Linked Open Data (LOD) at Schema-Level and knowledge described in Chinese is an important part of the LOD project. Previous work generally ignored the rules of word-order sensitivity and polysemy in Chinese or could not deal with the out-of-vocabulary (OOV) mapping task. There is still no efficient system for large-scale Chinese ontology mapping. In order to solve the problem, this study proposes a novel TongYiCiCiLin (TYCCL) and Sequence Alignment-based Chinese Ontology Mapping model, which is called TongSACOM, to evaluate Chinese concept similarity in LOD environment. Firstly, an improved TYCCL-based similarity algorithm is proposed to compute the similarity between atomic Chinese concepts that have been included in TYCCL. Secondly, a global sequence-alignment and improved TYCCL-based combined algorithm is proposed to evaluate the similarity between Chinese OOV. Finally, comparing the TongSACOM to other typical similarity computing algorithms, and the results prove that it has higher overall performance and usability. This study may have important practical significance for promoting Chinese knowledge sharing, reusing, interoperation and it can be widely applied in the related area of Chinese information processing.

The 1-bit band-pass delta-sigma modulator (BP-DSM) achieves high resolution by using the oversampling technique. This method allows direct RF signal transmission from a digitally modulated signal, using a 1-bit digital pulse train. However, it has been previously reported that the adjacent channel leakage ratio (ACLR) in a target frequency band degrades due to the pulse transition mismatch between rising and falling waveforms in the time domain. This paper clarifies that the spurious distortion in BP-DSM is caused by the asymmetricity of the waveform about the center of an eye pattern in the time axis, and proposes a 1-bit BP-DSM with the compensator consisting of a fractional delay filter and a binary data differentiator to cancel out the asymmetry in the target frequency band. This can accurately provide a wideband cancellation signal with more than 100MHz bandwidth, including the adjacent channel, within 50dB power dynamic range. Using long term evolution (LTE) signals with 5MHz bandwidth at 0.8GHz, we simulated the spurious distortion, performing various combinations of rising and falling times in the eye pattern, and the proposed 1-bit BP-DSM always achieved high ACLR, up to 60dB, in 140MHz bandwidth, under all conditions.

In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.

In accordance with Moore's law, recent design issues include shortening of time-to-market and detection of delay faults. Several studies with respect to formal techniques have examined the first issue. Using the equivalence checking, it is possible to identify whether large circuits are equivalent or not in a practical time frame. With respect to the latter issue, it is difficult to achieve 100% fault efficiency even for transition faults in full scan designs. This study involved proposing a redundant transition fault identification method using equivalence checking. The main concept of the proposed algorithm involved combining the following two known techniques, 1. modeling of a transition fault as a stuck-at fault with temporal expansion and 2. detection of a stuck-at fault by using equivalence checking tools. The experimental results indicated that the proposed redundant identification method using a formal approach achieved 100% fault efficiency for all benchmark circuits in a practical time even if a commercial ATPG tool was unable to achieve 100% fault efficiency for several circuits.

A new Internet Draft on benchmarking methodologies for IPv6 transition technologies including DNS64 was adopted by the Benchmarking Working Group of IETF. The aim of our effort is to design and implement a test program that complies with the draft and thus to create the world's first standard DNS64 benchmarking tool. In this paper, we disclose our design considerations and high-level implementation decisions. The precision of our special timing method is tested and found to be excellent. Due to the prudent design, the performance of our test program is also excellent: it can send more than 200,000 AAAA record requests using a single core of a desktop computer with a 3.2GHz Intel Core i5-4570 CPU. Its operation comprises all the functionalities required by the draft including checking the timeliness and validity of the answers of the tested DNS64 server. Our DNS64 benchmarking program, dns64perf++, is distributed as free software under GNU GPL v2 license for the benefit of the research, benchmarking and networking communities.

