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As the eutrophication of the water body becomes more and more serious, the algae in the water body grow in large quantities and eventually form harmful algal blooms, causing great harm to the marine ecosystem. Therefore, how to quickly and accurately identify algae and make precautions becomes the key to solving this problem. Currently, more than tens of thousands of microalgae species are known around the world, but publicly available data are sparse, many of the species are characterized similarly to each other, and it is currently challenging to train an effective classification model with limited data. Existing few-shot learning classification algorithms that utilize meta-learning for metrics can be a good solution to this problem. In this paper, an AlgaeClass_Net algorithm that combines an improved multi-scale feature fusion with a feature enhancement module is proposed for the fine-grained features of microalgae. Furthermore, it utilizes a metric learning approach to classify few-shot microalgae by calculating the distances between the feature vectors of samples in the query set and the feature vectors of samples in the support set. The experimental results showed that the method achieves 78.55% and 91.20% classification accuracies under different tasks of 5-way 1-shot and 5-way 5-shot, respectively, with an improvement of 3.51% and 4.97% on the suboptimal model, respectively. It provides new research ideas for the identification of marine microalgae and the development and utilization of marine renewable energy.

Large-scale renewable energy grid-connection will lead to a serious weakening of the inertia level of the power grid, which will have a non-negligible impact on the frequency stability of the system. This paper proposes an emergency control strategy for high proportion renewable power system that can make full use of the adjustment ability of multi-type power generations to maximize the frequency recovery effect after disturbances. Firstly, the frequency response characteristics of multi-type power generations in high proportion renewable energy power system are analyzed, and the frequency aggregation response models including thermal power units, wind turbine units and photovoltaic energy storage units are derived. On this basis, a system frequency emergency control model considering the adjustment characteristics of multi-type power generations is established. The objective function of the model is to minimize the frequency modulation cost after disturbance accident, while the constraint conditions include the output power of multi-type power generations and the transient index of frequency stability. The constraint of frequency change rate is transformed into the minimum inertia demand of the system, which can reduce the influence of imprecise measurement of disturbance occurrence time on the accuracy of scheme formulation. Finally, combined with the established example system model, it is verified that the theoretical calculation errors of the maximum frequency deviation and quasi-steady-state deviation against the actual values can be limited within 1% and the effectiveness of proposed emergency frequency control scheme is verified under different disturbances.

It is important in production to achieve accurate counting and density estimation of high-density culture fry under the environmental conditions of aquaculture scenarios in an efficient and accurate manner. However, none of the current methods for fry counting works well under the high-density and high-overlap conditions of real aquaculture scenarios. Therefore, in this paper, we propose a high-density farming fry monitoring network model, Super-Resolution GAN Density Estimate Attention Network (SGDAN), which incorporating an image enhancement algorithm and an attention mechanism, and we create a high-density farming fry dataset (HD-FryDataset) based on the environmental conditions of real aquaculture scenarios. The network model is designed to improve and optimize the targeted subnetworks for several key aspects of high-density fish fry monitoring work. Four subnetworks are included for image optimization, feature extraction, attention, and density map estimation. The experimental results show that the SGDAN network model achieved an average counting accuracy of 97.57% on the high-density culture fry dataset, which was 8.23% and 2.06% higher than those of MCNN and CSRNet, respectively. Additionally, the MAE and RMSE of the model were reduced by 71.9% and 67.3% and by 34.3% and 33.2% compared with those of MCNN and CSRNet, respectively. The model proposed in this paper also has a better ability to generate predictive density maps. The density maps generated by SGDAN have values of the evaluation metrics PSNR and SSIM of 20.33 and 0.933, respectively, which are 3.31 and 0.037 and 2.63 and 0.031 higher than those of MCNN and CSRNet. In general, the network model proposed in this paper outperforms existing network models in two applications: accurate counting of fry and generation of density maps for high-density culture in aquaculture. It also provides a good solution for digitizing the number of fry and visualizing the density of high-density culture in intelligent aquaculture systems.

