In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topology. This may lead to UANET service performance issues. In this study, for planning rapidly changing UAV swarms, we propose a dynamic value iteration network (DVIN) model trained using the episodic Q-learning method with the connection information

We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics, mismatches, and disturbances. Initially, the Hamilton-Jacobi-Bellman (HJB) equation associated with its performance function is derived for the original nonlinear systems. Unlike existing adaptive dynamic programming (ADP) approaches, this scheme uses a special non-quadratic

A 0.20–2.43 GHz fractional-N frequency synthesizer is presented for multi-band wireless communication systems, in which the scheme adopts low phase noise voltage-controlled oscillators (VCOs) and a charge pump (CP) with reduced current mismatch. VCOs that determine the out-band phase noise of a phase-locked loop (PLL) based frequency synthesizer are optimized using an automatic amplitude control technique

We deal with event-triggered H∞ controller design for discrete-time piecewise-affine systems subject to actuator saturation. By considering saturation information, a novel event-triggered strategy is proposed to conserve communication resources. A linear matrix inequality based condition is derived based on a piecewise Lyapunov function. This condition guarantees the stability of the closed-loop system

Ubiquitous power Internet of Things (IoT) is a smart service system oriented to all aspects of the power system, and has the characteristics of universal interconnection, human-computer interaction, comprehensive state perception, efficient information processing, and other convenient and flexible applications. It has become a hot topic in the field of IoT. We summarize some existing research work

We propose a new asymmetric pixel confusion algorithm for images based on the Rivest-Shamir-Adleman (RSA) public-key cryptosystem and Arnold map. First, the RSA asymmetric algorithm is used to generate two groups of Arnold transform parameters to address the problem of symmetrical distribution of Arnold map parameters. Second, the image is divided into blocks, and the first group of parameters is used

In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift

Success has been obtained using a semi-supervised graph analysis method based on a graph convolutional network (GCN). However, GCN ignores some local information at each node in the graph, so that data preprocessing is incomplete and the model generated is not accurate enough. Thus, in the case of numerous unsupervised models based on graph embedding technology, local node information is important

As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors, due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks, e.g., search and rescue, fire-fighting, reconnaissance, and surveillance. Cooperative path planning (CPP) is a key problem

In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error and range error are analyzed. They are influenced by the signal-to-noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the

Although deep neural networks (DNNs) have achieved great success in semantic segmentation tasks, it is still challenging for real-time applications. A large number of feature channels, parameters, and floating-point operations make the network sluggish and computationally heavy, which is not desirable for real-time tasks such as robotics and autonomous driving. Most approaches, however, usually sacrifice

Special curves in the Minkowski space such as Minkowski Pythagorean hodograph curves play an important role in computer-aided geometric design, and their usages are thoroughly studied in recent years. Bizzarri et al. (2016) introduced the class of Rational Envelope (RE) curves, and an interpolation method for G1 Hermite data was presented, where the resulting RE curve yielded a rational boundary for

Much recent progress in monaural speech separation (MSS) has been achieved through a series of deep learning architectures based on autoencoders, which use an encoder to condense the input signal into compressed features and then feed these features into a decoder to construct a specific audio source of interest. However, these approaches can neither learn generative factors of the original input for

Cyber-physical systems (CPSs) are becoming increasingly important in safety-critical systems. Particular risk analysis (PRA) is an essential step in the safety assessment process to guarantee the quality of a system in the early phase of system development. Human factors like the physical environment are the most important part of particular risk assessment. Therefore, it is necessary to analyze the

Cyber-physical systems (CPSs) have emerged as a potential enabling technology to handle the challenges in social and economic sustainable development. Since it was proposed in 2006, intensive research has been conducted, showing that the construction of a CPS is a hard and complex engineering process due to the nature of integrating a large number of heterogeneous subsystems. Among other approaches

Robotic swarms are usually designed in a bottom-up way, which can make robotic swarms vulnerable to environmental impact. It is particularly true for the widely used control mode of robotic swarms, where it is often the case that neither the correctness of the swarming tasks at the macro level nor the safety of the interaction among agents at the micro level can be guaranteed. To ensure that the behaviors

Cyber-physical systems (CPSs) are distributed assemblages of computing, communicating, and physical components that sense their environment, algorithmically assess the incoming information, and affect their physical environment. Thus, they share a common structure with other complex adaptive systems, and therefore share both the possible benefits and the probable harmful effects of emergent phenomena

In this perspective article, we first recall the historic background of human-cyber-physical systems (HCPSs), and then introduce and clarify important concepts. We discuss the key challenges in establishing the scientific foundation from a system engineering point of view, including (1) complex heterogeneity, (2) lack of appropriate abstractions, (3) dynamic black-box integration of heterogeneous systems

