3,135 articles
Sorting
Sorting is the problem of arranging a collection of items so their keys appear in a well-defined order, such as numerical or alphabetical order, and a central component in algorithm design.
Sorting algorithm
A sorting algorithm, also called a sort algorithm, is a method for rearranging a sequence of items into a specified order, such as ascending numerical or lexicographic order.
Sorting network
A sorting network is a fixed arrangement of comparators that sorts every possible input; its comparison order is data independent, making it suited to parallel sorting in hardware.
Sound event detection
Sound event detection (SED) is a machine learning task that identifies acoustic events in audio, outputting each event's class label with onset and offset times.
Soundex
Soundex is a phonetic algorithm that converts names, especially surnames, into letter-plus-three-digit codes by English sound, patented in 1918 and used to index United States censuses.
SoundHound AI
SoundHound AI, Inc. is a Santa Clara, California, voice and agentic AI company that builds speech recognition and conversational AI products for automotive, restaurant, and enterprise customers.
Source–filter model
The source–filter model represents voice production as a glottal sound source shaped by a vocal-tract filter whose resonances create formants; it underpins speech technology from LPC to neural vocoders.
South Korea AI Basic Act
The South Korea AI Basic Act, formally the Framework Act on the Development of Artificial Intelligence, is South Korea's comprehensive AI law, effective January 22, 2026.
Space-time adaptive processing
Space-time adaptive processing (STAP) is a radar signal processing method that jointly filters signals in space and time to suppress clutter and detect moving targets from airborne platforms.
spaCy
spaCy is an open-source natural language processing library for Python and Cython, first released in 2015 by Explosion, with pretrained pipelines supporting tagging, parsing, and named entity recognition in over 70 languages.
Spark: The Definitive Guide
Spark: The Definitive Guide (Big Data Processing Made Simple) is a 2018 O'Reilly book on Apache Spark by Bill Chambers and Matei Zaharia, covering Spark 2.0's structured APIs, RDDs, streaming, and MLlib, with a Databricks GitHub code repository; no second edition exists.
SPARQL
SPARQL (SPARQL Protocol and RDF Query Language) is the W3C-standardized query language and associated HTTP protocol for querying RDF graph data, a W3C Recommendation since 2008 and the standard query language for Linked Data.
Sparrow search algorithm
The sparrow search algorithm (SSA) is a swarm intelligence metaheuristic introduced in 2020 that mimics sparrow flock foraging and anti-predation behavior to find global optima.
Sparse autoencoder
A sparse autoencoder is an autoencoder whose training adds a sparsity penalty on the code layer, keeping few units active; it underlies unsupervised feature learning and language model interpretability.
Sparse Bayesian learning
Sparse Bayesian learning is a Bayesian machine learning method that fits regression and classification models by pruning irrelevant basis functions, producing sparse models with probabilistic outputs.
Sparse decomposition (signal processing)
Sparse decomposition is a signal processing method representing a signal as a linear combination of a few atoms from a redundant dictionary, used in compressed sensing and machine learning.
Sparse non-negative matrix factorization
Sparse non-negative matrix factorization (sparse NMF) decomposes a non-negative data matrix into two non-negative factors with user-set sparseness constraints, used in speech separation and hyperspectral unmixing.
Sparse optimization
Sparse optimization is a class of optimization methods that seek solutions with few nonzero variables, using sparsity-inducing penalties or constraints such as the lasso and basis pursuit.
Spatial database
A spatial database is a database management system extended with types, functions, and indexes for storing and querying geometric objects such as points, lines, and polygons.
Spatial pyramid matching
Spatial pyramid matching (SPM) is a computer vision technique that compares images by matching histograms of local features over increasingly fine sub-regions, introduced at CVPR 2006.
Spatio-temporal clustering
Spatio-temporal clustering is a data mining method that groups points close in both space and time, applied to GPS tracks, crime hotspots, and Twitter event detection.
Spatio-temporal data mining
Spatio-temporal data mining (STDM) extracts useful patterns from large datasets where every measurement carries a location and a timestamp, such as GPS trajectories, sensor readings, and evolving weather maps.
Spatiotemporal graph convolutional network
A spatiotemporal graph convolutional network (STGCN) is a neural architecture combining graph convolutions over sensor networks with temporal convolutions, introduced in 2018 for traffic forecasting and skeleton-based action recognition.
Speaker diarization
Speaker diarization is the artificial intelligence task of partitioning an audio or video recording into segments by speaker identity, answering who spoke when with anonymous labels such as Speaker A and Speaker B.
Speaker separation
Speaker separation is a signal processing and machine learning method that extracts one speech signal per speaker from an audio mixture of two or more overlapping voices.
Specially Designated Global Terrorist
A Specially Designated Global Terrorist (SDGT) is a person or entity the United States designates under Executive Order 13224 of 2001 for committing or supporting terrorism, blocking their property.
Speckle filtering
Speckle filtering is an image processing step that reduces the granular, multiplicative noise in synthetic aperture radar imagery while preserving edges, point targets, and texture.
Spectral clustering
Spectral clustering is a graph-based clustering method that partitions data points using the eigenvectors of a Laplacian matrix built from a similarity graph, suited to non-convex clusters such as spirals.
Spectral embedding
Spectral embedding is a family of nonlinear dimensionality reduction methods that represent data points or graph nodes in low-dimensional space using eigenvectors of a graph Laplacian built from pairwise similarities.
Spectral unmixing
Spectral unmixing, or unmixing, is a remote sensing technique that decomposes a pixel's spectrum into constituent material spectra, called endmembers, weighted by their fractional abundances, yielding sub-pixel information for mineral mapping and land-cover studies.