UAV-Assisted Data Offloading for Smart Container in Offshore Maritime Communications

Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention.However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station(BS). In this paper, we investigate the unmanned aerial vehicle(UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment,offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio.Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.

An image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree

This paper proposes an image encryption algorithm based on spatiotemporal chaos and middle order traversal of a binary tree.Firstly,other programming software is used to perform the middle order traversal,and the plaintext image is sorted according to the middle order traversal sequence on the permutation.Secondly,the chaotic sequence is generated using the coupled map lattice to set the chaotic interference value.Finally,the XOR operation between the adjacent pixel values of the replacement image is completed to generate the ciphertext matrix.The simulation and experimental results show that the proposed algorithm can resist typical attacks and has good robustness.

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

Analysis and Design of Feedback Controls Supporting Multiple Links and Heterogeneous Sources Transmission Control Protocol(TCP) Network Control System

An analysis and control approach is presented for the active queue management(AQM) problem in network control system supporting multiple links and heterogeneous sources transmission control protocol(TCP).Using additive increase multiplicative decrease(AIMD) model,some studies are carried out on multiple links and heterogeneous sources TCP network control system,and some conditions are derived to ensure the stabilization of the given feedback control system by exploiting a general LyapunovKrasovskii functional and some techniques for time-delay systems.And the controller gain is designed further.A simulation is to be provided to verify the algorithm in the paper.

Deep learning algorithm featuring continuous learning for modulation classifications in wireless networks

Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.

New chaotical image encryption algorithm based on Fisher–Yatess scrambling and DNA coding

Based on the Fisher–Yatess scrambling and DNA coding technology, a chaotical image encryption method is proposed. First, the SHA-3 algorithm is used to calculate the hash value of the initial password, which is used as the initial value of the chaotic system. Second, the chaotic sequence and Fisher–Yatess scrambling are used to scramble the plaintext,and a sorting scrambling algorithm is used for secondary scrambling. Then, the chaotic sequence and DNA coding rules are used to change the plaintext pixel values, which makes the ciphertext more random and resistant to attacks, and thus ensures that the encrypted ciphertext is more secure. Finally, we add plaintext statistics for pixel-level diffusion to ensure plaintext sensitivity. The experimental results and security analysis show that the new algorithm has a good encryption effect and speed, and can also resist common attacks.

Fuzzy Self-adaptive Proportional Integration Differential Control for Attitude Stabilization of Quadrotor UAV

The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional integration differential(PID) controllers,it is necessary to appropriately adjust the controller coefficients which are dependent on dynamic parameters of the quadrotor UAV and any changes in parameters and conditions could affect desired performance of the controller.In this paper,combining with PID control and fuzzy logic control,a kind of fuzzy self-adaptive PID control algorithm for attitude stabilization of the quadrotor UAV was put forward.Firstly,the nonlinear model of six degrees of freedom(6-DOF) for quadrotor UAV is established.Secondly,for obtaining the attitude of quadrotor,attitude data fusion using complementary filtering is applied to improving the measurement accuracy and dynamic performance.Finally,the attitude stabilization control simulation model of the quadrotor UAV is build,and the self-adaptive fuzzy parameter tuning rules for PID attitude controller are given,so as to realize the online self-tuning of the controller parameters.Simulation results show that comparing with the conventional PID controller,this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.

A novel pseudo-random coupled LP spatiotemporal chaos and its application in image encryption

In this paper, first, we investigate a novel one-dimensional logistic-PWLCM（LP） modulation map which is derived from the logistic and PWLCM maps. Second, we propose a novel PCLML spatiotemporal chaos in pseudo-random coupling method that can accelerate the system behavior of the fully spatial chaos. Here, because the better chaotic properties include a wide range of parameter settings and better ergodicity than a logistic map, the LP is used in PCLML as f（x）. The Kolmogorov–Sinai entropy density and universality and the bifurcation diagram are employed to investigate the chaotic behaviors of the proposed PCLML model. Finally, we apply the LP and PCLML chaotic systems to image encryption to improve the effectiveness and security of the encryption scheme. By combining self-generating matrix model M and dynamic substitution box（S-Box） methods, we design a new image encryption algorithm. Numerical simulations and security analysis have been carried out to demonstrate that the proposed algorithm has a high security level and can efficiently encrypt several different kinds of images into random-like images.

