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Networked robots have become crucial for unmanned applications since they can collaborate to complete complex tasks in remote/hazardous/depopulated areas. Due to the cost inefficiency of deploying cellular network infrastructure in these areas, hybrid satellite-UAV networks emerge as a promising solution. These networks provide seamless and on-demand connectivity for multiple robots with various task requirements, and support computation-intensive and latency-sensitive services through mobile edge computing (MEC)-based offloading. However, to complete tasks in limited times, the rapid collective movement of mobile robots may cause frequent service migration, and a large number of gathered robots may compete for limited bandwidth resources in satellite and UAV communications. As a result, offloading latency may increase significantly. To address this issue, the average completion time of multi-robot offloading in task-oriented satellite-UAV networks with MEC is formulated as an optimization problem. Unlike conventional mobility-aware MEC-based offloading schemes, joint optimization of mobility control, data offloading, and resource allocation is proposed using velocity control of multiple robots. According to Lyapunov optimization, the original optimization problem is simplified into minimizing the average completion time of offloading for all robots within UAV and satellite coverage. A multi-agent $Q$ -learning algorithm, including multi-group dual-agent $Q$ -learning, is proposed based on local state observation and global reward calculation. In each dual-agent $Q$ -learning, one agent is responsible for velocity control and communication resource allocation, while the other is responsible for data offloading and computational resource allocation. The convergence of the proposed multi-agent $Q$ -learning algorithm is also theoretically analyzed. Simulation results show that the proposed scheme can effectively reduce the offloading latency by up to 35% in the multi-robot environment over its conventional counterparts.

Due to privacy and sensitivity of the medical images, the creation of strong medical image encryption schemes has gained the attention of cryptographers. In this study, a robust medical image encryption scheme is developed involving a fuzzy subset of integers from 1 to 256 characterized by membership values. The S-box entries are determined using a precise mathematical formulation. Various standard analyses are performed to check the reliability and security of the newly devised S-box. Furthermore, this newly devised S-box is utilized to design proposed encrypting technique. A thorough benchmarking analysis that is specifically designed for image encryption schemes has been conducted in order to assess the algorithm’s performance. The findings of these analyses obtained through the proposed scheme, in comparison to some recently developed image encryption schemes; show that the proposed approach exhibits exceptional attributes and formidable cryptographic potency. Consequently, the algorithm not only showcases remarkable effectiveness but also holds great promise in the realm of securing medical images for telemedicine.

In this paper, the synchronization issue of chaotic Lur’e system (CLs) is investigated under the coupling memory sampled-data controller (SDC) with parameter uncertainties. First, using a Bernoulli distributed sequence, a more universal coupling controller that involves the factor of data transmission delay is proposed. Based on these vectors, an augmented Lyapunov-Krasovskii functional (LKF) is constructed for the CLs, where the LKF is derived as delay-dependent. The LKF is also based on the information of entire sampling interval and non-linear functional vector. Meanwhile, a relaxed second-stage affine Bessel–Legendre inequality (BLIY) is introduced to estimate integral terms generated during the derivation. On account of the improved functional and the relaxed integral inequality, an admissibility criteria via a coupling controller is acquired for the synchronous error system with less conservative and the framework of linear matrix inequalities (LMIs). Then, the memory SDC is designed to synchronize the driving and responding CLs. Finally, a simulation example is provided to verify the validity and superiority of the proposed design scheme.

Malicious traffic has posed a significant threat to current 5G networks. In the upcoming 6G era, with the rapid development of the Internet of Things (IoT), defending against malicious traffic has become even more challenging due to the diverse nature and widespread distribution of IoT devices. This paper presents a new distributed framework for detecting malicious traffic on the access side of the IoT. This framework shifts the line of defense forward, and could alleviate the resource-bottleneck problem of traditional detectors that are usually deployed at the victim side. In particular, we devise a transformer-based neural network, which is tailored for distributed malicious traffic detection at multiple detection points. Additionally, we develop a personalized federated learning-based collaborative algorithm to enable horizontal collaboration among multiple detection points by sharing neural network parameters. Different from the traditional federated learning framework which trains a high-precision global model, our proposed framework fully considers the differences in the distribution of IoT traffic at the entrance, and uses local traffic data to train personalized models with better local detection performance on the basis of the global model. The experimental results demonstrate that our approach achieves an average detection accuracy of 99.2% and an F1-score of 99.2% for all detection points using the N-BaIoT dataset. In comparison to methods lacking collaboration, our approach exhibits significant improvements in terms of accuracy, precision, recall, and F1-score. Moreover, our detection performance is comparable to that of centralized learning frameworks, despite sharing only the model parameters.

Distributed Denial of Service (DDoS) attacks pose a significant threat to the Internet of Vehicles, causing delays in driving operations and risking personal safety. With the emerging of Space-Air-Ground Integrated Sixth-Generation (6G) networks, DDoS defense faces new challenges, especially in open and complex Vehicle-to-Everything (V2X) connections. In this study, we reviewed the requisites and challenges of DDoS defense in V2X and introduced a tailored access-side defense architecture for Space-Air-Ground Integrated 6G V2X. The objective is to provide ultra-low latency and privacy-assured services despite limited communication resources. Our architecture uses access-side control points (CPs) for rapid attack responses, collaborating with local controllers to create a seamless defense perimeter at the network edge. This prevents malicious traffic infiltration. Incorporating proactive defense strategies and lightweight detection methods, our approach ensures high attack detection rates with low defense costs. Simulation results confirm its efficacy and advantages in defense and network efficiency. We also discuss open issues for future research to facilitate practical applications.