Raw data simulation is very important for system parameter design, performance evaluation, and route planning of bistatic synthetic aperture radar (SAR). However, most conventional simulation methods focus on improving the efficiency of echo simulation, without considering the impact of terrain fluctuations, resulting in insufficient fidelity of simulated echoes. In this article, a raw data simulation framework under undulating terrain for bistatic SAR with arbitrary configuration is proposed. In the framework, based on the prior information of digital elevation model of undulating terrain and the small facet theory, the time-variant scattering coefficient is computed first with the empirical formula related with the angle variation of transmitter and receiver for bistatic SAR. Then the shadow is judged from the perspectives of transmitter and receiver, respectively, and the formation of double shadow is discussed. At last, the above process was embedded into the time domain pulse by the pulse echo simulation algorithm, forming a framework for simulating bistatic SAR raw data under undulating terrain with arbitrary configuration, and the simulated and measured data experiments are illustrated to verify the effectiveness of the proposed framework.

Accurate shrimp seed counting is an important task in the aquaculture and stocking shrimp seeds processes. At present, most shrimp seed counting tasks are still performed manually, which is not only time-consuming and laborious but also has low accuracy, and this counting method cannot realize modern aquaculture. Therefore, to promote the modernization of aquaculture, a portable shrimp seed counting system is proposed in this paper. The system is based on Shrimpseed_Net, a modified CSRNet convolutional neural network, and can achieve higher accuracy in shrimp seed counting. The algorithm has been successfully implemented on a smartphone, enabling automatic counting of shrimp seeds within seconds by simply taking a picture of the shrimp seeds or selecting an image from the photo album to upload to the internal algorithm. Experimental results show that after only 50 iterations, the average absolute error of the algorithm is reduced to 17.28, with an accuracy rate of 95.53%. The system can accurately count shrimp seeds in real scenarios within a short period. The shrimp seed counting system studied in this paper can be widely used in the aquaculture industry to promote its development and enable intelligent aquaculture practices.

The applications of multiple underwater robots are becoming more widespread as the oceans continue to be explored. However, the cooperative control of multiple underwater robotic systems lacks an effective way to interact with information in the presence of weak communication in the water. This study proposes a brief binary optical-acoustic dual signals hybrid cooperative control method for overcoming this difficulty. It simplifies the information interaction rule to improve the efficiency of cooperative control while guaranteeing the range of underwater communication. This method includes an acoustic communication mechanism that enables the underwater robot to transmit signals over long distances and an optical communication mechanism that enables the transmission of signals in close situations. Avoiding information redundancy due to traditional underwater real-time communication, the underwater robots send brief binary signals through optical and acoustic sensors to realize cooperative control. An underwater robot equipped with a dual optical-acoustic system is designed in this paper combining the leader and follower formation structure. The triangular formation experiment and diamond formation experiment were also conducted in the pool to verify the effectiveness of this cooperative control method. The study provides a method for the cooperative control of multiple underwater robotic systems.

Uncertainties of intermittent renewables and complexities of load demands bring challenges to the optimal operation of the microgrid. This paper proposes a fully distributed method based on the consensus algorithm for solving the economic dispatch problem (EDP) of DC microgrids. The proposed distributed method is performed using a multiagent system. It only needs information from neighboring agents and does not need a central controller. A two-level control structure is adopted to realize the economic dispatch and voltage regulation simultaneously. The upper level obtains the optimal power reference of dispatchable agents and maintains the power balance of the system. The lower level performs an improved droop control to track the power reference and regulate the average bus voltage for the microgrid. The proposed method’s properties and convergence are analyzed using eigenvalue perturbation theory to obtain a faster convergence speed. By designing the adjacency matrix appropriately, the proposed method can ensure the system stable when the communication topology changes. Finally, several simulations are designed to verify the effectiveness of this novel control method.

This paper proposed distributed adaptive formation control for leader-follower bipartite time-varying formation (BTVF) of a nonlinear multi-agent system (MAS). The proposed nonlinear MAS satisfies the one-sided Lipschitz-type condition. In the topological graphs with directed spanning trees, the design of adaptive protocols does not depend on the known communication topology, which can avoid the use of global information. Given limited information, the proposed MAS can achieve desired formation tracking by utilizing an observer protocol and converting the bipartite formation control problem into a stability problem of system errors. The analysis results of the systematic errors by using Lyapunov candidate functions, indicate that the MAS can be globally stable with a certain convergence rate during operation. Finally, numerical simulations are presented to confirm the validity of the proposed approach.