An image encryption algorithm based on improved baker transformation and chaotic S-box

The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.

Novel dual-band antenna for multi-mode GNSS applications

A novel dual-band antenna is proposed for mitigating the multi-path interference in the global navigation satellite system（GNSS） applications. The radiation patches consist of a shortedannular-ring reduced-surface-wave（SAR-RSW） element and an inverted-shorted-annular-ring reduced-surface-wave（ISAR-RSW）element. One key feature of the design is the proximity-coupled probe feeds to increase impedance bandwidth. The other is the defected ground structure band rejection filters to suppress the interaction effect between the SAR-RSW and the ISAR-RSW elements. In addition, trans-directional couplers are used to obtain tight coupling. Measurement results indicate that the antenna has a larger than 10 d B return loss bandwidth and a less than 3 d B axial-ratio（AR） bandwidth in the range of（1.164 – 1.255） GHz and（1.552 – 1.610） GHz. The gain of the passive antenna in the whole operating band is more than 7 d Bi.

MEC Enabled Cooperative Sensing and Resource Allocation for Industrial IoT Systems

In industrial Internet of Things systems,state estimation plays an important role in multisensor cooperative sensing.However,the state information received by remote control center experiences random delay,which inevitably affects the state estimation performance.Moreover,the computation and storage burden of remote control center is very huge,due to the large amount of state information from all sensors.To address this issue,we propose a layered network architecture and design the mobile edge computing(MEC)enabled cooperative sensing scheme.In particular,we first characterize the impact of random delay on the error of state estimation.Based on this,the cooperative sensing and resource allocation are optimized to minimize the state estimation error.The formulated constrained minimization problem is a mixed integer programming problem,which is effectively solved with problem decomposition based on the information content of delivered data packets.The improved marine predators algorithm(MPA)is designed to choose the best edge estimator for each sensor to pretreat the sensory information.Finally,the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.

Fuzzy Inference System Design Based on Data Mining Concepts and Its Application in Time Series Forecasting

This paper adopts data mining(DM) technique and fuzzy system theory for robust time series forecasting.By introducing DM technique,the fuzzy rule extraction algorithm is improved to be more robust with the noises and outliers in time series.Then,the constructed fuzzy inference system(FIS) is optimized with a partition refining strategy to balance the system's accuracy and complexity.The proposed algorithm is compared with the WangMendel(WM) method,a benchmark method for building FIS,in comprehensive analysis of robustness.In the classical Mackey-Glass time series forecasting,the simulation results prove that the proposed method is able to predict time series with random perturbation more accurately.For the practical application,the proposed FIS is applied to predicting the time series of ship maneuvering motion.To obtain actual time series data records,the ship maneuvering motion trial is conducted in the Yukun ship of Dalian Maritime University in China.The time series forecasting results show that the FIS constructed with DM concepts can forecast ship maneuvering motion robustly and effectively.

A secure image protection algorithm by steganography and encryption using the 2D-TSCC

Based on the two-dimensional(2D)tan-sin-cos-coupling(2D-TSCC),a new image protection method is designed,this method includes steganography and encryption.First,a 2D-TSCC system is designed.The 2D-TSCC has a large parameter space in a hyperchaotic state.The chaotic trajectory fills the entire window.The chaotic sequence generated by the 2D-TSCC has a good pseudorandomness,so it can be used in steganography and encryption.Then,the amount of information contained in each bit of the cover image is analyzed,and the three bits which carry the least amount of information are selected.The secret image is hidden in these three bits base on the 2D-TSCC.Finally,the carrier image is scrambled and diffused by the 2D-TSCC.The ciphertext is generated in this way.Send the ciphertext to the recipient through channel transmission,and the recipient obtains the secret image by decrypting twice.

Disturbance Observer Based Sliding Mode Controller Design for Heave Motion of Surface Effect Ships

In order to damp the heave motion of surface effect ships(SESs),a sliding mode controller with a disturbance observer was designed.At first,a disturbance observer was proposed to estimate the unknown time-varying disturbance acting on SESs due to waves.Then,based on the disturbance,a slide mode controller was designed to minimize the magnitude of SES's heave motion position.It was theoretically proved that the designed sliding mode controller with the disturbance observer could guarantee the stability of the closed-loop heave motion control system of SESs.Simulations on a Norwegian Navy's SES were carried out and the simulation results illustrated the effectiveness of the proposed controller with the disturbance observer.