The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings to those of Internet-of-Things (IoT) devices. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disaster areas. However, satellite networks face the inherent challenges of low data rate and large latency, which may not support computationintensive and delay-sensitive IoT applications. Mobile Edge Computing (MEC) is a burgeoning paradigm by extending cloud computing capabilities to the network edge. Using MEC technologies, the resourcelimited IoT devices can access abundant computation resources with low latency, which enables the highly demanding applications while meeting strict delay requirements. Therefore, an integration of satellite communications and MEC technologies is necessary to better enable 6G IoT. In this survey, we provide a holistic overview of satellite-MEC integration. We first categorize the related studies based on three minimal structures and summarize current advances. For each minimal structure, we discuss the lessons learned and possible future directions. We also summarize studies considering the combination of minimal structures. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated satellite-MEC network.

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

Cardiac motion tracking from echocardiography can be used to estimate and quantify myocardial motion within a cardiac cycle. It is a cost-efficient and effective approach for assessing myocardial function. However, ultrasound imaging has the inherent characteristics of spatially low resolution and temporally random noise, which leads to difficulties in obtaining reliable annotation. Thus it is difficult to perform supervised learning for motion tracking. In addition, there is no end-to-end unsupervised method currently in the literature. This paper presents a motion tracking method where unsupervised optical flow networks are designed with spatial reconstruction loss and temporal-consistency loss. Our proposed loss functions make use of the pair-wise and temporal correlation to estimate cardiac motion from noisy background. Experiments using a synthetic 4D echocardiography dataset has shown the effectiveness of our approach, and its superiority over existing methods on both accuracy and running speed.

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km3. Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

Automatic pig counting and locating from camera images is one of the most important tasks in modern pig farming industry, which helps farmers to improve the efficiency of the livestock management in pig feeding, welfare estimation, unexpected events monitoring and etc. Due to the complex and diverse pigpen environment, complicated distributions of pig population and various motions of live pigs, traditional image processing techniques are not effective in counting and locating pigs in crowds. Thus, this task relies on manual labor heavily which is time-consuming and error-prone. In this paper, we propose an efficient and accurate pig counting method for top-view surveillance images in large-scale, crowded feeding scenes. The proposed method is composed of a novel density map generator and a density map estimation network architecture. The pigs in images are expressed as ellipses and their group density is generated with elliptical 2D Gaussian distribution. The proposed network is designed with efficient hybrid blocks including selective kernel convolution and vision transformer for feature extraction and density map regression. The total number of pigs in one image can be calculated by summing entire values in the density map. We also apply a modified K-means clustering algorithm on the density map to locate pig targets. To verify the effectiveness and precision of the proposed method, we evaluate our proposed method on our testing dataset. The Mean Absolute Error of counting numbers on testing images is 0.726. Due to lightweight design by using depth-wise separable convolutions and hybrid-vit blocks, our proposed method has very fast inference speed and will reduce dependency on computing resources substantially when deployed in pig farms. Based on the accurate estimated density maps of the testing images, pig locating can also achieve pretty good results. The modified K-means clustering algorithm proposed in this paper obtain target locating with 88.22% precision and 86.02% recall respectively. These results indicate our proposed method can accurately count pigs in piggery by density map estimation and locate pig targets even in crowded situations.

Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classification tasks in recent years. However, simple GA faces challenges, such as complex calculation, poor noise immunity, and slow convergence. This research proposes a two-step ensemble protocol for LULC classification using a grayscale-spatial-based GA model. The first ensemble framework uses fuzzy c-means to classify pixels into those that are difficult to cluster and those that are easy to cluster, which aids in reducing the search space for evolutionary computation. The second ensemble framework uses neighborhood windows as heuristic information to adaptively modify the objective function and mutation probability of the GA, which brings valuable benefits to the discrimination and decision of GA. In this study, three research areas in Dangyang, China, are utilized to validate the effectiveness of the proposed method. The experiments show that the proposed method can effectively maintain the image details, restrain noise, and achieve rapid algorithm convergence. Compared with the reference methods, the best overall accuracy obtained by the proposed algorithm is 88.72%.

Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies from the background. Informative bands are very important for the accuracy improvement of the detection technology. However, most of the abnormal targets segmentation methods focus on the usage of all the spectral features, thus are easily affected by redundant bands or feature noise. A hyperspectral anomaly detection algorithm based on subfeature ensemble is proposed in this article. The proposed method consists of the following steps. First, the bands of the original HSI are normalized and randomly divided into several subfeature sets according to different proportions. Second, six methods including the prior-based tensor approximation algorithm (PTA), Reed–Xiaoli method, a low-rank and sparse representation method, a low-rank and sparse matrix decomposition-based Mahalanobis distance method, the graph and total variation regularized low-rank representation-based method, and a method based on tensor principal component analysis are applied to detect anomalies on the original HSI, and the method with the best performance is used to obtain an enhanced feature set. Then, the enhanced features and the subfeatures are ensembled iteratively to construct a new dataset. Finally, the PTA method is operated on the dataset with ensemble features to get the final abnormal target results. Six hyperspectral datasets are used in the experiment. Seven methods are employed as comparisons. The results are analyzed from both qualitative and quantitative perspectives. Extensive experimental results illustrate that the proposed method performs best on all datasets.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep reinforcement learning (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional GAs remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize the contextual and hierarchical information of image objects in addition to solely using spectral information. In addition, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with the spatial-based GA offers a promising method [ensemble spatial–spectral genetic algorithm (E-nGA)] for automating the process of monitoring forest loss. The research in this article is presented in four parts. First, block-matching and 3-D filtering is performed to suppress noises while enhancing valuable information. The difference image is, then, generated using the image difference method. Afterward, context-based saliency detection and fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently used classification methods, as well as the simple GA, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a you-only-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

Focusing on the difficulty of completely extracting the surface damage caused by rolling bearing lubrication failure, an algorithm for extracting bearing lubrication fault is proposed, which is based on periodic optimum singular value decomposition (O-SVD) cascaded fast spectral correlation (FSC). Initially, conventional T-SVD with energy leakage defects was modified into O-SVD, which was used as the preprocessing unit for signal processing. Then, FSC calculation was performed on the reconstructed signals, eventually obtaining enhanced envelope spectrum with obvious features that could well preserve local details. Simulation and experimental results show that the proposed algorithm allows rather complete extraction of slight fault features resulting from poor lubrication under small sampling length and low signal-to-noise ratio (SNR), and has good applicability in extracting compound and composite fault features. The extracted signals have advantages over existing algorithms regarding fault resolution and local details preservation.

Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a unique characteristic called “step change” in which the marginal pattern changes when the load goes from one critical loading level (CLL) to another, and there is no change of marginal units within the interval of the two adjacent CLLs. Those loading intervals differ significantly in size. The randomly generated training dataset overfills intervals with large sizes and underfits intervals with small sizes, so it is biased. In this paper, three algorithms are proposed to construct a marginal pattern library to examine this bias according to different computational needs, and an enhancement algorithm is proposed to eliminate the bias in the load-ED training dataset generation. Three illustrative test cases demonstrate the proposed algorithms, and comparative studies are constructed to show the superiority of the enhanced, unbiased training dataset.

In this article, input-to-state stability (ISS) and stabilization are examined for sampled-data systems under deterministic aperiodic sampling and random sampling, respectively. Using the direct design method, the sampled-data systems are transformed into switched systems with switched time-varying delays. First, the ISS definition and criterion appropriate for these systems are provided. Based on this, the ISS criterion on sampled-data systems under deterministic aperiodic sampling is given. Second, after the stochastic ISS (SISS) definition and criterion are provided for switched nonlinear systems with randomly switching delays, the SISS criterion for the sampled-data systems under random sampling is provided. All of the ISS and SISS definitions are given in the form of $\mathcal {KL}$ function that is quite elegant and easy to work with. Then, sufficient conditions for input-to-state stabilization are obtained for sampled-data linear systems under deterministic aperiodic sampling and random sampling, respectively, via the Lyapunov-Krasovskii method. Finally, based on the criteria, a piecewise controller is designed by the matrix inequality approach for a sampled-data linear time invariant system, and simulation results are provided to illustrate our design method. The main conclusion of this article is that sampling intervals will affect the controller design of the systems, and the ISS properties are maintained using a piecewise controller.

Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning has been successfully applied to the HSI classification, a convolutional neural network (CNN) is one of the most representative of them. However, it is difficult to effectively improve the accuracy of minority classes under the problem of multiclass imbalance. In addition, ensemble learning has been successfully applied to solve multiclass imbalance, such as random forest (RF) This article proposes a novel enhanced-random-feature-subspace-based ensemble CNN algorithm for the multiclass imbalanced problem. The main idea is to perform random oversampling of training samples and multiple data enhancements based on random feature subspace, and then, construct an ensemble learning model combining random feature selection and CNN to the HSI classification. Experimental results on three public hyperspectral datasets show that the performance of the proposed method is better than the traditional CNN, RF, and deep learning ensemble methods.

Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. To solve this problem, a pixel cluster CNN and spectral-spatial fusion (SSF) algorithm for hyperspectral image classification with small-size training samples is proposed in this article. First, spatial information is extracted by the gray level co-occurrence matrix. Then, spatial information and spectral information are fused by means of bands superposition, forming spectral-spatial features. To expand the number of training samples, the pixels after SSF are combined into pixel clusters according to a certain rule. Finally, a CNN framework is utilized to extract effective features from the pixel clusters. Experiments based on three standard HSIs demonstrate that the proposed algorithm can get better performance than the conventional CNN and also outperforms other studied algorithms in the case of small training set.

Interference alignment (IA) is one of the most promising techniques to eliminate interference in multiuser multiantenna systems. Simultaneous wireless information and power transfer (SWIPT) technology is applied to harvest energy from radio frequency signals in green communications, while interference signal is assumed to be utilized as energy supply source in SWIPT. In this paper, an enhanced maximum signal to interference plus noise ratio (MAX-SINR) strategy with interference leakage power constraint is proposed, which realizes a tradeoff between information transmission and interference leakage in multiuser multiantenna SWIPT systems. Optimization problem is formulated and a three-step optimization solution is proposed. Finally, the problem can be transformed to a standard semi-definite programming (SDP) to obtain optimal power splitting factor and system sum-rate (spectral efficiency). Simulation results are presented to show that system sum-rate performance is enhanced with the increasing of receiving antenna numbers. The tradeoff between sum-rate and the harvested energy can be realized via the proposed IA strategy.

