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Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal design of different VLSI circuits, namely, the CMOS voltage reference circuit and the CMOS ring oscillator (RO). The optimization technique used here is the multiobjective differential evolution algorithm (MDEA). All the circuits are designed for 90 nm technology. The main objective of the CMOS voltage reference circuit is to minimize the voltage variation at the output. The targeted value of the reference voltage is 550 mV. A CMOS ring oscillator (RO) is designed depending on the performance parameters such as power consumption and phase noise. The optimal transistor sizing of each circuit is obtained from MDEA. Each circuit is implemented in SPICE by taking the optimal dimensions of the transistors, and the performance parameters are achieved. The designed voltage reference circuit achieves a reference voltage of 550 mV with 600 nW power dissipation. The reference voltage variation of 8.18% is observed due to temperature variation from −40°C to + 125°C. The MDEA-based optimal design of RO oscillates at 2.001 GHz frequency, has a phase noise of −87 dBc/Hz at 1 MHz offset frequency, and consumes 71 μW power. This work mainly aims to optimize the MOS transistors' sizes using MDEA for better circuit performance parameters. SPICE simulation has been carried out by using the optimal values of MOS transistor sizes to exhibit the performance parameters of the circuit. Simulation results establish that design specifications are closely met. SPICE results show that MDEA is a better technique for the optimal design of the above-mentioned VLSI circuits. [ABSTRACT FROM AUTHOR]

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%. [ABSTRACT FROM AUTHOR]

To address the phenomenon of serious psychological stress among college students, there are problems of high cost and subjectivity in assessing psychological stress by collecting physiological data, and this paper proposes a stress assessment method (Improved SMOTE + XGBoost) based on intelligent data collection, which divides stress levels into five levels. In the process of processing a large amount of data, there will be too little data. Therefore, this paper applies the improved SMOTE method to the data preprocessing, which can reduce the difficulty of collecting psychological stress test data while ensuring the amount of data. Firstly, we extracted features from cell phone data to generate samples, processed the samples by SMOTE, and then filtered features by XGBoost algorithm to filter features; meanwhile, we trained RF, SVM, BP, and KNN with the data before and after sampling and before and after feature screening, and the results showed that Improved SMOTE + XGBoost outperformed other methods. [ABSTRACT FROM AUTHOR]

Most of the existing resource allocation algorithms do not take into account the restriction conditions such as the delay limitation of different services and the service volume requirements of different STAs. Moreover, the surge in the number of smart terminal devices has brought great challenges to the access effect of WLAN systems. Therefore, 6G technology will surely replace the current technology as the main means of future communication technology. This paper combines 6G technology to study the smart system resource coordination algorithm and analyzes the calculation process and actual effect of the algorithm. Moreover, this paper analyzes the coordination of smart system resources and verifies the effect of 6G technology in the coordination of smart system resources through experimental research. The results of experimental research show that the smart system resource coordination algorithm based on 6G technology can effectively improve the resource coordination effect of smart systems and has certain anti-interference, which will promote the construction of smart systems in the future. [ABSTRACT FROM AUTHOR]

The installation of automotive electrical switches is a complex three-dimensional space assembly project which has high requirements for installation accuracy. In order to improve the installation effect of automotive electrical switches, this paper applies the PSO-BP neural network algorithm to automotive electrical switches and integrates PSO and ELM algorithms. The training speed of the ELM model is fast, the model generalizes the data well, and the noise data have little effect on the model. Moreover, this article combines simulation research to evaluate the effect of this algorithm. After confirming the performance of the effect, this paper uses a case study to study the effect of the application of the PSO-BP neural network algorithm to the automotive electrical switch. The research results show that the CAD-assisted 3D assembly system of automobile electrical switch considering PSO-BP neural network algorithm has a good effect. [ABSTRACT FROM AUTHOR]