Novel optimized routing algorithm for LEO satellite IP networks

A lot of routing algorithms have been proposed for low earth orbit（LEO） satellite IP networks in recent years,but most of them cannot achieve global optimization.The dynamic characters of LEO satellite networks are reflected in two aspects：topology and traffic change.The algorithms mentioned above are ＂hard routing＂ which only realize local optimization.A distributed soft routing algorithm combined with multi-agent system（MASSR） is proposed.In MASSR,mobile agents are used to gather routing information actively,and blackboard is introduced to achieve direct information exchange between agents.MASSR provides traffic adaptive routing and tracks the change of LEO satellite network topology.The performance of ant colony optimization（ACO） and MASSR are compared in Iridium constellation,and MASSR presents better end-to-end delay as well as enhanced robustness.

Sensing Time Optimization in Energy-Harvesting Cognitive Radio with Interference Rate Control

In this paper,an energy-harvesting cognitive radio(CR) is considered,which allows the transmitter of the secondary user(SU) to harvest the primary signal energy from the transmitter of the primary user(PU) when the presence of the PU is detected.Then the harvested energy is converted into the electrical power to supply the transmission of the SU at the detected absence of the PU.By adopting the periodic spectrum sensing,the average total transmission rate of the SU is maximized through optimizing the sensing time,subject to the constraints of the probabilities of false alarm and detection,the harvested energy and the interference rate control.The simulation results show that there deed exists an optimal sensing time that maximizes the transmission rate,and the maximum transmission rate of the energy-harvesting CR can better approach to that of the traditional CR with the increasing of the detection probability.

Robust Adaptive Path Following for Underactuated Surface Vessels with Uncertain Dynamics

柔韧的适应控制策略被开发强迫一个 underactuated 表面容器跟随一条引用路径，尽管有包括外长的骚乱和测量噪音的不明确的参数和未组织的无常的存在。参考路径能是一条曲线或一根直线。建议控制器被使用 Lyapunovs 直接方法和滑动模式控制和 backstepping 技术设计。因为容器的摇摆轴直接没被激活，二滑动表面被介绍，第一以追踪的巨浪运动，为 yaw 的错误和第二个错误打手势追踪错误。适应控制法律保证制服得到了追踪的错误的最终的固定。数字模拟结果被提供为路径列在后面 underactuated 表面容器验证建议控制器的有效性。

Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data

Proteasomes are responsible for the production of the majority of cytotoxic T lymphocyte(CTL) epitopes.Hence,it is important to identify correctly which peptides will be generated by proteasomes from an unknown protein.However,the pool of proteasome cleavage data used in the prediction algorithms,whether from major histocompatibility complex(MHC) I ligand or in vitro digestion data,is not identical to in vivo proteasomal digestion products.Therefore,the accuracy and reliability of these models still need to be improved.In this paper,three types of proteasomal cleavage data,constitutive proteasome(cCP),immunoproteasome(iCP) in vitro cleavage,and MHC I ligand data,were used for training cleave-site predictive methods based on the kernel-function stabilized matrix method(KSMM).The predictive accuracies of the KSMM+pair coefficients were 75.0%,72.3%,and 83.1% for cCP,iCP,and MHC I ligand data,respectively,which were comparable to the results from support vector machine(SVM).The three proteasomal cleavage methods were combined in turn with MHC I-peptide binding predictions to model MHC I-peptide processing and the presentation pathway.These integrations markedly improved MHC I peptide identification,increasing area under the receiver operator characteristics(ROC) curve(AUC) values from 0.82 to 0.91.The results suggested that both MHC I ligand and proteasomal in vitro degradation data can give an exact simulation of in vivo processed digestion.The information extracted from cCP and iCP in vitro cleavage data demonstrated that both cCP and iCP are selective in their usage of peptide bonds for cleavage.

Detecting overlapping communities based on vital nodes in complex networks

Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.