The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.

The issues of comparing the similarity or dissimilarity (distance) between structures have been studied. Especially, several distances between trees and their efficient algorithms have been proposed. However, all of the tree distances are defined based on mapping between vertices only, and they are helpless to compare the tree structures whose vertices and edges hold information. In this paper, we will propose a mapping between rooted and unordered trees based on vertex translation and edge translation, and then define a distance based on proposed mapping, and develop an efficient algorithm for computing proposed distance. Proposed distance can be used to compare the similarity or distance between two natural language sentences.

In this letter, we present an automatic approach of objective singing performance evaluation for untrained singers by relating acoustic measurements to perceptual ratings of singing voice quality. Several acoustic parameters and their combination features are investigated to find objective correspondences of the perceptual evaluation criteria. Experimental results show relative strong correlation between perceptual ratings and the combined features and the reliability of the proposed evaluation system is tested to be comparable to human judges.

The large computational complexity makes stereo matching a big challenge in real-time application scenario. The problem of stereo matching in a video sequence is slightly different with that in a still image because there exists temporal correlation among video frames. However, no existing method considered temporal consistency of disparity for algorithm acceleration. In this work, we proposed a scheme called the dynamic disparity range (DDR) to optimize matching cost calculation and cost aggregation steps by narrowing disparity searching range, and a scheme called temporal cost aggregation path to optimize the cost aggregation step. Based on the schemes, we proposed the DDR-SGM and the DDR-MCCNN algorithms for the stereo matching in video sequences. Evaluation results showed that the proposed algorithms significantly reduced the computational complexity with only very slight loss of accuracy. We proved that the proposed optimizations for the stereo matching are effective and the temporal consistency in stereo video is highly useful for either improving accuracy or reducing computational complexity.

Context-aware applications are a key aspect of pervasive computing. The core issue of context-aware application development is how to make the application behave suitably according to the changing context without coupling such context dependencies in the program. Several programming paradigms and languages have been proposed to facilitate the development, but they are either lack of sufficient flexibility or somewhat complex for programming and deploying. A reference programming model is proposed in this paper to make up inadequacy of those approaches. In the model, virtual tables constructed by system and maintained by space manager connect knowledge of both developer and space manager while separating dependency between context and application logic from base program. Hierarchy and architecture of the model are presented, and implementation suggestions are also discussed. Validation and evaluation show that the programming model is lightweight and easy to be implemented and deployed. Moreover, the model brings better flexibility for developing context-aware applications.

Spectral-amplitude coding (SAC) techniques in fiber-Bragg-grating (FBG)-based optical code-division multiple-access (OCDMA) systems were investigated in our previous work. This paper adopts the same network architecture to investigate the simultaneous reductions of multiple-access interference (MAI) and optical beat interference (OBI). The MAI is caused by overlapping wavelengths from undesired network coder/decoders (codecs) while the OBI is induced by interaction of simultaneous chips at adjacent gratings. It is proposed that MAI and OBI reductions may be obtained by use of: 1) a source spectrum that is divided into equal chip spacing; 2) coded FBGs characterized by approximately the same number of "0" and "1" code elements; and 3) spectrally balanced photo-detectors. With quasi-orthogonal Walsh-Hadamard coded FBGs, complementary spectral chips is employed as signal pairs to be recombined and detected in balanced photo-detectors, thus achieving simultaneous suppression of both MAIs and OBIs. Simulation results showed that the proposed OCDMA spectral-amplitude coding scheme achieves significant MAI and OBI reductions.

In this paper, we propose an on-line e-coin system with four parties: Consumer, Merchant, Bank and Issuer. The proposed system not only circulates anonymous e-coins but also protects the profits of the Merchant and the Consumer during a transaction. An e-coin, consisting of a secret value c and a public value c'=h(c) where h() is a secure one-way hash function with collision resistant property, is generated by its owner. The public value of a legal e-coin is published on the bulletin board of Issuer. Only the owner who releases the secret values of the published e-coins can spend money. Instead of Bank, Issuer has to be on-line to verify and replace the public values of the Consumer's e-coins with the Merchant's while the Consumer pays money to the Merchant in a transaction. Such a replacement represents that the coins are passed from one person to another.

In this paper, we investigate the electron-hole energy states and energy gap in three-dimensional (3D) InAs/GaAs quantum rings and dots with different shapes under external magnetic fields. Our realistic model formulation includes: (i) the effective mass Hamiltonian in non-parabolic approximation for electrons, (ii) the effective mass Hamiltonian in parabolic approximation for holes, (iii) the position- and energy-dependent quasi-particle effective mass approximation for electrons, (iv) the finite hard wall confinement potential, and (v) the Ben Daniel-Duke boundary conditions. To solve the 3D nonlinear problem without any fitting parameters, we have applied the nonlinear iterative method to obtain self-consistent solutions. Due to the penetration of applied magnetic fields into torus ring region, for ellipsoidal- and rectangular-shaped quantum rings we find nonperiodical oscillations of the energy gap between the lowest electron and hole states as a function of external magnetic fields. The nonperiodical oscillation is different from 1D periodical argument and strongly dependent on structure shape and size. The result is useful to study magneto-optical properties of the nanoscale quantum rings and dots.

