Showing papers in "Science in China Series F: Information Sciences in 2021"

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Abstract: The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Energy-efficient design for mmWave-enabled NOMA-UAV networks

Abstract: Owing to the recent advances of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave), these two technologies are combined in unmanned aerial vehicle (UAV) networks in this paper.However, energy efficiency has become a significant metric for UAVs owning to their limited energy.Thus, we aim to maximize the energy efficiency for mmWave-enabled NOMA-UAV networks by optimizing the UAV placement, hybrid precoding and power allocation.However, the optimization problem is complicated and intractable, which is decomposed into several sub-problems.First, we solve the UAV placement problem by approximating it into a convex one.Then, the hybrid precoding with user clustering is performed to better reap the multi-antenna gain. Particularly, three schemes are proposed, where the cluster head selection algorithm is adopted while considering different equivalent channels of users.Finally, the power allocation is optimized to maximize the energy efficiency, which is converted to convex and solved via an iterative algorithm.Simulation results are provided to evaluate the performance of the proposedschemes.

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Abstract: In this paper, we address simultaneous control of a flexible spacecraft’s attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures. Using Hamilton’s principle, the system dynamics is modeled as an infinite dimensional system captured using partial differential equations. Moreover, a novel adaptive fault tolerant control strategy is developed to suppress the vibrations of the flexible panel in the course of the attitude stabilization. To determine whether the system energies, angular velocities and transverse deflections, remain bounded and asymptotically decay to zero in the case wherein the number of actuator failures is infinite, a Lyapunov-based stability analysis is conducted. Finally, extensive numerical simulations are performed to demonstrate the performance of the proposed adaptive control strategy.

A survey of Blockchain consensus algorithms: mechanism, design and applications

Abstract: In 2008, Blockchain was introduced to the world as the underlying technology of the Bitcoin system. After more than a decade of development, various Blockchain systems have been proposed by both academia and industry. This paper focuses on the consensus algorithm, which is one of the core technologies of Blockchain. In this paper, we propose a unified consensus algorithm process model that is suitable for Blockchains based on both the chain and directed acyclic graph (DAG) structure. Subsequently, we analyze various mainstream Blockchain consensus algorithms and classify them according to their design in different phases of the process model. Additionally, we present an evaluation framework of Blockchain consensus algorithms and then discuss the security design principles that enable resistance from different attacks. Finally, we provide some suggestions for selecting consensus algorithms in different Blockchain application scenarios.

Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

Abstract: In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable Lyapunov-Krasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters $${\cal U}$$ , $${\cal G}$$ and $${\cal V}$$ . Finally, the filter gains and the optimal index α* are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

Finite-time command filtered adaptive control for nonlinear systems via immersion and invariance

Abstract: This paper investigates the problem of finite-time adaptive output tracking control for strict-feedback nonlinear systems with parametric uncertainties. Command signals and their derivatives are generated by a new command filter based on a second-order finite-time differentiator, which attenuates the chattering phenomenon. The parameter estimations are achieved by an immersion and invariance approach without requiring the certainty equivalence principle. The finite-time adaptive controller is constructed via a backstepping design method, a finite-time command filter, and a modified fractional-order error compensation mechanism. The proposed control strategy guarantees the finite-time boundedness of all signals in the closed-loop system, and the tracking error is driven into an arbitrarily small neighborhood of the origin in finite time. Finally, the new design technique is validated in a simulation example of the electromechanical system.

PSC-Net: learning part spatial co-occurrence for occluded pedestrian detection

Abstract: Detecting pedestrians, especially under heavy occlusion, is a challenging computer vision problem with numerous real-world applications. This paper introduces a novel approach, termed as PSC-Net, for occluded pedestrian detection. The proposed PSC-Net contains a dedicated module that is designed to explicitly capture both inter and intra-part co-occurrence information of different pedestrian body parts through a graph convolutional network (GCN). Both inter and intra-part co-occurrence information contribute towards improving the feature representation for handling varying level of occlusions, ranging from partial to severe occlusions. Our PSC-Net exploits the topological structure of pedestrian and does not require part-based annotations or additional visible bounding-box (VBB) information to learn part spatial co-occurrence. Comprehensive experiments are performed on three challenging datasets: CityPersons, Caltech, and CrowdHuman datasets. Particularly, in terms of log-average miss rates and with the same backbone and input scale as those of the state-of-the-art MGAN, the proposed PSC-Net achieves absolute gains of 4.0% and 3.4% over MGAN on the heavy occlusion subsets of CityPersons and Caltech test sets, respectively.

A novel synthesis method for reliable feedback shift registers via Boolean networks

Abstract: A random fault or a malicious attack can compromise the security of decryption systems Using a stable and monotonous feedback shift register (FSR) as the main building block in a convolutional decoder can limit some error propagation This work foucus on the synthesis of reliable (ie, globally stable and monotonous) FSRs using the Boolean networks (BNs) method First, we obtain an algebraic expression of the FSRs, based on which one necessary and sufficient condition for the monotonicity of the FSRs is given Then, we obtain the upper bound of the number of cyclic attractors for monotonous FSRs Furthermore, we propose one method of constructing n-stage reliable FSRs, and figure out that the number of reliable FSRs is $${{{2^{{2^{n - 4}}}}} \over {\phi (n)}}$$ (n > 5) times of that constructed by the existing method, where ϕ denotes the Euler’s totient function Finally, the proposed method and the obtained results are verified by some examples

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Abstract: Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.

Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance

Abstract: This paper investigates the cooperative output regulation problem for heterogeneous nonlinear uncertain multiagent networked systems subject to actuator failure, bounded matched or mismatched disturbances or disturbances produced by a given linear exosystem. Accurate information about nonlinearity, actuator failure and disturbance may be completely unknown. First, a distributed finite-time observer is designed to estimate the dynamics of the exosystem on a finite-time interval over a communication digraph. Then, a neural-adaptive control protocol is proposed. It is shown that (i) closed-loop multiagent systems are asymptotically stable, with output synchronization errors that tend to zero in the absence of mismatched disturbance, and (ii) the states of the closed-loop multiagent systems and the output synchronization errors are bounded in the presence of mismatched disturbance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control strategy.

Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach

Abstract: In order to protect the privacy and data security of mobile devices during the transactions in the industrial Internet of Things (IIoT), we propose a mobile edge computing (MEC)-based mobile blockchain framework by considering the limited bandwidth and computing power of small base stations (SBSs). First, we formulate a joint bandwidth and computing resource allocation problem to maximize the long-term utility of all mobile devices, and take into account the mobility of devices as well as the blockchain throughput. We decompose the formulated problem into two subproblems to decrease the dimension of action space. Then, we propose a deep reinforcement learning additional particle swarm optimization (DRPO) algorithm to solve the two subproblems, in which a particle swarm optimization algorithm is leveraged to avoid the unnecessary search of a deep deterministic policy gradient approach. Simulation results demonstrate the effectiveness of our method from various aspects.

Task-wise attention guided part complementary learning for few-shot image classification

Abstract: A general framework to tackle the problem of few-shot learning is meta-learning, which aims to train a well-generalized meta-learner (or backbone network) to learn a base-learner for each future task with small training data Although a lot of work has produced relatively good results, there are still some challenges for few-shot image classification First, meta-learning is a learning problem over a collection of tasks and the meta-learner is usually shared among all tasks To achieve image classification of novel classes in different tasks, it is needed to learn a base-learner for each task Under the circumstances, how to make the base-learner specialized, and thus respond to different inputs in an extremely task-wise manner for different tasks is a big challenge at present Second, classification network usually inclines to identify local regions from the most discriminative object parts rather than the whole objects for recognition, thereby resulting in incomplete feature representations To address the first challenge, we propose a task-wise attention (TWA) module to guide the base-learner to extract task-specific image features To address the second challenge, under the guidance of TWA, we propose a part complementary learning (PCL) module to extract and fuse the features of multiple complementary parts of target objects, and thus we can obtain more specific and complete information In addition, the proposed TWA module and PCL module can be embedded into a unified network for end-to-end training Extensive experiments on two commonly-used benchmark datasets and comparison with state-of-the-art methods demonstrate the effectiveness of our proposed method

Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology

Abstract: The “memory wall” problem or so-called von Neumann bottleneck limits the efficiency of conventional computer architectures, which move data from memory to CPU for computation; these architectures cannot meet the demands of the emerging memory-intensive applications. Processing-in-memory (PIM) has been proposed as a promising solution to break the von Neumann bottleneck by minimizing data movement between memory hierarchies. This study focuses on prior art of architecture level DRAM PIM technologies and their implementation. The key challenges and mainstream solutions of PIM are summarized and introduced. The relative limitations of PIM simulation are discussed, as well as four conventional PIM simulators. Finally, research directions and perspectives are proposed for future development.

Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem

Abstract: In this paper, we have formulated quantum beetle antennae search (QBAS), a meta-heuristic optimization algorithm, and a variant of beetle antennae search (BAS). We apply it to portfolio selection, a well-known finance problem. Quantum computing is gaining immense popularity among the scientific community as it outsmarts the conventional computing in efficiency and speed. All the traditional computing algorithms are not directly compatible with quantum computers, for that we need to formulate their variants using the principles of quantum mechanics. In the portfolio optimization problem, we need to find the set of optimal stock such that it minimizes the risk factor and maximizes the mean-return of the portfolio. To the best of our knowledge, no quantum meta-heuristic algorithm has been applied to address this problem yet. We apply QBAS on real-world stock market data and compare the results with other meta-heuristic optimization algorithms. The results obtained show that the QBAS outperforms swarm algorithms such as the particle swarm optimization (PSO) and the genetic algorithm (GA).

Event-triggered fault detection for nonlinear discrete-time switched stochastic systems: a convex function method

Abstract: To improve the safety and reliability of industrial processes, fault-detection problems in dynamic systems have attracted increasing research attention [1]. On the one hand, to enhance fault sensitivity, applying the H− performance index to measure the minimum impact of the fault on the residual signal has been proposed. On the other hand, due to inevitable external disturbances, the fault-detection filter should restrain the impact of interference signals below a prescribed level while detecting faults. Besides, because the network bandwidth of communication is limited, the eventtriggered fault-detection mechanism has been studied extensively to save network resources [2]. The main contributions of this study are as follows: (1) By considering the faults and external disturbances in the model, a mixed H−/H∞ fault-detection filtering is proposed to construct the residual signal. (2) An eventtriggered scenario is recommended to reduce the burden of communication processes by determining whether measurements should be transmitted or not. (3) Based on a new difference operator associated with Lyapunov functions [3, 4], which only depends on the mathematical expectation of white noise {wk}, a more practical result for the H−/H∞ fault detection filter for general nonlinear discrete switched stochastic systems is provided. The notations are provided in Appendix A. Problem description. In this study, we focus on the following discrete-time nonlinear switching stochastic system:

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Abstract: Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RIS-based information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

Properties and photodetector applications of two-dimensional black arsenic phosphorus and black phosphorus

Abstract: Two-dimensional (2D) black arsenic phosphorus (b-AsP), as an alloy of black phosphorus (b-P) with arsenic, has attracted great attention because of its outstanding electronic and optical properties, including high carrier mobility, tunable bandgap and in-plane anisotropy. B-AsP has a smaller bandgap (0.15–0.3 eV) than the b-P bandgap (0.3–2.0 eV), and thus can be used for mid-infrared photodetectors. In addition, both of them can form various van der Waals (vdW) heterojunctions with other 2D materials to realize novel functional optoelectronic devices. Here, we compare the basic characteristics of b-AsP and b-P, including crystal structure, optical properties, band structure, electrical properties and stability, and we summarize the update progress of b-AsP in photo detection, including representatives of phototransistor and photodiode devices. In the last part, the future research directions are discussed.

A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors

Abstract: This paper presents a robust QRS detection algorithm that is capable of detecting QRS complexes as well as accurately identifying R-peaks. The proposed bilateral threshold scheme combined with QRS watchdog greatly improves the detection accuracy and robustness, resulting in consistent detection performance on 9 available ECG databases. Simulations show that the proposed algorithm achieves good results on the datasets from both QTDB healthy database and MITDB arrhythmia database, i.e. the sensitivity of 99.99% and 99.88%, the precision of 99.98% and 99.88%, and the detection error rate of 0.04% and 0.31%, respectively. Furthermore, it also outperforms many existing algorithms on six other ECG databases, such as NSTDB, TWADB, STDB, SVDB, AFTDB, and FANTASIADB.

