This paper studies the design of cooperative beamforming (CB) and cooperative jamming (CJ) for the physical layer security of an amplify-and-forward (AF) relay network in the presence of multiple multi-antenna eavesdroppers. The secrecy rate maximization (SRM) problem of such a network is to maximize the difference of two concave functions, a problem which is non-convex and has no efficient solution. Based on the inner convex approximation (ICA) and semidefinite relaxation (SDR) techniques, we propose two novel low-complexity schemes to design CB and CJ for SRM in the AF network. In the first strategy, relay nodes adopt the CB only to secure transmission. Based on ICA, this design guarantees convergence to a Karush-Kuhn-Tucker (KKT) solution of the SDR of the original problem. In the second strategy, the optimal joint CB and CJ design is studied and the proposed joint design can guarantee convergence to a KKT solution of the original problem. Moreover, in the second strategy, we prove that SDR always has a rank-1 solution for the SRM problem. Simulation results show the superiority of the proposed schemes.

In this paper, VLSI architecture design of unified motion vector (MV) and boundary strength (BS) parameter decoder (PDec) for 8K UHDTV HEVC decoder is presented. The adoption of new coding tools in PDec, such as Advanced Motion Vector Prediction (AMVP), increases the VLSI hardware realization overhead and memory bandwidth requirement, especially for 8K UHDTV application. We propose four techniques for these challenges. Firstly, this work unifies MV and BS parameter decoders for line buffer memory sharing. Secondly, to support high throughput, we propose the top-level CU-adaptive pipeline scheme by trading off between implementation complexity and performance. Thirdly, PDec process engine with optimizations is adopted for 43.2k area reduction. Finally, PU-based coding scheme is proposed for 30% DRAM bandwidth reduction. In 90nm process, our design costs 93.3k logic gates with 23.0kB line buffer. The proposed architecture can support real-time decoding for 7680x4320@60fps application at 249MHz in the worst case.

8K Ultra High Definition Television (UHDTV) requires extremely high throughput for video decoding based on H.265. In H.265, intra coding could significantly enhance video compression efficiency, at the expense of an increased computational complexity compared with H.264. For intra prediction of 8K UHDTV real-time H.265 decoding, the joint complexity and throughput issue is more difficult to solve. Therefore, based on the divide-and-conquer strategy, we propose a new VLSI architecture in this paper, including two techniques, in order to achieve 8K UHDTV H.265 intra prediction decoding. The first technique is the LUT based Reference Sample Fetching Scheme (LUT-RSFS), reducing the number of reference samples in the worst case from 99 to 13. It further reduces the circuit area and enhances the performance. The second one is the Hybrid Block Reordering and Data Forwarding (HBRDF), minimizing the idle time and eliminating the dependency between TUs by creating 3 Data Forwarding paths. It achieves the hardware utilization of 94%. Our design is synthesized using Synopsys Design Compiler in 40nm process technology. It achieves an operation frequency of 260MHz, with a gate count of 217.8K for 8-bit design, and 251.1K for 10-bit design. The proposed VLSI architecture can support 4320p@120fps H.265 intra decoding (8-bit or 10-bit), with all 35 intra prediction modes and prediction unit sizes ranging from 4×4 to 64×64.

With multiple network interfaces are being widely equipped in modern mobile devices, the Multipath TCP (MPTCP) is increasingly becoming the preferred transport technique since it can uses multiple network interfaces simultaneously to spread the data across multiple network paths for throughput improvement. However, the MPTCP performance can be seriously affected by the use of a poor-performing path in multipath transmission, especially in the presence of network attacks, in which an MPTCP path would abrupt and frequent become underperforming caused by attacks. In this paper, we propose a multi-expert Learning-based MPTCP variant, called MPTCP-meLearning, to enhance MPTCP performance robustness against network attacks. MPTCP-meLearning introduces a new kind of predictor to possibly achieve better quality prediction accuracy for each of multiple paths, by leveraging a group of representative formula-based predictors. MPTCP-meLearning includes a novel mechanism to intelligently manage multiple paths in order to possibly mitigate the out-of-order reception and receive buffer blocking problems. Experimental results demonstrate that MPTCP-meLearning can achieve better transmission performance and quality of service than the baseline MPTCP scheme.

Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.

Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.

The wear leveling is a critical factor which significantly impacts the lifetime and the performance of flash storage systems. To extend lifespan and reduce memory requirements, this paper proposed an efficient wear leveling without substantially increasing overhead and without modifying Flash Translation Layer (FTL) for huge-capacity flash storage systems, which is based on selective replacement. Experimental results show that our design levels the wear of different physical blocks with limited system overhead compared with previous algorithms.

A moment-based method is proposed to estimate the illumination change between two images containing affinetransformed objects. The change is linearly modeled with parameters to be estimated by histograms due to its invariance of translation, rotation, and scaling. The parameters can be correctly estimated for an appropriate illumination change by normalizing the moments of the histograms.

The optimal design of an extrapolated impulse response (EIR) filter (in the mini-max sense) is a non-linear programming problem. In this paper, the optimal design of the EIR filter by the semi-infinite programming (SIP) is investigated and an iterative technique for optimally designing the EIR filter is proposed. The simulation experiment validates the effectiveness of the SIP technique and the proposed iterative technique in the optimal design of the EIR filter.

In this paper, a new method is proposed for supervised classification of ground cover types by using polarimetric synthetic aperture radar (SAR) data. The concept of similarity parameter between two scattering matrices is introduced for characterizing target scattering mechanism. Four similarity parameters of each pixel in image are used for classification. They are the similarity parameters between a pixel and a plane, a dihedral, a helix and a wire. The total received power of each pixel is also used since the similarity parameter is independent of the spans of target scattering matrices. The supervised classification is carried out based on the principal component analysis. This analysis is applied to each data set in image in the feature space for getting the corresponding feature transform vector. The inner product of two vectors is used as a distance measure in classification. The classification result of the new scheme is shown and it is compared to the results of principal component analysis with other decomposition coefficients, to demonstrate the effectiveness of the similarity parameters.

A context-aware system always needs to adapt its behaviors according to context changes; therefore, modeling context-aware requirements is a complex task. With the increasing use of mobile computing, research on methods of modeling context-aware requirements have become increasingly important, and a large number of relevant studies have been conducted. However, no comprehensive analysis of the challenges and achievements has been performed. The methodology of systematic literature review was used in this survey, in which 68 reports were selected as primary studies. The challenges and methods to confront these challenges in context-aware requirement modeling are summarized. The main contributions of this work are: (1) four challenges and nine sub-challenges are identified; (2) eight kinds of methods in three categories are identified to address these challenges; (3) the extent to which these challenges have been solved is evaluated; and (4) directions for future research are elaborated.

Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.

Existing studies on transportation mode detection from global positioning system (GPS) trajectories mainly adopt handcrafted features. These features require researchers with a professional background and do not always work well because of the complexity of traffic behavior. To address these issues, we propose a model using a sparse autoencoder to extract point-level deep features from point-level handcrafted features. A convolution neural network then aggregates the point-level deep features and generates a trajectory-level deep feature. A deep neural network incorporates the trajectory-level handcrafted features and the trajectory-level deep feature for detecting the users' transportation modes. Experiments conducted on Microsoft's GeoLife data show that our model can automatically extract the effective features and improve the accuracy of transportation mode detection. Compared with the model using only handcrafted features and shallow classifiers, the proposed model increases the maximum accuracy by 6%.

With the advent of the Internet of Things (IoT), short packet transmissions will dominate the future wireless communication. However, traditional coherent demodulation and channel estimation schemes require large pilot overhead, which may be highly inefficient for short packets in multipath fading scenarios. This paper proposes a novel pilot-free short packet structure based on the association of modulation on conjugate-reciprocal zeros (MOCZ) and tail-biting convolutional codes (TBCC), where a noncoherent demodulation and decoding scheme is designed without the channel state information (CSI) at the transceivers. We provide a construction method of constellation sets and demodulation rule for M-ary MOCZ. By deriving low complexity log-likelihood ratios (LLR) for M-ary MOCZ, we offer a reasonable balance between energy and bandwidth efficiency for joint coding and modulation scheme. Simulation results show that our proposed scheme can attain significant performance and throughput gains compared to the pilot-based coherent modulation scheme over multipath fading channels.

As a typical surface defect of tunnel lining structures, cracking disease affects the durability of tunnel structures and poses hidden dangers to tunnel driving safety. Factors such as interference from the complex service environment of the tunnel and the low signal-to-noise ratio of the crack targets themselves, have led to existing crack recognition methods based on semantic segmentation being unable to meet actual engineering needs. Based on this, this paper uses the Unet network as the basic framework for crack identification and proposes to construct a multi-kernel convolution cascade enhancement (MKCE) model to achieve accurate detection and identification of crack diseases. First of all, to ensure the performance of crack feature extraction, the model modified the main feature extraction network in the basic framework to ResNet-50 residual network. Compared with the VGG-16 network, this modification can extract richer crack detail features while reducing model parameters. Secondly, considering that the Unet network cannot effectively perceive multi-scale crack features in the skip connection stage, a multi-kernel convolution cascade enhancement module is proposed by combining a cascaded connection of multi-kernel convolution groups and multi-expansion rate dilated convolution groups. This module achieves a comprehensive perception of local details and the global content of tunnel lining cracks. In addition, to better weaken the effect of tunnel background clutter interference, a convolutional block attention calculation module is further introduced after the multi-kernel convolution cascade enhancement module, which effectively reduces the false alarm rate of crack recognition. The algorithm is tested on a large number of subway tunnel crack image datasets. The experimental results show that, compared with other crack recognition algorithms based on deep learning, the method in this paper has achieved the best results in terms of accuracy and intersection over union (IoU) indicators, which verifies the method in this paper has better applicability.

This letter introduces a prime-factor Galois field Fourier transform (PF-GFFT) architecture to frequency domain decoding (FDD) of cyclic codes. Firstly, a fast FDD scheme is designed which converts the original single longer Fourier transform to a multi-dimensional smaller transform. Furthermore, a ladder-shift architecture for PF-GFFT is explored to solve the rearrangement problem of input and output data. In this regard, PF-GFFT is considered as a lower order spectral calculation scheme, which has sufficient preponderance in reducing the computational complexity. Simulation results show that PF-GFFT compares favorably with the current general GFFT, simplified-GFFT (S-GFFT), and circular shifts-GFFT (CS-GFFT) algorithms in time-consuming cost, and is nearly an order of magnitude or smaller than them. The superiority is a benefit to improving the decoding speed and has potential application value in decoding cyclic codes with longer code lengths.

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.

Since ISAR is widely applied in many occasions and provides high resolution images of the target, ISAR countermeasures are attracting more and more attention. Most of the present methods of deception jamming are not suitable for engineering realization due to the heavy computation load or the large calculation delay. Deception jamming against ISAR requires large computation resource and real-time performance algorithms. Many studies on false target jamming assume that the jammer is able to receive the target echo or transmit the jamming signal to the real target, which is sometimes not possible. How to impose the target property onto the intercepted radar signal is critical to a deception jammer. This paper proposes a jamming algorithm based on parallel convolution and one-bit quantization. The algorithm is able to produce a single false target on ISAR image by the jammer itself. The requirement for computation resource is within the capabilities of current digital signal processors such as FPGA or DSP. The method processes the samples of radar signal in parallel and generates the jamming signal at the rate of ADC data, solving the problem that the real-time performance is not satisfied when the input data rate for convolution is far higher than the clock frequency of FPGA. In order to reduce the computation load of convolution, one-bit quantization is utilized. The complex multiplication is implemented by logical resources, which significantly reduces the consumption of FPGA multipliers. The parallel convolution jamming signal, whose date rate exceeds the FPGA clock rate, is introduced and analyzed in detail. In theory, the bandwidth of jamming signal can be half of the sampling frequency of high speed ADC, making the proposed jamming algorithm able to counter ultra-wideband ISAR signals. The performance and validity of the proposed method are verified by simulations. This jamming method is real-time and capable of producing a false target of large size at the low cost of FPGA device.

In lossy image/video encoding, there is a compromise between the number of bits and the extent of distortion. Optimizing the allocation of bits to different sources, such as frames or blocks, can improve the encoding performance. In intra-frame encoding, due to the dependency among macro blocks (MBs) introduced by intra prediction, the optimization of bit allocation to the MBs usually has high complexity. So far, no practical optimal bit allocation methods for intra-frame encoding exist, and the commonly used method for intra-frame encoding is the fixed-QP method. We suggest that the QP selection inside an image/a frame can be optimized by aiming at the constant perceptual quality (CPQ). We proposed an iteration-based bit allocation scheme for H.264/AVC intra-frame encoding, in which all the local areas (which is defined by a group of MBs (GOMBs) in this paper) in the frame are encoded to have approximately the same perceptual quality. The SSIM index is used to measure the perceptual quality of the GOMBs. The experimental results show that the encoding performance on intra-frames can be improved greatly by the proposed method compared with the fixed-QP method. Furthermore, we show that the optimization on the intra-frame can bring benefits to the whole sequence encoding, since a better reference frame can improve the encoding of the subsequent frames. The proposed method has acceptable encoding complexity for offline applications.

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.