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

We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection (FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10

The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning

Precise web page classification can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a web page classification method based on heterogeneous features and

Infrared Earth sensors are widely used in attitude-determination and control systems of satellites. The main deficiency of static infrared Earth sensors is the requirement of a small field of view (FOV). A typical FOV for a static infrared Earth sensor is about 20° to 30°, which may not be sufficient for low-Earth-orbiting micro-satellites. A novel compact infrared Earth sensor with an FOV of nearly

With the development of computer science, more and more hardware implementations can be reproduced by software programming, bringing compact, cheap, and fast components to imaging instrumentation. In recent years, computational methods have been introduced into spectral detection, and computational spectrum acquisition implementations have emerged. This paper highlights the advantages of computational

Traceability link recovery (TLR) is an important and costly software task that requires humans establish relationships between source and target artifact sets within the same project. Previous research has proposed to establish traceability links by machine learning approaches. However, current machine learning approaches cannot be well applied to projects without traceability information (links),

Unfortunately the second author’s name has been misspelt. It should be read: Abbas NIKNAFS.

The fractional logistic map holds rich dynamics and is adopted to generate chaotic series. A watermark image is then encrypted and inserted into the original images. Since the encryption image takes the fractional order within (0, 1], it increases the key space and becomes difficult to attack. This study provides a robust watermark method in the protection of the copyright of hardware, images, and

Multi-focus image fusion is an increasingly important component in image fusion, and it plays a key role in imaging. In this paper, we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets. The original image is decomposed into a base layer and a detail layer. Furthermore, a new fractional-order spatial frequency is built to reflect

An autonomous five-dimensional (5D) system with offset boosting is constructed by modifying the well-known three-dimensional autonomous Liu and Chen system. Equilibrium points of the proposed autonomous 5D system are found and its stability is analyzed. The proposed system includes Hopf bifurcation, periodic attractors, quasi-periodic attractors, a one-scroll chaotic attractor, a double-scroll chaotic

With the rapid increase of the size of applications and the complexity of the supercomputer architecture, topology-aware process mapping becomes increasingly important. High communication cost has become a dominant constraint of the performance of applications running on the supercomputer. To avoid a bad mapping strategy which can lead to terrible communication performance, we propose an optimized

In this study, we focus on the controllability of fractional-order damped systems in linear and nonlinear cases with multiple time-varying delays in control. For the linear system based on the Mittag-Leffler matrix function, we define a controllability Gramian matrix, which is useful in judging whether the system is controllable or not. Furthermore, in two special cases, we present serval equivalent

A novel color image encryption algorithm based on dynamic deoxyribonucleic acid (DNA) encoding and chaos is presented. A three-neuron fractional-order discrete Hopfield neural network (FODHNN) is employed as a pseudo-random chaotic sequence generator. Its initial value is obtained with the secret key generated by a five-parameter external key and a hash code of the plain image. The external key includes

In this study, we discuss mainly the image denoising and texture retention issues. Usually, the time-fractional derivative has an adjustable fractional order to control the diffusion process, and its memory effect can nicely retain the image texture when it is applied to image denoising. Therefore, we design a new Rudin-Osher-Fatemi model with a time-fractional derivative based on a classical one,

We introduce the fractional-order global optimal backpropagation machine, which is trained by an improved fractional-order steepest descent method (FSDM). This is a fractional-order backpropagation neural network (FBPNN), a state-of-the-art fractional-order branch of the family of backpropagation neural networks (BPNNs), different from the majority of the previous classic first-order BPNNs which are

Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure. Smart devices lead to emerging technologies and play a vital role in rapid evolution. Smart devices have become a primary 24/7 need

With the rapid development of electronic information technology, digital signature has become an indispensable part of our lives. Traditional public key certificate cryptosystems cannot overcome the limitations of certificate management. Identity-based cryptosystems can avoid the certificate management issues. The development of quantum computers has brought serious challenges to traditional cryptography

Visual servo control rules that refer to the control methods of robot motion planning using image data acquired from the camera mounted on the robot have been widely applied to the motion control of robotic arms or mobile robots. The methods are usually classified as image-based visual servo, position-based visual servo, and hybrid visual servo (HVS) control rules. Mobile manipulation enhances the

Symbolic execution is an effective way of systematically exploring the search space of a program, and is often used for automatic software testing and bug finding. The program to be analyzed is usually compiled into a binary or an intermediate representation, on which symbolic execution is carried out. During this process, compiler optimizations influence the effectiveness and efficiency of symbolic

The applications of artificial intelligence (AI) and machine learning (ML) technologies in wireless communications have drawn significant attention recently. AI has demonstrated real success in speech understanding, image identification, and natural language processing domains, thus exhibiting its great potential in solving problems that cannot be easily modeled. AI techniques have become an enabler

To address indeterminism in the bilevel knapsack problem, an uncertain bilevel knapsack problem (UBKP) model is proposed. Then, an uncertain solution for UBKP is proposed by defining the PE Nash equilibrium and PE Stackelberg-Nash equilibrium. To improve the computational efficiency of the uncertain solution, an evolutionary algorithm, the improved binary wolf pack algorithm, is constructed with one

We present a real-time monocular simultaneous localization and mapping (SLAM) system with a new distributed structure for multi-UAV collaboration tasks. The system is different from other general SLAM systems in two aspects: First, it does not aim to build a global map, but to estimate the latest relative position between nearby vehicles; Second, there is no centralized structure in the proposed system

We use the advanced proximal policy optimization (PPO) reinforcement learning algorithm to optimize the stochastic control strategy to achieve speed control of the “model-free” quadrotor. The model is controlled by four learned neural networks, which directly map the system states to control commands in an end-to-end style. By introducing an integral compensator into the actor-critic framework, the

