Biblio

Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.

In-Band Full-Duplex (IBFD) Underwater Acoustic (UWA) communication technology plays a major role in enhancing the performance of Underwater acoustic sensor networks (UWSN). Self-Interference (SI) is one of the main inherent challenges affecting the performance of IBFD UWA communication. To reconstruct the SI signal and counteract the SI effect, this is important to estimate the short range channel through which the SI signal passes. Inaccurate estimation will result in the performance degradation of IBFD UWA communication. From the perspective of engineering implementation, we consider that the UWA communication modem shell has a significant influence on the short-range SI channel, which will limit the efficiency of self-interference cancellation in the analog domain to some degree. Therefore we utilize a simplified model to simulate the influence of the structure of the IBFD UWA communication modem on the receiving end. This paper studies the effect of acoustic-shell coupling on near-end self-interference signal of IBFD UWA communication modem. Some suggestions on the design of shell structure of IBFD UWA communication modem are given.

Computer virus detection technology is an important basic security technology in the information age. The current detection technology has a high success rate for the detection of known viruses and known virus infection technologies, but the development of detection technology often lags behind the development of computer virus infection technology. Under Windows system, there are many kinds of file viruses, which change rapidly, and pose a continuous security threat to users. The research of new file virus infection technology can provide help for the development of virus detection technology. In this paper, a new virus infection technology based on dynamic binary analysis is proposed to execute file virus infection. Using the new virus infection technology, the infected executable file can be detected in the experimental environment. At the same time, this paper discusses the detection method of new virus infection technology. We hope to provide help for the development of virus detection technology from the perspective of virus design.

Network dynamics, such as bandwidth fluctuation and unexpected latency, hurt users' quality of experience (QoE) greatly for media services over the Internet. In this work, we propose a neural adaptive transport (NAT) framework to tackle the network dynamics for Internet-scale interactive media services. The entire NAT system has three major components: a learning based cloud overlay routing (COR) scheme for the best delivery path to bypass the network bottlenecks while offering the minimal end-to-end latency simultaneously; a residual neural network based collaborative video processing (CVP) system to trade the computational capability at client-end for QoE improvement via learned resolution scaling; and a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) strategy to select the appropriate video bitrate for maximal QoE. We have demonstrated that COR could improve the user satisfaction from 5% to 43%, CVP could reduce the bandwidth consumption more than 30% at the same quality, and DRL-based ARS can maintain the smooth streaming with \textbackslashtextless; 50% QoE improvement, respectively.

With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.

Currently, security protection in Industrial Control Systems has become a hot topic, and a great number of defense techniques have sprung up. As one of the most effective approaches, area isolation has the exceptional advantages and is widely used to prevent attacks or hazards propagating. However, most existing methods for inter-area communication protection present some limitations, i.e., excessively depending on the analyzing rules, affecting original communication. Additionally, the network architecture and data flow direction can hardly be adjusted after being deployed. To address these problems, a dynamical and customized communication protection technology is proposed in this paper. In detail, a security inter-area communication architecture based on Software Defined Network is designed firstly, where devices or subsystems can be dynamically added into or removed from the communication link. And then, a security inspection method based on information entropy is presented for deep network behaviors analysis. According to the security analysis results, the communications in the network can be adjusted in time. Finally, simulations are constructed, and the results indicate that the proposed approach is sensitive and effective for cyber-attacks detection.

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.