Reciprocal translation between SAR and optical remote sensing images with cascaded-residual adversarial networks

Abstract: Despite the advantages of all-weather and all-day high-resolution imaging, synthetic aperture radar (SAR) images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by comparing side-by-side SAR and optical images to learn the mapping rules from SAR to optical. This paper attempts to develop machine intelligence that is trainable with large-volume co-registered SAR and optical images to translate SAR images to optical version for assisted SAR image interpretation. Reciprocal SAR-optical image translation is a challenging task because it is a raw data translation between two physically very different sensing modalities. Inspired by recent progresses in image translation studies in computer vision, this paper tackles the problem of SAR-optical reciprocal translation with an adversarial network scheme where cascaded residual connections and hybrid L1-GAN loss are employed. It is trained and tested on both spaceborne Gaofen-3 (GF-3) and airborne Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) images. Results are presented for datasets of different resolutions and polarizations and compared with other state-of-the-art methods. The Frechet inception distance (FID) is used to quantitatively evaluate the translation performance. The possibility of unsupervised learning with unpaired/unregistered SAR and optical images is also explored. Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.

Fault diagnosis of high-speed train bogie based on LSTM neural network

Abstract: Dear editor, With the extensive implementation of highspeed railway acceleration, higher requirements for safety and stability are put forward. In order to guarantee the safety of the high-speed train (HST), it is critical to detect the abnormal state in operation. Bogie that plays an important role in reducing vehicle vibration is connected to the track and the train body, and its health condition directly affects the safe operation of train.

Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach

Abstract: In this paper, we propose a recurrent neural network (RNN) for the tracking control of surgical robots while satisfying remote center-of-motion (RCM) constraints. RCM constraints enforce rules suggesting that the surgical tip should not go beyond the region of incision while tracking the commands of the surgeon. Violations of RCM constraints can result in serious injury to the patient. We unify the RCM constraints with the tracing control by formulating a single constrained optimization problem using a penalty-term approach. The penalty-term actively rewards the optimizer for satisfying the RCM constraints. We then propose an RNN-based metaheuristic optimization algorithm called “Beetle Antennae Olfactory Recurrent Neural Network (BAORNN)” for solving the formulated optimization problem in real time. The proposed control framework can track the surgeon’s commands and satisfy the RCM constraints simultaneously. Theoretical analysis is performed to demonstrate the stability and convergence of the BAORNN algorithm. Simulations using LBR IIWA14, a 7-degree-of-freedom robotic arm, are performed to analyze the performance of the proposed framework.

Stability for discrete-time uncertain systems with infinite Markov jump and time-delay

Abstract: In this paper, we developed a stability analysis for discrete-time uncertain time-delay systems governed by an infinite-state Markov chain (DUTSs-IMC). Some sufficient conditions for the considered systems to be exponential stability in mean square with conditioning (ESMS-C) are derived via linear matrix inequalities (LMIs), which can be examined conveniently. Under novel sufficient conditions, the equivalence among asymptotical stability in mean square (ASMS), stochastic stability (SS), exponential stability in mean square (ESMS), and ESMS-C has been established. Besides, numerical simulations are employed in result validation.

Recent progress of integrated circuits and optoelectronic chips

Abstract: Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called “Moore’s law” in the past 60 years, and now has entered the post Moore’s law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

A survey of prototype and experiment for UAV communications

Abstract: Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAV-enabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements of UAV communications.

Fault estimation and fault-tolerant control for linear discrete time-varying stochastic systems

Abstract: This paper presents a scheme for simultaneous fault estimation and fault-tolerant control of linear discrete time-varying stochastic systems. An observer is proposed to estimate the system state and the fault simultaneously. The estimation errors of both the system state and fault can achieve exponential stability in mean square sense even if the fault arbitrarily changes or is unbounded. The controllers of the drift term and diffusion term are designed separately, and then based on the estimated fault, the fault compensation is performed to realize fault tolerance. For the parameter design in the estimator and controllers, we provide two different algorithms via the cone complementarity linearization and the state transition matrix methods, respectively. As an extension, a class of quasi-linear systems is also discussed. A simulation example with two different fault types and an application in electromechanical servo systems are provided to illustrate the usefulness of the proposed scheme.

Why over-parameterization of deep neural networks does not overfit?