In order to explore the optimal control method of tennis racket string diameter, this paper applies the Kalman filter algorithm to the collection and processing of tennis sports parameters. When the Kalman filter uses the optimal gain (the Kalman filter enters the steady state), the corresponding cost function is established based on the noncorrelated nature of its residual sequence, which is used as the judgment condition for the sampling strategy switch to improve the stability of the system. In addition, this paper improves the real-time performance and calculation accuracy of data transformation through adaptive sampling strategy and adaptive scale factor, improves the stability and estimation accuracy of the system as a whole, and builds an intelligent monitoring system. Finally, this paper systematically studied the optimization control method of tennis racket string diameter and verified that the Kalman filter algorithm can play a certain role in the optimization control of tennis racket string diameter. [ABSTRACT FROM AUTHOR]

From the perspective of practical application, information popularity prediction is of positive significance for corporate marketing, advertising, traffic control, and risk management. This paper combines the fast K-nearest neighbor algorithm to predict and analyze the popularity of multimedia network information and improves the nonindependent and identically distributed KNN classification algorithm. Moreover, this paper proves that it is a superior measurement method when considering the nonindependent and identical distribution among data objects to measure similarity and the improved CS_KNN algorithm can greatly improve the classification performance. Finally, this paper constructs a prediction model of multimedia network information popularity based on the fast K neighbor algorithm. Through the experimental research results, it can be seen that the prediction effect of the multimedia network information popularity prediction system based on the fast K neighbor algorithm proposed in this study is very good. [ABSTRACT FROM AUTHOR]

In order to improve the cost control effect of the supply chain of intelligent manufacturing enterprises, this paper improves the IQC algorithm. In order to overcome the problem of multitype data processing in the supply chain, this paper describes the high-frequency NCS as a delta arithmetic system with input time-varying delay, high-frequency constraints, and actuator saturation. Moreover, through a double closed-loop feedback configuration, this paper combines the loop structure of the supply chain to analyze the stability of the actuator saturated high-frequency NCS and combines the experiments to verify the reliability of the improved algorithm. In addition, on the basis of the improved algorithm, this paper constructs an intelligent supply chain cost control system, evaluates its functional modules, sets up the system architecture, and analyzes the performance of the system in combination with experimental research. The experimental research results show that the supply chain cost control method of intelligent manufacturing enterprises based on the improved IQC algorithm proposed in this paper has good results and meets the operational needs of intelligent manufacturing enterprises. [ABSTRACT FROM AUTHOR]

An association rule mining algorithm is an algorithm that mines the association between things and is often used to mine the association knowledge between things. Association rule mining algorithms can find potential connections between different qualities of college students from the data of college students' life and learning, which can help teachers discover the problems and their own strengths of different students and achieve teaching according to their aptitude. The purpose of this paper is to solve some problems related to the associative rule extraction algorithm and to investigate the impact of applying the associative rule extraction algorithm in a college student quality assessment system. Based on the algorithm, a quality assessment system for college students has been developed. A modified script-based associative rule extraction algorithm is used to find the correlation between the quality and the ability of college students. The quality assessment data of college students are analyzed and studied. The results show that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33% and reduce the probability of outliers in the scoring process by 27%. It can be seen that the association rule extraction algorithm can be applied to college students' quality assessment system and also reduces the probability of encountering obstacles in accuracy and performance assessment. At the same time, this experiment also proves the robustness and feasibility of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the effect of automatic intelligent control, this paper improves the intelligent vector control algorithm, analyzes the performance of the algorithm with common power pulses as an example, and obtains an improved intelligent vector control algorithm suitable for modern intelligent systems. By making two mutually adjacent vectors nonidentical combination in time, the two vectors have the difference between zero and nonzero, and the vector can be rotated into a certain area. In addition, with the support of improved algorithms, this paper gets an automated intelligent control system based on intelligent control algorithms. Finally, this paper verifies the effect of the intelligent control system through experimental research. The analysis of the test results shows that the automated intelligent control system proposed in this paper has a better effect in intelligent control. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the "gain rate" optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms. [ABSTRACT FROM AUTHOR]

