Results for “beamforming”

Design, select, and analyze waveforms for radar, sonar, and active sensing using the Phased Array System Toolbox. Covers LFM, NLFM, FMCW, phase-coded, CW, stepped FM, custom IQ, ambiguity functions, sidelobe reduction, and Doppler tolerance. Key objects: phased.LinearFMWaveform, phased.NonlinearFMWaveform, phased.CustomFMWaveform, phased.PhaseCodedWaveform, phased.FMCWWaveform, phased.SteppedFMWaveform, phased.MFSKWaveform, phased.RectangularWaveform, nlfmspec2freq, shapespectrum, ambgfun, pambgfun, sidelobelevel, legendreseq, mlseq, radarWaveformGenerator.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Multimodal Few-Shot Learning with Frozen Language Models

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Use when composing corner-anchored laser accents with thin beams, a bright emitter node, bloom, and atmospheric glow or fog. Prefer this over mengto-webgl-laser when lasers are layout corner ornaments, not a fixed full-screen WebGL beam.

Analyze AMS waveform data using Mixed-Signal Blockset utilities: phase noise measurement, clock jitter, anti-aliased resampling, timing measurements, lock time, INL/DNL, ADC/DAC calibration, HSpice import. Use when analyzing time-domain voltage from PLL/VCO/clock simulations, measuring phase noise from variable-step solver output, computing jitter, or resampling non-uniform data.

Use when building cursor-following spotlight reveals that expose a second aligned image through a soft radial mask—hover-to-color, before-and-after, x-ray, material, texture, product-detail, or illustrated heroes where a desaturated or embossed base stays visible while another treatment follows an eased pointer.

Inspect, generate, transform, and visualize UV channels on StaticMesh assets (UVMappingService). Use when the user asks to check/add/remove UV channels, auto-unwrap UVs, transform (tile/offset/rotate) UVs, check UV health/islands, or export a UV layout image.

Use when you need a dark atmospheric background with drifting vertical light folds, screen-blended glow, and a concentrated luminous corner or lower-edge bloom — background atmosphere only, not a full layout system.

Matryoshka Representation Learning

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Design and implement real-time audio processing chains using Audio Toolbox streaming objects. Use when building frame-based audio processing loops, multiband filters, dynamic range control, parametric EQ, level metering, loudness metering, SPL metering, octave-band analysis, sample rate conversion, frequency-domain filtering (long impulse responses, custom filter banks), or audio chains in Simulink. Covers visualization (visualize method), interactive tuning (parameterTuner), MIDI control, and Audio Toolbox Simulink blocks. Use when the user says "real-time audio", "streaming audio", "audio filter", "compressor", "equalizer", "level meter", "loudness meter", "SPL meter", "octave bands", "crossover filter", "audio chain", "MIDI control", "convolution reverb", "impulse response streaming", "frequency-domain filter", or asks to process audio frame-by-frame.

Bandpass, lowpass, bandstop filter design — hairpin, coupled-line, combline, stub, SIW for frequency selection and harmonic rejection. TRIGGER: user asks to design, create, or analyze any RF filter (bandpass, lowpass, highpass, bandstop, hairpin, coupled-line, combline, stub, SIW). Invoke BEFORE writing code — filter class names differ from what you would guess. SKIP: EM simulation/S-parameter extraction of an existing filter (use matlab-analyze-em), general PCB layout assembly (use matlab-assemble-pcb-layout), material/stackup setup only (use matlab-manage-pcb-material), optimization sweeps (use matlab-optimize-pcb-design).

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Towards Open-World Segmentation of Parts