Labeling a salient region accurately in video with cluttered background and complex motion condition is still a challenging work. Most existing video salient region detection models mainly extract the stimulus-driven saliency features to detect the salient region in video. They are easily influenced by the cluttered background and complex motion conditions. It may lead to incomplete or wrong detection results. In this paper, we propose a video salient region detection framework by fusing the stimulus-driven saliency features and spatiotemporal consistency cue to improve the performance of detection under these complex conditions. On one hand, stimulus-driven spatial saliency features and temporal saliency features are extracted effectively to derive the initial spatial and temporal salient region map. On the other hand, in order to make use of the spatiotemporal consistency cue, an effective spatiotemporal consistency optimization model is presented. We use this model optimize the initial spatial and temporal salient region map. Then the superpixel-level spatiotemporal salient region map is derived by optimizing the initial spatiotemporal salient region map. Finally, the pixel-level spatiotemporal salient region map is derived by solving a self-defined energy model. Experimental results on the challenging video datasets demonstrate that the proposed video salient region detection framework outperforms state-of-the-art methods.

In this paper, we study the degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) multiway relay channel (mRC) with two relays, two clusters and K (K≥3) users per cluster. We consider a clustered full data exchange model, i.e., each user in a cluster sends a multicast (common) message to all other users in the same cluster and desires to acquire all messages from them. The DoF results of the mRC with the single relay have been reported. However, the DoF achievability of the mRC with multiple relays is still an open problem. Furthermore, we consider a more practical scenario where no channel state information at the transmitter (CSIT) is available to each user. We first give a DoF cut-set upper bound of the considered mRC. Then, we propose a distributed interference neutralization and retransmission scheme (DINR) to approach the DoF cut-set upper bound. In the absence of user cooperation, this method focuses on the beamforming matrix design at each relay. By investigating channel state information (CSI) acquisition, we show that the DINR scheme can be performed by distributed processing. Theoretical analyses and numerical simulations show that the DoF cut-set upper bound can be attained by the DINR scheme. It is shown that the DINR scheme can provide significant DoF gain over the conventional time division multiple access (TDMA) scheme. In addition, we show that the DINR scheme is superior to the existing single relay schemes for the considered mRC.

A miniaturized and bandwidth-enhanced implantable antenna is designed for wireless biotelemetry in the medical implantable communications service (MICS) frequency band of 402-405MHz. To reduce the antenna size and enhance the available bandwidth with regard to the reflection coefficients, a meandered planar inverted-F antenna (PIFA) structure is adopted on a dielectric/ferrite substrate which is an artificial magneto-dielectric material. The potential of the proposed antenna for the intended applications is verified through prototype fabrication and measurement with a 2/3 human muscle phantom. Good agreement is observed between the simulation and measurement in terms of resonant characteristics and gain radiation patterns; the bandwidth is enhanced in comparison with that of the ferrite-removed antenna, and antenna gain of -27.7dB is obtained in the measurement. Allowances are made for probable fabrication inaccuracies and practical operating environments. An analysis of 1-g SAR distribution is conducted to confirm compliance with the specific absorption rate limitation (1.6W/kg) of the American National Standards Institute (ANSI).

Frequencies around 300GHz offer extremely broad atmospheric transmission window with relatively low losses of up to 10dB/km and can be regarded as the ultimate platform for ultrahigh-speed wireless communications with near-fiber-optic data rates. This paper reviews technical challenges and recent advances in integrated circuits targeted at communications using these and nearby “terahertz (THz)” frequencies. Possible new applications of THz wireless links that are hard to realize by other means are also discussed.

In this letter, we propose an algorithm for the 2-dimensional (2D) direction of arrival (DOA) estimation of noncircular coherently distributed (CD) sources using the centrosymmetric array. For a centrosymmetric array, we prove that the angular signal distributed weight (ASDW) vector of the CD source has a symmetric structure. To estimate azimuth and elevation angle, we perform a 2D searching based on generalized ESPRIT algorithm. The significant superiority of the proposed algorithm is that, the 2D central directions of CD sources can be found independently of deterministic angular distributed function (DADF). Simulations results verify the efficacy of the proposed algorithm.

