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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