Publication

Methods and systems for noise resilient, pin-efficient and low power communications with sparse signaling codes

Mohammad Amin Shokrollahi, Seyed Armin Tajalli, Harm Cronie
2012
Patents

Abstract

In bus communications methods and apparatus, a first set of physical signals representing the information to be conveyed over the bus is provided, and mapped to a codeword of a sparse signaling code, wherein a codeword is representable as a vector of a plurality of components, some of which are quiescent components and some of which are non-quiescent components, wherein the number of quiescent components and non-quiescent components meet a sparseness requirement.

Official source

https://infoscience.epfl.ch/entities/publication/65d1aad6-887d-4f8f-8ac1-9f58ddd40afa

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Mohammad Amin Shokrollahi, Seyed Armin Tajalli, Harm Cronie
2012
Patents

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/65d1aad6-887d-4f8f-8ac1-9f58ddd40afa

About this result

Related concepts (28)

Sparse dictionary learning

Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims at finding a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than the one of the signals being observed.

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An operational amplifier (often op amp or opamp) is a DC-coupled high-gain electronic voltage amplifier with a differential input and, usually, a single-ended output. In this configuration, an op amp produces an output potential (relative to circuit ground) that is typically 100,000 times larger than the potential difference between its input terminals. The operational amplifier traces its origin and name to analog computers, where they were used to perform mathematical operations in linear, non-linear, and frequency-dependent circuits.

Sparse approximation

Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in , signal processing, machine learning, medical imaging, and more. Consider a linear system of equations , where is an underdetermined matrix and . The matrix (typically assumed to be full-rank) is referred to as the dictionary, and is a signal of interest.

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