FIR Filter Design: Frequency Sampling Method

This method is another way of designing linear phase FIR filters. The process of obtaining the desired frequency response is same. The difference starts from then on. The desired frequency response is sampled in the frequency domain and then its inverse is calculated, which gives the filter response. For practical design, we again used Scilab. The formulae for DFT and IDFT were incorporated into the code and the filter parameters were taken as user input. Plot function helped us verify the accurateness of the designed filter.

Basic Operations on DSP Processor

The theoretical aspects of DSP technology are not too difficult. We just have different algorithms for different operations. But, the real world doesn't work on just mathematics and algorithms. We need to have some physical hardware that will implement these operations. This is where the DSP processor comes in.
We used a custom board of the popular C2000 processor. The coding platform used was Code Composer Studio. Using the implementations of DSP algorithms in C language developed previously, the code was tweaked to work on-chip in embedded C. Basic operations were performed on the board such as addition and subtraction among others. We also implemented FFT algorithms. The difference in implementing on hardware is that we have to reference the registers too while writing the code while a simple C language implementation does not require this.

Patent Review: Blind dialing US 8126512 B2

Who would not like to be able to call or send a text without tapping away on our screens? Voice assistants try to make this task easier by listening to voice commands. These commands too, however, require extensive setup, as your phone first needs to learn your voice pattern and pronunciations.
The invention 'Blind Dialing' tries to do away with this need for setup by using Morse code. It listens to acoustic signals and using DSP algorithms to identifies the morse code pattern from it. It then compares the decoded data to a set of existing phone numbers or identifiers for phone numbers and dial. The claims of this patent include a wireless communication device for analysing the destination and source addresses, decode morse signals and perform filtering of noise using DSP algorithms.
The only drawback for this: the user will have to learn morse code!

https://www.google.co.in/patents/US8126512?dq=Blind+dialing+US+8126512+B2&hl=en&sa=X&ved=0ahUKEwjot6nJ-8jTAhVEp48KHZ7NCsUQ6AEIJjAA

FIR Filter Design: Windowing Method

If you have read previous posts, then you will be familiar with IIR filters. But how about FIR filters?

These are filters that have a Finite Impulse Response. In these, the order means the length of the impulse response. Broadly, the method for designing the filter is the same as IIR filters. You input the formulae in a Scilab code and run the program. But the formulae are different, meaning, the pen and paper method is different. 

We used a Hanning Window as the window function and wrote the code accordingly. During execution, the filter parameters like the attenuations and frequencies were given as user input. The plot function was used to verify the response of the designed filter. A notable difference between IIR and FIR designing is that much of the calculation is done in time domain rather than the transform domain.

Paper Review: Implementation of Morse Decoder TMS320C6748 DSP Development Kit

Morse Code has been used for communication right since the Second World War. Then, the radio operators used to decode the received morse signal by hand. But today, we have technology to make our life easier. What if we used processors to decode the incoming morse signal?

This paper did exactly that. The authors used a Digital Signal Processor to implement a real time morse decoding system. They used a noisy audio signal as in input, filtered the signal digitally and extracted the dots and dashes that make up the morse code. Thanks to the DSP algorithm, all of this could be done in real-time. An added advantage was, it used several basic DSP algorithms like the Cooley and Tukey FFT algorithm, making the setup useful for teaching purposes.

Paper Title: Implementation of Morse Decoder on the TMS320C6748 DSP Development Kit
Authors: Pavel Zahradnik and Boris Simak
Published At: 6th European Embedded Design in Education and Research, 2014
Publisher: IEEE

You can find the paper at http://ieeexplore.ieee.org/document/6924373/

Chebyshev Filter Design

When we can see ripples in a filter response, either in the stop band or the pass band, it's called a Chebyshev filter. Of course, to account for the ripple, we need to consider different equations.

The method for filter design, however, stays the same. Open Scilab, write a code and voila, your filter is ready! The only difference in the code for Chebyshev is the formula for calculating the parameters of the analog filter. The digitisation process stays the same.

We designed a Chebyshev 1 filter where the ripple exists only in the pass band and the stop band is free of it. The frequency response of the digital filter could be seen to be close enough to the desired response.

Butterworth Filter Design

Filters are essential in any system. In the classroom, it's easy to design filters for smaller orders using a pen and paper. But, in practical systems, very high orders are required to get the desired response. The order may go in tens. If you sit and go about designing such a filter, you'll spend the entire day and in the end, go crazy!

Scilab comes to the rescue in such situations. It's an open source tool for simulation. Of course, if you're willing to spend some money, then MATLAB will be better, and easier too. But one should always be familiar with open source tools. We wrote a code in Scilab to design a Butterworth filter and also digitise it. The transfer function of the filter was calculated in the Laplace (s) domain and then converted to the Z domain by using the Bilinear Transform Method.

Both, low pass and high pass filters were designed and their magnitude and frequency responses were simulated. We could see that we got a response very close to the desired one and the order was higher than 10 for each of the designs.