An uncorrelated asymmetric ZCZ (UA-ZCZ) sequence set is a special version of an asymmetric ZCZ (A-ZCZ) sequence set, which contains multiple subsets and each subset is a typical ZCZ sequence set. One of the most important properties of UA-ZCZ sequnence set is that two arbitrary sequences from different sequence subsets are uncorrelated sequences, whose cross-correlation function (CCF) is zeros at all shifts. Based on interleaved technique and an uncorrelated sequence set, a new UA-ZCZ sequence set is obtained via interleaving a perfect sequence. The uncorrelated property of the UA-ZCZ sequence sets is expected to be useful for avoiding inter-cell interference of QS-CDMA systems.

Shaped beam reflector antennas are widely used because they can achieve a shaped beam even with a single primary feed. Because coverage shapes depend on service areas, optimum primary radiators and reflector shapes are determined by the service areas. In this paper, we propose a simultaneous optimal design method of the primary radiator and reflector for the shaped beam antenna. Particle swarm optimization and the conjugate gradient method are adopted to optimize the primary radiator and reflector. The design method is applied to Japan coverage to verify its effectiveness.

A huge amount of content exists on the Internet, and contents from mobile devices are also present. Growth of the Internet of Things (IoT) is further accelerating this trend. Content oriented networks have been proposed as a new network architecture that conducts routing using the content's ID instead of an IP address. Content queries are routed on the content name itself instead of a destination address in these content oriented networks. When the content from a mobile device moves somewhere else, all the routing tables are generally re-created with the movement information that the mobile device sends. However, a routing scheme that uses ant colony optimization has attracted attention for supporting this process, but this optimization has a problem in that it cannot cope with moving contents and users sufficiently. In this paper, we propose a scheme that can cope with moving contents sources and users that require contents by using pheromones that are laid by these moving mobile devices. This proposed scheme can be applied to case of not only moving content sources but also the moving request users. Moreover, we conduct simulations to evaluate the performance of the proposed scheme.

The overdrive technique is widely used to eliminate motion blur in liquid-crystal displays (LCDs). However, this technique requires a large frame memory to store the previous frame. A reduction in the frame memory requires an image compression algorithm suitable for real-time data processing. In this paper, we present an algorithm based on multimode-color-conversion block truncation coding (MCC-BTC) to obtain a constant output bit rate and high overdrive performance. The MCC-BTC algorithm uses four compression methods, one of which is selected. The four compression modes either use the single-bitmap-generation method or the subsampling method for chrominance. As shown in the simulation results, the proposed algorithm improves the performance of both coding (up to 2.73dB) and overdrive (up to 2.61dB), and the visual quality is improved in comparison to other competing algorithms in literature.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

In this paper, we investigate the problem of maximizing the weighted sum outage rate in multiuser multiple-input single-output (MISO) interference channels, where the transmitters have no knowledge of the exact values of channel coefficients, only the statistical information. Unfortunately, this problem is nonconvex and very difficult to deal with. We propose a new, provably convergent iterative algorithm where in each iteration, the original problem is approximated as second-order cone programming (SOCP) by introducing slack variables and using convex approximation. Simulation results show that the proposed SOCP algorithm converges in a few steps, and yields a better performance gain with a lower computational complexity than existing algorithms.

