Category: Artificial Intelligence

Watch Out!

“Hear my words and bear witness to my vow:

Night gathers, and now my watch begins. It shall not end until my death. I am the fire that burns against the cold, the light that brings the dawn, the horn that wakes the sleepers, the shield that guards the realms of men.”

Night’s Watch Oath, Game of Thrones, by G. R. R. Martin

Due to my pretention to academic worthiness, I have Google Scholar alert me to all my new citations. That is, citations to My Papers. I got an anodyne alert the other day and, as usual, gave it a quick once-over. Anything new or interesting? Any new signal-processing twist or a machine-learning breakthrough, finally smashing the last vestiges of the old order? I’m referring here to The Literature [R205].

Well … no. But some modern AI-related weirdness is there, and it is a concerning variety of weirdness for researchers that attempt to learn from published technical work, and especially for those that attempt to use references in a published technical paper to dig a little deeper toward foundational material. Let’s take a look.

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Tide is Turning?

I see the first CSP Blog upturn since ChatGPT exploded in late 2022. But is it real?

The CSP Blog enjoyed year-over-year page-view and viewer-total increases from its beginning in 2015 to 2022. In 2023, page views and viewer totals fell from their highs in 2022. They fell further in 2024. But the trend has reversed itself here in 2025:

Great!

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CSPB.ML.2018R2.NF

A noise-free version of the 2018 CSP Blog dataset CSPB.ML.2018R2 is posted here. This allows researchers to correctly apply propagation-channel effects to the generated signals, and to easily add their own noise at whatever level they wish.

The format of the files is the same as CSPB.ML.2018R2, and the truth parameters for each file are the same as the truth parameters for the corresponding file in CSPB.ML.2018R2, except for SNR, which is infinite.

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CSPB.ML.2023G1

Another dataset aimed at the continuing problem of generalization in machine-learning-based modulation recognition. This one is a companion to CSPB.ML.2023, which features cochannel situations.

Quality datasets containing digital signals with varied parameters and lengths sufficient to permit many kinds of validation checks by signal-processing experts remain in short supply. In this post, we continue our efforts to provide such datasets by offering a companion unlabeled dataset to CSPB.ML.2023.

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Stupid Laws Getting In My Way

A kvetch.

As the generative-AI crowd continues to feast on copyrighted material of all kinds, they are getting pushback in the form of lawsuits from artists, writers, and journalists. I discussed this recently with Dan and Eunice on the CSP Blog.

Open AI in particular seems to believe they have some kind of divine right to pursue whatever business they want, whether it is legal or not. Because reasons … including national security … and “meeting the needs of today’s citizens.” But probably just greed and hubris.

In a statement to the UK’s House of Lords, Open AI says this, and I assume they did so with a straight face, which would have been admirably difficult:

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CSPB.ML.2022R2: Correcting an RNG Flaw in CSPB.ML.2022

For completeness, I also correct the CSPB.ML.2022 dataset, which is aimed at facilitating neural-network generalization studies.

The same random-number-generator (RNG) error that plagued CSPB.ML.2018 corrupts CSPB.ML.2022, so that some of the files in the dataset correspond to identical signal parameters. This makes the CSPB.ML.2018 dataset potentially problematic for training a neural network using supervised learning.

In a recent post, I remedied the error and provided an updated CSPB.ML.2018 dataset and called it CSPB.ML.2018R2. Both are still available on the CSP Blog.

In this post, I provide an update to CSPB.ML.2022, called CSPB.ML.2022R2.

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The Next Logical Step in CSP+ML for Modulation Recognition: Snoap’s MILCOM ’23 Paper [Preview]

We are attempting to force a neural network to learn the features that we have already shown deliver simultaneous good performance and good generalization.

ODU doctoral student John Snoap and I have a new paper on the convergence of cyclostationary signal processing, machine learning using trained neural networks, and RF modulation classification: My Papers [55] (arxiv.org link here).

Previously in My Papers [50-52, 54] we have shown that the (multitudinous!) neural networks in the literature that use I/Q data as input and perform modulation recognition (output a modulation-class label) are highly brittle. That is, they minimize the classification error, they converge, but they don’t generalize. A trained neural network generalizes well if it can maintain high classification performance even if some of the probability density functions for the data’s random variables differ from the training inputs (in the lab) relative to the application inputs (in the field). The problem is also called the dataset-shift problem or the domain-adaptation problem. Generalization is my preferred term because it is simpler and has a strong connection to the human equivalent: we can quite easily generalize our observations and conclusions from one dataset to another without massive retraining of our neural noggins. We can find the cat in the image even if it is upside-down and colored like a giraffe.

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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?

Let’s switch things up a bit here at the CSP Blog by presenting an interview on a technical topic. I interview two characters you might recall from the post on the Domain Expertise Trap: Engineers Dan Peritum and Eunice Akamai.

With the splashy entrance of large-language models like ChatGPT into everyday life and into virtually all aspects of science, engineering, and education, we all want to know how our jobs and careers could be affected by widespread use of artificial intelligence constructs like ChatGPT, Dall-E, and Midjourney. In this interview with a couple of my favorite engineers, I get a feel for how non-AI researchers and developers think about the coming changes, and of course how they view the hype, distortions, and fabrications surrounding predictions of those changes. You can find photos of the interviewees and brief biographies at the end of the post.

The interview transcript is carefully contrived lightly edited for believability clarity.

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ChatGPT and CSP

Am I out of a job?

Update January 31, 2023: I’ve added numbers in square brackets next to the worst of the wrong things. I’ll document the errors at the bottom of the post.

Of course I have to see what ChatGPT has to say about CSP. Including definitions, which I don’t expect it to get too wrong, and code for estimators, which I expect it to get very wrong.

Let’s take a look.

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The Domain Expertise Trap

The softwarization of engineering continues apace…

I keep seeing people write things like “a major disadvantage of the technique for X is that it requires substantial domain expertise.” Let’s look at a recent good paper that makes many such remarks and try to understand what it could mean, and if having or getting domain expertise is actually a bad thing. Spoiler: It isn’t.

The paper under the spotlight is The Literature [R174], “Interference Suppression Using Deep Learning: Current Approaches and Open Challenges,” published for the nonce on arxiv.org. I’m not calling this post a “Comments On …” post, because once I extract the (many) quotes about domain expertise, I’m leaving the paper alone. The paper is a good paper and I expect it to be especially useful for current graduate students looking to make a contribution in the technical area where machine learning and RF signal processing overlap. I especially like Figure 1 and the various Tables.

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Are Probability Density Functions “Engineered” or “Hand-Crafted” Features?

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.

One of the things the machine learners never tire of saying is that their neural-network approach to classification is superior to previous methods because, in part, those older methods use hand-crafted features. They put it in different ways, but somewhere in the introductory section of a machine-learning modulation-recognition paper (ML/MR), you’ll likely see the claim. You can look through the ML/MR papers I’ve cited in The Literature ([R133]-[R146]) if you are curious, but I’ll extract a couple here just to illustrate the idea.

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