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In computing, multiple instruction, single data (MISD) is a type of parallel computing architecture where many functional units perform different operations on the same data. Pipeline architectures belong to this type, though a purist might say that the data is different after processing by each stage in the pipeline.
Pandemonium architecture is a theory in cognitive science that describes how visual images are processed by the brain. It has applications in artificial intelligence and pattern recognition. The theory was developed by the artificial intelligence pioneer Oliver Selfridge in 1959. It describes the process of object recognition as the exchange of ...
Software architecture pattern is a reusable, proven solution to a specific, recurring problem focused on architectural design challenges, which can be applied within various architectural styles. [ 1 ]
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
Pages in category "Architectural pattern (computer science)" The following 32 pages are in this category, out of 32 total. This list may not reflect recent changes .
Inbox and outbox pattern "Queue-Based Load Leveling", also known as the "Storage First Pattern", is an architectural pattern in which a queue acts as a buffer between an invoker service (such as an API Gateway) and the destination (e.g., compute resources). [4] "Backends for frontends" pattern [5] "Public versus Published Interfaces" [6]
In computer science, zero instruction set computer (ZISC) refers to a computer architecture based solely on pattern matching and absence of (micro-)instructions in the classical [clarification needed] sense. These chips are known for being thought of as comparable to the neural networks, being marketed for the number of "synapses" and "neurons ...
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.