signal-processing-dsp-basics
Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.
npx skills add majiayu000/claude-skill-registry --skill signal-processing-dsp-basics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Signal Processing: DSP Basics Digital signal processing is what happens after an ADC and before a DAC — or inside the world of purely discrete-time signals like audio files, sensor logs, and computed waveforms. The core ideas of DSP are deceptively simple (sample, add, multiply, feed back) but the traps are numerous (aliasing, leakage, numerical underflow, phase distortion, quantization noise). This skill covers the fundamentals every embedded or systems engineer needs to avoid making expensive analog problems digital. **Agent affinity:** shima (DSP architecture and fixed-point implementation), horowitz (intuition and practical filter examples) **Concept IDs:** elec-data-conversion-dsp, elec-signal-ac-analysis ## Sampling and the Nyquist Criterion A continuous-time signal x(t) is sampled by taking its value at evenly spaced instants T_s apart, producing the discrete-time sequence x[n] = x(n * T_s). The sample rate f_s = 1 / T_s. **The Nyquist-Shannon sampling theorem.** A band-limited signal whose frequency content is strictly below f_s / 2 can be perfectly reconstructed from its samples. Any frequency content above f_s / 2 gets aliased — folded back into the band from 0 to f_s /
- Sampling and the Nyquist Criterion
- Technique 1 — Quantization
- Technique 2 — The Discrete Fourier Transform
- Technique 3 — Windowing and Spectral Leakage
- Technique 4 — Convolution and FIR Filters
- Technique 5 — IIR Filters
- Technique 6 — Fixed-Point vs Floating-Point
- Technique 7 — Decimation and Interpolation
- Common Mistakes
- Cross-References
- References
What does the signal-processing-dsp-basics skill do?
Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill signal-processing-dsp-basics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.