Our research is focused on examining a stereoscopic quality assessment model for stereoscopic images with disparate quality in left and right images for glasses-free stereo vision. In this paper, we examine the objective assessment model of 3-D images, considering the difference in image quality between each view-point generated by the disparity-compensated coding. A overall stereoscopic image quality can be estimated by using only predicted values of left and right 2-D image qualities based on the MPEG-7 descriptor information without using any disparity information. As a result, the stereoscopic still image quality is assessed with high prediction accuracy with correlation coefficient=0.98 and average error=0.17.

Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.

In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.

In this paper, we present a supervised learning method to seek out answers to the most frequently asked descriptive questions: reason, method, and definition questions. Most of the previous systems for question answering focus on factoids, lists or definitional questions. However, descriptive questions such as reason questions and method questions are also frequently asked by users. We propose a system for these types of questions. The system conducts an answer search as follows. First, we analyze the user's question and extract search keywords and the expected answer type. Second, information retrieval results are obtained from an existing search engine such as Yahoo or Google . Finally, we rank the results to find snippets containing answers to the questions based on a ranking SVM algorithm. We also propose features to identify snippets containing answers for descriptive questions. The features are adaptable and thus are not dependent on answer type. Experimental results show that the proposed method and features are clearly effective for the task.

Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

As the Internet represents a key social infrastructure, its reliability is essential if we are to survive failures. Physical connectivity of networks is also essential as it characterizes reliability. There are collaboration structures, which are topological structures where two or more nodes are connected to a node, and collaboration structures are observed in transcriptional regulatory networks and the router-level topologies of ISPs. These collaboration structures are related to the reliability of networks. The main objective of this research is to find whether an increase in collaboration structures would improve reliability or not. We first categorize the topology into a three-level hierarchy for this purpose, i.e., top-level, middle-level, and bottom-level layers. We then calculate the reliability of networks. The results indicate that the reliability of most transcriptional regulatory networks is higher than that of one of router-level topologies. We then investigate the number of collaboration structures. It is apparent that there are much fewer collaboration structures between top-level nodes and middle-level nodes in router-level topologies. Finally, we confirm that the reliability of router-level topologies can be improved by rewiring to increase the collaboration structures between top-level and middle-level nodes.

This paper describes a technique for overcoming the model shrinkage problem in automatic speech recognition (ASR), which allows application developers and users to control the model size with less degradation of accuracy. Recently, models for ASR systems tend to be large and this can constitute a bottleneck for developers and users without special knowledge of ASR with respect to introducing the ASR function. Specifically, discriminative language models (DLMs) are usually designed in a high-dimensional parameter space, although DLMs have gained increasing attention as an approach for improving recognition accuracy. Our proposed method can be applied to linear models including DLMs, in which the score of an input sample is given by the inner product of its features and the model parameters, but our proposed method can shrink models in an easy computation by obtaining simple statistics, which are square sums of feature values appearing in a data set. Our experimental results show that our proposed method can shrink a DLM with little degradation in accuracy and perform properly whether or not the data for obtaining the statistics are the same as the data for training the model.

This paper presents a color-based method for medicine package recognition, called a linear manifold color descriptor (LMCD). It describes a color distribution (a set of color pixels) of a color package image as a linear manifold (an affine subspace) in the color space, and recognizes an anonymous package by linear manifold matching. Mainly due to low dimensionality of color spaces, LMCD can provide more compact description and faster computation than description styles based on histogram and dominant-color. This paper also proposes distance-based dissimilarities for linear manifold matching. Specially designed for color distribution matching, the proposed dissimilarities are theoretically appropriate more than J-divergence and canonical angles. Experiments on medicine package recognition validates that LMCD outperforms competitors including MPEG-7 color descriptors in terms of description size, computational cost and recognition rate.

A Drude-critical points (D-CP) model for considering metal dispersion is newly incorporated into the frequency-dependent FDTD method using the simple trapezoidal recursive convolution (TRC) technique. Numerical accuracy is investigated through the analysis of pulse propagation in a metal (aluminum) cladding waveguide. The TRC technique with a single convolution integral is found to provide higher accuracy, when compared with the recursive convolution counterpart. The methodology is also extended to the unconditionally stable FDTD based on the locally one-dimensional scheme for efficient frequency-dependent calculations.

We propose a Junction-Based Traffic Aware Routing (JTAR) protocol for Vehicular Ad Hoc Networks (VANETs) in sparse urban environments. A traffic aware optimum junction selection solution is adopted in packet-forwarding, and a metric named critical-segment is defined in recovery strategy. Simulation results show that JTAR can efficiently increase the packet delivery ratio and reduce the delivery delay.

We present an analytical nonlinear adiabatic theory of the microwave electron device that we call the Autophase Microwave Tube (AMT). In contrast to the well-known Traveling Wave Tube (TWT), the AMT exploits a highly efficient non-synchronous beam-wave interaction for the amplification (or generation) of the HF electromagnetic waves, and, differently from klystron and such hybrid devices as twystron, it employs a continuous beam-wave interaction. Because of these distinctive features, the AMT presents a special class of microwave electron devices, which feature very high electronic efficiency (which tends to 100%) and large bandwidth. Here, we develop the theory that allows one to find the profiles of static longitudinal electric or magnetic field (or both) over the device length, which yield negligible de-bunching together with highly efficient amplification (generation) of the HF electromagnetic wave. The analysis of electron motion in the bunch is performed by means of Lyapunov stability theory. The numerical example illustrates the possibility of achieving the electronic efficiency of AMT as high as 92%. We compare different autophase regimes in the AMT and show that the profiling of the longitudinal static magnetic focusing field in the helix AMT with the non-azimuthally symmetric wave has many advantages with respect to other regimes.

