In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

Three technical problems should be solved urgently in cyberspace security: the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. Artificial intelligence (AI) algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace security. Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for cyberspace security applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in machine learning (ML), deep learning (DL), and some popular optimization algorithms, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

For complex functions to emerge in artificial systems, it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature. In this paper, we present a comprehensive survey of pursuit-evasion, which is a critical problem in biological groups. First, we review the problem of pursuit-evasion from three different perspectives: game theory, control theory and artificial intelligence, and bio-inspired perspectives. Then we provide an overview of the research on pursuit-evasion problems in biological systems and artificial systems. We summarize predator pursuit behavior and prey evasion behavior as predator-prey behavior. Next, we analyze the application of pursuit-evasion in artificial systems from three perspectives, i.e., strong pursuer group vs. weak evader group, weak pursuer group vs. strong evader group, and equal-ability group. Finally, relevant prospects for future pursuit-evasion challenges are discussed. This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement. In an indoor environment, the current mainstream localization scheme uses two-dimensional (2D) laser light detection and ranging (LiDAR) to build an occupancy grid map with simultaneous localization and mapping (SLAM) technology; it then locates the robot based on the known grid map. However, such solutions work effectively only in those areas with salient geometrical features. For areas with repeated, symmetrical, or similar structures, such as a long corridor, the conventional particle filtering method will fail. To solve this crucial problem, this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor. First, the mobile robot is remote-controlled to move from the starting position to the end along a middle line. In the moving process, a grid map is built using the laser-based SLAM method. At the same time, a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy. The keyframes are associated with the robot’s poses through timestamps. Second, a moving strategy is proposed, based on the extracted range features of the laser scans, to decide on an initial rough position. This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose. Third, the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results show that our method is effective and robust for global localization. The localization success rate reaches 98.8% while the average moving distance is only 0.31 m. In addition, the method works well when the mobile robot is kidnapped to another position in the corridor.

Ground-breaking optical wireless power transfer (OWPT) techniques have gained significant attention from both academia and industry in recent decades. Powering remote systems through laser diodes (LDs) to either operate devices or recharge batteries offers several benefits. Remote LDs can remove the burden of carrying extra batteries and can reduce mission time by removing battery swap-time and charging. Apart from its appealing benefits, laser power transfer (LPT) is still a challenging task due to its low transfer efficiency. In this paper, we discuss the necessity and feasibility of OWPT and discuss several projects, working principle, system design, and components. In addition, we show that OWPT is an essential element to supply power to Internet-of-Things (IoT) terminals. We also highlight the impacts of dynamic OWPT. We outline several OWPT techniques including optical beamforming, distributed laser charging (DLC), adaptive-DLC (ADLC), simultaneous lightwave information and power transfer (SLIPT), Thing-to-Thing (T2T) OWPT, and high intensity laser power beaming (HILPB). We also deal with laser selection, hazard analysis, and received photovoltaic (PV) cell selection for OWPT systems. Finally, we discuss a range of open challenges and counter measures. We believe that this review will be helpful in integrating research and eliminating technical uncertainties, thereby promoting progress and innovation in the development of OWPT technologies.

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.

In this paper, the problem of controllability of Boolean control networks (BCNs) with multiple time delays in both states and controls is investigated. First, the controllability problem of BCNs with multiple time delays in controls is considered. For this controllability problem, a controllability matrix is constructed by defining a new product of matrices, based on which a necessary and sufficient controllability condition is obtained. Then, the controllability of BCNs with multiple time delays in states is studied by giving a necessary and sufficient condition. Subsequently, based on these results, a controllability matrix for BCNs with multiple time delays in both states and controls is proposed that provides a concise controllability condition. Finally, two examples are given to illustrate the main results.

In the field of reversible data hiding (RDH), designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects. In this paper, we propose a new RDH method, including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules. In the predictor part, we first design a transformer-based predictor. Then, we propose an image division method to divide the image into four parts, which can use more pixels as context. Compared with other predictors, the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones, making it more accurate in reducing the embedding distortion. In the embedding strategy part, we first propose a complexity measurement with pixels in the target blocks. Then, we develop an improved prediction error ordering rule. Finally, we provide an embedding strategy including multiple embedding rules for the first time. The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images, and experimental results show that the performance of our RDH method is leading the field.

This paper presents a precision centimeter-range positioner based on a Lorentz force actuator using flexure guides. An additional digital-to-analog converter and an operational amplifier (op amp) circuit together with a suitable controller are used to enhance the positioning accuracy to the nanometer level. First, a suitable coil is designed for the actuator based on the stiffness of the flexure guide model. The flexure mechanism and actuator performance are then verified with finite element analysis. Based on these, a means to enhance the positioning performance electronically is presented together with the control scheme. Finally, a prototype is fabricated, and the performance is evaluated. This positioner features a range of 10 mm with a resolution of 10 nm. The proposed scheme can be extended to other systems.

