A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.

Recently, H. Liu et al. [H. Liu, M. Liang, and H. Sun, A secure and efficient certificateless aggregate signature scheme, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol.E97-A, no.4, pp.991-915, 2014] proposed a new certificateless aggregate signature (CLAS) scheme and demonstrated that it was provably secure in the random oracle model. However, in this letter, we show that their scheme cannot provide unforgeability, i.e., an adversary having neither the user's secret value nor his/her partial private key can forge a legal signature of any message.

Recently, Shao et al. [M. Shao and Y. Chin, A privacy-preserving dynamic id-based remote user authentication scheme with access control for multi-server environment, IEICE Transactions on Information and Systems, vol.E95-D, no.1, pp.161–168, 2012] proposed a dynamic ID-based remote user authentication scheme with access control for multi-server environments. They claimed that their scheme could withstand various attacks and provide anonymity. However, in this letter, we will point out that Shao et al.'s scheme has practical pitfalls and is not feasible for real-life implementation. We identify that their scheme is vulnerable to two kinds of attacks and cannot provide anonymity.

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

In this letter, we exploit a new framework for detecting the non-specific object via combing the top-down and bottom-up cues. Specifically, a novel supervised discriminative dictionaries learning method is proposed to learn the coupled dictionaries for the object and non-object feature spaces in terms of the top-down cue. Different from previous dictionary learning methods, the new data reconstruction residual terms of coupled feature spaces, the sparsity penalty measures on the representations and an inconsistent regularizer for the learned dictionaries are all incorporated in a unitized objective function. Then we derive an iterative algorithm to alternatively optimize all the variables efficiently. Considering the bottom-up cue, the proposed discriminative dictionaries learning is then integrated with an unsupervised dictionary learning to capture the objectness windows in an image. Experimental results show that the non-specific object detection problem can be effectively solved by the proposed dictionary leaning framework and outperforms some established methods.

Providing students with useful feedback on faulty programs can effectively help students fix programs. Spectrum-Based Fault Location (SBFL), which is a widely studied and lightweight technique, can automatically generate a suspicious value of statement ranking to help users find potential faults in a program. However, the performance of SBFL on student programs is not satisfactory, to improve the accuracy of SBFL in student programs, we propose a novel Multi-Correct Programs based Fault Localization (MCPFL) approach. Specifically, We first collected the correct programs submitted by students on the OJ system according to the programming problem numbers and removed the highly similar correct programs based on code similarity, and then stored them together with the faulty program to be located to construct a set of programs. Afterward, we analyzed the suspiciousness of the term in the faulty program through the Term Frequency-Inverse Document Frequency (TF-IDF). Finally, we designed a formula to calculate the weight of suspiciousness for program statements based on the number of input variables in the statement and weighted it to the spectrum-based fault localization formula. To evaluate the effectiveness of MCPFL, we conducted empirical studies on six student program datasets collected in our OJ system, and the results showed that MCPFL can effectively improve the traditional SBFL methods. In particular, on the EXAM metric, our approach improves by an average of 27.51% on the Dstar formula.

This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

In this letter, we investigate the price-based power allocation for non-orthogonal multiple access (NOMA) networks, where the base station (BS) can admit the users to transmit by pricing their power. Stackelberg game is utilized to model the pricing and power purchasing strategies between the BS and the users. Based on backward induction, the pricing problem of the BS is recast into the non-convex power allocation problem, which is equivalent to the rate allocation problem by variable replacement. Based on the equivalence problem, an optimal price-based power allocation algorithm is proposed to maximize the revenue of the BS. Simulation results show that the proposed algorithm is superior to the existing pricing algorithm in items of the revenue of BS and the number of admitted users.

The accurate and fast estimation of link price is the key component of network-based congestion control schemes. A fast estimation method A2DPM is presented. Multiple hashes on IP identifier of packet header are adopted to accelerate the side information transmission, so accurate estimation of maximum price on the flow forwarding path can be realized after the receipt of just a few probe packets, and the sender is capable of reacting to congestion more quickly, making it suitable to meet the demands of dynamic networks.

Image binarization refers to convert gray-level images into binary ones, and many binarization algorithms have been developed. The related algorithms can be classified as either high quality computation or high speed performance. This letter presents an algorithm that ensures both benefits at the same time. The proposed algorithm intelligently segments input images into several sub-image, after which the sub-image binarization is performed independently. Experimental results reveal that our algorithm provides the appropriate quality with the medium speed.

