matlab-signal-vision-ai

MATLAB Signal Vision AI

Use this skill when the core artifact is a processed signal, image, video, point cloud, neural network, or AI experiment.

Workflow

1. Identify data modality, sampling rate, image size, color space, labels, and expected outputs.
2. Confirm toolbox availability before using specialized APIs.
3. Build a tiny reproducible subset first.
4. Save intermediate outputs so failures are inspectable.
5. Validate with numerical metrics, visual artifacts, and data shape checks.

Preferred APIs

• Signal: designfilt, filter, spectrogram, pwelch, findpeaks, wavelet functions.
• Vision: imread, im2gray, imresize, imbinarize, regionprops, detectSURFFeatures, bboxOverlapRatio.
• Deep learning: dlnetwork, trainnet, datastores, minibatchqueue, pretrained models when licensed.
• Lidar/medical imaging: use domain importers and viewers when available; keep metadata.

Validation

Choose relevant checks:

• Signal-to-noise ratio, peak frequency, filter stability.
• Image dimensions, pixel range, mask area, IoU, accuracy, F1.
• Training loss decreases on a smoke subset.
• Inference output has expected class names, boxes, or mask shape.
• Exported figure or video is nonempty.

Reproducibility

Record random seed, split policy, pretrained model version, GPU availability, and dataset hash or URL.

Tools

r = mas_spectrum_check(x, fs, struct('window','hann','nfft',[]));
r.leakage  r.cycles  r.concentration  r.parsevalErr  r.taperBias  r.warnings
o = mas_ofdm_ber([0 2 4 6]);   o.berSim  o.berTheory  o.relErr  o.verdict
m = mas_image_metrics(predMask, trueMask);
m.iou  m.dice  m.precision  m.recall  m.perObject  m.objectIouWorst
s = mas_split_seed(labels, struct('trainFrac',0.7,'seed',0,'stratify',true));
s.trainIdx  s.valIdx  s.noOverlap  s.noGap  s.reproducible  s.classBalance

A spectrum is only trustworthy after two checks

mas_spectrum_check runs both, because a spectrum can be wrong while looking completely normal: