A concurrent system consists of multiple processes that are run simultaneously. The execution orders of these processes are defined by a scheduler. In model checking techniques, the scheduling policy is closely related to the search algorithm that explores all of the system states. To ensure the correctness of the system, the scheduling policy needs to be taken into account during the verification. Current approaches, which use fixed strategies, are only capable of limited kinds of policies and are difficult to extend to handle the variations of the schedulers. To address these problems, we propose a method using a domain-specific language (DSL) for the succinct specification of different scheduling policies. Necessary artifacts are automatically generated from the specification to analyze the behaviors of the system. We also propose a search algorithm for exploring the state space. Based on this method, we develop a tool to verify the system with the scheduler. Our experiments show that we could serve the variations of the schedulers easily and verify the systems accurately.

Despite the widespread use of deep learning for speech emotion recognition, they are severely restricted due to the information loss in the high layer of deep neural networks, as well as the degradation problem. In order to efficiently utilize information and solve degradation, attention-based dense long short-term memory (LSTM) is proposed for speech emotion recognition. LSTM networks with the ability to process time series such as speech are constructed into which attention-based dense connections are introduced. That means the weight coefficients are added to skip-connections of each layer to distinguish the difference of the emotional information between layers and avoid the interference of redundant information from the bottom layer to the effective information from the top layer. The experiments demonstrate that proposed method improves the recognition performance by 12% and 7% on eNTERFACE and IEMOCAP corpus respectively.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

Wireless communication security has been increasingly important nowadays. Directional modulation (DM) is seen as a promising wireless physical layer security technology. Traditional DM is a transmit-side technology that projects digitally modulated information signals in the desired directions (or at the desired locations) while simultaneously distorting the constellation formats of the same signals in other directions (or at all other locations). However, these directly exposed digitally modulated information signals are easily intercepted by eavesdroppers along the desired directions (or around the desired locations). A new DM scheme for secure point-to-multipoint communication based on the spread spectrum assisted orthogonal frequency diverse array (short for SS-OFDA-M-DM) is proposed in this paper. It can achieve point-to-multipoint secure communication for multiple cooperative receivers at different locations. In the proposed SS-OFDA-M-DM scheme, only cooperative users that use specific DM receivers with right spread spectrum parameters can retrieve right symbols. Eavesdroppers without knowledge of spread spectrum parameters cannot intercept useful signals directly at the desired locations. Moreover, they cannot receive normal symbols at other locations either even if the right spread spectrum parameters are known. Numerical simulation results verify the validity of our proposed scheme.

In this paper, we propose a new technique for the transient scattering problem of periodically arrayed dispersion media for the TE case by using a combination of the Fourier series expansion method (FSEM) and the fast inversion Laplace transform (FILT) method, and analyze the pulse response for various widths of the dispersion media. As a result, we clarified the influence of the dispersion media with an air region on the resulting waveform.

Hydrophobic DNA (H-DNA) nano-film was formed as the surface modifier on a thin glass plate working as a slab optical waveguide (SOWF). Cytochrom c (cytc) molecules were immobilized from aqueous solution with direct contacting to the H-DNA nano-film for 30 minutes. From SOWG absorption spectral changes during automated solution exchange (SE) processes, it was found that about 28.1% of cytc molecules was immobilized in the H-DNA nano-film with keeping their reduction functionality by reducing reagent.

This article proposes a prosody correction method based on partial model adaptation for Chinese-accented Japanese hidden Markov model (HMM)-based text-to-speech synthesis. Although text-to-speech synthesis built from non-native speech accurately reproduces the speaker's individuality in synthetic speech, the naturalness of the synthetic speech is strongly degraded. In the proposed model, to improve the naturalness while preserving the speaker individuality of Chinese-accented Japanese text-to-speech synthesis, we partially utilize HMM parameters of native Japanese speech to synthesize prosody-corrected synthetic speech. Results of an experimental evaluation demonstrate that duration and F0 correction are significantly effective for improving naturalness.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

Break arcs are generated in a 48VDC resistive circuit. Circuit current I0 when electrical contacts are closed is changed from 50A to 300A. The break arcs are observed by a high-speed camera with appropriate settings of exposure from horizontal direction. Length of the break arcs L is measured from images of the break arcs. Time evolutions of the length L and gap voltage Vg are investigated. The following results are obtained. By appropriate settings of the high-speed camera, the time evolution of the length L is obtained from just after ignition to before arc extinction. Tendency of increase of the length L is similar to that of increase of the voltage Vg for each current I0.