In this paper, a simple yet efficient texture representation is proposed for texture classification by exploring the joint statistics of local quantized patterns (jsLQP). In order to combine information of different domains, the Gaussian derivative filters are first employed to obtain the multi-scale gradient responses. Then, three feature maps are generated by encoding the local quantized binary and ternary patterns in the image space and the gradient space. Finally, these feature maps are hybridly encoded, and their joint histogram is used as the final texture representation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art LBP based and even learning based methods for texture classification.

Recently, Lee et al. [Y. Lee, E. Kim, S. Seok, and M. Jung, A smartcard-based user authentication scheme to ensure the PFS in multi-server environments, IEICE Transactions on Communications, vol.E95-B, no.2, pp.619–622, 2012] proposed a smartcard-based user authentication scheme for multi-server environments. They claimed that their scheme could withstand various attacks and provide the perfect forward secrecy (PFS). However, in this letter, we will point out that their scheme is vulnerable to three kinds of attacks and cannot provide the PFS.

Maximizing the profit of datacenter networks (DCNs) demands to satisfy more flows' requirements simultaneously, but existing schemes always allocate resource based on single flow attribute, which cannot carry out accurate resource allocation and make many flows failed. In this letter, we propose Highest Priority Flow First (HPFF) to maximize DCN profit, which allocates resource for flows according to the priority. HPFF employs a utility function that considers multiple flow attributes, including flow size, deadline and demanded bandwidth, to calculate the priority for each flow. The experiments on the testbed show that HPFF can improve the network profit by 6.75%-19.7% and decrease the number of failed flow by 26.3%-83.3% compared with existing schemes under real DCN workloads.

The aim of this research is to study the power energy cost reduction of the mobile telecom industry through the supervisor control and data acquisition (SCADA) system application during globalization and liberalization competition. Yet this management system can be proposed functions: operating monitors, the analysis on load characteristics and dropping the cost of management.

In this paper, we propose an inconsistency resolution method based on a new concept, insecure backtracking role mapping. By analyzing the role graph, we prove that the root cause of security inconsistency in distributed interoperation is the existence of insecure backtracking role mapping. We propose a novel and efficient algorithm to detect the inconsistency via finding all of the insecure backtracking role mappings. Our detection algorithm will not only report the existence of inconsistency, but also generate the inconsistency information for the resolution. We reduce the inconsistency resolution problem to the known Minimum-Cut problem, and based on the results generated by our detection algorithm we propose an inconsistency resolution algorithm which could guarantee the security of distributed interoperation. We demonstrate the effectiveness of our approach through simulated tests and a case study.

This paper proposes a security violation detection method for RBAC based interoperation to meet the requirements of secure interoperation among distributed systems. We use role mappings between RBAC systems to implement trans-system access control, analyze security violation of interoperation with role mappings, and formalize definitions of secure interoperation. A minimum detection method according to the feature of RBAC system in distributed environment is introduced in detail. This method reduces complexity by decreasing the amount of roles involved in detection. Finally, we analyze security violation further based on the minimum detection method to help administrators eliminate security violation.

In this paper, we propose a novel joint rate distortion optimization (JRDO) model for intra prediction coding. The spatial prediction dependency is exploited by modeling the distortion propagation with a linear fitting function. A novel JRDO based Lagrange multiplier (LM) is derived from this model. To adapt to different blocks' distortion propagation characteristics, we also introduce a generalized multiple Lagrange multiplier (MLM) framework where some candidate LMs are used in the RDO process. Experiment results show that our proposed JRDO-MLM scheme is superior to the H.264/AVC encoder.

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

Software diversity can be utilized in cyberspace security to defend against the zero-day attacks. Existing researches have proved the effectiveness of diversity in bringing security benefits, but few of them touch the problem that whether there is a positive correlation between the security and the diversity. In addition, there is little guidance on how to construct an effective diversified system. For that, this paper develops two diversity metrics based on system attribute matrix, proposes a diversity measurement and verifies the effectiveness of the measurement. Through several simulations on the diversified systems which use voting strategy, the relationship between diversity and security is analyzed. The results show that there is an overall positive correlation between security and diversity. Though some cases are against the correlation, further analysis is made to explain the phenomenon. In addition, the effect of voting strategy is also discussed through simulations. The results show that the voting strategy have a dominant impact on the security, which implies that security benefits can be obtained only with proper strategies. According to the conclusions, some guidance is provided in constructing a more diversified as well as securer system.

Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learning-based object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.