The sequential method is easy to integrate with existing large-scale alternating current (AC) power flow solvers and is therefore a common approach for solving the power flow of AC/direct current (DC) hybrid systems. In this paper, a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed. The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result. First, the AC/DC network is intuitively represented by a graph and stored in a graph database (GDB) to expedite data processing. Considering the interconnection of AC grids via high-voltage direct current (HVDC) links, the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis. For each area, the fast-decoupled power flow (FDPF) is employed with node-based parallel computing (NPC) and hierarchical parallel computing (HPC) to quickly identify system states. Furthermore, to reduce the alternate iterations in the sequential method, a new decoupled approach is utilized to achieve a good initial estimate for the New-ton-Raphson method. With the improved initial estimate, the sequential method can converge in fewer iterations. Consequently, the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system. The performance is verified on standard IEEE 300-bus system, extended large-scale systems, and a practical 11119-bus system in China.

The phase induced intensity noise (PIIN) may seriously reduce the signal-to-noise ratio (SNR) of continuous-wave coherent Doppler lidar (CW-CDL), thereby limiting its performance in practical applications. In this paper, we propose to use optical frequency domain reflectometry (OFDR) technique to locate the PIIN sources and evaluate their intensities. According to the OFDR information, we demonstrate that the PIIN in our CW-CDL system is mainly induced by the crosstalk of polarization beam splitter. Then, the PIIN is reduced by setting fiber delay line, polarization control, and AR-coating. Finally, a wind Doppler signal at a distance of 80 meters is obtained on a clear day, and a typical SNR of 19.4 dB is achieved.

In order to improve the security and efficiency of image encryption, many researchers have continuously proposed new image encryption schemes in recent years. However, these image encryption schemes have not been fully analyzed and evaluated. In this paper, a newly reported image encryption scheme based on Feistel network and dynamic Deoxyribonucleic Acid (DNA) encoding is deeply and comprehensively investigated. This encryption scheme mainly adopts four encryption steps to encrypt the plain image, which are Generation of chaotic sequences, Hill encryption, Feistel network, and Pixel diffusion. Our analyses show that there are some problems in the secret key design and encryption process of this encryption scheme. After pointing out and analyzing these problems, we have made several necessary improvements to this encryption scheme and proposed the corresponding chosen-plaintext attack algorithm. The subsequent simulation tests and analyses have confirmed the effectiveness and feasibility of the proposed attack algorithm. Finally, for the problems in this encryption scheme and some current image encryption schemes, improvement suggestions are presented to provide references for the designers of future image encryption schemes.

As we know, improving security is one of the most important motivations for designing new image encryption schemes. However, the security of some new image encryption schemes has not been fully analyzed and evaluated. In view of this, a novel image encryption scheme based on the pixel-level filtering and the DNA-level diffusion (PFDD) is analyzed and evaluated in this paper. After comprehensive analysis and study, we find that PFDD does not have its claimed ability to resist chosen-plaintext attacks (CPAs). Therefore, under the conditions of CPAs, we cryptanalyze the entire encryption process of PFDD, and successfully crack it by our proposed attack algorithm. Then, The effectiveness and feasibility of the proposed attack algorithm are verified theoretically and experimentally. Finally, for several representative problems in current chaotic image encryption schemes, we put forward some improvement suggestions for future researchers.

With the growing concerns about energy saving and security problems in wireless sensor networks (WSNs), we investigated a joint energy saving scheduling and secure routing algorithm for critical event reporting in WSN. When a critical event has been detected, the notification must be safely sent to the sink node through the uplink transmission, and the sink node needs to broadcast the alarm messages to the whole network through the downlink transmission. In the uplink, a joint power allocation and secure routing strategy (JPASR) has been proposed to maximize the routing secure connection probability (RSCP) under the constraint of power. Meanwhile, combined with the level-by-level sleeping scheduling method, the energy-saving and secure uplink transmission can be guaranteed. In the downlink, an energy-first multi-point relays set selection mechanism (EFMSS) is designed to choose the backbone nodes to broadcast messages, and the backbone nodes are waken up by the same level-by-level sleeping scheduling method as the uplink transmission. With the two-step procedure, the critical events are appropriately dealt with and the responses are broadcast to the whole network. Extensive simulations results demonstrate that the energy saving and security performances of the proposed method are superior to the existing ones.

To promote the progress of fault diagnosis system, this study proposes an intelligent and effective fault diagnosis algorithm based on data-driven. Firstly, we propose a new time-frequency analysis method named second-order time-reassigned multisynchrosqueezing transform (STMSST) based on Gaussian-modulated linear group delay (GLGD) model to deal with the vibration signals of fault object for obtaining time-frequency images with high resolution. Then, an improved training method named evenly mini-batch training method is combined with convolutional neural network (CNN) to train and learn fault features from those obtained time-frequency images. Further, the proposed fault diagnosis algorithm is tested on the Case Western Reserve University (CWRU) bearing dataset and the Machinery Failure Prevention Technology (MFPT) Society dataset, respectively, and the experimental results indicate that the feature representation and training effect in our method is superior to state-of-the-art fault diagnosis methods. Finally, the proposed method is applied on the loudspeaker pure-tone detection dataset, which achieves the loudspeaker anomaly diagnosis, and the diagnosis result has verified that the method can meet the needs of practical engineering.