Federated learning (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data. However, researchers working on FL face several unique challenges, especially in the context of heterogeneity. Heterogeneity in data distributions, computational capabilities, and scenarios among clients necessitates the development of customized models and objectives in FL. Unfortunately, existing works such as FedAvg may not effectively accommodate the specific needs of each client. To address the challenges arising from heterogeneity in FL, we provide an overview of the heterogeneities in data, model, and objective (DMO). Furthermore, we propose a novel framework called federated mutual learning (FML), which enables each client to train a personalized model that accounts for the data heterogeneity (DH). A “meme model” serves as an intermediary between the personalized and global models to address model heterogeneity (MH). We introduce a knowledge distillation technique called deep mutual learning (DML) to transfer knowledge between these two models on local data. To overcome objective heterogeneity (OH), we design a shared global model that includes only certain parts, and the personalized model is task-specific and enhanced through mutual learning with the meme model. We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios. The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting.

In this paper, the optimization problem subject to N nonidentical closed convex set constraints is studied. The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem. To this end, with the push-sum framework improved, the distributed optimization algorithm is newly designed, and its strict convergence analysis is given under the assumption that the involved graph is strongly connected. Finally, simulation results support the good performance of the proposed algorithm.

To solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in traditional focused crawling methods, in this paper, we propose a novel focused crawler using an improved tabu search algorithm with domain ontology and host information (FCITS_OH), where a domain ontology is constructed by formal concept analysis to describe topics at the semantic and knowledge levels. To avoid crawling visited hyperlinks and expand the search range, we present an improved tabu search (ITS) algorithm and the strategy of host information memory. In addition, a comprehensive priority evaluation method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks. Experimental results on both tourism and rainstorm disaster domains show that the proposed focused crawlers overmatch the traditional focused crawlers for different performance metrics.

Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes. However, the distortion of objects during data acquisition and representation is seldom considered. In this paper, we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations. Based on this, a feature fusion framework is proposed to fully use the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data. Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline. In addition, the fusion strategies can be easily integrated into other detectors, such as the You Only Look Once (YOLO) series. The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments. Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver's experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

With the substantial increase in image transmission, the demand for image security is increasing. Noise-like images can be obtained by conventional encryption schemes, and although the security of the images can be guaranteed, the noise-like images cannot be directly previewed and retrieved. Based on the rank-then-encipher method, some researchers have designed a three-pixel exact thumbnail preserving encryption (TPE2) scheme, which can be applied to balance the security and availability of images, but this scheme has low encryption efficiency. In this paper, we introduce an efficient exact thumbnail preserving encryption scheme. First, blocking and bit-plane decomposition operations are performed on the plaintext image. The zigzag scrambling model is used to change the bit positions in the lower four bit planes. Subsequently, an operation is devised to permute the higher four bit planes, which is an extended application of the hidden Markov model. Finally, according to the difference in bit weights in each bit plane, a bit-level weighted diffusion rule is established to generate an encrypted image and still maintain the same sum of pixels within the block. Simulation results show that the proposed scheme improves the encryption efficiency and can guarantee the availability of images while protecting their privacy.

The problem of data right confirmation is a long-term bottleneck in data sharing. Existing methods for confirming data rights lack credibility owing to poor supervision, and work only with specific data types because of their technical limitations. The emergence of blockchain is followed by some new data-sharing models that may provide improved data security. However, few of these models perform well enough in confirming data rights because the data access could not be fully under the control of the blockchain facility. In view of this, we propose a rightconfirmable data-sharing model named RCDS that features symbol mapping coding (SMC) and blockchain. With SMC, each party encodes its digital identity into the byte sequence of the shared data by generating a unique symbol mapping table, whereby declaration of data rights can be content-independent for any type and any volume of data. With blockchain, all data-sharing participants jointly supervise the delivery and the access to shared data, so that granting of data rights can be openly verified. The evaluation results show that RCDS is effective and practical in data-sharing applications that are conscientious about data right confirmation.

