Previous SPTK Post: Digital Filters Next SPTK Post: TBD Let’s really get into the mathematical details of “IQ data,” a phrase that appears in many CSP Blog posts and an awful lot of machine-learning papers on modulation recognition. Just what are “I” and “Q” anyway? Jump Straight to the Significance of IQ Data in CSP Bandpass Signals and Their Complex Representation To set the stage, we review the idea of a bandpass signal which, in the context of manmade radio-frequency signals, means modulating a lowpass message or sensing signal. ‘Modulating’ here simply means multiplying by a sine wave, be ..read more

An advantage of using and understanding the statistics of communication signals ™, the basics of signal processing, and the rich details of cyclostationary signal processing is that a practitioner can deal with, to some useful degree, unknown unknowns. The unknown unknowns I’m talking about here on the CSP Blog are, of course, signals. We know about the by-now-familiar known-type detection, multi-class modulation-recognition, and RF scene-analysis problems, in which it is often assumed that we know the signals we are looking for, but we don’t know their times of arrival, some of their paramete ..read more

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. CSPB.ML.2023 is a two-part dataset with 120,000 binary data files. The first 60,000 are single-signal files and the last 60,000 are two-signal files created by combining pairs of the single-signal files. This means that many of the two-signal files contain cochannel signals, but not all, sinc ..read more

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 ..read more

Previous SPTK Post: The Z Transform   Next SPTK Post: TBD Linear shift-invariant systems are often called digital filters when they are designed objects as opposed to found objects, which are models, really, of systems occurring in the natural world. A basic goal of digital filtering is to perform the same kind of function as does an analog filter, but it is used after sampling rather than before. In some cases, the digitally filtered signal is then converted to an analog signal. These ideas are illustrated in Figure 1. Figure 1. A typical role for a linear shift-invariant system, or digital f ..read more

Let’s take a look at a recent perspectives-style paper published in the IEEE Signal Processing Magazine called “On the Concept of Frequency in Signal Processing: A Discussion [Perspectives],” (The Literature [R195]). While I criticize the paper directly, I’m hoping to use this post to provide my own perspective, and perhaps a bit of a tutorial, on the interrelated concepts of frequency, infinity, sine waves, and signal representations. I appreciate tutorial papers in the signal-processing literature (see, for example, my positive post on Candan’s article about the Dirac delta [impulse] functio ..read more

I am very pleased to announce that my signal-processing, machine-learning, and modulation-recognition collaborator and friend John Snoap has successfully defended his doctoral dissertation and is now Dr. Snoap! I started working with John after we met in the Comments section of the CSP Blog way back in 2019. John was building his own set of CSP software tools and ran into a small bump in the road and asked for some advice. Just the kind of reader I hope for–independent-minded, gets to the bottom of things, and embraces signal processing. As we interacted over email and zoom it became clear th ..read more

Previous SPTK Post: Practical Filters Next SPTK Post: TBD In this Signal Processing ToolKit post, we look at the discrete-time version of the Laplace Transform: The Z Transform. Jump straight to the Significance of the Z Transform in CSP. From the sampling theorem, we know that we can focus on regularly spaced samples of any bandlimited continuous-time signal and we will not lose any information about in doing so, provided we sample often enough. The impulse-sampled signal , given by is therefore equivalent, in an information sense, to itself, and since this signal can be constructed from ..read more

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. The CSPB.ML.2022 dataset is aimed at understanding the generalization prope ..read more

I’ve had to update the original Challenge for the Machine Learners post, and the associated dataset post, a couple times due to flaws in my metadata (truth) files. Those were fairly minor, so I just updated the original posts. But a new flaw in CSPB.ML.2018 and CSPB.ML.2022 has come to light due to the work of the estimable research engineers at Expedition Technology. The problem is not with labeling or the fundamental correctness of the modulation types, pulse functions, etc., but with the way a random-number generator was applied in my multi-threaded dataset-generation technique. I’ll explai ..read more