Signal Processing for Communication Systems
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
FFT Lab path:Complex DFT, bins & fftshift → Magnitude & phase → Spectral leakage → Plot: Matlab / PythonTool: FFT bin frequency & resolution calculator 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 … 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
Key focus: Learn how to generate color noise using auto regressive (AR) model. Apply Yule Walker equations for generating power law noises: pink noise, Brownian noise. Auto-Regressive (AR) model An uncorrelated Gaussian random sequence \(x[n]\) can be transformed into a correlated Gaussian random sequence \(y[n]\) using an AR time-series model. If a time series random … Read more
The aim of this article is to demonstrate the application of spectral factorization method in generating colored noise having Jakes power spectral density. Before continuing, I urge the reader to go through this post: Introduction to generating correlated Gaussian sequences. In spectral factorization method, a filter is designed using the desired frequency domain characteristics (like … Read more
Gaussian Minimum Shift Keying basics: MSK as CPM, Gaussian filter BT, smoother phase, and spectral compactness vs plain MSK.
Analytic Signal Lab (same on every page in this path) 1. Hilbert / FFT → 2. Envelope & IF → 3. Phase demodulation (you are here) Tools: Hilbert / analytic signal demo Key focus: Demodulation of phase modulated signal by extracting instantaneous phase can be done using Hilbert transform. Hands-on demo in Python & Matlab. … Read more
Extract amplitude envelope, instantaneous phase, and instantaneous frequency via the analytic signal — practical Hilbert/FFT notes.
How the analytic signal and Hilbert transform give envelope and instantaneous phase/frequency, FFT implementation tips, and links to the envelope follow-on.