This work presents a novel design of Ka-band (33 GHz) filtering packaging antenna (FPA) that features broadband and great filtering response, and is based on glass packaging material and through-glass via (TGV) technologies. Compared to traditional packaging materials (printed circuit board, low temperature co-fired ceramic, Si, etc.), TGVs are more suitable for miniaturization (millimeter-wave three-dimensional (3D) packaging devices) and have superior microwave performance. Glass substrate can realize 3D high-density interconnection through bonding technology, while the coefficient of thermal expansion (CTE) matches that of silicon. Furthermore, the stacking of glass substrate enables high-density interconnections and is compatible with micro-electro-mechanical system technology. The proposed antenna radiation patch is composed of a patch antenna and a bandpass filter (BPF) whose reflection coefficients are almost complementary. The BPF unit has three pairs of λ_g/4 slots (defect microstrip structure, DMS) and two λ_g/2 U-shaped slots (defect ground structure, DGS). The proposed antenna achieves large bandwidth and high radiation efficiency, which may be related to the stacking of glass substrate and TGV feed. In addition, the introduction of four radiation nulls can effectively improve the suppression level in the stopband. To demonstrate the performance of the proposed design, a 33-GHz broadband filtering antenna is optimized, debugged, and measured. The antenna could achieve |S₁₁|<-10 dB in 29.4-36.4 GHz, and yield an impedance matching bandwidth up to 21.2%, with the stopband suppression level at higher than 16.5 dB. The measurement results of the proposed antenna are a realized gain of ~6.5 dBi and radiation efficiency of ~89%.

To improve the embedding capacity of reversible data hiding in encrypted images (RDH-EI), a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit (MSB) prediction. First, according to the smoothness of the image, the image is partitioned into blocks based on adaptive quadtree partitioning, and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images. In the data embedding stage, the adaptive MSB prediction method proposed by Wang and He (2022) is improved by taking the upper-left pixel in the block as the target pixel, to predict other pixels to free up more embedding space. To the best of our knowledge, quadtree partitioning is first applied to RDH-EI. Simulation results show that the proposed method is reversible and separable, and that its average embedding capacity is improved. For gray images with a size of 512×512, the average embedding capacity is increased by 25565 bits. For all smooth images with improved embedding capacity, the average embedding capacity is increased by about 35530 bits.

When multiple central processing unit (CPU) cores and integrated graphics processing units (GPUs) share off-chip main memory, CPU and GPU applications compete for the critical memory resource. This causes serious resource competition and has a negative impact on the overall performance of the system. We describe the competition for shared-memory resources in a CPU-GPU heterogeneous multi-core architecture, and a shared-memory request scheduling strategy based on perceptual and predictive batch-processing is proposed. By sensing the CPU and GPU memory request conditions in the request buffer, the proposed scheduling strategy estimates the GPU latency tolerance and reduces mutual interference between CPU and GPU by processing CPU or GPU memory requests in batches. According to the simulation results, the scheduling strategy improves CPU performance by 8.53% and reduces mutual interference by 10.38% with low hardware complexity.

The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images, we propose a recurrent network based on filter-cluster attention (FCA), the main body of which consists of three units: difference concern, gate recurrent, and iterative residual. The network performs multi-stage recursive learning on low-light images, and then extracts deeper feature information. To compute more accurate dependence, we design a novel FCA that focuses on the saliency of feature channels. FCA and self-attention are used to highlight the low-light regions and important channels of the feature. We also design a dense connection pyramid (DenCP) to extract the color features of the low-light inversion image, to compensate for the loss of the image’s color information. Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

Satellite Internet of Things (IoT) is a promising way to provide seamless coverage to a massive number of devices all over the world, especially in remote areas not covered by cellular networks, e.g., forests, oceans, mountains, and deserts. In general, satellite IoT networks take low Earth orbit (LEO) satellites as access points, which solves the problem of wide coverage, but leads to many challenging issues. We first give an overview of satellite IoT, with an emphasis on revealing the characteristics of IoT services. Then, the challenging issues of satellite IoT, i.e., massive connectivity, wide coverage, high mobility, low power, and stringent delay, are analyzed in detail. Furthermore, the possible solutions to these challenges are provided. In particular, new massive access protocols and techniques are designed according to the characteristics and requirements of satellite IoT. Finally, we discuss several development trends of satellite IoT to stimulate and encourage further research in such a broad area.

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited data transformations fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations, the auto-generated transformations lack explainability concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization. We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study, respectively. A recommendation algorithm is designed to compute optimal transformations, which can reveal specified types of patterns and maintain explainability. We demonstrate the effectiveness of our approach through two cases and a user study.

Gas sensors have received extensive attention because of the gas pollution caused by rapid construction of urbanization and industrialization. Gas sensors based on semiconductor metal oxide (SMO) have the advantages of high response, excellent repeatability, stability, and cost-effectiveness, and have become extremely important components in the gas sensor field. Materials with regular structures and controllable morphology exhibit more consistent and repeatable performance. However, during the process of material synthesis, because of the uncontrollability of the microcosm, nanomaterials often show irregularities, unevenness, and other shortcomings. Thus, the synthesis of gas sensors with well-aligned one-dimensional (1D) structures, two-dimensional (2D) layered structures, and three-dimensional (3D) hierarchical structures has received extensive attention. To obtain regular structured nanomaterials with desired morphologies and dimensions, a template-assisted synthesis method with low cost and controllable process seems a very efficient strategy. In this review, we introduce the morphology and performance of SMO sensors with 1D, 2D, and 3D structures, discuss the impact of a variety of morphologies on gas sensor performance (response and stability), and shed new light on the synthesis of gas sensing materials with stable structure and excellent performance.

