Modelling a Noisy Communication Signal in MATLAB for the Analog to Digital Conversion Process
Practical signal modeling treats receiver noise as a fixed power source, not something tied to the transmitted waveform. Parth demonstrates why using MATLAB's awgn(sig,SNR,'measured') can misrepresent an analog front end and provides a short function that scales your signal so the added AWGN produces the desired receiver noise variance. This prepares realistic inputs for upcoming ADC simulations.
Delay estimation by FFT
Markus Nentwig presents a practical FFT-based algorithm to estimate and correct integer and fractional sample delays between two signals, returning a scaled, aligned replica and delay estimate. The method combines coarse cross-correlation with a phase-slope linear regression on weighted spectra to achieve subsample timing accuracy. The article also discusses accuracy limits, phase-unwrapping pitfalls, and how to use the error-vector spectrum to reveal distortion in lab measurements.
Polyphase filter / Farrows interpolation
Markus Nentwig shows how polyphase filtering and the Farrow interpolator provide a practical, computation‑efficient way to realize sub-sample delays and variable resampling. He starts from the upsample-filter-decimate view, explains how polyphase decomposition reduces per-phase work, then describes how the Farrow structure fits polynomials to coefficient banks for continuous fractional-delay control. The post includes warnings about filter choices and links to code and references.
Smaller DFTs from bigger DFTs
Introduction
Let's consider the following hypothetical situation: You have a sequence $x$ with $N/2$ points and a black box which can compute the DFT (Discrete Fourier Transform) of an $N$ point sequence. How will you use the black box to compute the $N/2$ point DFT of $x$? While the problem may appear to be a bit contrived, the answer(s) shed light on some basic yet insightful and useful properties of the DFT.
On a related note, the reverse problem of computing an $N$...
Matlab Programming Contest
Love puzzles or want to sharpen your MATLAB skills? Christopher Felton highlights MathWorks' biannual MATLAB programming contest, a week-long set of clever algorithm challenges that require only base MATLAB. Whether you're experienced or new, you can compete, compare solutions, or simply study others' code when later phases disclose submissions. No toolboxes or mex files allowed, so it's a pure programming playground for learning and bragging rights.
Maximum Likelihood Estimation
Any observation has some degree of noise content that makes our observations uncertain. When we try to make conclusions based on noisy observations, we have to separate the dynamics of a signal from noise.
Knowledge Mine for Embedded Systems
A little-known interactive portal makes learning embedded systems surprisingly practical and visual. The site is organized into four main areas: embedded systems design, design lifecycle, design methods, and design tools. Each section uses clickable system block diagrams so you can jump from a block, for example a MAC unit, to a focused page with detailed explanations. It’s a handy, ready reference for DSP and embedded engineers.
Maximum Likelihood Estimation
Any observation has some degree of noise content that makes our observations uncertain. When we try to make conclusions based on noisy observations, we have to separate the dynamics of a signal from noise.
Deesspee #5
Peter Kootsookos's Deesspee #5 is a very short micro-post simply titled "Computers". It acts as a minimalist flag in the Deesspee series pointing readers toward the computing topic on DSPRelated; click through to view the original entry and any context or discussion. This compact post is useful if you track the author's brief topic markers or short-format updates.