We propose multi-objective social learning pigeon-inspired optimization (MSLPIO) and apply it to obstacle avoidance for unmanned aerial vehicle (UAV) formation. In the algorithm, each pigeon learns from the better pigeon but not necessarily the global best one in the update process. A social learning factor is added to the map and compass operator and the landmark operator. In addition, a dimension-dependent

Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism

With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has

Lower-limb assisted exoskeletons are widely researched for movement assistance or rehabilitation training. Due to advantages of compliance with human body and lightweight, some cable-driven prototypes have been developed, but most of these can assist only unidirectional movement. In this paper we present an untethered cable-driven ankle exoskeleton that can achieve plantarflexion-dorsiflexion bidirectional

Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end,

Point set registration has been a topic of significant research interest in the field of mobile intelligent unmanned systems. In this paper, we present a novel approach for a three-dimensional scan-to-map point set registration. Using Gaussian process (GP) regression, we propose a new type of map representation, based on a regionalized GP map reconstruction algorithm. We combine the predictions and

The maximum ocean depth so far reported is about 11 000 m, and is located in the Mariana Trench in the Western Pacific Ocean. The hybrid unmanned underwater vehicle, Haidou, is developed to perform scientific survey at the deepest parts of the Earth oceans. For vehicles working at the full-ocean depth, acoustic positioning is the most effective and popular method. The 11 000 m class acoustic positioning

In this paper we provide a tutorial on the background of warehouse automation using robotic networks and survey relevant work in the literature. We present a new cyber-physical control method that achieves safe, deadlock-free, efficient, and adaptive behavior of multiple robots serving the goods-to-man logistic operations. A central piece of this method is the incremental supervisory control design

There has been a framework sketched for learning deep bidirectional intelligence. The framework has an inbound that features two actions: one is the acquiring action, which gets inputs in appropriate patterns, and the other is A-S cognition, derived from the abbreviated form of words abstraction and self-organization, which abstracts input patterns into concepts that are labeled and understood by self-organizing

A dynamic quantitative theory and measurement of power or dominance structures are proposed. Such power structures are represented as directed networks. A graph somewhat similar to the Lorenz curve for inequality measurement is introduced. The changes in the graph resulting from network dynamics are studied. Dynamics are operationalized in terms of added nodes and links. Study of dynamic aspects of

The Internet of Things (IoT) has been evolving for more than a decade. Technological advancements have increased its popularity, but concerns and risks related to IoT are growing considerably along with the increased number of connected devices. In 2013, a new cryptography-based infrastructure called blockchain emerged with the potential to replace the existing cloud-based infrastructure of IoT through

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire

Since the dawn of the Internet of Things (IoT), data and system security has been the major concern for developers. Because most IoT devices operate on 8-bit controllers with limited storage and computation power, encryption and decryption need to be implemented at the transmitting and receiving ends, respectively, using lightweight ciphers. We present novel architectures for hardware implementation

Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool

The effect of a constant current (CC) power supply on the CC ocean observation system is a problem that once was neglected. The dynamic characteristics of the CC power supply may have great influence on the whole system, especially the voltage behavior in the event of load change. This needs to be examined. In this paper, a method is introduced to check whether the CC power supply can satisfy the dynamic

In a dissipative gyroscopic system with four degrees of freedom and tensorial variables in contravariant (right upper index) and covariant (right lower index) forms, a Lagrangian-dissipative model, i.e., {L, D}-model, is obtained using second-order linear differential equations. The generalized elements are determined using the {L, D}-model of the system. When the prerequisite of a Legendre transform

The study of tourism destination images is of great significance in the tourism discipline. Tourism user-generated content (UGC), i.e., the feedback on tourism websites, provides rich information for constructing a destination image. However, it is difficult for tourism researchers to obtain a relatively complete and intuitive destination image due to the unintuitive destination image display, the

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in

Biological neurons can receive inputs and capture a variety of external stimuli, which can be encoded and transmitted as different electric signals. Thus, the membrane potential is adjusted to activate the appropriate firing modes. Indeed, reliable neuron models should take intrinsic biophysical effects and functional encoding into consideration. One fascinating and important question is the physical

Membrane diffractive optical elements formed by fabricating microstructures on the substrates have two important characteristics, ultra-light mass (surface mass density < 0.1 kg/m2) and loose surface shape tolerances (surface accuracy requirements are on the order of magnitude of centimeter). Large-aperture telescopes using a membrane diffractive optical element as the primary lens have super large

This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing

While many techniques have been developed for the design of different types of antennas, such as wire antenna, patch antenna, lenses, and reflectors, these cannot be said general-purpose strategies for the synthesis and design of antennas to achieve the performance characteristics specified by users. Recently, there has been an increasing need for the development of antenna design techniques because

A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions

Keyword search is an alternative for structured languages in querying graph-structured data. A result to a keyword query is a connected structure covering all or part of the queried keywords. The textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword query. Many previous works examined these properties after retrieving all of the candidate

A multi-band multi-antenna system has become an important trend in the development of mobile communication systems. However, strong mutual coupling tends to occur between antenna elements with a small space, distorting array antennas’ performance. Therefore, in the multiple-input multiple-output (MIMO) antenna system, high isolation based on miniaturization of the antenna array has been pursued. We

Graph is a significant data structure that describes the relationship between entries. Many application domains in the real world are heavily dependent on graph data. However, graph applications are vastly different from traditional applications. It is inefficient to use general-purpose platforms for graph applications, thus contributing to the research of specific graph processing platforms. In this

Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics. However, the content of many articles is still far from complete. In this paper, we propose EncyCatalogRec, a system to help generate a more comprehensive article by recommending catalogs. First, we represent articles and catalog items as embedding vectors, and obtain similar articles via the locality