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The analysis of scientific data, specially in different kinds of cosmological studies, has to deal with the increment in data volume. These studies include the calculation of correlation functions such as the Two-point Three-Dimensional Correlation Function. To get the final estimator value for these functions, it is necessary to construct histograms for storing large number counts. Histograms are...
A enhanced principal component analysis (PCA), termed as Two-step PCA (TS-PCA), is proposed to handle the dynamic characteristic of industry processes. Differently from the traditional dynamic PCA (DPCA) using the “time lag shift” structure, TS-PCA adopts a new structure to present the dynamic property in the process data. By using this new structure, TS-PCA can extract the time-uncorrelated components...
The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear...
Peer code review and continuous integration often interleave with each other in the modern software quality management. Although several studies investigate how non-technical factors (e.g., reviewer workload), developer participation and even patch size affect the code review process, the impact of continuous integration on code reviews is not yet properly understood. In this paper, we report an exploratory...
This paper analyzes the performance of different implementations of a three-point angular correlation function. This function is used in the study of large scale distribution of galaxies in a variety of computational platforms. The function is based on histogram construction and presents a large computational cost. This cost dramatically increases with the size of the datasets. The implementation...
Emerging technologies such as Spin-transfer torque magnetic random-access memory (STT-MRAM) are considered potential candidates for implementing low-power, high density storage systems. The vulnerability of such nonvolatile memory (NVM) based cryptosystems to standard side-channel attacks must be thoroughly assessed before deploying them in practice. In this paper, we outline a generic Correlation...
In the scenario of multiple access communication, the spectral sidelobes and peak-to-average power radio (PAPR) problems of orthogonal frequency division multiplexing (OFDM) must not be ignored. This paper proposes a novel precoding scheme that ahead of inverse discrete Fourier transformation (IDFT) is presented to jointly suppress the in-band-out-of-subband (IBOSB) radiation and high PAPR in orthogonal...
Spearman's rank relationship coefficient is a nonparametric (dispersion free) rank measurement. Spearman's coefficient is not a measure of the direct relationship between two factors, as a few ”analysts” proclaim. Pearson's relationship coefficient is the covariance of the two factors separated by the result of their standard deviations. The possibility of the paper is to look at the estimations of...
This work presents methods to automatically find optimal parameter settings for convolutional neural networks (CNNs) by using an evolutionary algorithm called particle swarm optimization (PSO). Even though the parameter space is extremely large (> 10 20), we experimentally show that a better parameter setting can be found for Alexnet configuration for five different image datasets. We have also...
This paper proposes an improved cloud-element model for information security risk assessment. Combined with the characteristics of dynamic balance of information security risk, we propose the control means and uncontrollable factors as information security risk evaluation index. The method of constructing the sample cloud has been expatiated, and the correlation between the cloud droplet and the sample...
In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure...
Aiming at the problem of expert weighting in group decision making, an expert weighting method based on D-S evidence theory is studied. Three ways such as the distance, grey correlation and the combination of these two methods are used to measure the deviation degree of the experts' opinions on the scheme, construct mass function based on this, and uses the D-S synthesis rule to carry on the information...
In this paper we proposed a simple and effective method of image blurred region detection is based on RGB color space feature information and local standard deviation of image. According to re-blurred the partially blurred image by a Gaussian function, compare the difference of RGB feature information in different regions of image, and then combined with the local standard deviation of image as the...
Examination and identification of tool marks is traditionally carried out under a comparison microscope by forensic scientists. This manual process is dependent on the experience of the examiner, including subjectivity. In order to improve the reliability and repeatability of the process in criminal investigation and trial, automation and quantification for tool mark examination is demanded. Shear...
Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed...
In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models for continuous Gaussian data, these models capture both the temporal correlation structure and the discrete marginal distribution of count data. We focus on the properties, parameter estimation, and model...
Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting...
Conventional pixel-domain block matching temporal (inter) prediction is suboptimal, since it ignores the underlying spatial correlation. Hence in our recent research we proposed transform domain temporal prediction (TDTP), wherein spatially decorrelated transform coefficients are individually predicted. Later we proposed extended block TDTP (EB-TDTP), which fully exploits spatial correlation around...
Random stepped FM radar that transmits random frequency narrowband pulses can avoid interference between adjacent radar systems. The random stepped FM radar also offers spectrum hole since it consists of independent pulses with different frequency. Thus it is expected to coexist with the existing radar systems. In this paper, we propose to adopt Khatri-Rao product array processing to spectrum hole...
Tremendous interest prevails in developing a convenient and effective wearable medical device for evaluating sleep at home. The current study presents the clinical validation of VitalPatch®, a wireless adhesive patch sensor for screening of sleep architecture compared to the gold standard polysomnograph (PSG). A group of 45 volunteers were attached to a standard 22-channel PSG and a VitalPatch sensor...
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