This paper describes, for the first time, an experimental study on the layout design considerations of GaAs HBT MMIC switchable-amplifier-chain-based power amplifiers (SWPAs) for CDMA handsets. The transient response of the quiescent current and output power (Pout) in GaAs HBT power amplifiers that consist of a main chain and a sub-chain is often affected by a thermal coupling between power stages and their bias circuits in the same chain or a thermal coupling between power stages and/or their bias circuits in different chains. In particular, excessively strong thermal coupling inside the MMIC SWPA causes failure in 3GPP-compliant inner loop power control tests. An experimental study reveals that both the preheating in the main/sub-chains and appropriate thermal coupling inside the main chain are very effective in reducing the turn-on delay for the two-parallel-amplifier-chain topology; for example, i) the sub-power stage is arranged near the main power stage, ii) the sub-driver stage is placed near the main driver stage and iii) the main driver bias circuit is placed near the main power stage and the sub-power stage. The SWPA operating in Band 9 (1749.9 to 1784.9 MHz), which was designed and fabricated from the foregoing considerations, shows a remarkable improvement in the Pout turn-on delay: a reduced power level error of 0.74 dB from turn-off to turn-on in the sub-amplifier chain and a reduced power level error of over 0.30 dB from turn-off to turn-on in the main amplifier chain. The main RF power measurements conducted with a 3.4-V supply voltage and a Band 9 WCDMA HSDPA modulated signal are as follows. The SWPA delivers a Pout of 28.5 dBm, a power gain (Gp) of 28 dB, and a PAE of 39% while restricting the ACLR1 to less than -40 dBc in the main amplifier chain. In the sub-amplifier chain, 17 dBm of Pout, 23.5 dB of Gp, and 27% of PAE are obtained at the same ACLR1 level.

Image quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development.

In this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

This letter presents a novel memory-based architecture for radix-2 fast Walsh-Hadamard-Fourier transform (FWFT) based on the constant geometry FWFT algorithm. It is composed of a multi-function Processing Engine, a conflict-free memory addressing scheme and an efficient twiddle factor generator. The address for memory access and the control signals for stride permutation are formulated in detail and the methods can be applied to other memory-based FFT-like architectures.

It is estimated that 80% of the information entering the human brain is obtained through the eyes. Therefore, it is commonly believed that drawing human attention to particular objects is effective in assisting human activities. In this paper, we propose a novel image modification method for guiding user attention to specific regions of interest by using a novel saliency map model based on spatial frequency components. We modify the frequency components on the basis of the obtained saliency map to decrease the visual saliency outside the specified region. By applying our modification method to an image, human attention can be guided to the specified region because the saliency inside the region is higher than that outside the region. Using gaze measurements, we show that the proposed saliency map matches well with the distribution of actual human attention. Moreover, we evaluate the effectiveness of the proposed modification method by using an eye tracking system.

Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment problem (Markov Random Field framework), which is observed to be sensitive to parameter setting. Meanwhile, it is also observed that large object variations and complicated backgrounds dramatically decrease the existing MGC performance. To this end, we propose a new object proposal based MGC model, with the aim of avoiding tedious parameter setting, and improving MGC performance. Our main idea is to formulate MGC as new region proposal selection task. A new energy function in term of proposal is proposed. Two aspects such as the foreground consistency within each single image group, and the group consistency among image groups are considered. The energy minimization method is designed in EM framework. Two steps such as the loop belief propagation and foreground propagation are iteratively implemented for the minimization. We verify our method on ICoseg dataset. Six existing cosegmentation methods are used for the comparison. The experimental results demonstrate that the proposed method can not only improve MGC performance in terms of larger IOU values, but is also robust to the parameter setting.

Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of spatial kernel in this framework, a huge adjacency matrix is adopted to determine the adjacency relation between each pair of voxels and thus it leads to very high computational complexity in the spatial kernel calculation. The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced. The improvement is verified by experimental results on classification of Alzheimer patients and cognitively normal controls.