The k-out-of-n: G(F) majority voter consists of n components (or modules) and a number of the components are required to be operating correctly for the overall system to be correct. As per the state discretization of the components, such a system is usually classified as either a binary system or a multi-state system. In practice, the operating conditions of different components may contribute differently to the operation of the entire system. In this manuscript, the k-out-of-n: G(F) majority voter is generalized as a consecutive-weighted-k-out-of-n: G(F) voter with either binary states or multiple states. To overcome the drawbacks of existing approaches, a stochastic analysis is proposed for assessing the system reliability. In the stochastic analysis, the input signal probabilities are encoded into non-Bernoulli sequences with fixed numbers of 0s and 1s for the Boolean case, or randomly permuted sequences for the multi-state scenario. By using stochastic logic, the reliability of a general system consisting of consecutive-weighted-k-out-of-n majority voters is efficiently and accurately predicted. The results are validated by an analysis of several case studies. Although the accuracy of the stochastic analysis is closely related with the employed sequence length, it is shown that a stochastic approach is more efficient than a universal generating function (UGF) method, while still retaining an acceptable accuracy.

The introduction of IPsec into software-defined networking (SDN) can secure communication in an SDN southbound interface, i.e., communication between the controllers and the switches. However, due to the static configuration of IPsec cryptographic algorithms, the invocation of these algorithms cannot dynamically self-adapt to traffic fluctuations in SDN southbound communication. To address the contradiction between link security and communication performance incurred by IPsec encryption, an evaluation model to find a trade-off between communication performance and link security is presented in this paper. An invocation mechanism based on the Free-to-Add (FTA) method is also proposed to optimize the invocation mode of cryptographic algorithms in traditional IPsec. Based on the real-time network status and the impact of the IPsec encryption process on the network latency and throughput, a feedback-based scheduling scheme is designed to enable the IPsec algorithms in use to be flexibly replaced and synchronously switched, and two policies are applied to determinate the appropriate encryption algorithm(s). The validity and effectiveness of the FTA-based mechanism are verified and evaluated on an SDN/OpenFlow platform in which IPsec security gateways are deployed. The feedback-based scheduling scheme is evaluated in terms of packet processing latency, distribution of optional encryption intensity, and the hit rate of encryption intensity.

To satisfy the growing demand for broadband maritime communications, space-ground integrated maritime communication networks (MCNs) arise, which are envisioned to take full advantage of both satellites and terrestrial shore-based networks. In practice, due to the frequent beam hopping of satellites and the limited number of geographically available onshore base-station sites, the space-ground integrated MCN usually presents a highly non-cellular network structure, leading to both challenging blind zones and coverage areas with severe interference. In this paper, we optimize the coverage performance by exploiting the marine environment information. Particularly, the transmit antenna correlation is estimated using the location and mobility information of scatterers on the sea, such as lighthouses, reefs, islands, and vessels. The position and attitude information of satellites are also utilized for interference estimation. Based on that, we optimize the input covariance and precoding matrix to maximize the ergodic sum capacity for all mobile terminals within the coverage. It is a complicated non-convex problem, especially because the ergodic sum capacity is difficult to be expressed straightly without the expectation operator. We introduce an upper bound of the ergodic sum capacity using the path loss and the transmit antenna correlation estimated from the environment information, and then propose an iterative algorithm to solve the problem by solving a set of convex subproblems. The proposed environment-aware scheme is evaluated using the real-world geographic information of a coastal area of China. Simulation results demonstrate that the proposed scheme can greatly improve the ergodic sum capacity and the energy efficiency compared with existing approaches, and achieve dynamic coverage to match the non-cellular network structure.

This paper proposes a 16-bit 6-channel high-voltage successive approximation register (SAR) ADC with an optimized 5 + 5 + 6 segmented capacitor array. The lower 10 bits of the capacitor array are all composed of unit capacitors without any calibration unit. Without calibration, the lower 10 bits of the capacitor array can ensure 10-bit conversion accuracy. Every of the upper 6 bits of the capacitor array contains a linearity calibration unit. The linearity error of the upper 6 bits is calibrated by the linearity calibration unit. The 16-bit is manufactured by a $0.6~\mu m$ standard COMS process, and the total chip area of 6-channel ADC including pads is 6.6mm $\times6.6$ mm. As for single channel SAR ADC, the area is 0.9mm $\times2.0$ mm. The measurement results show that the effective conversion accuracy of the SAR ADC reaches 13 bits by using novel differential nonlinearity (DNL) and integral nonlinearity (INL) calibration methods. The power is 80mW, corresponding to a Figure of Merit (FOM) of 48 pJ/conv.-step.

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS).

Integrated community energy system (ICES) enables multi-energy synergy and impulses interaction of integrated demand response (IDR) from the demand side. Nevertheless, the randomness lies in IDR resources and renewable generation in the ICES configuration issue still lacks thorough analysis. Furthermore, the widely focused concern of resiliency urges more proactive consideration of potential emergencies at the planning stage of ICES. To this end, this paper proposed a resilience-oriented stochastic ICES configuration framework considering IDR influence. First, generalized IDR models are set up in detail with elaborate fuzzy feature analysis of price-responsive multi-energy loads. Then, the vulnerability indicators of tie lines and converting devices of the ICES are first introduced to demonstrate the occasional outage in the normal operation and blackout in the emergent case. In addition, the worst-case conditional value-at-risk (WCVaR) theory is innovatively integrated to the traditional risk-neutral model, which is formulated as a two-stage stochastic chance-constrained programming problem, aiming to combine portfolio with minimizing the worst-case cost caused by a disaster. The models are then transformed into mixed-integer linear programming problems via several linearization techniques. Finally, the results of the case studies showed the established model's superiority in improving the overall economy (reduce 6.94% of the worst-case cost) and system resiliency (decrease the value-at-risk by 3.20%) against an unforeseen hurricane. Meanwhile, the ICES reliability was also improved by declining 25.65% of unintended load curtailment faced with an emergency. The proposed approach provided an efficient preventive portfolio scheme of the IDR-integrated ICES for decision-makers' different risk preferences of natural hazards.

