equal to the number of ripples .LPF and HPF were designed according to the input specifications and then were compared with the values obtained from the graph. We observed that the number of poles in butterworth filter is more than chebyshev filter.

EXP5:Butterworth IIR Filter

In this experiment a digital filter was designed using analog filter. Here we used scilab software. Digital butterworth filter can be designed using two methods impulse invariant method and BLT method. For HPF and LPF,all poles lie in the unit circle.Hence filters are stable. But  the value of Ap in HPF and As in LPF are not completely verified, so we need to increase the order of the filter.

EXP4: Overlap Add and Overlap Save

Output of FIR filter is convolution of input signal, x[n] and impulse response of filter, h[n]. Practically length of input signal is very long and and convolution becomes complicated. Therefore for filtering real time input signal there are two methods.
1. Overlap Add Method (OAM) 2. Overlap Save Method (OSM)
In both the methods the input signal is decomposed into small parts. In OAM ,length of decomposed signal is L,where L=N-M+1. Linear convolution of decomposed input signal and h[n] is carried out and final output is found out by shifting and adding individual outputs. In OSM ,input is decomposed by adding some preceding values. Circular convolution is done with h[n] after zero padding h[n].Final output of OSM is obtained after discarding the extra preceded values. Thus, OAM and OSM are block processing techniques.

EXP3: Fast Fourier Transform

As DFT is computationally is slow, in order to reduced the calculations and increase the speed drastically, Fast Fourier Transform i.e FFT was introduced. In theory sessions,we learnt Radix-2 FFT algorithms.
In this lab session we studied and executed FFT of 4pt and 8pt sequence using DITFFT. We also calculated the no. of computations required to find out FFT of a sequence. Comparing it with no.of computations required of DFT we concluded that FFT is much faster compared to DFT.

EXP2: Discrete Fourier Transform

Discrete Fourier Transform (DFT) is frequency sampled version of Discrete Time fourier transform(DTFT). DFT gives periodic results.
In this experiment,we performed 4-point and 8-point DFT. First we performed 4pt DFT. Later we appended the 4pt signal with 4 zeros and performed 8-point DFT. We also added zeros at even places of 4pt signal and performed 8pt DFT. We plotted the approximate magnitude spectrum of all the DFT signals.
We observed that expansion of signal in time domain gives compressed signal in frequency domain.
We also calculated the no.of computations required to get DFT of a signal and concluded that DFT is computationally slow.

EXP1: Convolution and Correlation

Convolution and correlation are very important from signals and systems point of view. Linear Convolution gives output of the system whereas correlation tells us about the similarity of two signals.
In theory sessions we implemented these mathematically. In lab session we are learning its practical implementation using C programming. We thoroughly studied the codes of linear convolution,linear using circular convolution,correlation. While studying the codes we revised the concepts of C programming learnt in first year especially the concept of call by reference which has been used in all the codes.

We compiled and executed the codes using terminal window of ubuntu. For linear convolution,we first entered the length of input signals,L=length of first signal and M=length of second signal and then entered the values.We found out that length of output signal i.e N=L+M-1. Similarly we performed linear using circular convolution and correlation.We concluded that auto correlation of delayed signal is same as original signal and the value of auto correlation at n=0 is the energy of the signal.