wzyn20051216 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
- Identify data modality, sampling rate, image size, color space, labels, and expected outputs.
- Confirm toolbox availability before using specialized APIs.
- Build a tiny reproducible subset first.
- Save intermediate outputs so failures are inspectable.
- 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.