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In this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closed-loop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper.

In this paper, we propose a fiber-optic time synchronization technique based on the bidirectional wavelength division multiplexing (WDM) transmission over a fiber link. The effect of fiber dispersion on the uncertainty of the system is effectively suppressed by properly allocating the forward and backward wavelength maps. Thus, only an initial easy-to-implement calibration in a back-to-back configuration instead of multiple complicated link calibrations is required. To compensate the propagation delay fluctuations, a large-dynamic-range time pre-compensator with high precision is implemented at the local site, enabling a time signal at the remote site is accurately synchronized with the clock at the local site. Our experiments demonstrate that stabilities in terms of time deviation (TDEV) of less than 29.8 ps at 1s and 5.2 ps at 104s and the clock difference of less than 28 ps can be achieved for the fiber link up to 200 km.

Reversible data hiding (RDH) is a technique that slightly alters digital media (e.g. images or videos) to embed secret messages while the original digital media can be completely recovered without any error after the hidden messages have been extracted. In the past more than one decade, hundreds of RDH algorithms have been reported, and among these algorithms, the difference histogram shifting (DHS) based methods have attracted much attention. With DHS-based RDH, high capacity and low distortion can be achieved efficiently. But there occurs one problem that, with DHS, the difference values to embed secret bits are explored, and the other difference values are shifted to create vacant spaces, it will cause the difference value histogram changing significantly and draw the attention of steganalyzers. So, this paper proposed a new idea for RDH based on the difference value and with statistical features maintained (SFM) with simple implementation and high scalability, we embed the secret messages by keeping the difference values that need to be modified in the original range, and the other difference values would not be shifted. In addition, we need the original difference values as the key to extract the secret messages. In order to expand the embedding capacity further, we designed two algorithms that embed message in two different difference values and four different difference values, and these two methods are named SFM_A and SFM_B respectively. SFM_B can support greater amount of embedded message than SFM_A, but brings greater changes to the original image, which could lead to the decline of PSNR and SSIM. The experimental results show that through our method, the histogram of difference values is well maintained, and the degree of distortion of the image is improved at the same time.

In recent years among data hiding technologies, Reversible Data Hiding(RDH) technology has attracted widespread interest and application, which is to hide the secret information in a carrier image and recover the original carrier image losslessly to extract the secret information. Current research on RDH algorithms mainly involving frequency domain, spatial domain, and encryption domain. Based on the Prediction-Error Expansion(PEE) methods, as spatial domain approaches, achieved great progress in the past decade. However, there is a defect in the state-of-the-art methods that with the embedded payload increased, the distortion rate of the cover image increased simultaneously. To solve the problem, we proposed a refined reversible data hiding algorithm based on the PEE method with simple implementation. We improved an effective predictor that all the remaining pixels can be predicted in the embedding process, except for those in the first row, the first column, the last row, and the last column in the original image. The extraction process is the reverse of the embedding process that the embedded information and the original carrier is restored without damage. Our work utilized the correlation between image pixels better to solve the inherent contradiction between payload and distortion rate in the state-of-the-art data hiding algorithms. Proven through experiments, our method achieved a large embedding capacity while keeping the image distortion rate and computing complexity low.

With the popularity of Apps, the products in one domain become more and more similar to each other, and developers start to find the break from other domains. However, facing the large-scale data resource in App stores, it is difficult to identify the related domains, let alone gain useful features from the products in them. In this paper, we propose an approach to help developers learn information of features related to their App from the products in different domains. Firstly, we provide the method to extract features as well as their relationships from App descriptions to describe one domain. Then, the similar features shared by different domains are identified as the bridges for searching the potential information which may be re-used by the developers. Finally, we provide the framework of an interactive recommendation system to let developers gain and understand the information easily. To evaluate our approach, we conducted experiments based on the dataset on Google Play. The results show that the average precision of our approach for finding similar features between different domains can reach 82.38%, and the survey on developers indicate that the information recommended by our approach is useful for updating Apps and inspiring developers generate innovative ideas.

