Herein, we analytically derive the effective index and field distribution of the LP01 mode of a step-index N-sided regular-polygonal-core fiber. To do this, we utilize the lowest-order non-anomalous approximation of the π/N expansion. These properties are also calculated numerically and the results are compared the with approximations.

Recently proposed irregular networks can reduce the latency for both on-chip and off-chip systems with a large number of computing nodes and thus can improve the performance of parallel applications. However, these networks usually suffer from deadlocks in routing packets when using a naive minimal path routing algorithm. To solve this problem, we focus attention on a lately proposed theory that generalizes the turn model to maintain the network performance with deadlock-freedom. The theorems remain a challenge of applying themselves to arbitrary topologies including fully irregular networks. In this paper, we advance the theorems to completely general ones. Moreover, we provide a feasible implementation of a deadlock-free routing method based on our advanced theorem. Experimental results show that the routing method based on our proposed theorem can improve the network throughput by up to 138 % compared to a conventional deterministic minimal routing method. Moreover, when utilized as the escape path in Duato's protocol, it can improve the throughput by up to 26.3 % compared with the conventional up*/down* routing.

Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.

In this paper, we investigate the maximum induced matching problem (MaxIM) on C5-free d-regular graphs. The previously known best approximation ratio for MaxIM on C5-free d-regular graphs is $left(rac{3d}{4}-rac{1}{8}+rac{3}{16d-8} ight)$. In this paper, we design a $left(rac{2d}{3}+rac{1}{3} ight)$-approximation algorithm, whose approximation ratio is strictly smaller/better than the previous one when d≥6.

This paper presents an analysis of random number generators based on continuous-time chaotic oscillators. Two different methods for random number generation have been studied: 1) Regular sampling of a chaotic waveform, and 2) Chaotic sampling of a regular waveform. Kernel density estimation is used to analytically describe the distribution of chaotic state variables and the probability density function corresponding to the output bit stream. Random bit sequences are generated using analytical equations and results from numerical simulations. Applying the concepts of autocorrelation and approximate entropy, randomness quality of the generated bit sequences are assessed to analyze relationships between the frequencies of the regular and chaotic waveforms used in both random number generation methods. It is demonstrated that in both methods, there exists certain ratios between the frequencies of regular and chaotic signal at which the randomness of the output bit stream changes abruptly. Furthermore, both random number generation methods have been compared against their immunity to interference from external signals. Analysis shows that chaotic sampling of regular waveform method provides more robustness against interference compared to regular sampling of chaotic waveform method.

Affine projection sign algorithm (APSA) is an important adaptive filtering method to combat the impulsive noisy environment. However, the performance of APSA is poor, if its regularization parameter is not well chosen. We propose a variable regularization APSA (VR-APSA) approach, which adopts a gradient-based method to recursively reduce the norm of the a priori error vector. The resulting VR-APSA leverages the time correlation of both the input signal matrix and error vector to adjust the value of the regularization parameter. Simulation results confirm that our algorithm exhibits both fast convergence and small misadjustment properties.

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

Developing a robust appearance model is a challenging task due to appearance variations of objects such as partial occlusion, illumination variation, rotation and background clutter. Existing tracking algorithms employ linear combinations of target templates to represent target appearances, which are not accurate enough to deal with appearance variations. The underlying relationship between target candidates and the target templates is highly nonlinear because of complicated appearance variations. To address this, this paper presents a regularized kernel representation for visual tracking. Namely, the feature vectors of target appearances are mapped into higher dimensional features, in which a target candidate is approximately represented by a nonlinear combination of target templates in a dimensional space. The kernel based appearance model takes advantage of considering the non-linear relationship and capturing the nonlinear similarity between target candidates and target templates. l2-regularization on coding coefficients makes the approximate solution of target representations more stable. Comprehensive experiments demonstrate the superior performances in comparison with state-of-the-art trackers.

In this paper we show a simple heuristic for constructing smaller automata for a set of regular expressions, based on suffix sorting: finding common prefixes and suffixes in regular expressions and merging them. It is an important problem in network security. We applied our approach to random and real-world regular expressions. Experimental results showed that our approach yields up to 12 times enhancement in throughput.

Image sensor communication (ISC), derived from visible light communication (VLC) is an attractive solution for outdoor mobile environments, particularly for intelligent transport systems (ITS). In ITS-ISC, tracking a transmitter in the image plane is critical issue since vehicle vibrations make it difficult to selsct the correct pixels for data reception. Our goal in this study is to develop a precise tracking method. To accomplish this, vehicle vibration modeling and its parameters estimation, i.e., represetative frequencies and their amplitudes for inherent vehicle vibration, and the variance of the Gaussian random process represnting road surface irregularity, are required. In this paper, we measured actual vehicle vibration in a driving situation and determined parameters based on the frequency characteristics. Then, we demonstrate that vehicle vibration that induces transmitter displacement in an image plane can be modeled by only Gaussian random processes that represent road surface irregularity when a high frame rate (e.g., 1000fps) image sensor is used as an ISC receiver. The simplified vehicle vibration model and its parameters are evaluated by numerical analysis and experimental measurement and obtained result shows that the proposed model can reproduce the characteristics of the transmitter displacement sufficiently.