High-voltage direct current (HVDC) technology based on Line Commutated Converter (LCC) is widely used in modern power systems due to its numerous benefits. However, as a typical power electronic device, LCC-HVDC has caused significant changes in the dynamic behavior of modern power systems due to largely replaced conventional power supplies in power systems. The interactions among sending-terminal and receiving-terminal system become stronger and have caused some stability issues, but the mechanism behind this has yet to be fully revealed. In order to analyze the interactions among sending-terminal and receiving-terminal system of LCC-HVDC, a small-signal model of modular HSS modeling is proposed based on Harmonic State Space (HSS) theory. Comparison of time-domain response with detailed switching model shows that the proposed can well reflect the dynamic behavior of the detailed switching model. Based on the model, the influence of controller’s parameters on system stability is analyzed, as well as the dynamic interactions of LCC-HVDC among sending-terminal and receiving-terminal system are investigated. Analytical results demonstrate that there exists interactions among sending-terminal and receiving-terminal system which have an influence on the stability of the system.

Aspect sentiment triplet extraction (ASTE) is one of the important subtasks of aspect-based sentiment analysis, it aims at detecting the aspect terms, opinion terms, and the corresponding sentiment polarity, simultaneously. Most methods directly employ GCNs to capture the syntactic dependency information in ASTE. However, these methods may lead to error propagation. Besides, the GCN-based methods are weak at capturing sequence information and long-distance information. The general neural networks such as LSTM are good at capturing this kind of information. However, these general neural networks are weak at modeling syntactic dependency information. To alleviate the above problems, we propose a novel interactive dual channel network (IDCN) for ASTE. In IDCN, an interactive word pair generating (IWPG) module is designed to model the sequence information, long-distance dependency information, and correlation relations between word pairs, simultaneously. In the IWPG module, the dual channels can learn different representations. Based on these representations, the informative word-pair representations can be learned by the interaction mechanism of dual channels. Besides, we design the syntactic dependency fusion module to model the syntax dependency information by constructing word pair dependency relation tensors and pooling mechanism, which can naturally inject the syntactic dependency knowledge into the general neural networks and reduce error propagation. Abundant experiments have been performed on multiple datasets. The experimental results show that IDCN acquires state-of-the-art results and validates the effectiveness of IDCN.

In this article, we investigate a multi-user multiple-input-single-output full-duplex system with simultaneous wireless information and power transfer under the non-linear energy harvesting model, where channel state information is imperfect. Since the channel estimation error is considered in our proposed system, the self-interference cannot be canceled perfectly. For such a system, a robust optimization problem that maximizes the minimum average total harvested power by jointly optimizing the uplink and downlink beamforming matrices is formulated. As the problem is non-linear and non-convex under channel estimation errors, a novel Newton method based iterative optimization algorithm is proposed to solve it efficiently. In each iteration, the semidefinite relaxation technique and S-Procedure method are applied to obtain the optimal beamforming vectors, which are proved to be optimal solutions with rank-one. Simulation results reveal that the proposed robust design can achieve better system performance than the non-robust scheme, both of which are based on the non-linear energy harvesting model.

The wide-field surveillance camera plays a critical role in space debris detection and the visible light situational awareness required for early warning. However, stray light in such a system has always been a serious problem. Current methods cannot effectively eliminate the interference of stray light, which directly lead to the inability to accurately segment the target and background, which greatly reduce the accuracy of target recognition. To solve this problem, we proposed an accurate stray light elimination method based on recursion multi-scale gray-scale morphology (RMGM). First, we defined two structural operators with different domains. These two structural operators can make full use of the difference information between the target region and the surrounding background region, which is the basic premise to ensure high-precision correction. Then we used the two structural operators with different domains to perform morphological processing on the surveillance image to eliminate stray light. Finally, in order to ensure that targets with different sizes in the surveillance image are not lost, we adopt a recursion multi-scale method. We increase the size of structural operator and perform the morphological operation again on the estimated and eliminated stray light non-uniform background in order to retrieve the lost larger size target. In addition, we add an automatic decision mechanism on the recursion multi-scale method by using corresponding threshold judgment. Further experimental results on real captured image datasets show that compared with other methods, the proposed RMGM method can simultaneously have high-precision stray light elimination effect, high-precision target retention rate, and faster computation time.

