OSEK/VDX, a standard for an automobile OS, has been widely adopted by many manufacturers to design and develop a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more complex applications are be developed based on the OSEK/VDX OS. However, how to ensure the reliability of developed applications is becoming a challenge for developers. To ensure the reliability of developed applications, model checking as an exhaustive technique can be applied to discover subtle errors in the development process. Many model checkers have been successfully applied to verify sequential software and general multi-threaded software. However, it is hard to directly use existing model checkers to precisely verify OSEK/VDX applications, since the execution characteristics of OSEK/VDX applications are different from the sequential software and general multi-threaded software. In this paper, we describe and develop an approach to translate OSEK/VDX applications into sequential programs in order to employ existing model checkers to precisely verify OSEK/VDX applications. The value of our approach is that it can be considered as a front-end translator for enabling existing model checkers to verify OSEK/VDX applications.

Heterogeneous networks (HetNet) with different radio access technologies have been deployed to support a range of communication services. To manage these HetNets efficiently, some interworking solutions such as MIH (media independent handover), ANQP (access network query protocol) or ANDSF (access network discovery and selection function) have been studied. Recently, the millimeter-wave (mm-wave) based HetNet has been explored to provide multi-gigabits-per-second data rates over short distances in the 60GHz frequency band for 5G wireless networks. WiGig (Wireless Gigabit Alliance) is one of the available radio access technologies using mm-wave. However, the conventional interworking solutions are not sufficient for the implementation of LTE (Long Term Evolution)/WiGig HetNets. Since the coverage area of WiGig is very small due to the high propagation loss of the mm-wave band signal, it is difficult for UEs to perform cell discovery and handover if using conventional LTE/WLAN (wireless local area networks) interworking solutions, which cannot support specific techniques of WiGig well, such as beamforming and new media access methods. To solve these problems and find solutions for LTE/WiGig interworking, RAN (radio access network)-level tightly coupled interworking architecture will be a promising solution. As a RAN-level tightly coupled interworking solution, this paper proposes to design a LTE/WiGig protocol adaptor above the protocol stacks of WiGig to process and transfer control signaling and user data traffic. The proposed extended control plane can assist UEs to discover and access mm-wave BSs successfully and support LTE macro cells to jointly control the radio resources of both LTE and WiGig, so as to improve spectrum efficiency. The effectiveness of the proposal is evaluated. Simulation results show that LTE/WiGig HetNets with the proposed interworking solution can decrease inter-cell handover and improve user throughput significantly. Moreover, the downlink backhaul throughput and energy efficiency of mm-wave HetNets are evaluated and compared with that of 3.5GHz LTE HetNets. Results indicate that 60GHz mm-wave HetNets have better energy efficiency but with much heavier backhaul overhead.

In social websites, users acquire information from adjacent neighbors as well as distant users by seeking along hyperlinks, and therefore, information diffusions, also seen as processes of “user infection”, show both cascading and jumping routes in social networks. Currently, existing analysis suffers from the difficulty in distinguishing between the impacts of information seeking behaviors, i.e. random walks, and other factors leading to user infections. To this end, we present a mechanism to recognize and measure influences of random walks on information diffusions. Firstly, we propose the concept of information propagation structure (IPS), which is also a directed acyclic graph, to represent frequent information diffusion routes in social networks. In IPS, we represent “jumping routes” as virtual arcs and regard them as the traces of random walks. Secondly, we design a frequent IPS mining algorithm (FIPS). By considering descendant node infections as a consequence of ancestor node infections in IPS, we can use a Bayesian network to model each IPS, and learn parameters based on the records of information diffusions passing through the IPS. Finally, we present a quantitative description method of random walks influence, the method is based on Bayesian probabilistic inferring in IPS, which is used to determine the ancestors, whose infection causes the infection of target users. We also employ betweenness centralities of arcs to evaluate contributions of random walks to certain infections. Experiments are carried out with real datasets and simulations. The results show random walks are influential in early and steady phases of information diffusions. They help diffusions pass through some topology limitations in social networks.