Quaternion algebra has been used to apply the fractional Fourier transform (FrFT) to color images in a comprehensive approach. However, the discrete fractional random transform (DFRNT) with adequate basic randomness remains to be examined. This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform (DQFrFT) watermarking and three-dimensional chaotic logistic map (3D-CLM) encryption. First, we describe quaternion DFRNT (QDFRNT), which generalizes DFRNT to handle quaternion signals effectively, and then use QDFRNT to perform color medical image adaptive watermarking. To efficiently evaluate QDFRNT, this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal. Moreover, it uses the human vision system’s (HVS) masking qualities of edge, texture, and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine (SVM) techniques. The limitations of watermark embedding are also explained to conserve watermarking energy. Second, 3D-CLM encryption is employed to improve the system’s security and efficiency, allowing it to be used as a multistage privacy system. The proposed security system is effective against many types of channel noise attacks, according to simulation results.

We investigate a kind of vehicle routing problem with constraints (VRPC) in the car-sharing mobility environment, where the problem is based on user orders, and each order has a reservation time limit and two location point transitions, origin and destination. It is a typical extended vehicle routing problem (VRP) with both time and space constraints. We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user (or passenger) experience. To solve the scheduling model more accurately, a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer, and a spatiotemporal distance embedded hybrid ant colony algorithm (HACA-ST) is proposed. The algorithm can be divided into two stages. First, through spatiotemporal clustering, the spatiotemporal distance between users is the main measure used to classify customers in categories, which helps provide heuristic information for problem solving. Second, an improved ant colony algorithm (ACO) is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route. Computational analysis is carried out based on existing data sets and simulated urban instances. Compared with other heuristic algorithms, HACA-ST reduces the length of the shortest route by 2%–14% in benchmark instances. In VRPC testing instances, concerning the combined cost, HACA-ST has competitive cost compared to existing VRP-related algorithms. Finally, we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

Disk failure prediction methods have been useful in handing a single issue, e.g., heterogeneous disks, model aging, and minority samples. However, because these issues often exist simultaneously, prediction models that can handle only one will result in prediction bias in reality. Existing disk failure prediction methods simply fuse various models, lacking discussion of training data preparation and learning patterns when facing multiple issues, although the solutions to different issues often conflict with each other. As a result, we first explore the training data preparation for multiple issues via a data partitioning pattern, i.e., our proposed multi-property data partitioning (MDP). Then, we consider learning with the partitioned data for multiple issues as learning multiple tasks, and introduce the model-agnostic meta-learning (MAML) framework to achieve the learning. Based on these improvements, we propose a novel disk failure prediction model named MDP-MAML. MDP addresses the challenges of uneven partitioning and difficulty in partitioning by time, and MAML addresses the challenge of learning with multiple domains and minor samples for multiple issues. In addition, MDP-MAML can assimilate emerging issues for learning and prediction. On the datasets reported by two real-world data centers, compared to state-of-the-art methods, MDP-MAML can improve the area under the curve (AUC) and false detection rate (FDR) from 0.85 to 0.89 and from 0.85 to 0.91, respectively, while reducing the false alarm rate (FAR) from 4.88% to 2.85%.

In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its tracking accuracy is the main criterion. To improve the tracking accuracy, in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified deep feedforward neural network (MDFNN) is then proposed. In MDFNN, a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN) based tracking methods under the considered scenarios.

In this paper, an efficient image encryption scheme based on a novel mixed linear–nonlinear coupled map lattice (NMLNCML) system and DNA operations is presented. The proposed NMLNCML system strengthens the chaotic characteristics of the system, and is applicable for image encryption. The main advantages of the proposed method are embodied in its extensive key space; high sensitivity to secret keys; great resistance to chosen-plaintext attack, statistical attack, and differential attack; and good robustness to noise and data loss. Our image cryptosystem adopts the architecture of scrambling, compression, and diffusion. First, a plain image is transformed to a sparsity coefficient matrix by discrete wavelet transform, and plaintext-related Arnold scrambling is performed on the coefficient matrix. Then, semi-tensor product (STP) compressive sensing is employed to compress and encrypt the coefficient matrix. Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit XOR operation. The NMLNCML system is applied to generate chaotic elements in the STP measurement matrix of compressive sensing and the pseudo-random sequence in DNA operations. An SHA-384 function is used to produce plaintext secret keys and thus makes the proposed encryption algorithm highly sensitive to the original image. Simulation results and performance analyses verify the security and effectiveness of our scheme.