The traditional solid-state drive buffer management algorithm generally adopts fixed structures and parameters, leading to their poor adaptability. For example, after the underlying flash translation layer (FTL) or the upper workload is changed, the traditional algorithm's performance fluctuates significantly. Focusing on this problem, based on the cross-layer aware method, we propose an Advanced Adaptive Least Recently Used buffer management algorithm (AALRU). The core idea of the AALRU is that by sensing the characteristics of the upper workload and the status of the underlying FTL, the AALRU adaptively adjusts its structure, parameters, and write-back strategy to optimize the buffer's performance. First, the AALRU divides the buffer into two parts: read buffer and write buffer, and their proportion is adjusted by sensing the read-write characteristics of the workload and the underlying read-write latency. Second, the AALRU employs different granularities to manage the buffer. On one hand, for data loading and migrating, the AALRU adopts page-level granularity, which can avoid the problem of hot and cold data page entanglement in block management, and thus improve the buffer hit ratio. On the other hand, for data writing back to the FTL, the AALRU adopts block-level granularity, which can enhance the continuity of write requests and thus reduce the underlying FTL's garbage collection overhead. Finally, when the clustered data are written back, by sensing the underlying FTL's garbage collection status, the AALRU adaptively adjusts the page-padding trigger threshold to reconstruct the continuity of the write-back data, which can mitigate the underlying FTL's garbage collection overhead. The experimental results show that the AALRU has the best adaptability to different FTLs and test workloads, and it can achieve optimal or near-optimal results.

Unlike aerial base station enabled by a single unmanned aerial vehicle (UAV), aerial coordinated multiple points (CoMP) can be enabled by a UAV swarm. In this case, the management of multiple UAVs is important. This paper considers the power allocation strategy for a UAV swarm-enabled aerial network to enhance the physical layer security of the downlink transmission, where an eavesdropper moves following the trajectory of the swarm for better eavesdropping. Unlike existing works, we use only the large-scale channel state information (CSI) and maximize the secrecy throughput in a whole-trajectory-oriented manner. The overall transmission energy constraint on each UAV and the total transmission duration for all the legitimate users are considered. The non-convexity of the formulated problem is solved by using max-min optimization with iteration. Both the transmission power of desired signals and artificial noise (AN) are derived iteratively. Simulation results are presented to validate the effectiveness of our proposed power allocation algorithm and to show the advantage of aerial CoMP by using only the large-scale CSI.

According to the research works of the literatures on chaotic cryptanalysis, many recent chaotic image encryption algorithms cannot resist chosen-plaintext attack. Although some chaotic image encryption algorithms introduce plain-image information, they still violate some design requirements of modern cryptosystems. Therefore, after analyzing many literatures related to chaotic image encryption, we propose a plain-image-related chaotic image encryption algorithm based on the deoxyribonucleic acid (DNA) sequence operation and discrete logarithm. As we know, the discrete logarithm calculation is a complex non-linear calculation, introducing it can increase the complexity of the encryption process. More importantly, discrete logarithm values are significantly different under different generators, and the multiplicative group adopted in the proposed algorithm has as many as 128 generators. Therefore, by using two generators determined by the secret key and the hash value of the plain-image respectively, we not only expand the key space, but also greatly improve the plain-image sensitivity and plain-image relevancy of the encryption process. In addition, the proposed algorithm improves the way of using the hash value of the plain-image. In this way, once the secret key is selected, chaotic sequences can be generated in advance and reused to improve the encryption efficiency. Similarly, the proposed algorithm also introduces the DNA sequence operation with high parallelism to further improves the encryption efficiency. Simulation tests and comparative analyses show that the proposed algorithm can not only resist common attacks, but also outperform some recent chaotic image encryption algorithms in many aspects.

Decision making by participants in supply chains is fundamentally constrained in online and offline (O2O) supply chain management. Enterprises ultimately seek selling strategies that can increase their sustainability in highly competitive marketplaces. In making purchase decisions, customers like to “touch and feel” products, and providing this experience to customers must be considered when calculating the costs for retailers of customers returning products. Therefore, in this study, we discuss the influence of selling strategies and the retailer cost of handling returns according to different distribution channels. The O2O supply chain decision models under a duopoly and monopoly are constructed to obtain the wholesale price and selling price, as well as the maximum profits of game players. One important result is that when the retailer's cost of handling returns is within a certain range, the profits of the supplier and O2O supply chain in the duopoly context (Model D) are higher than those in the monopoly context (Model O). However, regardless of the retailer cost of handling returns, an e-retailer's profits are always higher than the sum of the two retailers' profits. For consumer surplus, when the retailer's cost of handling returns meets certain conditions, the consumer surplus is lower in the monopoly context (Model O) than in the duopoly context (Model D) and creates more utility for consumers in a duopolistic market. The research is beneficial for companies seeking to establish sound pricing and sales strategies in the emerging field of O2O commerce, enhancing companies' long-term economic performance.

Robust chaos in the discrete system is suggested to have practical as well as theoretical importance since it can obtain reliable operation in the chaotic mode. However, it receives only moderate attention and only focuses on a finite chaotic parameter space and small Lyapunov exponents. This paper introduces a two-dimensional smooth map and studies its robustness of chaos in the infinite parameter space. Then, a compound operation-based optimization control method is introduced to increase the map complexity in the measure of Lyapunov exponent. The introduced method is simple and provides a new pathway for exploring the robustness and complexity of discrete chaotic system. Finally, we design a chaos-based pseudo-random number generator (CPNG) based on the optimized robust chaotic map, and the careful analysis shows that the proposed CPNG has high quality of randomness and has passed the rigorous National Institute of Standards and Technology (NIST) test.