The sparse representation models have been widely applied in image super-resolution. The certain optimization problem is supposed and can be solved by the iterative shrinkage algorithm. During iteration, the update of dictionaries and similar patches is necessary to obtain prior knowledge to better solve such ill-conditioned problem as image super-resolution. However, both the processes of iteration and update often spend a lot of time, which will be a bottleneck in practice. To solve it, in this paper, we present the concept of image quality difference based on generalized Gaussian distribution feature which has the same trend with the variation of Peak Signal to Noise Ratio (PSNR), and we update dictionaries or similar patches from the termination strategy according to the adaptive threshold of the image quality difference. Based on this point, we present two sparse representation algorithms for image super-resolution, one achieves the further improvement in image quality and the other decreases running time on the basis of image quality assurance. Experimental results also show that our quantitative results on several test datasets are in line with exceptions.

In this paper, a new class of low-hit-zone (LHZ) frequency-hopping sequence sets (LHZ FHS sets) is constructed based upon the Cartesian product, and the periodic partial Hamming correlation within its LHZ are studied. Studies have shown that the new LHZ FHS sets are optimal according to the periodic partial Hamming correlation bounds of FHS set, and some known FHS sets are the special cases of this new construction.

Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into different categories. However, ACA needs to be complemented with other algorithms for the classification process. In this paper, we present a fuzzy anomaly detection system that works in two phases. In the first phase, the training phase, we propose ACA to determine clusters. In the second phase, the classification phase, we exploit a fuzzy approach by the combination of two distance-based methods to detect anomalies in new monitored data. We validate our hybrid approach using the KDD Cup'99 dataset. The results indicate that, compared to several traditional and new techniques, the proposed hybrid approach achieves higher detection rate and lower false positive rate.

Service chaining (SC) is a method for realizing a service by transferring flows among several service functions (SFs) that process packets. A route among SFs is called a service path (SP). Service chaining is being developed to reduce costs, increase flexibility, and shorten time-to-market. SC technologies are expected to be applied to carrier networks so that large communication carriers benefit from them. We assume that SPs process the traffic of services that treat all users in the same way such as an Internet access service for home users. An SP processes flows from several users. We do not assume that each SP is assigned to a user. Because a carrier network accommodates many users, each service will be heavily utilized. Therefore, it is assumed that the amount of traffic of a service is larger than the resource of an SF apparatus. Several SPs are required to process the traffic. SPs are supposed to meet two requirements. One is guaranteeing minimum bandwidth. The other is reducing the number of SF apparatuses, i.e., high resource utilization. Resource utilization depends on the combination of the resource quantities of SF apparatuses. Network operators have to determine the bandwidth of each SP within the range from the minimum bandwidth to the resource quantities of SF apparatuses to maximize resource utilization. Methods for determining the bandwidth of each SP have not been proposed for meeting the two requirements. Therefore, we propose a resource allocation method for this purpose. The proposed method determines the bandwidth of each SP on the basis of the combination of the resource quantities of SF apparatuses for guaranteeing the minimum bandwidth and maximizing resource utilization and allocates necessary resources to each SP. We also evaluate the proposed method and confirm that it can guarantee the minimum bandwidth of SPs and achieve high resource utilization regardless of the combination of the resource quantities of SF apparatuses. Although SF apparatuses are generally produced without considering the combinations of resource quantities of SF apparatuses in SPs, the proposed method can provide more options for selecting SF apparatuses.

In this paper, we propose a filtering approach based on global motion estimation (GME) and global motion compensation (GMC) for pre- and postprocessing of video codecs. For preprocessing a video codec, group of pictures (GOP), which is a basic unit for GMC, and reference frames are first defined for an input video sequence. Next, GME and GMC are sequentially performed for every frame in each GOP. Finally, a block-based adaptive temporal filter is applied between the GMC frames before video encoding. For postprocessing a video codec at the decoder end, every decoded frame is inversely motion-compensated using the transmitted global motion information. The holes generated during inverse motion compensation can be filled with the reference frames. The experimental results show that the proposed algorithm provides higher Bjontegaard-delta peak signal-to-noise ratios (BD-PSNRs) of 0.63 and 0.57 dB on an average compared with conventional H.264 and HEVC platforms, respectively.