In this paper, we investigate the secrecy outage performance in simultaneous wireless information and power transfer (SWIPT) systems taking artificial noise assistance into account. Multiple antennas in the source and a single antenna in both the legitimate receiver and the eavesdropper are assumed. Specifically, the transmitted signal at the source is composed of two parts, where the first part is

We demonstrate a graphene-metasurface structure for tunable wide-incident-angle terahertz wave absorption, which involves depositing planar arrays of Omega-shaped graphene patterns on a silicon dioxide substrate. We also discuss how the graphene Fermi-level layer and various substrates affect the absorption characteristics. The absorption of the proposed terahertz absorber is above 80% at an incident

We study the containment control problem for high-order heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes. To achieve the containment control objective and reduce communication consumption among agents, a distributed event-triggered control scheme is proposed by applying the backstepping method, Lyapunov functional approach, and neural networks. Then, the results

Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios. In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm, to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface. Dual activation has two steps. First, we multiply the activated weights of the last

The demise of Dennard’s scaling has created both power and utilization wall challenges for computer systems. As transistors operating in the near-threshold region are able to obtain flexible trade-offs between power and performance, it is regarded as an alternative solution to the scaling challenge. A reduction in supply voltage will nevertheless generate significant reliability challenges, while maintaining

The super giant slalom (Super-G) is a speed event in alpine skiing, in which the skier trajectory has a significant influence on the athletes’ performances. It is a challenging task to determine an optimal trajectory for the skiers along the entire course because of the complexity and difficulty in the convergence of the optimization model. In this study, a trajectory optimization model for alpine

Discriminative correlation filters (DCF) are efficient in visual tracking and have advanced the field significantly. However, the symmetry of correlation (or convolution) operator results in computational problems and does harm to the generalized translation equivariance. The former problem has been approached in many ways, whereas the latter one has not been well recognized. In this paper, we analyze

We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control, target state estimation, and network topology control. We first present the communication topology and basic notations for network connectivity, and introduce the distributed Kalman consensus filter. Then, convergence and boundedness

Given that the existing image denoising methods damage the texture details of an image, a new method based on fractional integration is proposed. First, the fractional-order integral formula is deduced by generalizing the Cauchy integral, and then the approximate value of the fractional-order integral operator is estimated by a numerical method. Finally, a fractional-order integral mask operator of

Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically categorize the deep RL algorithms and applications, and provide a detailed review over existing deep RL

We investigate the solution and stability of continuous-time cross-dimensional linear systems (CCDLSs) with dimension bounded by V-addition and V-product. Using the integral iteration method, the solution to CCDLSs can be obtained. Based on the new algebraic expression of the solution and the Jordan decomposition method of matrix, a necessary and sufficient condition is derived for judging whether

Unfortunately the corresponding author’s ORCID was incorrect. It should be: Fan ZHANG, https://orcid.org/0000-0001-7456-8377

General purpose graphics processing units (GPGPUs) can be used to improve computing performance considerably for regular applications. However, irregular memory access exists in many applications, and the benefits of graphics processing units (GPUs) are less substantial for irregular applications. In recent years, several studies have presented some solutions to remove static irregular memory access

Large-scale datasets are driving the rapid developments of deep convolutional neural networks for visual sentiment analysis. However, the annotation of large-scale datasets is expensive and time consuming. Instead, it is easy to obtain weakly labeled web images from the Internet. However, noisy labels still lead to seriously degraded performance when we use images directly from the web for training

Run-length limited (RLL) codes can facilitate reliable data transmission and provide flicker-free illumination in visible light communication (VLC) systems. We propose novel high-rate RLL codes, which can improve error performance and mitigate flicker. Two RLL coding schemes are developed by designing the finite-state machine to further enhance the coding gain by improving the minimum Hamming distance

Opinion question machine reading comprehension (MRC) requires a machine to answer questions by analyzing corresponding passages. Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages, opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the

A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts (FOC) systems with mismatched uncertainties and disturbances. The primary objective of the proposed observer is to minimize the effects of unknown bounded disturbances on the estimation of errors. A more practicable output-feedback passive controller is proposed using an adaptive

In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a traffic balancing strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware Q-network enhanced geographic routing protocol based on greedy perimeter stateless routing (GPSR), for

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation

Developing new saturable absorbers for use in the mid-infrared region has practical significance for short-pulsed lasers and related scientific and industrial applications. The performance of gold nanorods (GNRs) as saturable absorbers at novel mid-infrared wavelengths needs to be evaluated even though these benefit from ultrafast nonlinear responses and broadband saturable absorption. Passive Q-switching