A smart city's vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The "Eigen-trick" method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle's score, and the CH is selected based on the maximum vehicle's score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) has become a very popular technology with a wide range of applications. It consists of several spatially distributed sensors that work collaboratively to monitor a given region of interest (ROI). The limited energy available for each sensor node is a crucial restriction that affects the overall performance of the network. Therefore, energy efficiency is a major concern in WSNs. Over the years, many techniques have been developed and used to reduce energy consumption in WSNs. Clustering is one of the most effective energy-saving techniques that significantly can improve the efficiency of WSNs in terms of the network lifetime, energy consumption, and the number of received packets. In this paper, an energy-efficient algorithm for cluster head (CH) selection based on a newly formulated fitness function and using the manta ray foraging optimization (MRFO) is proposed. The objective function for the proposed formulation takes into account different network parameters such as the average distance between the CH and the sensors in its cluster, the distance between CHs and the base station (BS), residual energy, and CH balancing. The proposed algorithm is tested by running many simulations under a variety of conditions. The simulation results showed that the proposed algorithm has a better performance than that of some other algorithms reported in the literature in terms of energy consumption, networks lifetime, and the number of received packets. [ABSTRACT FROM AUTHOR]

This paper proposes an interphase power control algorithm (IPCA) and submodule power control algorithm (SPCA) based on state transition PWM (ST-PWM) for cascaded H-bridge inverter with unbalanced DC sources. In this method, the correction value helps control the interphase power distribution and submodule power distribution by directly modifying the duty cycle of each switching state. In order to achieve linear modulation, an in-depth analysis of ST-PWM is conducted. Combining ST-PWM and IPCA, balanced AC currents and accurate power control on DC side can be achieved with an unbalanced DC source. The proposed strategies are simulated using MATLAB/Simulink and tested on a five-level laboratory prototype. Simulation and experimental results demonstrate the advantage and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network's ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex event pattern in big data and strengthen research on key technologies of the Internet of Things and computer time matching algorithms, this paper studies the problem based on Hadoop clustering algorithm. Firstly, based on the subtype attribute of event type, the maximum value is selected as the final attribute value of the event after weighting the event. Secondly, cluster analysis is conducted on the Internet of Things flow dataset through the relationship between complex events. Finally, the simulation test is carried out on the simulation dataset of complex event relationship, and the simulation test of clustering algorithm comparison is carried out. The experimental results show that the clustering accuracy of different datasets is above 85%, and the clustering accuracy of causality reaches 96.07% when the dataset is 5G. Therefore, the algorithm proposed has high feasibility, good stability, and high speed and effectiveness for complex event processing. This study has certain practical significance for solving the problem of complex event pattern in big data. [ABSTRACT FROM AUTHOR]

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%. [ABSTRACT FROM AUTHOR]

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness. [ABSTRACT FROM AUTHOR]

In the matching pursuit algorithm of compressed sensing, the traditional reconstruction algorithm needs to know the signal sparsity. The sparsity adaptive matching pursuit (SAMP) algorithm can adaptively approach the signal sparsity when the sparsity is unknown. However, the SAMP algorithm starts from zero and iterates several times with a fixed step size to approximate the true sparsity, which increases the runtime. To increase the run speed, a sparsity preestimated adaptive matching pursuit (SPAMP) algorithm is proposed in this paper. Firstly, the sparsity preestimated strategy is used to estimate the sparsity, and then the signal is reconstructed by the SAMP algorithm with the preestimated sparsity as the iterative initial value. The method reconstructs the signal from the preestimated sparsity, which reduces the number of iterations and greatly speeds up the run efficiency. [ABSTRACT FROM AUTHOR]

Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. Vehicle detection can be exploited to accomplish several tasks such as computing the distances to other vehicles, which can help the driver by warning to slow down the vehicle to avoid collisions. In this paper, we propose an efficient real-time vehicle detection method following two steps: hypothesis generation and hypothesis verification. In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. In the second step, two-dimensional discrete wavelet transform (2D-DWT) is used to extract features from the hypotheses generated in the first step and then to classify them as vehicles and nonvehicles. The choice of the classifier is very important due to the pivotal role that plays in the quality of the final results. Therefore, SVMs and AdaBoost are two classifiers chosen to be used in this paper and their results are compared thereafter. The results of the experiments are compared with some existing system, and it showed that our proposed system has good performance in terms of robustness and accuracy and that our system can meet the requirements in real time. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient cryptosystem for digital image encryption and authentication. The cryptosystem is a hybrid scheme that uses symmetric and asymmetric approaches. The first one is used to encrypt the host image by utilizing a chaos-based key generator. The second one is used to encrypt the initial secret key and the owner's signature that permit authentication. The algorithm is evaluated and validated by its application on several types of standard images and tools such as the statistical analysis, the key, and the performance analysis. The results indicate that the proposed cryptosystem provides high performance and enhanced security. The NIST 800-22 is used for testing the pseudorandom numbers generation (PRNG). The obtained simulation results are better than those cited in the recent works in terms of execution time and security level and low computational complexity. [ABSTRACT FROM AUTHOR]

The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability. [ABSTRACT FROM AUTHOR]

DC microgrids are gaining more attention compared to AC microgrids due to their high efficiency and uncomplicated interconnection of renewable sources. In standalone DC microgrid, parallel-connected converters connect the storage system to the load. To achieve equal current sharing among parallel converters, several methods have been presented, but they vary in their current sharing performance, complexity, cost, and reliability. In DC microgrid, the conventional droop control method is preferred because it is more competitive in terms of cost, suitability, and reliability compared to the master-slave control method. However, the conventional droop method cannot ensure equal current sharing due to the mismatches in parameters of parallel-connected converters. To address this limitation, a control algorithm that supervises a modified droop method to achieve precise current sharing between parallel modules is proposed in this paper. The control algorithm is based on the percentage of current sharing for each module to the total load current. The output current measurement of each converter is compared to the total load current and is used to modify the nominal voltage for each converter. The effectiveness of the proposed algorithm is verified by MATLAB simulation model and experimental results. [ABSTRACT FROM AUTHOR]

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user's behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user's interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time. [ABSTRACT FROM AUTHOR]

In this paper, a novel chaotic circuit with one memcapacitor is designed, which has abundant dynamic behaviors. As a potential application, combining the DNA binding mode and the characteristics of the chaotic system, a new image encryption scheme is proposed. Firstly, the original image is processed and generates a set of hash values by the SHA-3 algorithm. The generated hash value and original image are performed XOR operation. The hash values are processed to generate the initial value of the chaotic system by the Hamming distance. Furthermore, the elliptic curve with the hyperchaotic sequence is used to construct the Hill encryption matrix, and the plaintext image is permuted and encrypted. At last, the proposed chaotic system is used to scramble the image. The simulation results and theoretical analysis show that the chaotic system has abundant dynamic behaviors, and the proposed algorithm has a better anti-interference ability for image and information encryption. [ABSTRACT FROM AUTHOR]

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov's decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers' bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach. [ABSTRACT FROM AUTHOR]

Based on the Adomian decomposition method and Lyapunov stability theory, this paper constructs a fractional-order memristive hyperchaos. Then, the 0–1 test analysis is applied to detect random nature of chaotic sequences exhibited by the fractional-order systems. Comparing with the corresponding integer-order hyperchaotic system, the fractional-order hyperchaos possesses higher complexity. Finally, an image encryption algorithm is proposed based on the fractional-order memristive hyperchaos. Security and performance analysis indicates that the proposed chaos-based image encryption algorithm is highly resistant to statistical attacks. [ABSTRACT FROM AUTHOR]

Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods. [ABSTRACT FROM AUTHOR]

Clustering algorithms have been explored in recent years to solve hotspot clustering problems in integrated circuit design. With various applications in design for manufacturability flow such as hotspot library generation, systematic yield optimization, and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequality to solve the problem of minimizing cluster count while satisfying given constraints. Apart from minimizing cluster count, we generate representative clips that best represent the clusters formed. We achieve a better cluster count for both formulations in most test cases as compared to the results published in the literature in the ICCAD 2016 contest benchmarks as well as the reference results reported in the ICCAD 2016 contest website. [ABSTRACT FROM AUTHOR]

In this paper, an orthogonal regularized kernel canonical correlation analysis algorithm (ORKCCA) is proposed. ORCCA algorithm can deal with the linear relationships between two groups of random variables. But if the linear relationships between two groups of random variables do not exist, the performance of ORCCA algorithm will not work well. Linear orthogonal regularized CCA algorithm is extended to nonlinear space by introducing the kernel method into CCA. Simulation experimental results on both artificial and handwritten numerals databases show that the proposed method outperforms ORCCA for the nonlinear problems. [ABSTRACT FROM AUTHOR]

It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image. [ABSTRACT FROM AUTHOR]

Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced. [ABSTRACT FROM AUTHOR]

For electric power steering system (EPS), road interference, noise of the sensor, and the uncertainty of the steering system may make EPS control effect and the driver's road sense worse. EPS system which takes advantage of good current tracking ability, good anti-interference ability, and good operation stability is becoming more and more important in automotive research. The traditional H∞ control algorithm can solve the system uncertainty theoretically, but it cannot solve the contradiction between robustness and performance without considering the performance of the system. Therefore, this paper proposes a EPS current tracking method based on the hybrid sensitivity H∞ control algorithm, which takes the current tracking performance as one of the control objectives, so that the system can maximize the robustness and performance. Firstly, the dynamic model of EPS is established. Then, the two-degree-of-freedom vehicle model and tire model are introduced. The state space equation of the system is constructed on the basis of the system state space with random disturbance signals, the hybrid sensitivity H∞ controller is designed in the sensitivity index design, and the proposed algorithm can use weighting function to minimize the performance of the current tracking error as well as the robustness of the yaw rate error in response to robustness. Simulation analysis and experimental verification of EPS system are also carried out. The results show that the control method of the hybrid sensitivity H∞ can better achieve EPS target current tracking, effectively suppress the effect of external interference and noise, improve the system performance and robustness, ensure the driver get good road sense, and improve the system of steering stability. [ABSTRACT FROM AUTHOR]

Spectrum sensing is one of the key technologies in wireless wideband communication. There are still challenges in respect of how to realize fast and robust wideband spectrum sensing technology. In this paper, a novel nonreconstructed sequential compressed wideband spectrum sensing algorithm (NSCWSS) is proposed. Firstly, the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm. Secondly, the algorithm uses the strategy of compressed sensing without reconstruction, which thus ensures the sensing agility of the algorithm. The algorithm is simulated and analyzed by using the centralized cooperative sensing. The theoretical analysis and simulation results reveal that, under the condition of ensuring the certain detection probability, the proposed algorithm effectively reduces complex computation of signal reconstruction, significantly reducing the wideband spectrum sampling rate. At the same time, in the cognitive wideband communication scenarios, the algorithm also achieves a better defense against the SSDF attack in spectrum sensing. [ABSTRACT FROM AUTHOR]

