SIGNAL PROCESSING APPLICATION - MORSE CODE GENERATOR

    This was a group experiment for analysis and implementation of Morse code generator and its application. It was performed by Akash Bangera, Saish Desai, Ninad Chitnis, Vaibhavi Dichwalkar, Shrinish Donde.

PATENT REVIEW:

EXTENSIVE MORSE CODE PROCESSING SYSTEM
PATENT NUMBER:  US005095179A
INVENTOR: Lewis Ho, No.27, Alley 20, Lane 61, Wu-Te St., Chung-Pu Hsiang, Taiwan
PUBLICATION DATE: 10/03/1992
FILING DATE: 26/07/1990
SUMMARY:
    The invention relates generally to telegraphic systems, and more particularly, to an electronic apparatus and code converting method for transmission and reception of morse code signals. Morse code is a widely used telegraph code which assigns a combination of short (dot) and long (dash) pulse signals to different characters. A telegraph key unit is used to key in short and long pulse signals. The telegraph key unit is connected to a wireless radio unit which transmits the pulse signals to a remote radio unit. The receiving party audibly distinguishes the received pulse signals and communicates with the sending party in a similar manner. One of the main drawbacks of the above described morse code telegraphic system is that not everybody can operate such a system.

PATENT LINK: https://docs.google.com/viewer?url=patentimages.storage.googleapis.com/pdfs/US5095179.pdf



PAPER REVIEW:

MORSE CODE DECODER USING PIC MICROCONTROLLER
PUBLISHER: Ms. N.S. Bakde, Prof A.P. Thakare
PUBLISHED IN: International Journal of Science, Engineering and Technology Research (IJSETR) 
SUMMARY:
    Morse code is designed to be read by humans without a decoding device, making it useful for sending automated digital data in voice channels. For emergency signalling, Morse code can be sent by way of improvised sources that can be easily "keyed" on and off. The design presented here is physically simpler, although the software is considerably more complex. The goal of this project was to produce a system that will decode morse code signals from a possibly noisy audio source, and display the decoded text on a LCD screen through a PIC microcontroller interface. The system might also produce morse code via input from keyboard. It consists of a handheld unit that can receive morse code, via audio input or direct signal connection, and translate it for display on LCD screen.

PAPER LINK: http://ijsetr.org/wp-content/uploads/2013/08/IJSETR-VOL-1-ISSUE-5-200-205.pdf

OPERATION ON DSP PROCESSOR

    The aim of this experiment is to perform basic operations on DSP Processor. The operations were performed on a DSP Processor(TMS320F28335). The TI Code Composer Studio(CCS) was used for programming the DSP processor. The various operations carried out were Arithmetic Operations, Logical Operations and Shifting Operations.
    The same operations can also be performed using general purpose processor. But, DSP Processor is faster than general purpose processors.

FIR FILTER DESIGN USING FREQUENCY SAMPLING METHOD

    The aim of the experiment was to design digital filter using frequency sampling method. The magnitude and phase spectrum were plotted for LPF and HPF. 
    It was observed that the phase plot is similar. Also, if the order of LPF and HPF are same, then the phase plot for both the filters is also same. The observed and calculated values of As and Ap were verified.

FIR FILTER DESIGN USING WINDOWING METHOD

    The aim of the experiment was to design digital filter using windowing method and study the spectrum. The input parameters were passband attenuation(Ap), stopband attenuation(As), passband frequency(Fp), stopband frequency(Fs) and sampling frequency(F). The magnitude and phase spectrum for LPF and BPF using Hanning window were plotted. 
    The observed and calculated values of Ap and As were compared. Thus, the values were verified. The phase spectrum was observed to be linear.

DIGITAL CHEBYSHEV FILTER DESIGN

    The aim of this experiment was to design Digital Chebyshev Filter. This experiment was performed using Scilab. Digital chebyshev filter was designed using BLT method.
    The imput parameters were pass band attenuation(Ap), stop band attenuation(As), pass band frequency(Fp), stop band frequency(Fs) and sampling frequency(F). The observed and theoretical values of Ap and As were compared. More the order of the filter, less will be the difference between observed and theoretical Ap and As values.

DIGITAL BUTTERWORTH FILTER DESIGN

    The aim of this experiment was to design digital butterworth filter. This experiment was performed using Scilab. The input parameters were passband attenuation(Ap), stopband attenuation(As), passband frequency(Fp), stopband frequency(Fs) and sampling frequency(F).The theoretical and observed values of Ap and As were compared.
    It was observed that all the poles lie in the unit circle for high pass and low pass digital filter. Thus, the filters are stable. Greater the order of filter, greater is the accuracy in the theoretical and observed values. 

FILTERING OF LONG DATA SEQUENCE

        The aim of this experiment was to perform filtering of long data sequence using Overlap Add Method(OAM) and Overlap Save Method(OSM). These methods are used to find discrete convolution i.e. output of FIR filter for a very long input signal x(n).

        OAM and OSM divides the input sequence into smaller blocks. The filtering of individual blocks, which are smaller in length, is done by DFT and IDFT and then fitted one after the other. The overlapped portion is discarded in OSM whereas it is added in OAM to get the required output sequence. Thus, OAM and OSM are block processing techniques.

        They are suitable for real time signal processing. They are used for decreasing the delay obtained in getting the output.

FAST FOURIER TRANSFORM

        The aim of this experiment was to perform Fast Fourier Transform(FFT) of N point signal. We found out the FFT of 4 point and 8 point input sequence using DITFFT. We also performed Inverse Fast Fourier Transform(IFFT) to verify our original input signal.
        We observed that, input sequence order and output sequence order in Radix-2 FFT algorithm is in bit reversal manner. We compared the real and complex additions as well as the real and complex multiplications of FFT with DFT and came to a conclusion that FFT improves operational efficiency by parallel processing. Thus, FFT is computationally fast algorithm.

DISCRETE FOURIER TRANSFORM

        The aim of this experiment was to perform Discrete Fourier Transform(DFT) of N point signal. We found out the DFT of input signal x(n), zero padded x(n) and expanded signal. We also plotted the magnitude spectrum of these signals. We also performed Inverse Discrete Fourier Transform(IDFT) to verify our original input signal.
        We observed that, as the length of the signal increases by zero padding, frequency spacing decreases, approximation error decreases and resolution of spectrum increases. DFT gives approximated spectrum. Expansion of signal in time domain gives compressed spectra in frequency domain. Also, DFT is computationally slow. We found out the total number of real additions and multiplications using the code and verified it with the formulae available for these parameters.

DISCRETE CONVOLUTION AND CORRELATION

        The aim of this experiment was to study mathematical operations such as Linear Convolution, Circular Convolution, Linear Convolution using Circular Convolution and Correlation. In our practical session, we implemented these operations using C codes to find the output of the system and degree of similarity between the two signals.

        First part of this experiment was to find convolution between two signals x(n) and h(n). We found out that circular convolution gives aliased output.

        Second part of this experiment was to find correlation between two signals. We found the auto-correlation of input signal x(n) and also of delayed input signal. Auto-correlation output signal is an even signal. The auto-correlation of delayed input signal is same as that of auto-correlation of original signal. The application of correlation is to find the degree of similarity of two signals.