Infinity, Periodicity, and Frequency: Comments on a Recent Signal-Processing Perspectives Paper ([R195])
If a tool isn’t appropriate for your problem, don’t blame the tool. Find another one.
For completeness, I also correct the CSPB.ML.2022 dataset, which is aimed at facilitating neural-network generalization studies.
KIRK: Everything that is in error must be sterilised. NOMAD: There are no exceptions. KIRK: Nomad, I made an error in creating you. NOMAD: The creation of perfection is no error. KIRK: I did not create perfection. I created error.
We are attempting to force a neural network to learn the features that we have already shown deliver simultaneous good performance and good generalization.
CSP Blog Interview: Why We Still Need Human Signal Processors with Engineers E. Akamai and D. Peritum
What do practicing engineers think of using large-language models like ChatGPT in their research, development, and writing tasks? And is there a future for humans in signal processing?
Latest Paper on CSP and Deep-Learning for Modulation Recognition: An Extended Version of My Papers 
Another step forward in the merging of CSP and ML for modulation recognition, and another step away from the misstep of always relying on convolutional neural networks from image processing for RF-domain problem-solving.
The cyclostationarity of frequency-shift-keyed signals depends strongly on the way the carrier phase evolves over time. Many distinct cycle-frequency patterns and spectral correlation shapes are possible.
Final Update on “Future Posts” Poll: So among the CSP Blog readers that voted, I think the consensus is to produce more “on brand” posts on CSP and the Signal-Processing ToolKit. Also, there is significant interest in doing CSP with GNU Radio, which I have considerable experience with, and so…
Sometimes MATLAB’s resample.m gives results that can be trouble for subsequent CSP.
By the pricking of my thumbs, something wicked this way comes …
CSP can be used to separate cochannel contemporaneous signals. The involved signal-processing structure is linear but periodically time-varying.
The next step in dataset complexity at the CSP Blog: cochannel signals.
How can we train a neural network to make use of both IQ data samples and CSP features in the context of weak-signal detection?
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! As usual in these end-of-year reveries, I will show some highlights from the CSP Blog…
The CSP Blog recently received a comment from a signal processor that needed a small amount of debugging help with their python spectral correlation estimator code. The code uses a form of the time-smoothing method and aims to compute and plot the spectral correlation estimate as well as the corresponding…
Correcting the Record: Comments On “Wireless Signal Representation Techniques for Automatic Modulation Classification,” by X. Liu et al
It’s too close to home, and it’s too near the bone …
Neural Networks for Modulation Recognition: IQ-Input Networks Do Not Generalize, but Cyclic-Cumulant-Input Networks Generalize Very Well
Neural networks with CSP-feature inputs DO generalize in the modulation-recognition problem setting.
Epistemic Bubbles: Comments on “Modulation Recognition Using Signal Enhancement and Multi-Stage Attention Mechanism” by Lin, Zeng, and Gong.
Another brick in the wall, another drop in the bucket, another windmill on the horizon …
What is the Minimum Effort Required to Find ‘Related Work?’: Comments on Some Spectrum-Sensing Literature by N. West [R176] and T. Yucek [R178]
Starts as a personal gripe, but ends with weird stuff from the literature.
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…
Elegy for a Dying Field: Comments on “Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning,” by F. Wei et al
Black-box thinking is degrading our ability to connect effects to causes.
Neural networks with I/Q data as input do not generalize in the modulation-recognition problem setting.
Wow, Elsevier, Just … Wow. Comments On “Cyclic Correntropy: Properties and the Application in Symbol Rate Estimation Under Alpha-Stable Distributed Noise,” by S. Luan et al.
Can we fix peer review in engineering by some form of payment to reviewers?
Let’s take a look at an even faster spectral correlation function estimator. How useful is it for CSP applications in communications?
Shifted Dataset for the Machine-Learning Challenge: How Well Does a Modulation-Recognition DNN Generalize? [Dataset CSPB.ML.2022]
Another RF-signal dataset to help push along our R&D on modulation recognition.
An interesting paper on the true nature of the impulse function we use so much in signal processing.
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…
What are the ranges of spectral frequency and cycle frequency that we need to consider in a discrete-time/discrete-frequency setting for CSP?
The Signal-Processing Equivalent of Resume-Padding? Comments on “A Robust Modulation Classification Method Using Convolutional Neural Networks” by S. Zhou et al.
Does the use of ‘total SNR’ mislead when the fractional bandwidth is very small? What constitutes ‘weak-signal processing?’
Some examples of random variables encountered in communication systems, channels, and mathematical models.
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…
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.
Comments on “Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification,” by B. Hamdaoui et al
Another post-publication review of a paper that is weak on the ‘RF’ in RF machine learning.
Spectral correlation surfaces for real-valued and complex-valued versions of the same signal look quite different.
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…
The Machine Learners think that their “feature engineering” (rooting around in voluminous data) is the same as “features” in mathematically derived signal-processing algorithms. I take a lighthearted look.
What happens when a cyclostationary time-series is treated as if it were stationary?
The third DeepSig dataset I’ve examined. It’s better!
To aid navigating the CSP Blog, I’ve added a new page called “All CSP Blog Posts.” You can find the page link at the top of the home page, or in various lists on the right side of the Blog, such as “Pages” and “Site Navigation.” Let me know in…
In which my life is made a little harder.
What are the unique parts of the multidimensional cyclic moments and cyclic cumulants?
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…
Do we need to consider all cycle frequencies, both positive and negative? Do we need to consider all delays and frequencies in our second-order CSP parameters?
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…
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…
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…
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…
A PSK/QAM/SQPSK data set with randomized symbol rate, inband SNR, carrier-frequency offset, and pulse roll-off.
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. If you subscribe to MATLAB’s Communication Toolbox, you have access to an implementation of the SSCA: commP25ssca.m.…
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…
Comments on “Detection of Almost-Cyclostationarity: An Approach Based on a Multiple Hypothesis Test” by S. Horstmann et al
The statistics-oriented wing of electrical engineering is perpetually dazzled by [insert Revered Person]’s Theorem at the expense of, well, actual engineering.
‘Can a Machine Learn the Fourier Transform?’ Redux, Plus Relevant Comments on a Machine-Learning Paper by M. Kulin et al.
Reconsidering my first attempt at teaching a machine the Fourier transform with the help of a CSP Blog reader. Also, the Fourier transform is viewed by Machine Learners as an input data representation, and that representation matters.
The costs strongly depend on whether you have prior cycle-frequency information or not.
Unlike conventional spectrum analysis for stationary signals, CSP has three kinds of resolutions that must be considered in all CSP applications, not just two.
How do we efficiently estimate higher-order cyclic cumulants? The basic answer is first estimate cyclic moments, then combine using the moments-to-cumulants formula.
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