Signal Processing for Communication Systems
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Key focus: Demodulation of phase modulated signal by extracting instantaneous phase can be done using Hilbert transform. Hands-on demo in Python & Matlab. Phase modulated signal: The concept of instantaneous amplitude/phase/frequency are fundamental to information communication and appears in many signal processing application. We know that a monochromatic signal of form x(t) = a cos(ω … Read more
Key focus: Learn how to use Hilbert transform to extract envelope, instantaneous phase and frequency from a modulated signal. Hands-on demo using Python & Matlab. If you would like to brush-up the basics on analytic signal and how it related to Hilbert transform, you may visit article: Understanding Analytic Signal and Hilbert Transform Introduction The … Read more
Key focus of this article: Understand the relationship between analytic signal, Hilbert transform and FFT. Hands-on demonstration using Python and Matlab. Introduction Fourier Transform of a real-valued signal is complex-symmetric. It implies that the content at negative frequencies are redundant with respect to the positive frequencies. In their works, Gabor [1] and Ville [2], aimed … Read more
“What is the best filter that I should use? FIR or IIR ?” is often the question asked by many. There exists two different types of Linear Time Invariant (LTI) filters from transfer function standpoint : FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) filters and myriad design techniques for designing them. The mere fact … Read more
Key focus: Discuss the definition of logical effort, the idea behind it and various associated terminologies. Introduction In today’s digital world the most important aspect of any processor is how fast can it function and support multiple applications. Often, the chip design engineers are confronted with bewildering questions in the design process of a logic: … Read more
In the previous post, Interpretation of frequency bins, frequency axis arrangement (fftshift/ifftshift) for complex DFT were discussed. In this post, I intend to show you how to interpret FFT results and obtain magnitude and phase information. Outline For the discussion here, lets take an arbitrary cosine function of the form \(x(t)= A cos \left(2 \pi … Read more
Key focus: Interpret FFT results, complex DFT, frequency bins, fftshift and ifftshift. Know how to use them in analysis using Matlab and Python. Four types of Fourier Transforms: Often, one is confronted with the problem of converting a time domain signal to frequency domain and vice-versa. Fourier Transform is an excellent tool to achieve this … Read more
Root Mean Square (RMS) value is the most important parameter that signifies the size of a signal. Defining the term “size”: In signal processing, a signal is viewed as a function of time. The term “size of a signal” is used to represent “strength of the signal”. It is crucial to know the “size” of … Read more
Since you have landed on this page, probably you are looking for an opportunity to write for this website. I assume that you have looked around and you know that I take this website seriously. I am committed to adding value to my readers and to you as an author. I am currently accepting guest posts from … Read more
We live in the age of smart phones that can be loaded with numerous applications to communicate with each other. What’s next ? Where do we go from here ? Students at MIT Media Labs has answered the call with a novel approach of “Physical Telepresence” that provides the ability to remotely render shapes of … Read more