This paper discusses the largest common similar substructure (in short, LCSS) problem for trees. The problem is, for all pairs of "substructure of A and that of B," to find one of them, denoted by A and B', such that A is most similar to B' and the sum of the number of vertices of A and that of B' is largest. An algorithm for the LCSS problem for unrooted and unordered trees (in short, trees) and that for trees embedded in a plane (in short, Co-trees) are proposed. The time complexity of the algorithm for trees is O (max (ma, mb)2 NaNb) and that for CO-trees is O (mambNaNb), where, ma (mb) and Na (Nb) are the largest degree of a vertex of tree Ta (Tb) and the number of vertices of Ta (Tb), respectively. It is easy to modify the algorithms for enumerating all of the LCSSs for trees and CO-trees. The algorithms can be applied to structure-activity studies in chemistry and various structure comparison problems.

Several distances between trees have been proposed. However, most of the reports on distances have dealt with rooted and ordered trees. This paper proposes two distances between unrooted and cyclically ordered trees (CO-trees) and their computing methods. A CO-tree is a tree embedded in a plane. These distances are defined based on Tai's mapping (TM) and a strongly structure preserving mapping (SSPM) between CO-trees. The time complexities to compute the distances between two CO-trees Ta and Tb are OT (N 2aN 2b) for the distance based on a TM and OT(mambNaNb) for that on an SSPM, respectively, where ma(mb) and Na(Nb) are the largest degree of a vertex and the number of vertices of Ta(Tb), respectively. The space complexities of both methods are Os(NaNb). Those distances can be applied to the clustering of CO-trees.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several simpler methods that utilized time of arrival (TOA) at three base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. From the viewpoint of geometric approach, for each a TOA value measured at any BS, one can generate a circle. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are much easier by using linear LOP to determine the MS. Numerical results demonstrate that the calculation time of using linear LOP is much less than employing circular LOP. Although the location precision of using linear LOP is only reduced slightly. However, the proposed efficient methods by using linear LOP can still provide precise solution of MS location and reduce the computational effort greatly. In addition, the proposed methods with less effort can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.

Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

Erasure codes have been considered as one of the most promising techniques for data reliability enhancement and storage efficiency in modern distributed storage systems. However, erasure codes often suffer from a time-consuming coding process which makes them nearly impractical. The opportunity to solve this problem probably rely on the parallelization of erasure-code-based application on the modern multi-/many-core processors to fully take advantage of the adequate hardware resources on those platforms. However, the complicated data allocation and limited I/O throughput pose a great challenge on the parallelization. To address this challenge, we propose a general multi-threaded parallel coding approach in this work. The approach consists of a general multi-threaded parallel coding model named as MTPerasure, and two detailed parallel coding algorithms, named as sdaParallel and ddaParallel, respectively, adapting to different I/O circumstances. MTPerasure is a general parallel coding model focusing on the high level data allocation, and it is applicable for all erasure codes and can be implemented without any modifications of the low level coding algorithms. The sdaParallel divides the data into several parts and the data parts are allocated to different threads statically in order to eliminate synchronization latency among multiple threads, which improves the parallel coding performance under the dummy I/O mode. The ddaParallel employs two threads to execute the I/O reading and writing on the basis of small pieces independently, which increases the I/O throughput. Furthermore, the data pieces are assigned to the coding thread dynamically. A special thread scheduling algorithm is also proposed to reduce thread migration latency. To evaluate our proposal, we parallelize the popular open source library jerasure based on our approach. And a detailed performance comparison with the original sequential coding program indicates that the proposed parallel approach outperforms the original sequential program by an extraordinary speedups from 1.4x up to 7x, and achieves better utilization of the computation and I/O resources.

In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in a single frequency network (SFN) system and investigates the frequency resource allocation problem. Firstly, we propose that D2D user equipments (DUEs) do not share frequency with cellular user equipments (CUEs) in the same SFN, but reuse frequency with CUEs in other SFNs, by which the interference between D2D and cellular communications can be avoided. Then, under the principle that two nearest D2D multicast groups cannot reuse the same frequency, the study develops a distance-based fair frequency resource allocation (DFRA) algorithm. The DFRA algorithm ensures control of the interference within a reasonable range and fairly allocate the available frequency resources to the D2D multicast groups. Numerical simulation results show that the proposed resource allocation algorithm is effective in improving the data rate and reducing the outage probability for D2D communications.

During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a well-known open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented.

A new hybrid brain-computer interface (BCI), which is based on sequential controls by eye tracking and steady-state visual evoked potentials (SSVEPs), has been proposed for high-speed spelling in virtual reality (VR) with a 40-target virtual keyboard. During target selection, gaze point was first detected by an eye-tracking accessory. A 4-target block was then selected for further target selection by a 4-class SSVEP BCI. The system can type at a speed of 1.25 character/sec in a cue-guided target selection task. Online experiments on three subjects achieved an averaged information transfer rate (ITR) of 360.7 bits/min.

Huynen has already provided a method to decompose a Mueller matrix in order to retrieve detailed target information in a polarimetric radar system. However, this decomposition sometimes fails in the presence of small error or noise in the elements of a Mueller matrix. This paper attempts to improve Huynen's decomposition method. First, we give the definition of stable decomposition and present an example, showing a problem of Huynen's approach. Then two methods are proposed to carry out stable decompositions, based on the nonlinear least square method and the Newton's method. Stability means the decomposition is not sensitive to noise. The proposed methods overcomes the problems on the unstable decomposition of Mueller matrix, and provides correct information of a target.