CHEBYSHEV FILTER DESIGN

EXPERIMENT NO. 5

In this experiment we designed a digital Chebyshev filters from an analog chebyshev filter.These filters have a steeper roll off and unlike butterworth filters which are monotonic, they have ripples in their stop band or pass band.BLT was used for converting the impulse response in H(s) to H(z). It was noticed that in the output magnitude response, there is a ripple in the pass band with the total number of valleys and peaks equal to order of the filter. Chebyshev filter had a smaller order as compared to Butterworth filter.We also observed that the no of valleys or peaks is equal to the order of the filter.For the same parameters the order of chebyshev filter is less than of butterworth filters.

OVERLAP ADD AND SAVE METHOD

This method is the one that is of more practical use as it allows a very long continous signal to be processed. We use the overlap add method to compute the convolution of a signal of a long length.Which is broken down into small sequences and after performing the operations on each of them,they are combined to obtain the final output.Breaking down i.e. buffering small portions of the signal also allows usinf FFT for processing, which allows better computational efficiency.


the code can be found at https://docs.google.com/document/d/1koSHOnOtbPnJ37__APPRIpDNYDh2eEkD9CqLOtg5etw/edit

DSP APPLICATION

EXPERIMENT 10

This was research based experiment wherein we studied an application of dsp processor.We formed a group of 5 students. The members were Nikita Pagar, Golappagouda Patil, Anushree Mhatre, Apoorva Raut and Sahil Rai. The topic we selected was “Detection and Processing of Electromyography signals”.

the paper name   

EMG Pattern Recognition Based on
Artificial Intelligence Techniques

the patent   

RECOGNIZING FINGER GESTURES FROM
FOREARM EMG SIGNALS

Pub. No.: US 2013/0232095 A1

Applicant: MICROSOFT CORPORATION
 Inventors: Desney Tan, Kirkland,
Morris, Bellevue,  T. Scott
Saponas, Seattle,  Ravin

This paper presents an electromyographic (EMG)

pattern recognition method to identify motion commands for
the control of a prosthetic arm by evidence accumulation based
on artificial intelligence with multiple parameters. The integral
absolute value, variance, autoregressive (AR) model coefficients,
linear cepstrum coefficients, and adaptive cepstrum vector are
extracted as feature parameters from several time segments of
EMG signals.

A machine learning model is trained by instructing a user to
perform Various prede?ned gestures, sampling signals from
EMG sensors arranged arbitrarily on the user’s forearm With
 respect to locations of muscles in the forearm, extracting
(Us) feature samples from the sampled signals, labeling the feature
samples according to the corresponding gestures instructed to
be performed, and training the machine learning model With
the labeled feature samples

DSP Processor

Experiment 9 of the course was the first hardware based experiment. The experiment was demonstrated by our seniors who explained it really well and made sure that we got our doubts cleared about the experiment. The DSP Processor used was the Texas Instruments TMS320C5505 DSP processor. The DSP processor was programmed using Code Composer Studio (CCS). Matlab was used to design the filter and to obtain the coefficients of transfer function. A real time audio signal was taken via the microphone, and background noise was eliminated after passing through the filter.

FREQUENCY SAMPLING METHOD

In this experiment we used frequency sampling as a way to implement a digital filter.The main idea of the frequency sampling design method is that a desired frequency response can be approximated by sampling at N evenly spaced points.We observed that the no of lobes in the frequency response increases as the order of the filter increases.The filter's response also gets better with an increase in the filter order.

THE CODE CAN BE FOUND https://docs.google.com/document/d/176C5dgcETgEag8frRuD0c5IujrezYSRofv_Gwdo2YU0/edit

Digital Filter design using Windowing Method

In this lab experiment,used a particular windowing function based upon the attenuation in stop band.The different types of windowing functions we have learnt in class were 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 where the attenuation is maximum for the Blackman window and minimum for rectangular window.

THE CODE CAN BE FOUND AT
https://docs.google.com/document/d/1QTUh5d1IkRcVrRv5YaXU1yExOXdLxdeFiVzz7oV2uZ8/edit

BUTTERWORTH FILTER DESIGN

 the experiments on Filters, required us to find output of a digital Butterworth filter. The Butterworth filter was designed using Transfer Domain Method - Bilinear Transformation (BLT). User was prompted to input values like Attenuation in Stop band (As) and Pass band (Ap) as well as Pass band frequency, Stop band frequency and sampling frequency. Subsequently, Order of filter, cut off frequency, normalised LPF and denormalised filter were calculated. Transfer function was computed and the filter was obtained. Both LPF and HPF were calculated and their magnitude response was observed. It was observed that magnitude response is maximally flat in both stop band and pass band.

THE CODE CAN BE FOUND AT https://docs.google.com/document/d/15QM0rGZNQAsTLD9hyonYlxq9tboYebGRvwTNzNeXQwk/edit