This study focuses on the design of electrical stimulator for retinal prosthesis. The stimulator must be designed such that the occurrence of electrolysis or any irreversible process in the electrodes and flexible lead is prevented in order to achieve safe stimulation over long periods using the large number of electrodes. Some types of biphasic current pulse circuits, charge balance circuits, and AC power delivery circuits were developed to address this issue. Electronic circuitry must be introduced in the stimulator to achieve the large number of electrodes required to obtain high quality of vision. The concept of a smart electrode, in which a microchip is embedded inside an electrode, is presented for future retinal prostheses with over 1000 electrodes.

Malware phylogeny refers to inferring the evolutionary relationships among instances of a family. It plays an important role in malware forensics. Previous works mainly focused on tree-based model. However, trees cannot represent reticulate events, such as inheriting code fragments from different parents, which are common in variants generation. Therefore, phylogenetic networks as a more accurate and general model have been put forward. In this paper, we propose a novel malware phylogenetic network construction method based on splits graph, taking advantage of the one-to-one correspondence between reticulate events and netted components in splits graph. We evaluate our algorithm on three malware families and two benign families whose ground truth are known and compare with competing algorithms. Experiments demonstrate that our method achieves a higher mean accuracy of 64.8%.

Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.

It inevitably comes out information overload problem with the increasing available data on e-commence websites. Most existing approaches have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction. However, the existing approaches are unable to perform user prediction and item prediction, since they just treat the ratings as real numbers and learn nothing about the ratings' embeddings in the training process. In this paper, motivated by relation prediction in multi-relational graph, we propose a novel embedding model, namely RPEM, to solve the problem including the tasks of rating prediction, user prediction and item prediction simultaneously for recommendation systems, by learning the latent semantic representation of the users, items and ratings. In addition, we apply the proposed model to cross-domain recommendation, which is able to realize recommendation generation in multiple domains. Empirical comparison on several real datasets validates the effectiveness of the proposed model. The data is available at https://github.com/yuzhaour/da.

A novel real-valued ESPRIT (RV-ESPRIT) algorithm is proposed to estimate the direction of arrival (DOA) and direction of departure (DOD) for noncircular signals in bistatic MIMO radar. By exploiting the property of signal noncircularity and Euler's formula, a new virtual array data of bistatic MIMO radar, which is twice that of the MIMO virtual array data, is established with real-valued sine and cosine data. Then the receiving/transmitting selective matrices are constructed to obtain the receiving/transmitting rotationally invariant factors. Compared to the existing angle estimation methods, the proposed algorithm has lower computational load. Simulation results confirm the effectiveness of the RV-ESPRIT.

Numerous methods have been developed to handle lighting variations in the preprocessing step of face recognition. However, most of them only use the high-frequency information (edges, lines, corner, etc.) for recognition, as pixels lied in these areas have higher local variance values, and thus insensitive to illumination variations. In this case, information of low-frequency may be discarded and some of the features which are helpful for recognition may be ignored. In this paper, we present a new and efficient method for illumination normalization using an energy minimization framework. The proposed method aims to remove the illumination field of the observed face images while simultaneously preserving the intrinsic facial features. The normalized face image and illumination field could be achieved by a reciprocal iteration scheme. Experiments on CMU-PIE and the Extended Yale B databases show that the proposed method can preserve a very good visual quality even on the images illuminated with deep shadow and high brightness regions, and obtain promising illumination normalization results for better face recognition performance.

A new class of visible light communication (VLC) systems, namely image sensor (IS) based VLC systems, has emerged. An IS consists of a two-dimensional (2D) array of photodetectors (PDs), and then VLC systems with an IS receiver are capable of exploiting the spatial dimensions invoked for transmitting information. This paper aims for providing a brief survey of topics related to the IS-based VLC, and then provides a matrix representation of how to map a series of one dimensional (1D) symbols onto a set of 2D symbols for efficiently exploit the associate grade of freedom offered by 2D VLC systems. As an example, the matrix representation is applied to the symbol mapping of layered space-time coding (L-STC), which is presented to enlarge the coverage of IS-based VLC that is limited by pixel resolution of ISs.