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Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud...
Prefix Scan (or simply scan) is an operator that computes all the partial sums of a vector. A scan operation results in a vector where each element is the sum of the preceding elements in the original vector up to the corresponding position. Scan is a key operation in many relevant problems like sorting, lexical analysis, string comparison, image filtering among others. Although there are libraries...
Given a set of points P⊄ R^d and a kernel k, the Kernel Density Estimate at a point x∊R^d is defined as \mathrm{KDE}_{P}(x)=\frac{1}{|P|}\sum_{y\in P} k(x,y). We study the problem of designing a data structure that given a data set P and a kernel function, returns approximations to the kernel density} of a query point in sublinear time}. We introduce a class of unbiased estimators...
Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations...
We present nonlinear Schur-type orthogonal representations of nonlinear filters of the Volterra-Wiener class for higher-order and non-Gaussian stochastic processes, and propose efficient and numerically attractive solutions of the orthogonal transformations (innovations, stochastic modeling) for this class of processes.
The complexity of multidimensionality is one of the frequently encountered problems in the high-dimensional data space. The fact that multidimensionality in the data space increases and reaches great numbers brings about the problem that the number of non-informative ones among the features associated with the target class increases along with the dataset complexity. The fact that all features included...
CNN involves large number of convolution of feature maps and kernels, necessary for extracting useful features for accurate classification. However, it requires significant amount of computationally intensive power and area hungry multiplications limiting its deployment on embedded devices under resource constrained scenario. To address this problem, we propose modified distributed arithmetic based...
A novel projection twin support vector machine (PTSVM), termed as NPTSVM, is presented in this paper for binary classification. Although this method determines two projection vectors using the same way as PTSVM, it has more advantages than existing PTSVMs. First, NPTSVM does not have to calculate inverse matrices during the learning process, which makes the training speed of NPTSVM be much faster...
Convolutional neural networks (CNNs), in which several convolutional layers extract feature patterns from an input image, are one of the most popular network architectures used for image classification. The convolutional computation, however, requires a high computational cost, resulting in an increased power consumption and processing time. In this paper, we propose a novel algorithm that substitutes...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
Firstly, aiming at the characteristics of accuracy and complexity of uncertain information representation. the interval grey number extended the general grey number. Then, the concept of extended general grey number is put forward based on grey number theory, which is used to represent complex uncertain information. Secondly, according to the essence of the kernel and degree of greyness of general...
Automatic blobs detection constitutes a basic but difficult problem. In this work a new fast blobs detection technique based on a scale-space representation of the original image, is proposed. The scale-space representation is constructed by using a new simplified form of the Fast Radial Symmetry Transform to precisely detect the essential blobs. From the experiments we have conducted, the proposed...
The exponential growth of available data has increased the need for interactive exploratory analysis. Dataset can no longer be understood through manual crawling and simple statistics. In Geographical Information Systems (GIS), the dataset is often composed of events localized in space and time; and visualizing such a dataset involves building a map of where the events occurred.We focus in this paper...
We study the sparse matrix product problem where the input matrices are sparse. Starting with an original DO- loop nest structured algorithm, different versions involving body kernels such as GAXPY, AXPY and DOT are generated by the loop interchange technique. We particularly focus on the GAXPY- Row body kernel where the matrices are acceded row-wise. Various versions corresponding to the most used...
This note suggests some investigations about the complexity of multivariate problems based on quantized information rather than standard information. The extreme case of binary information is studied on two classical examples: the integration of multivariate Lipschitz functions, for which it is shown that adaptivity of the quantization process is beneficial, and the integration of multivariate trigonometric...
The High Efficiency Video Coding (HEVC) standard significantly saves coding bit-rate over the proceeding H.264 standard, but at the expense of extremely high encoding complexity. In fact, the coding tree unit (CTU) partition consumes a large proportion of HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, we propose in this paper a complexity...
SVM is an effective method for fault classification. However, it has computational problem while dealing with high dimensional datasets. To overcome this issue, we propose an efficient dimension reduction technique named random projection combined with the SVM for fault classification in this paper. Compared with well known dimension reduction method like PCA, it provides better tradeoff between pairwised...
Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN...
Low-density parity-check convolutional codes (LDPC-CC) have interesting error correction features. They have a great potential to become a key error-correcting codes for enhancing reliability of modern digital communication systems, optical systems and storage devices. On the implementation side, however, the design of low-cost low-power and high-throughput LDPC-CC decoders remains challenging. This...
The paper considers the class of discrete-time, single-input, single-output, nonlinear dynamical systems described by a polynomial difference equation. This class, call polynomial time-invariant, is a proper generalization of the linear time-invariant model class. The identification data is assumed to be generated in the errors-in-variables setting, where the input and the output noise is zero mean,...
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