The last mile problem of E-grocery Distribution comprises one of the most costly and highest polluting components of the supply chain in which companies deliver goods to end customers. To reduce transport cost and fuel emissions, a new element of ground-based delivery services, autonomous delivery vehicles (ADVs), is included in the E-grocery distribution system for improving delivery efficiency. Thus, the objective of this study is to optimize a two-echelon distribution network for efficient E-grocery delivery, where conventional vans serve the delivery in the first echelon and ADVs serve delivery in the second echelon. The problem is formulated as a two-echelon vehicle routing problem with mixed vehicles (2E-VRP-MV) with a nonlinear objective function, in which the total transport and emission costs are optimized. This optimization is based on the flow assignment at each echelon and to realize routing choice for both the van and ADV. A two-step clustering-based hybrid Genetic Algorithm and Particle Swarm Optimization (C-GA-PSO) algorithm is proposed to solve the problem. First, the end customers are clustered to the intermediate depots, named satellites, based on the minimized distance and maximized demand. To enhance the efficiency of resolving the 2E-VRP-MV-model, a hybrid GA-PSO algorithm is adopted to solve the vehicle routing problem. Computational results of up to 21, 32, 50, and 100 customers show the effectiveness of the methods developed here. At last, the impacts of the layout of the depot-customer and customer density on the total cost are analyzed. This study sheds light on the tactical planning of the multi-echelon sustainable E-grocery delivery network.

Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks.

Lysine acetylation (Kace) is a conservative protein posttranslational modification (PTM) closely related to various metabolic diseases. Therefore, Kace sites identification is of great significance for investigating metabolic disease treatments. Existing studies have shown that protein structural properties contain highly useful structural information, which provides a strong basis for identifying PTMs. During the feature learning process, features at different levels are complementary, and taking them into consideration can effectively improve the quality of the features. However, existing deep-learning methods used protein sequence-level information as input without considering the protein structural properties. Furthermore, only high-level features were focused on, resulting in considerable information loss and weakening the prediction results. Therefore, we propose a novel deep-learning model based on modular densely connected convolutional networks (MDC) for Kace sites prediction, called MDC-Kace. MDC-Kace introduces the protein structural properties and combines them with the original protein sequence and the amino acid physicochemical properties to construct the site's feature space. Then, modular densely connected convolutional networks are used to capture the information of features at different levels and reduce information loss and crosstalk during the feature learning step. We add a squeeze-excitation layer to evaluate the importance of different features and improve the network abstraction ability to identify potential Kace sites. The experimental results of ten-fold cross-validation and independent testing in human, Mus musculus and Escherichia coli datasets showed that our MDC-Kace model outperforms the existing Kace site predictors and can predict potential Kace sites effectively. MDC-Kace can be available at https://github.com/lianglianggg/MDC-Kace.

The link prediction can be used to seek missing or future links in the network, so it has become a hot research topic. The network generally contains two types of information: the topological structure of network formed by the connection between nodes, and the attribute information of nodes. However, the existing topology-based link prediction algorithms consider little attribute information. In this paper, a novel algorithm called Network Embedding with Attribute Deep Fusion for Link Prediction (NEADF-LP) is proposed. We get the embedded vectors with topological structure and attribute information by structure encoder and attribute encoder respectively, and fuse two vectors deeply. Compared with mainstream baselines on CiteSeer and Cora datasets, the results show that the deep fusion of topological structure and attribute information improve the accuracy of link prediction effectively.