In the field of non-isolated light-emitting diode (LED) driver, the omnipresent buck-based LED constant current driver shows poor efficiency in occasions of high step-down ratio due to the marginal operating duty cycle. In order to overcome such drawback and to provide an alternative topological scheme for an LED driver, a Watkins-Johnson topology-based solution is proposed in this paper, by improving the original circuit structure into a dual low-side power switch version and introducing a feedback network for regulating the output current to a constant level. Theoretical analysis, including accurate model construction, steady-state analysis, dynamic analysis, and design consideration, is conducted. Also, an experimental prototype is realized to drive all kinds of LED arrays for acquiring electrical parameters and evaluating performance. The innovative points include invoking the low-side-ize approach to realize the power circuit transformation, and furthermore, theWatkins-Johnson topology is first applied to the constant current power supply. The proposed LED driver holds salient features of low-terminal current ripple, ease of driving, high-voltage conversion ratio tolerance, and inherent energy recovery functionality. The application scope covers low-voltage dc input or automotive lighting use.

Emerging communications technologies, such as IPv6 and 5G, will enable massive numbers of devices to connect to the Internet of Things. With the scale of networking equipment increasing, how to effectively use extensive IoT data is an increasingly urgent issue. The interaction relationships between IoT devices based on various application requirements have not received enough attention in traditional data mining methods. However, IoT devices require a large amount of information to interact in practical applications every moment, which produces a variety of semantic relationship. Different from the traditional methods, this paper employs graph structure to represent the semantic relations of IoT data, which exploits fine-grained semantic information more efficiently. Our contributions are as follows: 1) we propose the IoT data representation framework by converting semantic relations into graph structure, 2) the framework can leverage different meta-paths in the graph to measure the similarity among entities in the IoT from different perspectives, and 3) we conduct a cluster experiment based on regularization improved matrix factorization for different application scenarios that consider the semantic similarity. We demonstrate our method using a real-world dataset, and the experimental results show the practicability and effectiveness of our proposed approach. This paper presents a new research angle to analyze semantic data in the IoT.

It is hard to select and read suitable documents due to the rapidly growing number of scholarly documents. Keyphrases can be considered as the gist of a document so that a researcher can select the documents that they want using keyphrase queries. However, there are also many scholarly documents without any keyphrases tagged by the authors or other researchers. Automatic keyphrase extraction can help researchers to quickly extract keyphrases. This paper proposed an unsupervised approach for keyphrase extraction using graph-based ranking and topic-based clustering under the assumption that we only use the within-collection resources. We use graph-based ranking to describe the relevance between two words and topic-based clustering to embed semantical information into words. In this paper, we assume that each word has its own meaning, and each meaning can be considered as a topic, though we know nothing about these meanings. We use topic-based clustering to assign the “correct meaning” to the “correct word”. In addition, by taking the relevance among phrases into consideration and only using within-collection resources, we can use the graph-based ranking in our approach. The edges in a graph that are built for phrases can describe the hidden relevance between two phrases, and the weights that are set for edges can measure the connection between two phrases. Then, after using the position feature, our approach consists of an enhanced graph-based ranking and a topic-based clustering. The experiments are run on four datasets: KDD, WWW, GSN and ACM. The results indicate that our approach has better performance than the state-of-the-art methods.

With the rapid development of technologies, the ecological control strategies of connected and autonomous vehicle (CAV) technologies are gaining more and more attention. In this paper, a rule-based ecological cruise control, called the ecological smart driver model (EcoSDM), is proposed to improve the fuel efficiency of an individual vehicle and the traffic flow. By adjusting the spacing between the leading and the following vehicles, EcoSDM provides smoother deceleration and acceleration than the enhanced intelligent driver model (Enhanced-IDM) and the smart driver model (SDM). The linear stability of EcoSDM is analyzed both theoretically and numerically. The numerical results validate the results of theoretical analysis. Moreover, the simulations results show that EcoSDM outperforms the Enhanced-IDM and SDM in terms of stabilization effect on homogeneous traffic flow. In addition, the calibrated VT-Micro model is used to estimate the fuel consumption of CAVs and manually driven vehicles. The result shows that CAVs have better fuel economy than the human-driven vehicles, which is consistent with existing studies. The EcoSDM outperforms Enhanced-IDM and SDM in terms of fuel consumption. For the EcoSDM-equipped CAVs, the fuel saving benefit is greatest when a CAV is at the front of the platoon.

