DSP Application Patent and Paper Review

10th Experiment

In this experiment we were asked to find out an application for signal processing of one dimensional signals.This was a group experiment. We had to find patents and IEEE papers related to single application.

Domain :  Bio-medical Applications.

Objective : Signal Acquisition and Conditioning of Biomedical signals (Examples : ECG- ElectroCardioGram and EMG- ElectroMyoGraphy)

Group Members: Aditya Parkhi, Abhisu Mishra, Nikhil Nandoskar, Meet Nathwani

Patents reviewed by Group members:

Aditya Parkhi :Remotely Interogated Biomedical Signal

Abhisu Mishra: Pace Pulse Detection

Nikhil Nandoskar: Signal Acquisition and Conditioning of Biomedical signals

Meet Nathwani: Wireless ECG system

 Patent : Portable electrocardiographic monitor based on digital signal processor.

Patent Number: CN2565394Y

Patent inventors; White, Zhang Yonghong , Hu Bing Yi , Hu Peng

Publication date: Aug 13, 2003

Patent Review:

This patent describes the process of working on ECG real-time detection, and wireless transmission of ECG. In order to achieve this purpose a portable ECG monitor which achieves the purpose of cardiac disease remote monitoring, and which will be used in the physical and psychological disease surveillance in smart home system was developed.A portable ECG Monitor was used and interfaced with Digital Signal Processor

Digital Signal Processor used was TMS320F2812. It served as the core controller and  completed the tasks like signal collection, storage, processing, waveform display and transmission.

Paper : Development of an Embedded System and MATLAB-based GUI for online Acquisition and Analysis of ECG Signal.

Paper Authors : R Gupta, J.N. Bera, Madhuchhand Mitra

Publication Details : Measurement 43(9):1119-1126 · January 2003

Paper review : 

This paper illustrates a low-cost method for online acquisition of ECG signal for storage and processing using a MATLAB-based Graphical User Interface (GUI). The single lead ECG is sampled and after digitization, fed to a microcontroller-based embedded system to convert the ECG data to a serial bit-stream. This serial data stream is then transmitted to a desktop Personal Computer and a software which stores it automatically in a temporary data file. The original ECG data is reconstructed from the digital data set by a conversion formula. The MATLAB-based GUI is designed to perform online analysis on the ECG data to compute the different time-plane features and display the same on the GUI along with the ECG signal plot.

Group IEEE Paper : Analysis and Filtering of ECG, EMG and EEG signals.

We as a group prepared IEEE format paper on the topic of Analysis and Filtering of ECG, EMG and EEG signals.

We studied how these signals are obtained as input and filtered using DSP processor and MATLAB.

Plagiarism was checked and the resukt obtained was 96% unique.

DSP Proceesor

9th Experiment

This was the first hardware based demo of signal processing. Our senior conducted this for us. The session was conducted using the TMS320F28375 DSP board. Emulation was observed of real time audio input in class demonstration.  We learned to perform basic arithmetic functions-

1. Basic Arithmetic operations - ADD,SUB,MUL,DIV. 

2. Bitwise operations - AND, NOT

3. Shifting operation - SHIFT LEFT/RIGHT AND ROTATE LEFT/RIGHT

It was concluded that a large number of mathematical operations can be performed and repeatedly on this processor since it is a specialised microprocessor.

FIR Filter Design Using Frequency Sampling

8th Experiment

In this experiment we design LPF and HPF using frequency sampling method. Input specifications similar to the other filter designing methods were taken and the magnitude response was plotted. In this method, desired frequency response is sampled and samples obtained are taken as DFT coefficients, and then h(n) is calculated using IDFT. It is seen that as order increases, number of lobes in stop band also increases. Since phase is linear the output signal won’t contain any distortions.

FIR Filter Design using Window Function

7th Experiment 

The 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.A low pass and Band pass filter was designed. The magnitude and phase plot of both the filters was plotted using scilab. In this method, the desired impulse response is multiplied with window function w(n) to obtain h(n) which after Z-transfrom  gave H(z). The phase  plot being linear, there will be no distortion at the output.

Chebyshev Filter Design

6th Experiment

The filter was designed just the way it was discussed in the class. The input parameters were As Ap Pass Band the input parameters As ,Ap pass band frequency and stop band frequency and sampling frequency.The pole zero plot was also drawn and the values of As and Ap were compared from the magnitude spectrum. The poles lie within the unit cirle. The number of ripples representated the order of the filter.

1. In the magnitude response of the Low Pass Filter there were ripples in the pass band whereas the stop band is monotonic and there is no ripple.

2. In the magnitude response of the High Pass, there were ripple in the stop band whereas the response is monotonic in the pass band.

Butterworth Filter Design

5th Experiment

The butterworth filter design was done for Low Pass and High Pass. It was done with specific input parameters like As, Ap , Pass Band frequency, Stop Band Frequency and Sampling frequency. H(z) for each were calculated and so was the order of the filter. The theoretical and observed values were compared. Maginitude spectrum and pole zero plot was drawn and it was observed that butterworth filters are monotonic in its pass band and stop band.

Filtering Of Long Data Sequence

4th Experiment

Linear Convolution was performed using Overlap Add method  for which the length of the ip signal x(n) was 12 and h(n) was 3. The program was done by breaking the ip signal in 2 parts of length 6 each and the length of the decomposed signal was 8. So two outputs were generated and the final output was stored in y(n) of length 14. Overlap Save Method involved the same procedure and in the earlier case x1(n) and x2(n) but in OSM x1(n) s2(n) and x3(n) were generated. It was noted that OAM and OSM can be used to filter long ip sequences using FFT.