The classic demand-based flash translation layer (DFTL) algorithm is well-known since it can solve the contradiction between mapping flexibility and the size of mapping cache by dynamically loading mapping entries. However, DFTL failed to utilize the spatial locality and hot-cold characteristics of the request and had an inefficient mapping entry eviction scheme. This paper proposes an adaptive read-write partitioning flash translation layer algorithm (ARWFTL). First, the cache mapping table (CMT) is divided into the read CMT and the write CMT. The size of the two can be adaptively adjusted by sensing the characteristics of the upper workload and the read-write latency of the underlying flash page. Second, a priority eviction window is set at the tail of the write CMT to evict the clean mapping entry firstly. When there is no clean mapping entry in the priority eviction window, the tail mapping entry and other mapping entries that belong to the same translation page are clustered to write back into the translation page. Then, other written back mapping entries are set to be clean and the tail mapping entry is evicted. Third, a hot data window is set at the head of the write CMT to recognize the hot and cold data of write requests. Then, the hot and cold data are stored in different data blocks of flash to avoid hot and cold data entanglement and reduce valid page migrations in garbage collection. Experimental results show that, compared with DFTL, ARWFTL can reduce the translation page write counts, the valid page migration counts, the block erase counts, and the average response time by 92.8%, 47.7%, 31.7%, and 31.4%, respectively. In addition, ARWFTL is also superior to the other recent DFTL-based improved algorithms, and even exceeds the pure page-level FTL in some indicators.

Physical layer security (PLS) in the presence of multiple eavesdroppers over Nakagami-m fading channels for cooperative decode-and-forward (DF) relay network that consists of one source, one destination, and multiple relays is investigated. Different from the recent PLS system model that only considers one eavesdropper during eavesdropping attack, this paper extends the one-eavesdropper case to a multiple-eavesdropper scenario and investigates joint relay and eavesdropper selection (JRES) strategy against eavesdropping attack over Nakagami-m fading channels. In the proposed strategy, the best relay is selected via the maximum relay forward channel capacity. In addition, since eavesdroppers are noncooperative, the worst case is considered. Namely, the wiretap channel between the best relay and the chosen eavesdropper has the maximum capacity. Traditional direct transmission and opportunistic relay selection (ORS) scheme in the presence of one eavesdropper over Rayleigh channel are regarded as benchmarks. Moreover, a security-reliability tradeoff (SRT) performance is analyzed, where the reliability performance is expressed by outage probability (OP), while the security performance is measured by intercept probability (IP). Closed-form expressions of OP and IP are derived. The numerical results show that the proposed JRES scheme outperforms the traditional direct transmission and the ORS scheme in the presence of one eavesdropper over Rayleigh channel. The SRT performance is enhanced obviously with the increasing of relay numbers and Nakagami channel fading factor m for a given number of eavesdroppers, which extends the PLS and SRT performance analysis to a more general case in a cooperative DF relay network.

In recent years, many scholars have been committed to improving the security and practicability of chaotic image encryption. In addition, their proposed schemes mainly focus on two aspects, new or improved chaotic systems and new or improved encryption processes. However, according to cryptanalysis works of scholars, encryption processes of chaotic image encryption schemes deserve more attentions. In this paper, a recently reported chaotic image encryption scheme named the 2D logistic-adjusted-sine-map-based image encryption scheme is comprehensively investigated, and some security, practicability, and rationality problems are found. Therefore, we first point out these problems existing in the reported encryption scheme and make some improvements in practicability and rationality. Next, under the conditions of chosen plaintext attack, we cryptanalyze it and propose a corresponding attack algorithm. For our attack algorithm, simulation test results show that it can completely recover plain images without knowing any secret key related information. Finally, we also present some possible improvements for the security problems of chaotic image encryption scheme under study.

The use of the unmanned aerial vehicle (UAV) as the aerial base stations can provide wireless communication services in the form of UAV-based small cells (USCs). Thus, the major design challenge that needs to be addressed is the coverage maximization of such USCs in the presence of co-channel interference generated by multiple UAVs operating within a specific target area. Consequently, the efficient deployment strategy is imperative for USCs while optimizing the coverage area performance to compensate for the impact of interference. To this end, this paper presents a coordinated multi-UAV strategy in two scenarios. In the first scenario, symmetric placement of UAVs is assumed at a common optimal altitude and transmit power. In the second scenario, asymmetric deployment of UAVs with different altitudes and transmit powers is assumed. Then, the coverage area performance is investigated as a function of the separation distance between UAVs that are deployed in a certain geographical area to satisfy a target signal-to-interference-plus-noise ratio (SINR) at the cell boundary. Finally, the system-level performance of a boundary user is studied in terms of the coverage probability. The numerical results unveil that the SINR threshold, the separation distance, and the number of UAVs and their formations should be carefully selected to achieve the maximum coverage area inside and to reduce the unnecessary expansion outside the target area. Thus, this paper provides important design guidelines for the deployment of multiple UAVs in the presence of co-channel interference.

Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, many studies based on the cross-layer design philosophy have been conducted in both wired and wireless networks. The Lagrangian duality technique has been widely adopted to solve the cross-layer optimization problem, but it is slowly convergent and sensitive to the iterative step size, especially for large networks. In this paper, a joint congestion control and power allocation second-order algorithm (JCCPA) is proposed, in which the joint optimization problem is modeled as a network utility maximization (NUM) framework, and the primal-dual interior-point method is used to solve the NUM problem. The JCCPA updates the primal variables and dual variables simultaneously according to their Newton directions; as a result,resulting in a faster convergence speed is reached. Moreover, the matrix-splitting technology is utilized to decompose the computation of the Hessian matrix and its inverse into different nodes and links so that the distributed update of source rate and node power can be implemented. The simulation results demonstrate that the JCCPA not only significantly improves energy efficiency but also has a faster convergence speed and is insensitive to step size.

Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other subjects (source domains) to train a model for classifying EEG data from the new subjects (target domains). However, the model trained using other subjects EEG data may degrade the classification performance of the target subject, when there exists the substantial inter-subject variability of EEG data. In this paper, to account for the domain shift between different subjects, we propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition. Specifically, a special end-to-end convolutional neural network (CNN) is firstly adopted to automatically extract deep features from the raw EEG data. Then, maximum mean discrepancy (MMD) is used to minimize the distribution discrepancy of deep features between source and target subjects. Finally, a center-based discriminative feature learning (CDFL) method is used to force the deep features closer to their corresponding class centers and make the inter-class centers more separable, so that it is possible to further improve the recognition performance of target domain EEG data. Experiments on public EEG datasets prove the effectiveness of the proposed method. This study may promote the practical use of EEG signal processing technology and expand its application range.

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme.

As various video services become popular, video streaming will dominate the mobile data traffic. The H.264 standard has been widely used for video compression. As the successor to H.264, H.265 can compress video streaming better, hence it is gradually gaining market share. However, in the short term H.264 will not be completely replaced, and will co-exist with H.265. Using H.264 and H.265 standards, three types of frames are generated, and among different types of frames exist dependencies. Since the radio resources are limited, using dependencies and quantities of frames in buffers, an appropriate time division transmission policy can be applied to transmit different types of frames sequentially, in order to avoid the occurrence of video carton or decoding failure. Polling models with batch Markovian arrival process (BMAP) and across-queue state-dependent service discipline are considered to be effective means in the design and optimization of appropriate time division transmission policies. However, the BMAP and across-queue state-dependent service discipline of the polling models lead to the large state space and several coupled state transition processes, which complicate the performance analysis. There have been very few researches in this regard. In this paper, a polling model of this type is analyzed. By constructing a supplementary embedded Markov chain and applying the matrix-analytic method based on the semi-regenerative process, the expressions of important performance measures including the joint queue length distribution, the customer blocking probability and the customer mean waiting time are obtained. The analysis will provide inspiration for analyzing the polling models with BMAP and across-queue state-dependent service discipline, to guide the design and optimization of time division transmission policies for transmitting the video compressed by H.264 and H.265.

This paper studies the secure transmission issue for simultaneous wireless information and power transfer in a multiuser multiple-input-multiple-output system with multiple external eavesdroppers, where the transmitter broadcasts independent confidential messages to different legitimate receivers. Each receiver can be seen as an internal eavesdropper intended by other receivers. Our objective is to achieve the robust beamforming design under imperfect channel state information, in which the total transmission power is minimized with constraints on the achievable secrecy rate and the energy harvesting. Since the problem is nonconvex, we propose a two-level optimization scheme. For the inner problem, we investigate two conservative relaxation approaches, large-deviation inequality and Bernstein-type inequality (BTI), to reformulate the outage secrecy rate constraints into some convex ones, yielding a convex optimization by semidefinite programming (SDP) relaxation. For the outer problem, it is a K-variable optimization problem, which can be solved via the novel line-dimensional-like search method. Moreover, we characterize the rank profile of SDP relaxed solution for these two approaches. Specifically, the optimal solution is proved to be rank-one. Numerical results are provided to verify the performance of the proposed algorithms, where the LDI-based scheme outperforms the BTI-based scheme in terms of energy efficiency.

In recent years, hybrid chaotic image encryption schemes combined with the DNA encryption technology or other technologies have become a research focus of many researchers. However, some researchers neglect security analyses of proposed schemes under the chosen plaintext attack, therefore leaving security vulnerabilities. Considering this situation, we carefully investigate a recently reported hyper-chaotic image encryption scheme using the DNA encryption technology. In this paper, we first point out some issues identified in the reported scheme and make some necessary improvements, and then cryptanalyze it and propose a chosen plaintext attack algorithm. With the proposed attack algorithm, theoretical analyses and test results show that it can completely recover plain images without knowing any secure key related information. Finally, some suggestions for improving the security and practicability of the reported hyper-chaotic image encryption scheme are presented.

Blind channel estimation (BCE) which does not require the knowledge of the source signals becomes increasingly important in various underwater acoustic applications. Unlike the conventional methods that utilize the statistical properties of the received signals, this paper proposes an acoustic model based BCE method that exploits the properties of the acoustic propagation and the acoustic environmental information. This method is implemented by localizing the source with a matched-field processing algorithm first, then, uses the physical model of the ocean waveguides to determine the channel impulse response. Its performance is investigated by making comparisons with the mode-based artificial time reversal and the ray-based synthetic time reversal methods, using the array data measured from the SWellEx-96 experiment at four source-array ranges. Cross-correlation coefficient and normalized projection misalignment are adopted to evaluate the performance of the proposed method, and the simulation results for different multipath channels confirm the effectiveness of the proposed method.