5th generation wireless systems are coming. While we are excited about the delay-free high speeds 5G will bring, security problems are becoming more and more serious. Increasingly rampant Distributed Denial of service (DDoS) attacks, particularly Distributed Reflection Denial of Service (DrDoS) attacks with User Datagram Protocols (UDPs) have developed into a global problem. This article presents a design, implementation, analysis, and experimental evaluation of an authentication scheme, a defense against UDP DrDoS attacks, by which attackers cleverly use rebound server farms to bounce a flood of packets to a target host. We call our solution IEWA because it combines the concepts of increasing expenses and weak authentication. In this paper, we apply IEWA to Network Time Protocol (NTP). First, we simulate and compare the original and improved protocols. Next, we verify the effectiveness of our proposed scheme. We show that our improved scheme is safer than the original scheme. Finally, we compare our solution with existing state-of-the-art schemes, using indicators such as communication overhead, server storage costs, client storage costs, computation costs of server and computation costs of client. We find that our scheme improves system stability and security, reduces communication overhead, server storage cost and computational costs. Our solution not only improves the NTP protocol to mitigate DrDoS attacks, but also strengthens other UDP protocols that are vulnerable to DrDoS attacks. Therefore, our solution can be used as a solution to UDP DrDoS attacks in 5G Networks.

Coauthorship prediction is challenging yet important for academic collaboration and novel research topics discovery. The challenges lie in the dynamics of social or organizational relationships, changing preferences of suitable collaborators, and the evolution of research interests or topics. However, most current approaches and systems developed so far are mainly based on past coauthorships from a static viewpoint and do not capture the above evolving characteristics in coauthoring. Accordingly, this paper proposes a time-aware approach to capture the evolving coauthorships from online academic databases in terms of capturing the dynamics of social relationships and research interests. In particular, in order to understand the underlying factors influencing researchers to make choices of coauthors, we incorporate choice modeling based on utility theory. More specifically, our model conducts a series of pairwise choices over a poset induced by a utility function so as to learn the preference over all candidate coauthors. To complete the model inference, a gradient-based algorithm is devised to efficiently learn the model parameters for large-scale data. Finally, extensive experiments conducted on a real-world dataset show that our approach consistently outperforms other state-of-the-art methods.

Mobile crowdsensing (MCS) is a new sensing framework that empowers normal mobile devices to participate in sensing tasks. The key challenge that degrades the performance of MCS is selfish mobile users who conserve the resources (e.g., CPU, battery, and bandwidth) of their devices. Thus, we introduce energy harvesting (EH) as rewards into MCS, and thus provide more possibilities to improve the quality of service (QoS) of the system. In this paper, we propose a game theoretic approach for achieving sustainable and higher quality sensing task execution in MCS. The proposed solution is implemented as a two-stage game. The first stage of the game is the system reward game, in which the system is the leader, who allocates the task and reward, and the mobile devices are the followers who execute the tasks. The second stage of the game is called the participant decision-making game, in which we consider both the network channel condition and participant's abilities. We analyze the features of the second stage of the game and show that the game admits a Nash equilibrium (NE). Based on the NE of the second stage of the game, the system can admit a Stackelberg equilibrium, at which the utility is maximized. Simulation results demonstrate that the proposed mechanism can achieve a better QoS and prolong the system lifetime while also providing a proper incentive mechanism for MCS.

