Signal Processing Application
6 weeks · 0 milestones
Design and implement a signal processing application for a defined signal and task: filter design (low-pass, high-pass, band-pass, or band-stop), spectral analysis, noise reduction, or feature extraction. The application must be non-trivial — a real signal with real noise characteristics, not a textbook ideal case. Required documentation: signal specification (frequency content, noise type, SNR, sample rate), filter or algorithm specification (design method, order, cutoff frequencies, ripple), frequency response plot of the designed filter or algorithm, performance validation comparing before and after processing with quantitative metrics (SNR improvement, stopband attenuation achieved), and a documented sensitivity analysis testing at least one design parameter. Preferred proof: a real signal from physical measurement hardware. Accessible alternative: Python with scipy.signal and numpy (free), MATLAB Online free tier, or GNU Octave (free) applied to publicly available signal datasets (PhysioNet ECG data, NOAA seismic data, urban noise datasets). Proof artifacts: the filter or algorithm specification (design artifact) and the frequency response and performance validation plots (analysis artifact). Verification: an electrical or signal processing engineer reviews the performance analysis — 'this filter removes the noise but what useful signal components are also attenuated?' — requiring you to quantify the trade-off.
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3 milestones
Identify a real or realistic signal processing problem: noise reduction in audio, ECG artefact removal, vibration analysis for a rotating machine, digital communications filter design, or image processing. Define the problem quantitatively: signal frequency content, noise spectrum, sampling rate, acceptable attenuation, and passband/stopband requirements. Evaluate ≥2 candidate algorithm approaches (FIR vs IIR filter, FFT-based analysis vs wavelet transform, etc.) and select the approach with documented justification. Signal processing is constraint-driven — an algorithm that meets the stopband attenuation but violates the latency requirement is the wrong algorithm.
Proof required
Submit your problem definition document (≥500 words): the signal processing problem, quantitative requirements (frequency, sampling rate, attenuation, latency, or equivalent), ≥2 candidate algorithms evaluated, and the selected algorithm with justification.
What gets checked
- Signal processing requirements are quantitative — frequency values in Hz, attenuation in dB, sampling rate in samples/second — not stated as 'remove noise'
- ≥2 candidate algorithms are evaluated against the requirements — not a free choice without comparison
- Selected algorithm justification addresses why it meets the constraints the alternatives do not