Results 11 to 20 of about 10,276 (261)
Sparse Convolution for Approximate Sparse Instance
CoRR, 2023 Computing the convolution $A \star B$ of two vectors of dimension $n$ is one of the most important computational primitives in many fields. For the non-negative convolution scenario, the classical solution is to leverage the Fast Fourier Transform whose time complexity is $O(n \log n)$.
Xiaoxiao Li +2 more
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Frontiers in Human Neuroscience, 2014 Entrained behavior coordinates, predicts, and modulates multi-scale rhythmic gestures with high spatio-temporal precision even as it shows flexible adaptation in response to perturbation (Clayton et al., 2005; Altenmuller et al., 2006; Phillips-Silver et al., 2010).
Eric eBarnhill, Eric eBarnhill
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Sparse Recovery Using Sparse Matrices [PDF]
Proceedings of the IEEE, 2010 In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals.
Gilbert, Anna, Indyk, Piotr
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Anomaly target detection has been a hotspot of the hyperspectral imagery (HSI) processing in recent decades. One of the key research points in the HSI anomaly detection is the accurate descriptions of the background and anomaly targets.
Yan Zhang +6 more
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Research on compressive sensing of strong earthquake signals for earthquake early warning
Geomatics, Natural Hazards & Risk, 2021 Earthquake early warning is an effective method to reduce casualties and losses. Based on the theory of compressive sensing, this paper proposes a strong earthquake signal processing architecture based on compressive sensing for difficulties of the ...
Jiening Xia +4 more
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Frontiers in Computational Neuroscience, 2021 Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory inputs.
Shirin Dora +4 more
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Estimating optimal sparseness of developmental gene networks using a semi-quantitative model. [PDF]
PLoS ONE, 2017 To estimate gene regulatory networks, it is important that we know the number of connections, or sparseness of the networks. It can be expected that the robustness to perturbations is one of the factors determining the sparseness.
Natsuhiro Ichinose +2 more
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An Insightful Overview of the Wiener Filter for System Identification
Applied Sciences, 2021 Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice.
Laura-Maria Dogariu +3 more
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Sparse Sparse Bundle Adjustment [PDF]
Procedings of the British Machine Vision Conference 2010, 2010 Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras ...
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Deterministic sparse FFT for M-sparse vectors [PDF]
Numerical Algorithms, 2017 In this interesting paper, the authors present a new deterministic sparse (inverse) fast Fourier transform (FFT), where the resulting vector \(\mathbf x \in {\mathbb C}^N\) with \(N = 2^J\) is \(M\)-sparse, i.e., \(\mathbf x\) contains only \(M\) nonzero components. This new algorithm which generalizes the sparse FFT of \textit{G. Plonka} and \textit{K.
Gerlind Plonka +3 more
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