Topic
Encoding (memory)
About: Encoding (memory) is a research topic. Over the lifetime, 7547 publications have been published within this topic receiving 120214 citations. The topic is also known as: memory encoding & encoding of memories.
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TL;DR: In this article, the effects of MTL stimulation on memory performance were studied in five patients undergoing invasive electrocorticographic monitoring during various phases of a memory task (encoding, distractor, recall).
41 citations
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26 May 2006
TL;DR: In this article, a feature extraction module receives an audio frequency waveform encoding the digital signal and generates a feature vector representing the digital data signal, and a bit sequence estimation module analyzes the feature vector and generates an estimated bit sequence corresponding to the data signal.
Abstract: A receiver with a time diversity combining component recovers a digital data signal transmitted over a voice channel of a digital wireless telecommunications network. A feature extraction module receives an audio frequency waveform encoding the digital data signal and generates a feature vector representing the digital data signal. A bit sequence estimation module analyzes the feature vector and generates an estimated bit sequence corresponding to the digital data signal. A memory stores the feature vector if the estimated bit sequence contains errors. A time diversity combining component generates a second estimated bit sequence by analyzing the first feature vector in combination with one or more feature vectors stored in the memory.
41 citations
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TL;DR: Modular encoding for neural networks based on attribute grammars is developed, a new encoding designed to generate the structure of neural networks and parameters with evolutionary algorithms, while explicitly enabling these three above-mentioned principles.
Abstract: Recent work in the evolutionary computation field suggests that the implementation of the principles of modularity (functional localization of functions), repetition (multiple use of the same sub-structure) and hierarchy (recursive composition of sub-structures) could improve the evolvability of complex systems. The generation of neural networks through evolutionary algorithms should in particular benefit from an adapted use of these notions. We have consequently developed modular encoding for neural networks based on attribute grammars (MENNAG), a new encoding designed to generate the structure of neural networks and parameters with evolutionary algorithms, while explicitly enabling these three above-mentioned principles. We expressed this encoding in the formalism of attribute grammars in order to facilitate understanding and future modifications. It has been tested on two preliminary benchmark problems: cart-pole control and robotic arm control, the latter being specifically designed to evaluate the repetition capabilities of an encoding. We compared MENNAG to a direct encoding, ModNet, NEAT, a multi-layer perceptron with a fixed structure and to reference controllers. Results show that MENNAG performs better than comparable encodings on both problems, suggesting a promising potential for future applications.
41 citations
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30 Nov 1983TL;DR: One chip microprocessor, which is more particularly designed to execute culation algorithms of a public code encoding system formed by a public function and a secret inverse function of the type comprising at least one programmable read-only memory, a processing unit and an input/output device, was designed in this article.
Abstract: One chip microprocessor, which is more particularly designed to execute culation algorithms of a public code encoding system formed by a public function and a secret inverse function of the type comprising at least one programmable read-only memory, a processing unit and an input/output device, wherein it comprises a memory, in which is recorded at least one algorithm corresponding to the performance of said secret function, and wherein the programmable read-only memory contains the secret parameters constituting the secret code and recorded in an area of the read-only memory which is inaccesible from the outside, the processing unit including the multiplying circuits necessary for the execution of the algorithm.
41 citations
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26 Aug 1999TL;DR: An improved teleconferencing data capture, encoding, and decoding architecture incorporates the audio encoding and video encoding functions in capture encoder hardware devices, and incorporates the video decoding function in a video decoder hardware device as discussed by the authors.
Abstract: An improved teleconferencing data capture, encoding, and decoding architecture incorporates the audio encoding and video encoding functions in capture encoder hardware devices, and incorporates the video decoding function in a video decoder hardware device. The video decoder and an audio decoder are able to analyze incoming data packets and are communicably linked to their respective capture encoder devices, or to a single capture encoder device if both audio and video capture and encoding functions are incorporated in a single device, so that the capture and/or encoding functions may be modified during the course of a teleconference.
41 citations