Cross-corpus speech emotion recognition, which learns an accurate classifier for new test data using old and labeled training data, has shown promising value in speech emotion recognition research. Most previous works have explored two learning strategies independently for cross-corpus speech emotion recognition: feature matching and sample reweighting. In this paper, we show that both strategies are important and inevitable when the distribution difference is substantially large for training and test data. We therefore put forward a novel multiple kernel learning of joint sample and feature matching (JSFM-MKL) to model them in a unified optimization problem. Experimental results demonstrate that the proposed JSFM-MKL outperforms the competitive algorithms for cross-corpus speech emotion recognition. [ABSTRACT FROM AUTHOR]

A new type of student information management system is designed to implement student information identification and management based on fingerprint identification. In order to ensure the security of data transmission, this paper proposes a data encryption method based on an improved AES algorithm. A new S-box is cleverly designed, which can significantly reduce the encryption time by improving ByteSub, ShiftRow, and MixColumn in the round transformation of the traditional AES algorithm with the process of look-up table. Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm. [ABSTRACT FROM AUTHOR]

Flat surface detection is one of the most common geometry inferences in computer vision. In this paper we propose detecting printed photos from original scenes, which fully exploit angular information of light field and characteristics of the flat surface. Unlike previous methods, our method does not need a prior depth estimation. The algorithm rectifies the mess epipolar lines in the epipolar plane image (EPI). Then feature points are extracted from light field data and used to compute an energy ratio in the depth distribution of the scene. Based on the energy ratio, a feature vector is constructed and we obtain robust detection of flat surface. Apart from flat surface detection, the kernel rectification algorithm in our method can be expanded to inclined plane refocusing and continuous depth estimation for flat surface. Experiments on the public datasets and our collections have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The selection of feature subset is a crucial aspect in speech emotion recognition problem. In this paper, a Reordering Features with Weights Fusion (RFWF) algorithm is proposed for selecting more effective and compact feature subset. The RFWF algorithm fuses the weights reflecting the relevance, complementarity, and redundancy between features and classes comprehensively and implements the reordering of features to construct feature subset with excellent emotional recognizability. A binary-tree structured multiple-kernel SVM classifier is adopted in emotion recognition. And different feature subsets are selected in different nodes of the classifier. The highest recognition accuracy of the five emotions in Berlin database is 90.549% with only 15 features selected by RFWF. The experimental results show the effectiveness of RFWF in building feature subset and the utilization of different feature subsets for specified emotions can improve the overall recognition performance. [ABSTRACT FROM AUTHOR]

Algorithms for harmonic detection and compensation are important guarantees for an active power filter (APF) to achieve the harmonic control function and directly determine the overall performance. Existing algorithms usually need a large amount of computation, and the compensation effect of specified order harmonic is also limited. DC side capacitor voltage at sudden change of load is affected by the algorithm as well. This paper proposes a new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF), in which a band-pass filter with center frequency of 6kth harmonic is designed in fundamental frequency SRF to extract random harmonic current with two different frequencies of (6k±1)th harmonic in stationary reference frame. This new algorithm can rapidly detect any specified harmonic, and it can adjust the power factor to compensate reactive power. Meanwhile, it has few impacts on DC side capacitor voltage under complicated operating conditions such as sudden change of load. The correctness and effectiveness of this new algorithm are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity. [ABSTRACT FROM AUTHOR]

With the rapid growth of aged population in China, it is urgent to design a safe and effective monitoring system for the nursing homes. An optimized scheme and high performance security and privacy protection for monitoring system have already become the focus studied especially. So this paper proposed a health monitoring system with lightweight security and privacy protection for nursing homes. Dual-band RFID, virtual routing location algorithm, and diet and exercise data collection based on RFID were adopted to obtain the location and health information. And that fused a mobile authentication protocol based on Hash function to realize security access and privacy protection, which can improve security and reduce the complexity of calculation and the implementation cost compared with the typical authentication protocols. The experiment results show that the ratio of relative network delay is below 35%. The system has strong real-time, high security, more comprehensive data, and lower cost of computation and communication. It can satisfy the requirements of health monitoring for nursing homes. [ABSTRACT FROM AUTHOR]