The use of machine learning (ML) algorithms to conduct prediction or analysis tasks in a data center networking (DCN) environment is gaining increasing attention today. Recent research in traffic prediction, abnormal traffic monitoring, and routing selection has led to significant progress by making full use of historical data and improved ML models. However, such approaches face challenges when dealing with graphical data. These approaches have limited capabilities to explore the information that is hiding in the network's topological structure. To solve these challenges, we study the problem of representation learning in DCN topologies. To serve as a bridge, we proposed a novel method, “Topology2Vec,”to learn the network topology and represent the nodes using low-dimensional vectors, which is useful in many topology-related tasks. Both network structure and performance are considered in our method to ensure that the representation can adapt to different requirements. To evaluate the effectiveness, we demonstrate this method in a controller placement problem as a typical use case using topological data from real-world data centers. The experiments show that the use of “Topology2Vec”as a premise has produced better results in terms of network latency.

In order to reduce the size and cost of a medium-power light-emitting diode (LED) driver, a novel single-stage LED driver integrating a boost converter with a half-bridge LLC resonant converter has been proposed. This topology is composed of a boost power factor correction circuit operating in a discontinuous conduction mode and an isolated LLC circuit unit with soft-switching characteristics. The proposed LED driver only adopted two switch components, thus benefiting from control simple and without employing an extra switch driver and a control circuit. The operating principle and characteristics of the proposed LED driver are analyzed in detail. In addition, experimental results of a laboratory prototype for supplying a 100-W/70-V LED lighting systems from 220-V/50-Hz ac line voltage are presented to verify well the correctness of the theoretical analysis and parameters’ design.

In recent years, the Internet has become an indispensable part of people's lives, and it offers increasingly comprehensive information tailored to people's personal preferences as well as commodity attribute information. Consequently, many researchers have used external information to improve recommendation technology. However, most previous studies consider only adding single relationship types, such as social networking friend-relationships. In the real world, considering multiple types of external relations can more accurately determine the reason why a user selected an item. To address this problem, in this paper, we propose a hybrid method called the semantic preference-based personalized recommendation on heterogeneous information networks (SPR), which combines user feedback scores with heterogeneous information networks. This method can improve recommendation problems by considering multiple types of external relationships. To apply the method, we first introduce a similarity measure between users based on a user's potential preferences in the meta-path and design the recommended model at the global and individual level. Finally, we perform experiments on two real-world data sets, finding that the SPR method achieves better results compared with the several widely employed and the state-of-the-art recommendation methods.

The multimodal properties of the grinding wheel matrix significantly impact grinding performance, while research on the interactions among these properties remains notably limited. To investigate the latent relationship between the microstructure and the bending strength of the bronze matrix, a visual quantization model based on the microstructure of the Cu-Sn alloy samples was established. The proposed model integrated a image segmentation network module, a quantitative characterization module, and a multivariate prediction module. The enhancement of the segmentation network is based on the synergistic combination of full-scale feature fusion with attention mechanism. The quantitative characterization parameters of metallographic microstructure features are proposed, and the most prominent intercorrelation between these parameters is studied from multiple dimensions. The results show that the modified image segmentation network exhibits superior performance compared to Unet, as evidenced by a 3% increase in Mean Intersection over Union (MIOU). The optimized output strategy ( $\eta Dd$ -PSO-SVR) can contribute to the model’s prediction accuracy of material bending strength (MSE = 23.558,R $^{2}=0.934$ ). Finally, this work shows that the microscopic information demonstrates great adaptability for the machine learning models in predicting bending strength.

