Skill UI

Offers MATLAB and GNU Octave guidance for matrix math, data import/export, visualizations, statistics, signal processing, differential equations, and general scripting—helpful when writing or translating MATLAB/Octave code and running scripts under either interpreter.

NeuroKit2 is a Python biosignal processing toolkit for ECG, EEG, EDA, RSP, EMG, and EOG data that automates cleaning, feature extraction, and HRV/complexity analysis for psychophysiology, clinical, and HCI studies.

SciPy provides open-source algorithms and utilities for scientific computing, including integration, optimization, statistics, and signal processing, enabling researchers and engineers to build reliable numerical applications.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This skill guides authors through the critical process of positioning their manuscript for Acta Electronica Sinica (AES). It helps authors rigorously assess if their topic falls within the scope of electronic information science (e.g., IC, communication, signal processing) and determines the appropriate journal (AES vs. CJE). It covers scope definition, innovation assessment, and manuscript type selection, ensuring submissions are highly relevant and meet the required academic level.

A comprehensive guide for generating and packaging reproducible evidence for signal processing submissions to ICASSP. It details what reviewers prioritize (scoring scripts, dataset splits, logs) and outlines best practices to ensure that headline metrics are easily regenerated, thereby maximizing review credibility and post-publication impact.

A comprehensive guide for designing and auditing empirical experiments in signal processing, audio, and speech domains. It emphasizes matching standard metrics (e.g., WER, SI-SDR, PSNR) to specific tasks, establishing current strong baselines, and systematically sweeping operating conditions (like SNR or reverberation time) to ensure robust, reliable, and publication-ready results.

A comprehensive guide for authors submitting to major signal processing conferences like ICASSP. It details how to audit, define, and precisely position a paper's novelty by comparing it against specific literature lanes (SPS journals, conferences, and ML venues). Learn to articulate the technical delta against the nearest work in a constrained, four-page format.

A comprehensive guide for researchers submitting signal processing papers, detailing how to achieve rigorous reproducibility for conferences like ICASSP. It emphasizes that simply providing a model is insufficient; authors must pin down the 'scoring ruler,' data versions, front-end parameters, and computational seeds to ensure every reported metric can be independently verified.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

A comprehensive guide for researchers determining the best conference or journal for their signal processing contribution. It helps users identify the core signal-processing primitive (e.g., estimator, transform, representation) to correctly route work between major venues like ICASSP, Interspeech, ICIP, or general ML conferences.