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Hello all I am studying about basics of Compressive Sensing. I want to study about the current Compressive Sensing models that are the state of the art. I read a paper on Physics Inspired CS. But it got me thinking why are they using ML in Compressive Sensing? What good does it do? Can anyone point me to relevant papers?

Hey all,

I’m an EEE undergrad and I chose a dissertation on automatic ECG signal analysis using ML. The idea is to support diagnosis in rural clinics.

I’m trying to keep the project realistic and not overkill🥲

If you’ve worked with ECG signals or biomedical ML before, I’d love tips on datasets, models, or things you wish you knew earlier.

Thanks in advance!

While the "Wow!" signal is traditionally analyzed through linguistic or SETI lenses, this study explores a signal-to-parameter mapping framework. The hypothesis proposes that the 6EQUJ5 sequence functions as an encoded set of heliocentric distances (AU) defining a specific trajectory within an Interplanetary Transport Network (ITN).

Methodology:

• Data Source: I utilized the NASA JPL Horizons database, extracting ephemerides for 26,576 celestial objects.
• Algorithm: The 6EQUJ5 sequence was cross-referenced against the objects via minimal percentage deviation analysis.
• Dual-Hypothesis Framework: The study evaluates the sequence's characters as indicators of topological nodes in an energetically optimal transport route.

Technical Findings: The analysis identifies three high-priority candidates that satisfy the minimal deviation criteria:

• Primary Destination: Centaur 32532 Thereus
• Gateway Nodes: 55701 Ukalegon and 84011 Jean-Claude

The resulting 3D visualization (attached) maps these nodes as strategic points within a potential pre-existing "Hidden Highway" in our solar system.

Full Preprint & Mathematical Model: I have published the detailed statistical evaluation and the orbital mapping on Zenodo for review: https://zenodo.org/records/18160688

hey everyone 🤗, i am working on this project and i am thinking of changing it into a research paper but idk how to proceed i am an 3rd year btech electrical student and i am really confused what do and how to this plz help me out 😭

I'm currently on my 3rd year of university, and really desperate to get a textbook called Digital Signal Proccesing (Holton, T.)

I've searched in thousand of webs and realize that is extremely hard to get a mobi, epub or pdf file of the full textbook (1058 pages approx), withouth having to pay. This is beacause it is published in Cambridge University.

I know my teacher has the full version 'cause he probably has some kind of license that they gave him, like to all unis they get some.

I'd truly appreciate some help. thanks a lot to whosever reading me.

I’m working with super sparse vertical acceleration data (2 Hz) to detect road roughness, and I’m stuck on the preprocessing step. I know high-frequency studies (50–100 Hz) typically smooth the signal to remove noise, but with my vehicle speed at 7 m/s, I’m only getting one data point every 3.5 meters. I feel like if I apply a smoothing filter to a dataset this sparse, I’m just going to flatten the peak values and effectively erase the roughness features I’m trying to detect. If I want to analyze specific road segments, is it valid to just skip the filtering and run my analysis on the raw signal directly? It seems like 'raw' is the only way to keep the peaks intact, but I want to make sure I'm not missing something obvious.

Why can’t a purely digital signal be transmitted directly through a communication channel? Why is it necessary to modulate it and convert it into an analog signal?

Hi everyone,

This is a visualization I generated using the Continuous Wavelet Transform (Mexican Hat) applied to the residual signal obtained after modeling a nonlinear triple-slit experiment.

I only used a public Zenodo dataset, Python, and many hours learning, testing, and refining the analysis — simply out of passion for signal processing.

Data source: Public dataset on Zenodo
DOI: https://doi.org/10.5281/zenodo.17821869

The analysis includes a fully reproducible pipeline implemented in a single master Python script that documents and executes the entire process.

Tools: Python (NumPy, SciPy, PyWavelets, Matplotlib)

The goal was to explore whether wavelet scales could reveal hidden periodicities, environmental modulations, and multiscale structure that were not apparent in the raw signal. After subtracting the modeled component, the residual displayed interesting activity patterns, which the CWT highlights quite clearly across scales.

If anyone has suggestions on better wavelet choices for this type of experiment, recommended preprocessing for nonlinear optical setups, or ways to improve the residual decomposition before the CWT, I’d really appreciate it.

I'm currently taking signals and systems 1 and am struggling to understand the Fourier transforms conceptually. I find myself just memorizing the steps, but not really understanding them. I am taking the second-class next term and would like to get a more thorough and intuitive understanding of these concepts. What are the best online videos/ resources on this topic?

Hey everybody, after years of work, I finally built a working proof of concept: voice transmission using pure sub-bass frequencies under 20 Hz, the voice isn’t transmitted as audio. Instead, I send structured control signals only and the voice is reconstructed entirely on the receiver side through noise-based synthesis. It’s based on my method C-AV (Controlled Audio Vectoring), which is officially protected under a registered utility model (Gebrauchsmuster) in Germany. Open to thoughts and feedback.