Signal Processing for Communication Systems
Markov chains are useful in computing the probability of events that are observable. However, in many real world applications, the events that we are interested in are usually hidden, that is we don’t observe them directly. These hidden events need to be inferred. For example, given a sentence in a natural language we only observe the … Read more
Key focus: Markov chains are a probabilistic models that describe a sequence of observations whose occurrence are statistically dependent only on the previous ones. ● Time-series data like speech, stock price movements.● Words in a sentence.● Base pairs on the rung of a DNA ladder. States and transitions Assume that we want to model the … Read more
Let’s see how to design a simple digital FIR filter to reject unwanted frequencies in an incoming signal. As a per-requisite, I urge you to read through this post: Introduction to digital filter design Background on transfer function The transfer function of a system provides the underlying support for ascertaining vital system response characteristics without … Read more
Key focus: Develop basic understanding of digital filter design. Learn about fundamentals of FIR and IIR filters and the design choices. Analog filters and digital filters are the two major classification of filters, depending on the type of signal signal they process. An analog filter, processes continuous-time signal analog signals. Whereas, digital filters process sampled, … Read more
Key focus: Let’s demonstrate basics of univariate linear regression using Python SciPy functions. Train the model and use it for predictions. Linear regression model Regression is a framework for fitting models to data. At a fundamental level, a linear regression model assumes linear relationship between input variables () and the output variable (). The input … Read more
Key focus: Generating simulated dataset for regression problems using sklearn make_regression function (Python 3) is discussed in this article. Problem statement Suppose, a survey is conducted among the employees of a company. In that survey, the salary and the years of experience of the employees are collected. The aim of this data collection is to … Read more
Often the most basic step in signal processing of audio files, one would like to visualize an audio sample file as time-series data. Audio sounds can be thought of as an one-dimensional vector that stores numerical values corresponding to each sample. The time-series plot is a two dimensional plot of those sample values as a … Read more
Key focus: Learn how to plot FFT of sine wave and cosine wave using Python. Understand FFTshift. Plot one-sided, double-sided and normalized spectrum using FFT. Introduction Numerous texts are available to explain the basics of Discrete Fourier Transform and its very efficient implementation – Fast Fourier Transform (FFT). Often we are confronted with the need … Read more
Key focus: Fundamentals of signal processing for machine learning. Speaker identification is taken as an example for introducing supervised learning concepts. Signal Processing A signal, mathematically a function, is a mechanism for conveying information. Audio, image, electrocardiograph (ECG) signal, radar signals, stock price movements, electrical current/voltages etc.., are some of the examples. Signal processing is … Read more
Key focus: Learn to generate Fibonacci sequence using Python. Python 3 is used in this tutorial. Fibonacci series is a sequence of numbers 0,1,1,2,3,5,8,13,… Let’s digress a bit from signal processing and brush up basic some concepts in python programming. Why python? Python is an incredibly versatile programming language that is used for everything from … Read more