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Several applications in numerical scientific computing involve very large sparse matrices with a regular or irregular sparse structure. These matrices can be stored using special compression formats (storing only non-zero elements) to reduce memory space and processing time. The choice of the optimal format is a critical process that involves several criteria. The general context of this work is to...
In this study we use triangular basis function set to solve second kind fuzzy integral equation that can be converted to a system of two integral equations in crisp case. We also consider collocation method for approximately solving the equation.
Single-image blind deconvolution is one of the most challenging fields in image processing which restores a sharp image from its blurred version. Nowadays blind deconvolution algorithms have made significant progress. However, the restoration of blurred images with little scale edges and periodic textures is still a hard work. To solve this problem, this paper proposes a new normalized sparse regularization...
In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by...
For the mathematical model of tug handling simulator, the locally optimal locally weighted learning (LWL) is proposed. Firstly, samples space rearrangement is taken to diminish the one-to-many mapping and non-separable of ship motion states. Secondly, distance metric is learned by leave-one-out cross validation for every sample, and this approach improves the nonlinearity mapping ability and robustness...
Precipitation nowcasting is an important component for accurate weather modeling and Doppler radar data acts as an important input for nowcasting models. In this work, we propose a deep learning based approach for radar echo states prediction. Our approach uses a hybrid structure of convolutions within Long Short Term Memory recurrent network structure and a discriminator network is added in the loss...
The growing demand for flexibility and cost reduction in the telecommunication landscape directs the focus of service development heavily to programmability and softwarization. In the domain of Network Function Virtualization (NFV), one of the goals is to replace dedicated hardware devices (such as switches, routers, firewalls) with software-based network functionalities, showing comparable performance...
The well known Weyl's asymptotic formula gives an approximation to the number Nω of eigenvalues (counted with multiplicities) on an interval [0, ω] of the Laplace-Beltrami operator on a compact Riemannian manifold M. In this paper we approach this question from the point of view of Shannon-type sampling on compact Riemannian manifolds. Namely, we show that Nω is comparable to cardinality of certain...
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint...
Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which...
Efficient test and diagnosis methods are required to ensure high levels of dependability of the electronic systems deployed to the market. These methods involve a trade-off in terms of accessibility to test nodes, test stimuli complexity, area overhead, and data processing that, altogether determine the impact that the involved operations have in the final cost, performance, and reliability presented...
Recent advances in photonics and imaging technology allow the development of cutting-edge, lightweight hyperspectral sensors, both push-broom/line-scanning and snapshot/frame. At the same time, emerging applications in robotics, food inspection, medicine and earth observation are posing critical challenges on real-time processing and computational efficiency, both in terms of accuracy and power consumption...
The problem of image enhancement for low-contrast images with the small-size objects is considered. The histogram-based method for contrast enhancement of low-contrast images with the small-size objects on the basis of the estimation of parameters of contrast distribution at boundaries of image elements for the various definitions of contrast kernels is proposed. The proposed method is intended for...
As seen in many studies the relationship of object oriented matrices of the software and the calculated maintenance effort metric is very complicated, complex and nonlinear in nature. So with this kind of behavior, we can have got a research area where we can work upon to minimize the maintenance effort which can be used to develop and deploy models and systems for the forecasting of software maintenance...
In this paper we propose a technique to deal with unknown random jitter in a band-limited Gaussian channel. The jitter caused by the deviation or displacement of signal pulses affects the performance of the communication system. We show that a sampling set as small as twice the baud rate is enough for good detection performance. Detection is done by means of a suboptimal algorithm with polynomial...
Heterogeneous Multi-Processor Systems-on-Chip, whether ARM or x86 based, promise further performance scalability by complementing temporal compute in CPUs/GPUs with spatial compute in digital circuitry. Dynamic partial reconfiguration (DPR) extends such compute architectures by making use of different spatial compute elements over time. Novel research [1] presents means for operating DPR by the Linux...
Network Function Virtualization (NFV) is a novel paradigm that enables flexible and scalable implementation of network services on cloud infrastructure. An important enabler for the NFV paradigm is software switching, which should satisfy rigid network requirements such as high throughput and low latency. Despite recent research activities in the field of NFV, not much attention was given to understand...
Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification...
As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between...
Big Data as expressed as "Big Graphs" are growing in importance. Looking forward, there is also increasing interest in streaming versions of the associated analytics. This paper develops an initial template for the relationship between "traditional" batch graph problems, and streaming forms. Variations of streaming problems are discussed, along with their relationship to existing...
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