Sparse coding
Accelarated Sparse Coding with Overcomplete Dictionaties for Image Processing Apllications
... Image processing problems have always been challenging due to the complexity of the signal. These problems comprise image enhancement, such as denoising, inpainting or digital zoom, decomposing the image into relevant ...
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Development of sparse coding and reconstruction subsystems for astronomical imaging
... a sparse coding, a reconstruction and an optical subsystems studied and developed, as they were successfully proved to be functional and integrated within a fully functional and selfcontained prototype for ...
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Synaptic learning rules and sparse coding in a model sensory system.
... Olfactory representations for single odors acquired during training with STDP on multiple sequences had firing patterns that were very similar to those observed after learning each odor alone (Figure 5). The random ...
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Inference of causal networks from time-varying transcriptome data via sparse coding.
... expressions is crucial in grouping and hypothesizing causal relationships from high dimensional transcriptome data. Typically, inference of the causal relationships can be ambiguous; there is significant literature in ...
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Image Deblurring Using Sparse-Land Model
... process. Sparse-land model is an emerging and powerful method to describe signals based on the sparsity and redundancy of their ...representations. Sparse coding is a key principle that underlies ...
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Network coding switch
... introducing sparse coding techniques, where not all coding symbols are combined without causing any negative ...full coding vector in the packet header, a small random seed can be sent to ...
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Face recognition via sparse representation
... In sparse coding this can be problematic, since the dictionaries will not have neither good reconstruction nor discriminate power, when dealing with small amount of labeled ...
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Sparse Spatial Coding: a novel approach for efficient and accurate object recognition
... Sparse Coding (SC) techniques are characterized by a class of algorithms that learn basis functions from unlabeled input data in order to capture high-level fea- ...
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Phase-coded pulse aperiodic transmitter coding
... Transmitter Coding (ATC), is based on transmitting the radar pulses in an aperiodic man- ner, which enables the estimation of the pulse-to-pulse corre- lations with a lag resolution higher than the target range ...
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Dense light field coding: a survey
... the coding grid of a block-wise image/video coding ...when coding a lenslet image with any HEVC-based solutions, such as: i) HEVC SCC standard; and ii) a learning-based prediction proposed in [223] ...
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Learning transcriptional regulatory relationships using sparse graphical models.
... To learn the parameters of the model from data, which is usually of high dimension and low sample size, we use L1 regularization as is done in [22] (see also [23–25]). This approach yields a sparse network, where ...
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Assessing a Sparse Distributed Memory Using Different Encoding Methods
... The Sparse Distributed Memory model was proposed for the first time in the 1980s, by Pentti Kanerva [1]. Kan- erva figured out that such a memory model, based on the use of high dimensional binary vectors, can ...
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Performance Evaluation of Algorithms for Sparse-Dense Matrix Product
... the sparse dense matrix product associated to four sparse compressing ...the sparse matrix is stored in three list arrays of size NNZ), namely COO_IJ_V1 (u=16), once optimised gave the best results ...
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Bell nonlocality using tensor networks and sparse recovery
... Based on the tensor network approach, we show a number of results. First, we show that nonsignaling correlations (in- cluding nonlocal correlations) can be mapped to hidden vari- able models governed by ...
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On information metrics for spatial coding
... 2001; Petersen et al., 2001; Belitski et al., 2008, 2010; Montemurro et al., 2008; Kayser et al., 2009; Pava˜o et al., 2014; Lopes-dos-Santos et al., 2015; Rossi-Pool et al., 2016; Vergara et al., 2016 ). Moreover, the ...
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Sparse error gait image: a new representation for gait recognition
... a sparse error component, for each of the input ...The sparse error component captures the larger variations in the GEIs, whose representation in the RPCA space are far from the low rank ...The ...
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XRCC2 coding region by high
... Homologous recombination (HR) is the major pathway for repairing double strand breaks (DSBs) in eukaryotes and XRCC2 is an essential component of the HR repair machinery. To evaluate the potential role of mutations in ...
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Nonlinear Network Coding: A Case Study
... In this section, we focus on transmitting messages of di- mension two. Firstly, we show that polynomial code is not as powerful as linear code in binary alphabet. Then, it is proved that polynomial code with the same ...
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Sparse and compositionally robust inference of microbial ecological networks.
... 16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While ...
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Scalable light field representation and coding
... LF coding algorithms that allow for scalability and random access: In order to enable LF content to be presented on various types legacy displays, such as 2D, and 3D/Stereo displays, as well as on newer LF ...
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