The collaborative control of the multi-agent system (MAS) marks the trend of intelligent transportation system (ITS). However, the collaborative control of MAS with flexible sampling periods remains a challenge, because under-driven systems are prone to random delays, data loss and sensor failures in semi-unstructured environment. Against the background of the semi-unstructured environment in a Dutch greenhouse, this paper puts forward a universal collaborative motion control algorithm for the MAS of automated guided vehicles (AGVs), in the light of the first-order dynamics of the system. The proposed algorithm is called continuous-step-rotate-run (CSRR). Besides, the enhanced depth image fusion positioning (EDIFP) scheme was designed to mitigate the disturbances on the control algorithm, arising from flexible sampling periods and data loss. To verify its effectiveness, the CSRR control algorithm was tested on an MAS of three under-driven BigPan AGVs. The results demonstrate that our algorithm can collaboratively control the AGVs in an effective and stable manner. The simple algorithm offers a desirable solution to the collaborative control of various MASs.

We investigate the generalized projective synchronization (GPS) control of fractional-order extended Hindmarsh-Rose (FOEHR) neuronal models with transcranial magneto-acoustical stimulation (TMAS) input. This improved neuronal model has advantages in describing the complex firing characteristics of neurons stimulated by alternating current. In this study, a master-slave neuron system consisting of two FOEHR neuronal models is assumed to be subject to uncertain model parameters and unknown external disturbances. To quantify the GPS error, we design a new error variable based on the properties of the fractional-order derivative and construct a related GPS error system. Fuzzy logic systems are introduced to approximate the unknown nonlinear dynamics of the error system. To ensure the synchronous firing rhythms of the master-slave neuron system, an adaptive fuzzy control algorithm is proposed under the Lyapunov approach, in which the adaptive parameters are robust to the estimation errors. By choosing the appropriate design parameters, the proposed control scheme enables the master-slave neuron system to achieve GPS in a finite amount of time and to be resilient to uncertain parameters and unknown disturbances. The simulation results demonstrate that after the designed control inputs are implemented, the states of the slave neuron synchronize with those of the master neuron in specified proportions, and the corresponding synchronization error converges towards an arbitrarily small neighborhood of zero.

Network representation learning is proposed to make it easier to perform complex inference processes on large-scale networks. It aims to represent each node in the network as a low-dimensional potential representation while preserving the structure and inherent features of the network. Most existing learning methods do not consider the comprehensive structure or the rich semantics of between nodes, which lead to incomplete embeddings. We attempt to find a way to learn network representation while keeping the network structure and relations between nodes to address this issue. In this paper, we propose a Community Aware and Relational Attention (CARA) method to enhance network representation learning. In CARA, community aware random walks capture both the community and local context from a global perspective to enhance the structure embedding. And we design a two-layer attention mechanism to model the semantic relationships between nodes, which can learn different text embedding for different neighbors. Then, these two embeddings are combined together to obtain the node representation. Experiments on four real-world networks show that CARA has better performance than other methods in link prediction and node classification.

Under a multiscenario environment with frequent bursts of data in the edge cloud, the resource allocation in the edge cloud will affect the stability of its nodes. To address this problem, a balanced virtual resource allocation model based on conflict conditions is proposed. Based on a thorough study of the similarity between task attributes and resources used by the host, the concept of a task backlog is implemented to achieve a preliminary balanced allocation of tasks; thus, a conflict condition based on the remaining resources of the physical and virtual machines is proposed. Further, a matrix of phased conflict coefficients is built to establish a balanced virtual machine allocation model. The results of experiments comparing the performance of the proposed model with that of other existing models indicate that the proposed model can reduce the virtual machine scheduling time by up to 8.33%, save up to 6.25% of host energy consumption, and improve the algorithm efficiency by 20.47% compared with the other algorithms. To avoid the local optimal problem caused by dynamic virtual machine migration, an improved ant colony algorithm is combined with the above model, and concepts of a pheromone volatility factor and suppression factor are implemented to optimize the pheromone measurement function and ensure that the virtual machine migration path is globally optimal. Overall, the model reduces the conflict rate of resources on the physical machine and can maintain stable operation under CPU usage fluctuation, thus realizing a balanced allocation of node resources.