Chromophoric dissolved organic matter (CDOM) is a crucial component of aquatic environments. Accurately quantifying the content of CDOM is essential for supporting lake water quality monitoring and management. In this study, we utilized quasi-analytical algorithms (QAA_v6 and QAA_CDOM) to estimate the CDOM concentration in the intricate freshwater environment of Erhai Lake. This was accomplished by utilizing in-situ hyperspectral data and water sample measurements. A quasi-analytical algorithm, named QAA_CDOM_680, was proposed to retrieve CDOM absorption coefficients from Sentinel-3 satellite images by optimizing the reference wavelength based on these two algorithms. The root mean square errors (RMSE) for QAA_V6, QAA_CDOM algorithm, and the new algorithm QAA_CDOM_680 are 0.615, 0.2235, and 0.1935, respectively. The calibration determination coefficients (R2) of the three algorithms are 0.44, 0.51, and 0.63, respectively. The new algorithm demonstrates a significant improvement in accuracy. The results of two satellite-ground synchronous inversions indicate that the calibration determination coefficients (R2) of CDOM absorption coefficients in the Erhai Lake research area are 0.42 and 0.48, respectively. This study has successfully completed the dynamic quantification monitoring of CDOM in Erhai Lake, contributing to the preservation of Erhai Lake’s water quality.

Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812.

Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes. Monitoring approaches based on radial basis kernel function have been intensively applied. However, an infinite dimension nonlinear mapping is redundant and inefficient. To improve the efficiency of traditional methods and consider the nonlinearity and dynamics simultaneously, this paper proposes canonical variate nonlinear principal component analysis (CV-NPCA) based on explicit polynomial mapping and combined statistic for detecting and identifying faults in nonlinear dynamic processes. There are two main contributions of the proposed method. First, explicit second-order polynomial mapping is introduced to combine CVA with PCA to simultaneously decrease the adverse effects of nonlinearity and dynamics. Second, the $Q_{c}$ statistic combining residual vectors with residual components is proposed, and a two-dimensional (2D) contribution plot and the variable with the largest contribution based on the $Q_{c}$ statistic are given for fault identification in the simulation study. Compared with the results of PCA, CVA, kernel principal component analysis (KPCA), nonlinear dynamic principal component analysis (NDPCA) and kernel entropy component analysis (KECA), the proposed method not only has relatively higher fault detection rates and identification rates but also has lower false alarm rates in the numerical simulation process and the benchmark Tennessee Eastman process.

In this work, we present and evaluate a three dimensional Convolutional Neural Network algorithm to accurately detect EEG abnormalities from multi-channel EEG signals. This research synthesizes several heterogeneous datasets, constructs a dataset 10 times larger than other datasets of its kind, uses all channel EEG signals as input, and preprocesses them into data structures that can reflect EEG spatio-temporal character, constructs and trains a 28-layer deep residual network, automatically extracts high-level features, and recognizes EEG anomalies. We collect and reorganize several heterogeneous data sets, and convert two-dimensional signal segments to three-dimensional frames after preprocessing. Thus we build a dataset of 14049 annotated samples with shape 512*11*11*1, of which 8866 are abnormal. On this dataset, we train a 28-layer convolutional network with residual blocks which classify EEG segments as normal or abnormal. Prediction on independent test sets using this trained model achieved an accuracy of 96.67%. The AUC is 99.93% and the RMSE is 0.0032. We compared the results of several methods and found that 3D frame data structure and deeper CNN model is better. The performance of our model also outperforms other related researches on EEG classification.

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-of-the-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning-based classifier. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and a shorter classification response time.

This paper proposes an active-clamped inverter (ACI) with duty cycle modulation for achieving simultaneous wireless power and data transmission (WPDT). The proposed method adjusts harmonic amplitudes by changing the duty cycle of the inverter, as a means to encode information alongside power using a shared wireless channel. This method generates a variable unsymmetrical current waveform corresponding with different duty cycles. By controlling the duty cycle, simultaneous power and data transfer can be achieved by using the fundamental and harmonic components respectively. The ACI is designed to operate in continuous current mode to reduce the variation of harmonic components, even under no load conditions. A simple ACI containing two switches is proposed to generate unsymmetrical current waveforms with a high efficiency and high circuit quality factor Q. Two communication methods are developed to balance the power transfer capability and signal-noise-ratio (SNR). Various separation distances and loads are tested, with experimental results showing the proposed system can achieve simultaneous power and data transfer across a shared channel in an inductive wireless power transfer.

