We designed a digital chebyshev filter using the analog filter and input specifications. Scilab software was used to write the code and also observe the output magnitude spectrum with frequency (Hz) on X axis and attenuation (dB) on Y axis.We have designed Chebyshev-I in which there are ripples in the pass band and no ripples in the stop band.It was observed similar input parameters the order of chebyshev filter was lesser compared to butterworth filter.
Wednesday, 26 April 2017
Tuesday, 25 April 2017
Patent review
Tuesday, 25 April 2017
Patent Review
Patent No: US3436754
Publication date: June 2, 1965
Inventors: L. D. Powner
Inventors: L. D. Powner
Title : BINARY TO INTERNATIONAL MORSE CODE CONVERTER
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.
Paper Review
Title : Adaptive Word processor based on Morse code
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 Link : https://drive.google.com/open?id=0B-MmFL7cJdFoeldEcnlkQXM4Vzg
Plagiarism Check Link : https://drive.google.com/drive/folders/0BxzVWYbaSXFrMUtVRzNZWXpLeTQ
Operations using DSP Processsor
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 which included Addition Subtraction Multiply Divide, Logical Operations like And and Not and Shifting Operations like Logical Shift Left, Logical Shift Right, Rotate Right, Rotate Left.
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.
Butterworth Filter
In this experiment we designed a digital filter from analog filter. The input specifications that were given are passband attenuation, stopband attenuation, passband frequency, stopband frequency and sampling frequency. Scilab software was used to code and observe the output plot with frequency on x axis and attenuation (in db) on Y axis.From the pole zero plot observed i we saw that in case of digital LPF ,poles lie inside the unit circle, thus the digital filter was stable in nature.
Monday, 13 March 2017
OAM And OSM
Overlap-Add and Overlap-Save method is used to perform linear convolution and circular convolution of long data sequences respectively. They are called as block processing techniques as the input signal is divided into blocks and then analysed. The OAM was performed by linear convolution using DITFFT and OSM by Circular Convolution using DITFFT. In case of OSM the input signal was modified and the final output signal was obtained by discarding few initial values from all the convolved outputs.
FFT
In order to overcome the slow computation drawback of DFT , Fast Fourier Transform(FFT) method was invented which performs the same task in less number of steps ,thereby increasing the speed. In this method the input sequence is broken down into two equal parts. This breaking down of the signal was continued till no further decomposition was possible. Radix-2 algorithm is used to perform the experiment. The counter was then added in the code to check the total number of calculations and it was proved that less steps are required to perform the task as compared to DFT.
The difference in the number of steps required goes on increasing as the length of input signal increase.
The difference in the number of steps required goes on increasing as the length of input signal increase.
Convolution and correlation
In todays experiment we studied Convolution which gives the output of the system and Correlation which gives the degree of similarity between the 2 signals which are being compared.
In linear convolution we found that if both the input signals are causal then the output signal is also causal . Also Aliased output was obtained in circular convulation . The length of output signal in case of linear convolution was found to be always greater than or equal to the that of the circular convolution.
In case of Auto correlation we found that the same output was produced even if the signal was delayed and always the ouput signal was an even signal. Also the Cross correlation of a signal with its delayed signal was same as that of the autocorrelation of input signal
In linear convolution we found that if both the input signals are causal then the output signal is also causal . Also Aliased output was obtained in circular convulation . The length of output signal in case of linear convolution was found to be always greater than or equal to the that of the circular convolution.
In case of Auto correlation we found that the same output was produced even if the signal was delayed and always the ouput signal was an even signal. Also the Cross correlation of a signal with its delayed signal was same as that of the autocorrelation of input signal
DFT
In this experiment we learned to obtain DFT signal from DTFT signal.DFT signal is basically obtained by sampling DTFT signal at w =2πk/N.
We also found that DFT gives approximated spectrum and as we increase the number of samples the approximation error decreases which in turn increase the resolution.This was achieved by doing zero padding. Also DFT produce priodic result with period equal to N.
We also found that DFT gives approximated spectrum and as we increase the number of samples the approximation error decreases which in turn increase the resolution.This was achieved by doing zero padding. Also DFT produce priodic result with period equal to N.
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