Action recognition is an important yet challenging task in computer vision. Attention mechanism not only tells where to focus but when to focus. It plays a key role in extracting discriminative spatial and temporal features for solving the task. In this paper, we propose an improved spatiotemporal attention model based on the two-stream structure to recognize the different actions in videos. Specifically, we first extract the intra-frame spatial features and inter-frame optical flow features for each video data. Then we implement an effective attention module, which sequentially infers attention maps along three separate dimensions: channel, spatial and temporal. After adaptive feature refinement based on the attention maps, we perform a temporal pooling process to squeeze the temporal dimension. Then, these achieved spatial and temporal features are fed into the spatial LSTM and temporal LSTM, respectively. Finally, we fuse the spatial feature, temporal feature and two-stream fusion feature to classify the actions in videos. Additionally, we also collect and construct a new Ping-Pong action dataset for subsequent human-robot interaction task from YouTube. It contains 2400 labeled videos for 4 categories. We compare with other action recognition algorithms and validate the feasibility and effectiveness of the proposed method on Ping-Pong action dataset and HMDB51 dataset.

The increasingly growing truck traffic volume data while limited truck weigh-in-motion weight data has posed great challenges for transport agencies to access the freight tonnage of all the truck traffic sites. By mapping a group of traffic sites with similar traffic patterns to a weigh-in-motion site, the clustered truck traffic data is expected to be smaller than the sum of all data from all traffic sites, and the cluster can be fully utilized in a period of time by transport agencies to evaluate the freight tonnage. This study developed a novel and implementable approach of integrating two complementary data, Weigh-in-Motion (WIM) weigh data and Telemetric Traffic Monitoring Sites (TTMSs) volume data, to produce truck traffic sites clustering. An improved k-means clustering with three attributes is fitted to the TTMS, which are the distances to the WIM sites (WIMSs), truck volumes in TTMS, and vehicle class distribution. The aforementioned methodology was tested in a case study in Florida using WIM data in 2012 and 2017. The proposed model might shed light on the statewide performance evaluation of freight traffic with low computing cost.

Due to the confidentiality of the classified information system, it is isolated from the external network, but it is necessary to exchange data with an external network. This makes data exchange security between classified networks an important problem. Previous research focused on using access control policies to limit data access at different security levels, using some security checks to detect the security of the data itself. The security threat of the access control policy itself will threaten the data exchange between the classified networks. Moreover, the data should be performed security check isolated from user during the physical transmission to prevent the attack behavior which bypass the data security check from the user side. At present, the cross-network isolation and data exchange models are designed based on fixed business scenarios and lack generality. Therefore, this paper proposes a Turing machine-based theoretical model for cross-network isolated data exchange. This model consists of two parts: the proof system module and the physical transmission channel module. The proof system module uses an interactive Turing machine to model the part of the operational security and data security check of the communicating party's authority to provide security and versatility. The physical transmission channel module isolates different networks and provides a detection mechanism independent of the communication parties to ensure the security and efficiency of the transmission. The proof given shows that the theoretical model can exchange data efficiently and safely. Finally, based on the given theoretical model, an intuitive cross-network isolation and data exchange function model is constructed in the form of a functional tree to illustrate the model versatility.

Kinematic calibration performance is heavily dependent on two factors-the ability of calibration configurations mapping kinematic parameter errors, and the un-modeled errors including joint clearance, thermal expansion, and measurement noise. Therefore, this paper deals with the calibration configuration optimization to reduce the impact of the two factors on calibration performance. We pay particular attention to establish an index for evaluating calibration configuration's quality. Different from other works, the proposed comprehensive quality index can simultaneously reflect configurations' observability and globality. Furthermore, the numerical methods are used to analyze the relationships between the comprehensive quality index and configuration number, and the relationships between calibration performance and configuration number. Based on the above relationships, we provide a feasible solution for determining the calibration configuration number of a specific manipulator. Based on the above work, configuration optimization model is established and solved by particle swarm optimization. The simulation of an eight degree-of-freedom manipulator illustrates the advantages of the proposed method. In 100 calibration simulations, optimized configurations perform better than random configurations, with the position accuracy increased by 43.86% and the attitude accuracy increased by 14.29%.