Regular expression matching is essential in network and big-data applications; however, it still has a serious performance bottleneck. The state-of-the-art schemes use a multi-pattern exact string-matching algorithm as a filtering module placed before a heavy regular expression engine. We design a new approximate string-matching filter using multiple q-grams; this filter not only achieves better space compactness, but it also has higher throughput than the existing filters.

A Tikhonov regularized RLS algorithm with an exponential weighting factor, i.e., a leaky RLS (LRLS) algorithm was proposed by the author. A quadratic version of the LRLS algorithm also exists in the literature of adaptive filters. In this letter, a cubic version of the LRLS filter which is computationally efficient is proposed when the length of the adaptive filter is short. The proposed LRLS filter includes only a divide per iteration although its multiplications and additions increase in number. Simulation results show that the proposed LRLS filter is faster for its short length than the existing quadratic version of the LRLS filter.

In this work, two new proposed NMF models are developed for facial expression recognition. They are called maximum volume constrained nonnegative matrix factorization (MV_NMF) and maximum volume constrained graph nonnegative matrix factorization (MV_GNMF). They achieve sparseness from a larger simplicial cone constraint and the extracted features preserve the topological structure of the original images.

A widely-used query on a graph is a regular path query (RPQ) whose answer is a set of tuples of nodes connected by paths corresponding to a given regular expression. Traditionally, evaluating an RPQ on a large graph takes substantial memory spaces and long response time. Recently, several studies have focused on improving response time for evaluating an RPQ by splitting an original RPQ into smaller subqueries, evaluating them in parallel and combining partial answers. In these works, how to choose split labels in an RPQ is one of key points of the performance of RPQ evaluation, and rare labels of a graph can be used as split labels. However there is still a room for improvement, because a rare label cannot guarantee the minimum evaluation cost all the time. In this paper, we propose a novel approach of selecting split labels by estimating evaluation cost of each split subquery with a unit-subquery cost matrix (USCM), which can be obtained from a graph in prior to evaluate an RPQ. USCM presents the evaluation cost of a unit-subquery which is the smallest possible subquery, and we can estimate the evaluation cost of an RPQ by decomposing into a set of unit-subqueries. Experimental results show that our proposed approach outperforms rare label based approaches.

Train-borne video captured from the camera installed in the front or back of the train has been used for railway environment surveillance, including missing communication units and bolts on the track, broken fences, unpredictable objects falling into the rail area or hanging on wires on the top of rails. Moreover, the track condition can be perceived visually from the video by observing and analyzing the train-swaying arising from the track irregularity. However, it's a time-consuming and labor-intensive work to examine the whole large scale video up to dozens of hours frequently. In this paper, we propose a simple and effective method to detect the train-swaying quickly and automatically. We first generate the long rail track panorama (RTP) by stitching the stripes cut from the video frames, and then extract track profile to perform the unevenness detection algorithm on the RTP. The experimental results show that RTP, the compact video representation, can fast examine the visual train-swaying information for track condition perceiving, on which we detect the irregular spots with 92.86% recall and 82.98% precision in only 2 minutes computation from the video close to 1 hour.

Cryptographic protocols enable participating parties to compute any function of their inputs without leaking any information beyond the output. A card-based protocol is a cryptographic protocol implemented by physical cards. In this paper, for constructing protocols with small numbers of shuffles, we introduce a new type of cards, regular polygon cards, and a new protocol, oblivious conversion. Using our cards, we construct an addition protocol on non-binary inputs with only one shuffle and two cards. Furthermore, using our oblivious conversion protocol, we construct the first protocol for general functions in which the number of shuffles is linear in the number of inputs.

Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of spatial kernel in this framework, a huge adjacency matrix is adopted to determine the adjacency relation between each pair of voxels and thus it leads to very high computational complexity in the spatial kernel calculation. The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced. The improvement is verified by experimental results on classification of Alzheimer patients and cognitively normal controls.

The solution of the standard 2-norm-based multiple kernel regression problem and the theoretical limit of the considered model space are discussed in this paper. We prove that 1) The solution of the 2-norm-based multiple kernel regressor constructed by a given training data set does not generally attain the theoretical limit of the considered model space in terms of the generalization errors, even if the training data set is noise-free, 2) The solution of the 2-norm-based multiple kernel regressor is identical to the solution of the single kernel regressor under a noise free setting, in which the adopted single kernel is the sum of the same kernels used in the multiple kernel regressor; and it is also true for a noisy setting with the 2-norm-based regularizer. The first result motivates us to develop a novel framework for the multiple kernel regression problems which yields a better solution close to the theoretical limit, and the second result implies that it is enough to use the single kernel regressors with the sum of given multiple kernels instead of the multiple kernel regressors as long as the 2-norm based criterion is used.

This letter proposes an Index based on Irregular Partition of data identifiers (IIP), to enable clients to quickly access multiple data items on a wireless broadcast channel. IIP improves the access time by reducing the index waiting time when clients access multiple data items, through the use of irregular partitioning of the identifier space of data items. Our performance evaluation shows that with respect to access time, the proposed IIP outperforms the existing index schemes supporting multiple data access.

In this letter, a local characteristic image restoration based on convolutional neural network is proposed. In this method, image restoration is considered as a classification problem and images are divided into several sub-blocks. The convolutional neural network is used to extract and classify the local characteristics of image sub-blocks, and the different forms of the regularization constraints are adopted for the different local characteristics. Experiments show that the image restoration results by the regularization method based on local characteristics are superior to those by the traditional regularization methods and this method also has lower computing cost.