In this letter we develop a software reliability modeling framework by introducing the Burr XII distributions to software fault-detection time. An extension to deal with software metrics data characterizing the product size, program complexity or testing expenditure is also proposed. Finally, we investigate the goodness-of-fit performance and compare our new models with the existing ones through real data analyses.

In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.

The sparse Fourier transform (SFT) seeks to recover k non-negligible Fourier coefficients from a k-sparse signal of length N (k«N). A single frequency signal can be recovered via the Chinese remainder theorem (CRT) with sub-sampled discrete Fourier transforms (DFTs). However, when there are multiple non-negligible coefficients, more of them may collide, and multiple stages of sub-sampled DFTs are needed to deal with such collisions. In this paper, we propose a combinatorial aliasing-based SFT (CASFT) algorithm that is robust to noise and greatly reduces the number of stages by iteratively recovering coefficients. First, CASFT detects collisions and recovers coefficients via the CRT in a single stage. These coefficients are then subtracted from each stage, and the process iterates through the other stages. With a computational complexity of O(klog klog 2N) and sample complexity of O(klog 2N), CASFT is a novel and efficient SFT algorithm.

This paper proposes and analyses an improved direction finding (DF) method that uses a rotating interferometer. The minimum sampling frequency is deduced in order to eliminate the phase ambiguity associated with a long baseline, the influence of phase imbalance of receiver is quantitatively discussed and the Root Mean Square Error (RMSE) of both bearing angle and pitch angle are also demonstrated. The theoretical analysis of the rotating interferometer is verified by simulation results, which show that it achieves better RMSE performance than the conventional method.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

High-availability seamless redundancy (HSR) is a fault-tolerant protocol for Ethernet networks that provides two frame copies for each frame sent. Each copy is forwarded on a separate physical path. HSR is a potential candidate for several fault-tolerant Ethernet applications including smart grid communications. However, one of the drawbacks of the HSR protocol is that it generates and circulates unnecessary frames within connected rings regardless of the presence of a destination node in the ring. This downside will degrade network performance and may deplete network resources. Previously, we proposed a simple but efficient approach to solving the above problem, namely, port locking (PL), which is based on the media access control address. The PL approach enables the network to learn the locations of the source and destination nodes gradually for each connection pair without using network control frames; the PL then prunes all the rings that do not contain the destination node by locking the corresponding ring's entrance ports at its QuadBox node. In this paper, we present an enhanced port-locking (EPL) approach that increases the number of pruned unused HSR rings. The analysis and corresponding simulation results show that the network traffic volume is significantly reduced for a large-sized HSR connected-rings network and consequently, network performance is greatly improved compared to the standard HSR protocol, and even PL.

A prompt rescue is a serious operation when a catastrophic disaster occurs. In an uncommunicable area where the existing communication systems are damaged, it is, however, difficult to collect SOS messages of victims. So far, we have proposed a smartphone application named SOSCast to collect SOS messages via device-to-device transmission in such an area. However, with the limitation of coverage area and battery consumption, it decreases the possibility of finding the victim due to the risk of losing the SOS messages. In this paper, thus, we propose an information-sharing cluster to virtually extend the communication coverage area and to secure the SOS messages by reducing the battery consumption. In the performance evaluation, compared with the original SOSCast, we showed that the proposed method can reduce battery consumption to secure the message through a prototype system and simulation experiments.