To develop the advanced and rich life, and the also economy and social activity continuously, various types of energy are necessary. At the same time, to protect the global environment and to prevent the depletion of natural resources, the effective and moreover efficient use of energy is becoming important. Electric power is one of the most important forms of energy for our life and society. This paper describes topics and survey results of technical trends regarding the electric power supply systems which are playing a core role as the important infrastructure to support the emergence of information-oriented society. Specifically, the power supply systems that enhance high power quality and reliability (PQR) are important for the steady growth of information and communication services. The direct current (DC) power, which has been used for telecommunications power systems and information and communications technologies (ICT), enables existing utilities' grid and distributed energy resources to keep a balance between supply and demand of small-scaled power systems or microgirds. These techniques are expected to be part of smartgrid technologies and facilitate the installation of distributed generators in mission critical facilities.

In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

In the scattering problem of periodic gratings, at a low grazing limit of incidence, the incident plane wave is completely cancelled by the reflected wave, and the total wave field vanishes and physically becomes a dark shadow. This problem has received much interest recently. Nakayama et al. have proposed “the shadow theory”. The theory was first applied to the diffraction by perfectly conductive gratings as an example, where a new description and a physical mean at a low grazing limit of incidence for the gratings have been discussed. In this paper, the shadow theory is applied to the analyses of multilayered dielectric periodic gratings, and is shown to be valid on the basis of the behavior of electromagnetic waves through the matrix eigenvalue problem. Then, the representation of field distributions is demonstrated for the cases that the eigenvalues degenerate in the middle regions of multilayered gratings in addition to at a low grazing limit of incidence and some numerical examples are given.

A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.

Current trends in modern out-of-order processors involve implementing deeper pipelines and a large instruction window to achieve high performance, which lead to the penalty of the branch misprediction recovery being a critical factor in overall processor performance. Multi path execution is proposed to reduce this penalty by executing both paths following a branch, simultaneously. However, there are some drawbacks in this mechanism, such as design complexity caused by processing both paths after a branch and performance degradation due to hardware resource competition between two paths. In this paper, we propose a new recovery mechanism, called Recovery Critical Misprediction (RCM), to reduce the penalty of branch misprediction recovery. The mechanism uses a small trace cache to save the decoded instructions from the alternative path following a branch. Then, during the subsequent predictions, the trace cache is accessed. If there is a hit, the processor forks the second path of this branch at the renamed stage so that the design complexity in the fetch stage and decode stage is alleviated. The most contribution of this paper is that our proposed mechanism employs critical path prediction to identify the branches that will be most harmful if mispredicted. Only the critical branch can save its alternative path into the trace cache, which not only increases the usefulness of a limited size of trace cache but also avoids the performance degradation caused by the forked non-critical branch. Experimental results employing SPECint 2000 benchmark show that a processor with our proposed RCM improves IPC value by 10.05% compared with a conventional processor.

Modern web applications incorporate many programmatic frameworks and APIs that are often pushed to the client-side with most of the application logic while contents are the result of mashing up several resources from different origins. Such applications are threatened by attackers that often attempts to inject directly, or by leveraging a stepstone website, script codes that perform malicious operations. Web scripting based malware proliferation is being more and more industrialized with the drawbacks and advantages that characterize such approach: on one hand, we are witnessing a lot of samples that exhibit the same characteristics which make these easy to detect, while on the other hand, professional developers are continuously developing new attack techniques. While obfuscation is still a debated issue within the community, it becomes clear that, with new schemes being designed, this issue cannot be ignored anymore. Because many proposed countermeasures confess that they perform better on unobfuscated contents, we propose a 2-stage technique that first relieve the burden of obfuscation by emulating the deobfuscation stage before performing a static abstraction of the analyzed sample's functionalities in order to reveal its intent. We support our proposal with evidence from applying our technique to real-life examples and provide discussion on performance in terms of time, as well as possible other applications of proposed techniques in the areas of web crawling and script classification. Additionally, we claim that such approach can be generalized to other scripting languages similar to JavaScript.

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

In this paper, we are concerned about content-based networking (CBN) at extreme scales, characterized by a large number of widely spread consumers, heterogeneous consumer requirements, huge volume of publications, and the scarcity of end-to-end bandwidth. We extend CBN with a generic service model that allows consumers to express their interests in future publications including cached content, but also to quantify the maximum amount of information they are willing to consume. We take advantage of this knowledge to pace the dissemination process and therefore, enhance the service efficiency. Early evaluation results show gains of up to 80% compared to a baseline CBN model.

Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. The curvature-based feature varies in position when multi-scale analysis is performed. Therefore, it is important to recognize the scale in order to detect the feature point. Numerous shape descriptors based on contour shapes have been developed in the field of pattern recognition and computer vision. A curvature scale-space (CSS) representation cannot be applied to a contour fragment and requires the tracking of feature points. In a gradient-based curvature computation, although the gradient computation considers the scale, the curvature is normalized with respect to not the scale but the contour length. The scale-invariant feature transform algorithm that detects feature points from an image solves similar problems by using the difference of Gaussian (DoG). It is difficult to apply the SIFT algorithm to a planar curve for feature extraction. In this paper, an automatic scale detection method for a contour fragment is proposed. The proposed method detects the appropriate scales and their positions on the basis of the difference of curvature (DoC) without the tracking of feature points. To calculate the differences, scale-normalized curvature is introduced. An advantage of the DoC algorithm is that the appropriate scale can be obtained from a contour fragment as a local feature. It then extends the application area. The validity of the proposed method is confirmed by experiments. The proposed method provides the most stable and robust scales of feature points among conventional methods such as curvature scale-space and gradient-based curvature.