In this paper, battery energy storage systems (BESSs) are integrated into wind farms (WFs) to mitigate the wind power fluctuations. This paper presents a formulation to optimize the operation schedule of the BESS, with the objective of minimizing the energy of the fluctuating component extracted from the output power of the BESS-integrated WF. The optimal operation schedule of the BESS is utilized to generate the charging/discharging instructions for the BESS to enhance the performances on mitigating the wind power fluctuations. Without proper control, the BESS switches between the charging and discharging states frequently and, thus, degrading batteries significantly, which results in a high operation cost of the BESS. To address this issue, the BESS is divided into two parts, which implement charging and discharging instructions, respectively. Sequential Monte Carlo simulation is applied to simulate the operation of the BESS-integrated WF on a typical day and the simulation results are used to evaluate the technical and economic performances on mitigating the fluctuations. The simulation results validate that the solution can mitigate the output power fluctuations with better performance and a less operation cost than the existing solutions.

This paper proposes a states feedback control method for Z-axis MEMS gyroscopes using fractional calculus and adaptive dynamic sliding mode control method. A new sliding mode control method is proposed to achieve trajectory tracking by adding a fractional order term in the conventional sliding manifold. The new proposed sliding surface contains integer order terms as well as fractional order terms and thus can provide an extra degree of freedom. Besides, in the presence of unknown system parameters, some adaptive laws containing the new designed sliding manifold are proposed to online tune controller parameters. All adaptive laws are derived in the stability framework and the stability of the control system is also guaranteed according to the Lyapunov stability theory and. Simulations results on a Z-axis vibrating gyroscope are provided to illustrate the effectiveness of the control method.

This paper proposes a simple and low-cost brightness and color control method for a wireless light emitting diode (LED) system. Since the open circuit voltage of the LED in the secondary side is approximately constant, the average current flowing through the LED reflects the average power received from the proposed wireless power transfer system. The brightness of the LED is adjusted by using a pulse width modulation (PWM) average power control method from the primary side directly. The same inductively coupled channel is utilized to establish a wireless communication channel to control the LED color based on a time-division multiplexing method. A wireless communication protocol is defined, and the worst-case condition for designing the primary PWM modulator identified to avoid the misinterpretation by the secondary demodulator between the power regulation and signal transfer. Experimental results have demonstrated the whole dimming capability of the proposed system with full range duty cycle regulation, and a three digit wireless communication channel developed in this paper can effectively control the selection of eight colors.

Considering a wireless social network, we investigate a cooperative jamming scheme based on the space power synthesis with unknown channel state information (CSI) of eavesdroppers. In particular, we provide a multiple jammers-based anti-eavesdropping model and formulate it by a superposition principle of various jamming signals in a free space. Based on the model, we analyze the superimposed effects of jammers with different locations in a fixed area, and then present corresponding jamming schemes to minimize synthetic jamming power at a legitimate receiver but satisfying basic interference in other locations. Furthermore, we also provide power allocation schemes to maximize the worst-case secrecy rate of a legitimate receiver. Numerical simulation results demonstrate that the secrecy performance of our cooperative jamming schemes can satisfy the requirements of secure transmission in a fixed area. Besides, our proposed power allocation schemes can further improve secrecy rate without known exact CSI of eavesdroppers.

This paper investigates the leader-following formation tracking problem (FTP) for multiple nonholonomic agent systems (MNASs) in the presence of external disturbances and parametric uncertainties, where both the kinematics and dynamics of the agents are taken into consideration. A novel finite-time distributed controller-estimator algorithm (DCEA) is designed to handle such a challenging problem. Based on Lyapunov stability method, the sufficient conditions for finite-time stability of the closed-loop system are derived. Finally, the simulation results are presented to demonstrate the effectiveness and the robustness of the proposed DCEA.