In this letter, a novel and highly efficient haze removal algorithm is proposed for haze removal from only a single input image. The proposed algorithm is built on the atmospheric scattering model. Firstly, global atmospheric light is estimated and coarse atmospheric veil is inferred based on statistics of dark channel prior. Secondly, the coarser atmospheric veil is refined by using a fast Tri-Gaussian filter based on human retina property. To avoid halo artefacts, we then redefine the scene albedo. Finally, the haze-free image is derived by inverting the atmospheric scattering model. Results on some challenging foggy images demonstrate that the proposed method can not only improve the contrast and visibility of the restored image but also expedite the process.

Single-instruction multiple-data (SIMD) extension provides an energy-efficient platform to scale the performance of media and scientific applications while still retaining post-programmability. However, the major challenge is to translate the parallel resources of the SIMD hardware into real application performance. Currently, all the slots in the vector register are used when compilers exploit SIMD parallelism of programs, which can be called sufficient vectorization. Sufficient vectorization means all the data in the vector register is valid. Because all the slots which vector register provides must be used, the chances of vectorizing programs with low SIMD parallelism are abandoned by sufficient vectorization method. In addition, the speedup obtained by full use of vector register sometimes is not as great as that obtained by partial use. Specifically, the length of vector register provided by SIMD extension becomes longer, sufficient vectorization method cannot exploit the SIMD parallelism of programs completely. Therefore, insufficient vectorization method is proposed, which refer to partial use of vector register. First, the adaptation scene of insufficient vectorization is analyzed. Second, the methods of computing inter-iteration and intra-iteration SIMD parallelism for loops are put forward. Furthermore, according to the relationship between the parallelism and vector factor a method is established to make the choice of vectorization method, in order to vectorize programs as well as possible. Finally, code generation strategy for insufficient vectorization is presented. Benchmark test results show that insufficient vectorization method vectorized more programs than sufficient vectorization method by 107.5% and the performance achieved by insufficient vectorization method is 12.1% higher than that achieved by sufficient vectorization method.

This paper proposes an effective signal processing scheme using a modulation code with two-dimensional (2D) run-length limited (RLL) constraints for bit-patterned media magnetic recording (BPMR). This 2D signal processing scheme is applied to be one of two-dimensional magnetic recording (TDMR) schemes for shingled magnetic recording on bit patterned media (BPM). A TDMR scheme has been pointed out an important key technology for increasing areal density toward 10Tb/in2. From the viewpoint of 2D signal processing for TDMR, multi-track joint decoding scheme is desirable to increase an effective transfer rate because this scheme gets readback signals from several adjacent parallel tracks and detect recorded data written in these tracks simultaneously. Actually, the proposed signal processing scheme for BPMR gets mixed readback signal sequences from the parallel tracks using a single reading head and these readback signal sequences are equalized to a frequency response given by a desired 2D generalized partial response system. In the decoding process, it leads to an increase in the effective transfer rate by using a single maximum likelihood (ML) sequence detector because the recorded data on the parallel tracks are decoded for each time slot. Furthermore, a new joint pattern-dependent noise-predictive (PDNP) sequence detection scheme is investigated for multi-track recording with media noise. This joint PDNP detection is embed in a ML detector and can be useful to eliminate media noise. Using computer simulation, it is shown that the joint PDNP detection scheme is able to compensate media noise in the equalizer output which is correlated and data-dependent.

This paper proposes a multiway power divider for wideband (4:1) beamforming arrays. The divider's input reflection characteristic (S11) is achieved using a multisection stepped-impedance transformer. Moreover, the divider's isolation (S32) bandwidth is increased by incorporating inductors and capacitors in addition to the conventional resistor only isolation networks of the divider. The analysis of the proposed divider and comparison with the previous research model was conducted with four-way configuration. A prototype of a wideband eight-way power divider is fabricated and measured. The measured fractional bandwidth is about 137% from 1.3 to 6.8GHz with the -10dB criteria of input reflection (S11), output reflection (S22) and isolation (S32) simultaneously.