We built a Tm:Ho co-doped fiber laser using a Ti3C2 MXene material as a saturable absorber (SA). The formation of vector solitons (VSs) and noise-like pulses (NLPs) was observed. The SA was prepared by dripping a Ti3C2 solution on a side-polished D-shaped fiber and then naturally vaporized. The VS is characterized by two coexisting sets of Kelly sidebands. By modulating the polarization controller

An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-in-space mode. A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images. The training dataset is constructed to achieve the networks’ optimal performance. Simulation results show that the proposed algorithm is highly robust to many

Generally, the distributed bundle adjustment (DBA) method uses multiple worker nodes to solve the bundle adjustment problems and overcomes the computation and memory storage limitations of a single computer. However, the performance considerably degrades owing to the overhead introduced by the additional block partitioning step and synchronous waiting. Therefore, we propose a low-overhead consensus

Smartphone, as a smart device with multiple built-in sensors, can be used for collecting information (e.g., vibration and location). In this paper, we propose an approach for using the smartphone as a sensing platform to obtain real-time data on vehicle acceleration, velocity, and location through the development of the corresponding application software and thereby achieve the green concept based

In this review, we introduce some chip-based waveguide biosensing and imaging techniques, which significantly reduce the complexity of the entire system. These techniques use a well-confined evanescent field to interact with the surrounding materials and achieve high sensitivity sensing and high signal-to-noise ratio (SNR) super-resolution imaging. The fabrication process of these chips is simple and

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front. Considering this situation, we propose a many-objective

Long-term coherent integration can remarkably improve the ability of detection and motion parameter estimation of radar for maneuvering targets. However, the linear range migration, quadratic range migration (QRM), and Doppler frequency migration within the coherent processing interval seriously degrade the detection and estimation performance. Therefore, an efficient and noise-resistant coherent integration

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual descriptions. It is highly desirable to explore a very deep neural network to capture the complicated nonlinear preferences. However, training a deeper recommender is not as effortless as simply adding layers. A deeper recommender suffers from the gradient vanishing/exploding issue

Crowd density estimation, in general, is a challenging task due to the large variation of head sizes in the crowds. Existing methods always use a multi-column convolutional neural network (MCNN) to adapt to this variation, which results in an average effect in areas with different densities and brings a lot of noise to the density map. To address this problem, we propose a new method called the segmentation-aware

Permanent magnet synchronous motor (PMSM) has been widely used in position control applications. Its performance is not satisfactory due to internal uncertainties and external load disturbances. To enhance the control performance of PMSM systems, a new method that has fast response and good robustness is proposed in this study. First, a modified integral terminal sliding mode controller is developed

In this study, we focus mainly on the problem of finding the minimum-length path through a set of circular regions by a fixed-wing unmanned aerial vehicle. Such a problem is referred to as the Dubins traveling salesman problem with neighborhoods (DTSPN). Algorithms developed in the literature for solving DTSPN either are computationally demanding or generate low-quality solutions. To achieve a better

We investigate the stability of Boolean networks (BNs) with impulses triggered by both states and random factors. A hybrid index model is used to describe impulsive BNs. First, several necessary and sufficient conditions for forward completeness are obtained. Second, based on the stability criterion of probabilistic BNs and the forward completeness criterion, the necessary and sufficient conditions

Images are widely used by companies to advertise their products and promote awareness of their brands. The automatic synthesis of advertising images is challenging because the advertising message must be clearly conveyed while complying with the style required for the product, brand, or target audience. In this study, we proposed a data-driven method to capture individual design attributes and the

Crowd counting has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for crowd counting. While traditional methods focus merely on solving one specific factor, we aggregate sufficient

We consider optimal two-impulse space interception problems with multiple constraints. The multiple constraints are imposed on the terminal position of a space interceptor, impulse and impact instants, and the component-wise magnitudes of velocity impulses. These optimization problems are formulated as multi-point boundary value problems and solved by the calculus of variations. Slackness variable

Passive bistatic radar detects targets by exploiting available local broadcasters and communication transmissions as illuminators, which are not designed for radar. The signal usually contains a time-varying structure, which may result in high-level range ambiguity sidelobes. Because the mismatched filter is effective in suppressing sidelobes, it can be used in a passive bistatic radar. However, due

We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC

With the growing amount of information and data, object-oriented storage systems have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of metadata plays an important role in improving the input/output performance of the entire system. Unbalanced load on the metadata server leads to a serious bottleneck

Dynamic channel assignment (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent reinforcement learning (RL)

Image classification is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An autoencoder is a special type of neural network, often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional autoencoder, incorporating