All Posts

SPTK: The Laplace Transform

The Laplace transform easily handles signals that are not Fourier transformable by introducing an exponential damping function inside the transform integral.

Blog Notes and Reader Poll

Update May 11, 2023: Please vote in the Reader Poll below (multiple times if you’d like) soon! As of today, CSP Applications and Signal Processing ToolKit are in the lead, with Rants and Datasets at the bottom. The CSP Blog is rolling along here in 2023! March 2023 broke a…

66,000+ Page Views in 2022!

The CSP Blog took a big step forward in 2022, with 66,700 67,965 page views and counting, which is 10,000 12,000 more than last year’s (record) number of about 56,000. Thanks to all my readers!

Critic and Skeptic Roundup

“That was excellently observed,” say I, when I read a passage in an author, where his opinion agrees with mine. When we differ, there I pronounce him to be mistaken. – Jonathan Swift

Blog Notes and Preview

May 2022 saw 6026 page views at the CSP Blog, a new monthly record! Thanks so much to all my readers, new and old, signal processors and machine learners, commenters and lurkers. My next non-ranty post is on frequency-shift (FRESH) filtering. I will go over cyclic Wiener filtering (The Literature…

SPTK: Sampling and The Sampling Theorem

The basics of how to convert a continuous-time signal into a discrete-time signal without losing information in the process. Plus, how the choice of sampling rate influences CSP.

One Last Time …

We take a quick look at a fourth DeepSig dataset called 2016.04C.multisnr.tar.bz2 in the context of the data-shift problem in machine learning.

Wave Walker DSP: A New Kind of Engineering Blog

A colleague has started up a website with lots of content on digital signal processing: Wave Walker DSP. This is, to me, a new kind of engineering blog in that it blends DSP mathematics and practice with philosophy. That’s an intriguing complement to my engineering blog, which I view as…

SPTK (and CSP): Random Processes

The merging of conventional probability theory with signal theory leads to random processes, also known as stochastic processes. The ideas involved with random processes are central to cyclostationary signal processing.

Worth the Price of a (Fancy) Cup of Coffee?

Just a reminder that if you are getting some value out of the CSP Blog, I would appreciate it if you could make a donation to offset my costs: I do pay WordPress to keep ads off the site! I also pay extra for a class of service that allows…

SPTK: The Analytic Signal and Complex Envelope

In signal processing, and in CSP, we often have to convert real-valued data into complex-valued data and vice versa. Real-valued data is in the real world, but complex-valued data is easier to process due to the use of a substantially lower sampling rate.

SPTK: Ideal Filters

Ideal filters have rectangular or unit-step-like transfer functions and so are not physical. But they permit much insight into the analysis and design of real-world linear systems.

50,000 Page Views in 2020

And counting … Last evening the CSP Blog crossed the 50,000 page-view threshold for 2020, a yearly total that has not been achieved previously! I want to thank each reader, each commenter, and each person that’s clicked the Donate button. You’ve made the CSP Blog the success it is, and…

All BPSK Signals

An analysis of DeepSig’s 2016.10A dataset, used in many published machine-learning papers, and detailed comments on quite a few of those papers.

New Look for a New Year and New Decade

2020 is the fifth full year of existence for the CSP Blog, and the beginning of a new decade that will be full of CSP explorations. I thought I’d freshen up the look of the Blog, so I’ve switched the theme. It is a cleaner look with fewer colors and…

CSP Resources: The Ultimate Guides to Cyclostationary Random Processes by Professor Napolitano

My friend and colleague Antonio Napolitano has just published a new book on cyclostationary signals and cyclostationary signal processing: Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations, Academic Press/Elsevier, 2020, ISBN: 978-0-08-102708-0. The book is a comprehensive guide to the structure of cyclostationary random processes and signals, and it…

Sponsoring the CSP Blog

I’ve decided to solicit donations to the CSP Blog through PayPal. For the past four years, I’ve been writing blog posts and doing my best to answer comments at no cost to my readers. And it has turned out very well indeed, thanks to all the people that stop by…

For the Beginner at CSP

Here is a list of links to CSP Blog posts that I think are suitable for a beginner: read them in the order given. How to Obtain Help from the CSP Blog Introduction to CSP How to Create a Simple Cyclostationary Signal: Rectangular-Pulse BPSK The Cyclic Autocorrelation Function The Spectral…

On The Shoulders

What modest academic success I’ve had in the area of cyclostationary signal theory and cyclostationary signal processing is largely due to the patient mentorship of my doctoral adviser, William (Bill) Gardner, and the fact that I was able to build on an excellent foundation put in place by Gardner, his…

100,000 Page Views!

The CSP Blog has reached 100,000 page views! Also, a while back it passed the “20,000 visitors” milestone. All of this for 53 posts and 10 pages. More to come! I started the CSP Blog in late 2015, so it has taken a bit over three years to get to…

MATLAB’s SSCA: commP25ssca.m

In this short post, I describe some errors that are produced by MATLAB’s strip spectral correlation analyzer function commP25ssca.m. I don’t recommend that you use it; far better to create your own function.

How we Learned CSP

We learned it using abstractions involving various infinite quantities. Can a machine learn it without that advantage?

Useful Signal Processing Blogs or Websites?

Update November 1, 2018: A site called feedspot (blog.feedspot.com) contacted me to tell me I made their “Top 10 Digital Signal Processing Blogs, Websites & Newsletters in 2018” list. Weirdly, there are only eight blogs in the list. What’s most important for this post is the other signal processing blogs…

CSP Patent: Tunneling

Tunneling == Purposeful severe undersampling of wideband communication signals. If some of the cyclostationarity property remains, we can exploit it at a lower cost.

CSP Blog Highlights

Welcome to the CSP Blog! To help new readers, I’m supplying here links to the posts that have gotten the most attention over the lifetime of the Blog. Omitted from this list are the more esoteric topics as well as most of the posts that comment on the engineering literature.…

Automatic Spectral Segmentation

Radio-frequency scene analysis is much more complex than modulation recognition. A good first step is to blindly identify the frequency intervals for which significant non-noise energy exists.

Blog Notes and How to Obtain Help with Your CSP Work

The CSP Blog has been getting lots of new visitors these past few months; welcome to all! Following the CSP Blog If you want to receive an email each time I publish a new post, look for the Follow Blog via Email widget on the right side of the Blog…

Cumulant (4, 2) is a Good Discriminator? Comments on “Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks,” by E. Rebeiz et al.

Let’s talk about another published paper on signal detection involving cyclostationarity and/or cumulants. This one is called “Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks,” (The Literature [R69]), and is authored by UCLA researchers E. Rebeiz and four colleagues. My focus on this paper is its idea that broad…

Machine Learning and Modulation Recognition: Comments on “Convolutional Radio Modulation Recognition Networks” by T. O’Shea, J. Corgan, and T. Clancy

Update October 2020: Since I wrote the paper review in this post, I’ve analyzed three of O’Shea’s data sets (O’Shea is with the company DeepSig, so I’ve been referring to the data sets as DeepSig’s in other posts): All BPSK Signals, More on DeepSig’s Data Sets, and DeepSig’s 2018 Data…

The Periodogram

The periodogram is the scaled magnitude-squared finite-time Fourier transform of something. It is as random as its input–it never converges to the power spectrum.

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4 thoughts on “All Posts”

        1. WordPress gave me a hint on how to restore the pretty version, which I’ve done, but at the expense of disallowing sharing of the posts and the “Like” button. I don’t care about “Likes,” so that’s fine, and people can always email or text the URLs themselves, so this workaround is fine indefinitely.

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