Abstract: Deep neural networks often come with a huge number of parameters, even larger than the number of training examples, but it seems that these over-parameterized models have not suffered from overfitting. This is quite strange and why over-parameterization does not overfit ? poses a fundamental question concerning the mysteries behind the success of deep neural networks. In conventional machine learning theory, let H denote the hypothesis space, m is the size of a training set with i.i.d. samples, then the gap between the generalization error and empirical error is often bounded by O( √

Ultra-reliable and low-latency communications: applications, opportunities and challenges

Abstract: In the upcoming 5G and beyond systems, ultra-reliable and low latency communication (URLLC) has been considered as the key enabler to support diverse mission-critical services, such as industrial automation, remote healthcare, and intelligent transportation. However, the two stringent requirements of URLLC: extremely low latency and ultra-strict reliability have posed great challenges in system designing. In this article, the basic concepts and the potential applications of URLLC are first introduced. Then, the state-of-the-art research of URLLC in the physical layer, link layer and the network layer are overviewed. In addition, some potential research topics and challenges are also identified.

Exponential stabilization of memristor-based neural networks with unbounded time-varying delays

Abstract: Dear editor, As a consequence of symmetry arguments, the memristor was predicted by Chua [1]. As the fourth basic circuit element, its memory characteristic and nanometer dimensions are devoid of resistors, capacitors, and inductors. In the field of the dynamical behavior analysis for memristive neural networks (MNNs), information exchange and signal transmission among different neurons are time-varying activities and discrete time delays are frequently supposed to be bounded, which implies that the current state of a neuron depend only on a part of its history. Actually, the current behavior of a neuron depends upon all its historical information. Consequently, discrete time delays in MNNs should be assumed to be time-varying and unbounded, which can exhibit the characteristics of the neurons in human brains. Many outstanding achievements on MNNs have already been investigated, but the discrete time delays of the investigated MNNs were all assumed to be bounded [2–4]. In this study, pth moment exponential stabilization for a class of MNNs with unbounded discrete time-varying delays under a designed controller is investigated. With the help of inequality techniques and theories of exponential stabilization, a sufficient condition is presented to ensure the stabilization of MNNs. A numerical example is given to illustrate the effectiveness of the theoretical results. Preliminaries. Consider a class of MNNs with discrete time-varying delays described by

Bi2O2Se/BP van der Waals heterojunction for high performance broadband photodetector

Abstract: Broadband photodetector has wide applications in the field of remote sensing, health monitoring and medical imaging. Two-dimensional (2D) materials with narrow bandgaps have shown enormous potential in broadband photodetection. However, the device performance is often restricted by the high dark currents. Herein, we demonstrate a high performance broadband photodetector by constructing Bi2O2Se/BP van der Waals heterojunction. The device exhibits a p-n diode behavior with a current rectification ratio of ∼20. Benifited from the low dark current of the heterojunction and the effective carrier separation, the device achieves the responsivity (R) of ∼ 500 A/W, ∼ 4.3 A/W and ∼ 2.3 A/W at 700 nm, 1310 nm and 1550 nm, respectively. The specific detectivity (D*) is up to ∼ 2.8 × 1011 Jones (700 nm), ∼ 2.4 × 109 Jones (1310 nm) and ∼ 1.3 × 109 Jones (1550 nm). Moreover, the response time is ∼ 9 ms, which is more than 20 times faster than that of individual BP (∼ 190 ms) and Bi2O2Se (∼ 180 ms) devices.

Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints

Abstract: Inspired by the practical operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this paper investigates adaptive trajectory tracking control problems in multiple six-rotor UAV systems with asymmetric time-varying output constraints and input saturation. Under model and disturbance uncertainties, six-rotor UAV systems are modeled as two non-strict-feedback systems, including attitude (inner-loop) and position (outer-loop) regulation systems. For the inner-loop design, the neural-based distributed adaptive attitude consensus control protocol is employed to realize the leader-follower consensus. Adaptive first-order sliding mode differentiators and an auxiliary dynamic system are introduced to address the “explosion of complexity” and saturation nonlinearity issues, respectively. Then, an event-triggered condition is predefined to alleviate the communication loads and reduce the number of messages to be transmitted from the controller to actuator. In addition, a class of asymmetric time-varying barrier Lyapunov functions are constructed for preventing the violation of time-varying output constraints. Accordingly, the proposed double-loop control strategies guarantee that all signals of UAV systems are semi-globally and uniformly bounded. Simulation results demonstrate that the proposed control method is effective.