DDoS attacks can prevent legitimate users from accessing the service by consuming resource of the target nodes, whose availability of network and service is exposed to a significant threat. Therefore, DDoS traffic perception is the premise and foundation of the whole system security. In this paper the method of DDoS traffic perception for SOA network based on time united conditional entropy was proposed. According to many-to-one relationship mapping between the source IP address and destination IP addresses of DDoS attacks, traffic characteristics of services are analyzed based on conditional entropy. The algorithm is provided with perception ability of DDoS attacks on SOA services by introducing time dimension. Simulation results show that the novel method can realize DDoS traffic perception with analyzing abrupt variation of conditional entropy in time dimension. [ABSTRACT FROM AUTHOR]

SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. In this paper, an improved SPRINT algorithm is proposed, which searches better candidate segmentation point for the discrete and continuous attributes. The experiment results demonstrate that the proposed algorithm can reduce the computation cost and improve the efficiency of the algorithm by improving the segmentation of continuous attributes and discrete attributes. [ABSTRACT FROM AUTHOR]

Cloud storage has been recognized as the popular solution to solve the problems of the rising storage costs of IT enterprises for users. However, outsourcing data to the cloud service providers (CSPs) may leak some sensitive privacy information, as the data is out of user’s control. So how to ensure the integrity and privacy of outsourced data has become a big challenge. Encryption and data auditing provide a solution toward the challenge. In this paper, we propose a privacy-preserving and auditing-supporting outsourcing data storage scheme by using encryption and digital watermarking. Logistic map-based chaotic cryptography algorithm is used to preserve the privacy of outsourcing data, which has a fast operation speed and a good effect of encryption. Local histogram shifting digital watermark algorithm is used to protect the data integrity which has high payload and makes the original image restored losslessly if the data is verified to be integrated. Experiments show that our scheme is secure and feasible. [ABSTRACT FROM AUTHOR]

An efficient path planning approach in mobile beacon localization for the randomly deployed wireless sensor nodes is proposed in this paper. Firstly, in order to improve localization accuracy, the weighting function based on the distance between nodes is constructed. Moreover, an iterative multilateration algorithm is also presented to avoid decreasing the localization accuracy. Furthermore, a path planning algorithm based on grid scan which can traverse entirely in sensor field is described. At the same time, the start conditions of localization algorithm are also proposed to improve localization accuracy. To evaluate the proposed path planning algorithm, the localization results of beacon nodes randomly deployed in sensor field are also provided. The proposed approach can provide the deployment uniformly of virtual beacon nodes among the sensor fields and the lower computational complexity of path planning compared with method which utilizes only mobile beacons on the basis of a random movement. The performance evaluation shows that the proposed approach can reduce the beacon movement distance and the number of virtual mobile beacon nodes by comparison with other methods. [ABSTRACT FROM AUTHOR]

As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time and matching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent. [ABSTRACT FROM AUTHOR]

High energy consumption in Ethernet Passive Optical Networks (EPONs) has caused intense research on energy-saving methods in recent years. The most common method for EPON energy saving is to cause optical network units (ONUs) to sleep which are idle or low loaded. With the popularity of multimedia applications, the ONUs carry not only unicast traffic but also more and more multicast traffic. However, existing studies mainly focus on ONUs that support only unicast traffic. The many differences between multicast traffic and unicast traffic make it necessary to design novel energy-saving methods. This paper proposes a multicast-traffic-oriented energy-saving algorithm for EPONs, called the energy-saving algorithm for multicast traffic (ESMT). To save as much energy as possible, the proposed algorithm uses a hybrid sleep mode (composed of a deep sleep state and an independent sleep mode) in which not only can ONU enter a deep sleep state, but also the ONU transmitter and receiver can sleep independently. Simulation results show that the proposed algorithm, which is oriented to multicast traffic in EPONs, is more energy efficient than other algorithms. [ABSTRACT FROM AUTHOR]