Data rate requirements, from consumer application to automotive and aerospace grew rapidly in the last years. This led to the development of a series of communication protocols (i.e. Ethernet, PCI-Express, RapidIO and SpaceFibre), which use more than one communication lane, both to speed up data rate and to increase link reliability. Some of these protocols, such as SpaceFibre, are able to detect real-time changes in the number of active lanes and to adapt the data flow appropriately, providing a flexible solution, robust to lane failures. This results in a real time varying data path in the lower layers of the data handling system. The aim of this paper is to propose the architecture of a hardware block capable of reading a fixed number of words from a host FIFO and shaping them on a real time variable number of words equal to the number of active lanes.

In this paper, we investigate different power allocation optimization problems with interferences for distributed antenna systems (DAS) with and without D2D communication, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with D2D communication under the constraints of the minimum SE requirements of user equipment (UE) and D2D pair, maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. We transform this non-convex objective function into a difference of convex functions (D.C.) then using the concave-convex procedure (CCCP) algorithm to solve the optimization problem. The second objective is maximizing energy efficiency (EE) of the DAS with D2D communication under the same constraints. We first exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we summarize the corresponding optimal power allocation algorithms and also use similar method to obtain optimal solutions of the same optimization problems in DAS. Simulation results are provided to demonstrate the effectiveness of the designed power allocation algorithms and illustrate the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication.

A novel image enhancement method for vein recognition is introduced. Inspired by observation that the intensity of the vein vessel changes rapidly during the smoothing process compared to that of background (i.e., skin tissue) due to its thin and long shape, we propose to exploit the smoothing speed as a restoration weight for the vein image enhancement. Experimental results based on the CASIA multispectral palm vein database demonstrate that the proposed method is effective to improve the performance of vein recognition.

The aim of this research is to support real-time drawingin talking by using multimodal user interface technologies. In this situation, if talking and drawing are considered as commands by mistake during presentation, it will disturb users' natural talking and drawing. To prevent this problem, we introduce two modes of a command mode and a free mode, and explore smooth mode switching techniques that does not interfere with users' natural talking and drawing. We evaluate four techniques. Among them, a technique that specifies the command mode after actions using a pen gesture was the most effective. In this technique, users could quickly draw diagrams, and specifying mode switching didn't interfere with users' natural talk.

A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.

To improve the efficiency of spectrum utilization, cognitive radio systems attempt to use temporarily unoccupied spectrum which is referred to as a spectrum hole. To this end, QoS (Quality of Service) is one of the most important issues in practical cognitive radio systems. In this article, an efficient spectrum management scheme using self-reserving spectrum is proposed to support QoS for cognitive radio users. The self-reservation of a spectrum hole can minimize service dropping probability by using the statistical characteristics of spectrum bands while using optimum amount of resources. In addition, it realizes seamless service for users by eliminating spectrum entry procedure that includes spectrum sensing, spectrum request, and spectrum grant. Performance analysis and intensive system level simulations confirm the efficiency of the proposed algorithms.

Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.

This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

This paper introduces a new noise generation algorithm for vocoder-based speech waveform generation. White noise is generally used for generating an aperiodic component. Since short-term white noise includes a zero-frequency component (ZFC) and inaudible components below 20 Hz, they are reduced in advance when synthesizing. We propose a new noise generation algorithm based on that for velvet noise to overcome the problem. The objective evaluation demonstrated that the proposed algorithm can reduce the unwanted components.

Blind nonlinear compensation for RF receivers is an important research topic in 5G mobile communication, in which higher level modulation schemes are employed more often to achieve high capacity and ultra-broadband services. Since nonlinear compensation circuits must handle intermodulation bandwidths that are more than three times the signal bandwidth, reducing the sampling frequency is essential for saving power consumption. This paper proposes a novel blind nonlinear compensation technique that employs sub-Nyquist sampling analog-to-digital conversion. Although outband distortion spectrum is folded in the proposed sub-Nyquist sampling technique, determination of compensator coefficients is still possible by using the distortion power. Proposed technique achieves almost same compensation performance in EVM as the conventional compensation scheme, while reducing sampling speed of analog to digital convertor (ADC) to less than half the normal sampling frequency. The proposed technique can be applied in concurrent dual-band communication systems and adapt to flat Rayleigh fading environments.