In order to tackle the rapidly increasing traffic, the 5th generation (5G) mobile communication system will introduce small cells using higher frequency bands with wider bandwidth to achieve super high bit rate transmission of several tens of Gbps. Massive multiple input multiple output (MIMO) beamforming (BF) is promising as one of the technologies that can compensate for larger path-loss in the higher frequency bands. Joint analog fixed BF and digital precoding have been proposed to reduce the cost of a Massive MIMO transceiver. However, the conventional scheme assumes the transmission of a few streams using well-known codebook-based precoding as the digital precoding, and both a selection method of the fixed BF weights and a digital precoder design, which are suitable for super high bit rate transmission using multiple streams, have not been studied. This paper proposes a joint fixed BF and CSI-based precoding (called FBCP) scheme for the 5G Massive MIMO systems. FBCP first selects the analog fixed BF weights based on a maximum total received power criterion, and then it calculates an eigenmode (EM) precoding matrix by exploiting CSI. This paper targets a 5G system achieving over 20Gbps in the 20GHz band as one example. Throughput performances of the Massive MIMO using the proposed FBCP are evaluated by link level simulations using adaptive modulation and coding and it is shown that the proposed FBCP with the optimum number of selected beams (baseband chains) can use higher level modulation, up to 256QAM, and higher coding rates and achieve throughputs close to 30Gbps while the cost and complexity can be reduced compared with the fully digital Massive MIMO.

We provide an overview of techniques for the photonic generation of arbitrary RF waveforms, particularly those suitable for impulse radio or multi-band ultrawideband (UWB)-over-fiber transmission, and chirped microwave waveforms, with an emphasis on microwave photonic filtering and optical spectral shaping followed by wavelength-to-time mapping. We discuss possibilities for integrating the various device and component technologies with silicon photonics.

Due to the fact that natural images are approximately sparse in Discrete Cosine Transform (DCT) or wavelet basis, the Compressive Sensing (CS) can be employed to decode both the host image and watermark with zero error, despite not knowing the host image. In this paper, Limited-Random Sequence (LRS) matrix is utilized to implement the blind CS detection, which benefits from zero error and lower complexity. The performance in Bit Error Rate (BER) and error-free detection probability confirms the validity and efficiency of the proposed scheme.

The fifth generation (5G) mobile communication technologies are attracting a lot of attention in terms of accommodating the huge traffic expected in the future. Millimeter wave communications, which utilize wide frequency bands, are attracting attention for the realization of the high capacity required in the 5G era. In millimeter wave communications, beamforming with massive antennas is expected to play a very important role in compensating the large propagation loss of millimeter waves. Because massive beamforming yields narrow beams, the search for the optimal beam could have considerable impact on the system. In this paper, we propose a new beam search method that can reduce the load of beam search significantly while keeping beamforming gain almost the same as that of the conventional method. The proposed method consists of three stages with the creation of a set of candidate beams in the first stage, selection of an initial beam in the second stage, and refinement of the selected beam in the third stage. In the first stage, the created set of candidate beams contains beams of various widths instead of beams of a uniform width to reduce the number of candidate beams in the set. Here, we leverage the property of millimeter waves according to which the fluctuation of millimeter wave propagation loss is spatially and temporally small because of the fewer multipaths, and therefore, the propagation loss has strong correlation with the user location. By using the decreased set of candidate beams, the beam search time can be reduced in the second stage. Then the beam refinement can increase the beamforming gain to increase user throughput in the third stage. To confirm the effects of the proposed beam search method, we conduct system level simulations by using a propagation model for millimeter wave communications proposed by MiWEBA, which is an international project between Europe and Japan. The results show that the proposed beam search method can reduce the number of candidate beams, and can therefore shorten the beam search time by about 39% without any degradation in outage probability compared with a conventional method.

