Update on DeepSig Datasets
‘Insufficient facts always invite danger.’ Spock in Star Trek TOS Episode “Space Seed”
The Altar of Optimality
Danger Will Robinson! Non-technical post approaching!
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 …
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.
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.
Some Concrete Results on Generalization in Modulation Recognition using Machine Learning
Neural networks with I/Q data as input do not generalize in the modulation-recognition problem setting.
The Domain Expertise Trap
The softwarization of engineering continues apace…
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?
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?’
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.
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.
Professor Jang Again Tortures CSP Mathematics Until it Breaks
In which my life is made a little harder.
On Impulsive Noise, CSP, and Correntropy
And I still don’t understand how a random variable with infinite variance can be a good model for anything physical. So there.
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 Set. The data set relating…
Comments on “Blind Cyclostationary Spectrum Sensing in Cognitive Radios” by W. M. Jang
We are all susceptible to using bad mathematics to get us where we want to go. Here is an example.
Comments on “Cyclostationary Correntropy: Definition and Application” by Fontes et al
I recently came across a published paper with the title Cyclostationary Correntropy: Definition and Application, by Aluisio Fontes et al. It is published in a journal called Expert Systems with Applications (Elsevier). Actually, it wasn’t the first time I’d seen this work by these authors. I had reviewed a similar paper in 2015 for a…
Yes, the CSP Blog uses the simplest idealized cyclostationary digital signal–rectangular-pulse BPSK–to connect all the different aspects of CSP. But don’t mistake these ‘textbook’ signals for the real world.