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sequencer-instrument

DNA sequencer from optics to basecalls

Illumina NovaSeq X/DRAGEN, ONT MinKNOW/Dorado, PacBio SMRT Link

SystemTechniques · 18Primitives · 54 sequencer-instrument Fpga dataflow pipelineshardware-os · medium: Fully pipelined fixed-latency FPGA logic: initiation interval, HLS, BRAM and DSP budgetingAuto exposure focus white balancemedia · medium: Camera 3A loops: metering-driven exposure, contrast/phase autofocus, gray-world/illuminant white balanceImage alignment registrationmedia · medium: Align frames via feature matching or pyramidal optical flow for burst merging and stitchingRadiometric calibrationmedia · medium: Convert sensor counts to reflectance: radiometric and atmospheric correctionBeam search decodingml-training · medium: Keep the k best partial sequences by summed log-probability at every step, set finished ones aside, and return the best (n-best) by a length-normalised score.›Dynamic batchingml-training · medium: Queue independent requests and dispatch them as one batch when it is full or the oldest has waited max_delay; bounded extra latency buys throughput.›Bayesian genotype likelihoodssearch-sort · medium: Per-site genotype likelihoods from read base qualities, combined with priors to call variantsConvolution image filtersgraphics · low: Separable 2D convolution kernels for blur, sharpen (unsharp mask) and edges with border handlingPeak detectionsignal-processing · low: Robust peak and event detection in noisy signals with adaptive thresholds and refractory periodsSeekable compressed streamcompression · low: Independently compressed blocks plus an index for random access (BGZF, seekable zstd, eStargz)Dma transfersembedded · low: DMA controllers: descriptors, circular buffers, cache coherence for zero-CPU data movementConnectionist temporal classificati…machine-learning · low: Alignment-free sequence loss that sums the probability of every frame-to-label alignment with a blank symbol, using a dynamic program.›Recurrent networksmachine-learning · low: GRU and LSTM cells for streaming sequence modelsConvolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Weight quantizationml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.›Hierarchical state machinesystems · low: Nest states so a substate inherits its parent's reactions and only overrides what differs, with ordered entry and exit actions along the path.›Color space conversiongraphics · low: Convert pixels between transfer curves (sRGB, linear light), primaries (sRGB, Display P3, XYZ) and Y'CbCr, using tables for the curve and 3x3 matrices for the rest.›PID controllerrobotics · low: Drive a measured value to a setpoint with three terms on the error: proportional, integral, derivative. The default feedback loop for motors, heaters, drones and gimbals.›Homogeneous transformsgeometry · low: 4x4 affine and projective matrices, homogeneous coordinates, view and projection, normal matricesImage pyramidsgraphics · low: Gaussian/Laplacian pyramids and tiled multi-resolution images for zoom and blendingImage warping remapgraphics · low: Resampling with lookup maps: resize, lens undistortion, homographies, bilinear/bicubicFeature detection orbrobotics · low: FAST corners with oriented BRIEF descriptors and Hamming matching for tracking and relocalizationRansacrobotics · low: Random sample consensus for robust model fitting with outliers (homographies, planes, poses)+ 11 more techniques+ 38 more primitives

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