The Internet of Things is one of the new emerging application domains that require delay tolerant network (DTN) support, where an end-to-end path between the source and the destination may not always exist. Due to the intermittent connectivity of DTN, the design of an efficient routing algorithm is the main challenge. In this paper, we first define a metric called message handling capacity to determine the ability of a node to forward messages. Then, we introduce a concept called connection strength to reflect the connection time between nodes and then integrate the concept into delivery predictability used by Prophet to determine the chance of a node completely delivering a message to the destination. Subsequently, we present a metric called quality of node (QoN), which is calculated by combining the relative weights of the message handling capacity and the improved delivery predictability. Finally, we present an adaptive spray and wait routing algorithm based on QoN (QoN-ASW). The QoN-ASW adaptively allocates the number of message copies between the encountered nodes according to the proportion of quality of node in the spray phase, which avoids the blindness of replica distribution. In addition, a forwarding scheme is implemented in the wait phase, which takes full advantage of encounter opportunities. In the simulation, we demonstrate the efficiency of integrating the connection strength into delivery predictability and compare the QoN-ASW with four existing DTN routing algorithms from four aspects. The simulation results show that the QoN-ASW can significantly improve the delivery rate and reduce the average delay while achieving a relatively low overhead.

The remote transmission of tumor ultrasound images is an important link in telemedicine. Based on the Galois field (GF), this paper proposes an encryption algorithm that ensures the security of tumor ultrasound images (TUIs) during the transmission. First, the grayscale image of the TUI was generated by the secure hash algorithm 1 (SHA-1) algorithm, and the resulting hash value was taken as the medical record summary, which was used to monitor the transmission of the TUI. Second, the 2D ciphertext image was expanded into 1D vectors, and the TUI was encrypted with the non-repetitive scrambling algorithm, coupled with two diffusion algorithms. The simulation results show that the coupled algorithm can ensure the safety of the TUIs in remote transmission.

Due to the complexity of the brain image itself, the brain image segmentation technology has become a bottle for further application and development of the system. Considering the inconsistency of intensity, partial volume effect, and noise in medical images, this paper studies the brain image segmentation technology based on the multi-weight probability. The multi-weight probability method mainly models the data set with outliers and non-Gaussian noise. First, the probabilistic local ELM model is established. Based on this, the Parzen window method is used to establish the probability distribution of the local model, and then, the probability distribution is used as the weight to fuse. All local models are used to build a global robustness model. The method successfully applied the brain and UCI examples and compared with traditional ELM, regularized ELM, and robust ELM. The results show that the probability weight ELM shows better modeling performance.

A simple pattern reconfigurable antenna based on monopole is presented in this paper. The antenna consists of four folded monopoles and a windmill-shape ground plane. The central patch is fed by a coaxial probe to decide which monopole is excited. Four reconfigurable modes can be achieved by changing the states of four PIN diodes mounted on the feeding lines. And the maximum radiation directions of the proposed antenna are deflected from φ = 30°, φ = 120°, φ = 210°, and φ = 300° at azimuth plane in four operating modes. The proposed antenna is simulated, fabricated, and measured. The experimental results indicate that the antenna can achieve a measured 10-dB bandwidth of 25.7% (3.32-4.3 GHz), a 10-dB front-to-back ratio of 17.7% (3.6-4.3 GHz), and a stable average gain of 3.3 dBi within the operating band. These characteristics make the designed antenna fit for C-band satellite communication system.

To solve the low attack path quantification degree and complex path finding in the industrial Internet of Things, a vulnerability assessment method based on attack graph and maximum flow is proposed. The method takes into account the factors influencing the attack behavior and relationship between network nodes. The attack risk is calculated by common vulnerability scoring system, which increases the attack path quantification degree. The maximum loss flow describes the attack path, evaluates the network vulnerability by maximum loss flow and loss saturation and represents the vulnerability relevance. Avoiding the repeat calculation and obtaining the potential key vulnerability path fast, the augmented road algorithm is used to find optimal attack path within global path. The result shows that the method is feasible and can evaluate the vulnerability and risk path objectively.