Non-verbal communication incorporating visual, audio, and contextual information is important to make sense of and navigate the social world. Individuals who have trouble with social situations often have difficulty recognizing these sorts of non-verbal social signals. In this article, we propose a training tool NOCOA+ (Non-verbal COmmuniation for Autism plus) that uses utterances in visual and audio modalities in non-verbal communication training. We describe the design of NOCOA+, and further perform an experimental evaluation in which we examine its potential as a tool for computer-based training of non-verbal communication skills for people with social and communication difficulties. In a series of four experiments, we investigated 1) the effect of temporal context on the ability to recognize social signals in testing context, 2) the effect of modality of presentation of social stimulus on ability to recognize non-verbal information, 3) the correlation between autistic traits as measured by the autism spectrum quotient (AQ) and non-verbal behavior recognition skills measured by NOCOA+, 4) the effectiveness of computer-based training in improving social skills. We found that context information was helpful for recognizing non-verbal behaviors, and the effect of modality was different. The results also showed a significant relationship between the AQ communication and socialization scores and non-verbal communication skills, and that social skills were significantly improved through computer-based training.

Information Centric Networking (ICN) had merits in terms of mobility, security, power consumption and network traffic. When a large-scale disaster occurred, the current communication system might be fragile and the server based network service might be unavailable due to the damages, network congestions, and power failure, etc. In this paper, we proposed an ICN based Disaster Information Sharing Service (DISS) [1], [2] system. DISS could provide robust information sharing service. Users could publish disaster information as a content message with the help of our DISS. In addition, by utilizing DISS's message naming strategy, users could retrieve disaster information even without a server connection. The ICN based DISS could reduce the probability of network congestion when a large number of simultaneous connections occurring. It could provide server-less service in poor network condition. DISS allows users retrieve disaster information from terminals or ICN nodes. During disasters, sharing information timely and effective could protect people from disaster, ensure people's safety.

In this paper, a threshold-based I-Q diversity combining scheme for ultra-high frequency (UHF) radio frequency identification (RFID) readers with a quadrature receiver is proposed in the aspect of improving the tag detection performance. In addition, the performance of the proposed scheme is evaluated as the closed-form expressions. In particular, its statistical characteristics are detailed and its performance is compared to that of conventional schemes over independent and identically distributed Rician fading conditions in terms of average signal-to-noise ratio (SNR), bit error rate (BER), and the average number of required combining process. Numerical results indicate that the proposed scheme enables processing power control through threshold control while meeting the required quality of service compared to conventional schemes.

Object extraction and tracking in a video image is basic technology for many applications, such as video surveillance and robot vision. Many moving object extraction and tracking methods have been proposed. However, they fail when the scenes include illumination change or light reflection. For tracking the moving object robustly, we should consider not only the RGB values of input images but also the shape information of the objects. If the objects' shapes do not change suddenly, matching positions on the cost matrix of exclusive block matching are located nearly on a line. We propose a method for obtaining the correspondence of feature points by imposing a matching position constraint induced by the shape constancy. We demonstrate experimentally that the proposed method achieves robust tracking in various environments.

This letter presents performance analysis in the high signal-to-noise ratio (SNR) region for matched filter (MF) precoding in single cell Massive MIMO systems. The outage probability function is derived in closed form, and the data rate of each user is also given. We have also presented asymptotic analysis in terms of data rate for MF when the number of users and the number of antennas grow without bounds. The expressions of these analytical results are rather simple and are thus convenient for overall performance evaluation. The simulation results show that the analysis are very accurate.

This paper proposes a Poisson denoising method with a union of directional lapped orthogonal transforms (DirLOTs). DirLOTs are 2-D non-separable lapped orthogonal transforms with directional characteristics under the fixed-critically-subsampling, overlapping, orthonormal, symmetric, real-valued and compact-support property. In this work, DirLOTs are used to generate symmetric orthogonal discrete wavelet transforms and then a redundant dictionary as a union of unitary transforms. The multiple directional property is suitable for representing natural images which contain diagonal textures and edges. Multiple DirLOTs can overcome a disadvantage of separable wavelets in representing diagonal components. In addition to this feature, multiple DirLOTs make transform-based denoising performance better through the redundant representation. Experimental results show that the combination of the variance stabilizing transformation (VST), Stein's unbiased risk estimator-linear expansion of threshold (SURE-LET) approach and multiple DirLOTs is able to significantly improve the denoising performance.