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Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy. [ABSTRACT FROM AUTHOR]

This article investigates the stability and synchronization of nonautonomous reaction–diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction–diffusion neural networks, the main innovation of this article is that the network coefficients are time-varying, and the delays are general (which means that fewer constraints are posed on delays; for example, the commonly used conditions of differentiability and boundedness are no longer needed). By Green’s formula and some analytical techniques, some easily checkable criteria on stability and synchronization for the underlying neural networks are established. These obtained results not only improve some existing ones but also contain some novel results that have not yet been reported. The effectiveness and superiorities of the established criteria are verified by three numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, the dual common-mode (CM) resonance technique is proposed for lowering the phase noise (PN) and flicker PN corner of LC voltage-controlled oscillators (VCOs) across wide tuning range. The dual CM resonator can provide high and resistive input impedance across a wide $2^{\mathbf {nd}}$ harmonic band without dedicated tuning. It can both prevent the tank-loading effect and reshape the output waveform. Novel implementation methods are demonstrated to realize dual CM resonance at high frequencies. For comparison, both the dual CM resonance VCO and a reference one with a tail filter are fabricated in 65-nm CMOS, achieving the measured frequency range of 8-11.15 and 7.98-11.3 GHz, respectively. In 1/f2 region, the proposed VCO shows an average PN of −139 dBc/Hz and the FOM of 190-192.3 dBc/Hz across the band, leading to an excellent FOM $_{\mathbf {T}}$ of larger than 200.3 dBc/Hz. The reference VCO shows the average PN and FOM of -137.7 and 190.5 dBc/Hz. In 1/f3 region, the former exhibits a low flicker PN corner of 280–350 kHz while the latter’s flicker PN corner varies largely from 290 to 950 kHz across the band. Both VCOs’ cores consume about 5 mW at 1 V supply and occupy approximately 0.13 mm2. [ABSTRACT FROM AUTHOR]

This article proposes a broadband high-efficiency parallel-circuit (PC) class-EF power amplifier (PA) along with a quarter-wavelength stub for the first time. By adding a reactive element ${X}$ , the design space is expanded so that the impedance terminations of the fundamental and harmonics are no longer fixed points. Based on the broadband capability of the proposed structure, using a GaN HEMT transistor, a proof-of-concept circuit implemented with the Chebyshev impedance transformation network is designed and fabricated. Experimental results demonstrate a high-efficiency broadband PA operating from 2.6-3.9 GHz, with the average efficiency of 71.5%, the measured output power (${P}_{\textit {out}}$) of 38.8-40.9 dBm and the power gain of 8.5-10.6 dB. [ABSTRACT FROM AUTHOR]

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The problem of the stochastic event-based distributed fusion estimation for a class of Gaussian systems is investigated. Considering the deterministic event-triggers destroying the Gaussian property of system states, the stochastic event-triggered mechanisms (SETMs) are used, which also can relieve the network transmission burden. Under the stochastic transmission schedules, a two-step fusion estimation method is developed. The first step, with the consideration of channel fading, the local estimation of each sensor is proposed by using the measurements from itself and its neighbors. The second step, the fusion algorithm is designed to eliminate the disagreements among local estimations of each sensor. Finally, experiment is carried out to demonstrate the advantages of the proposed distributed fusion estimation. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting. [ABSTRACT FROM AUTHOR]

Orthogonal polyphase sequence sets with desirable properties have numerous applications in sensing fields, e.g., a monostatic radar transmitting such waveforms can effectively counter the coherent repeater jamming (CRJ). However, due to imperfections in practical systems, the delicately designed sequences may suffer significant distortion and reduction in performance when transmitted as radar waveforms. In this paper, we focus on the problem of designing polyphase sequence sets with good correlation properties under spectral distortion. To address this issue, we first introduce a new metric called distortion correlation properties (DCorr), which quantifies the quality of such sequence sets. Subsequently, a majorization-minimization (MM)-based iterative algorithm is developed to solve the problem of minimizing DCorr. A number of numerical examples are provided to demonstrate the effectiveness of the proposed method in improving the correlation properties of polyphase sequence sets under spectral distortion. The results also validate that our proposed algorithms can effectively mitigate the performance reduction caused by the imperfections in practical radar systems simply. [ABSTRACT FROM AUTHOR]

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X → Y , and (2) no disturbance variable is contained in X , i.e., removing any component of X will weaken the causal relationship between X and Y , then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets. [ABSTRACT FROM AUTHOR]

Integrating Deep Neural Networks (DNNs) into the Internet of Thing (IoT) devices could result in the emergence of complex sensing and recognition tasks that support a new era of human interactions with surrounding environments. However, DNNs are power-hungry, performing billions of computations in terms of one inference. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures such as systolic arrays. Energy-efficient DNN accelerators skip bit-wise or word-wise sparsity in the input feature maps (ifmaps) and filter weights which means ineffectual computations are skipped. However, there is still room for pruning the effectual computations without reducing the accuracy of DNNs. In this paper, we propose a novel real-time architecture and dataflow by decomposing multiplications down to the bit level and pruning identical computations in spatial designs while running benchmark networks. The proposed architecture prunes identical computations by identifying identical bit values available in both ifmaps and filter weights without changing the accuracy of benchmark networks. When compared to the reference design, our proposed design achieves an average per layer speedup of ×1.4 and an energy efficiency of × 1.21 per inference while maintaining the accuracy of benchmark networks. [ABSTRACT FROM AUTHOR]

This article presents a 22–47 GHz wideband low-noise amplifier (LNA) with coupled L-type interstage matching inductors. The coupled inductors extend the bandwidth of LNA by moving zeros to lower frequencies to cancel the effect of poles. Meanwhile, asymmetric L-type inductors with different inductances can be designed separately to achieve better gain flatness. In addition, the quality factor of coupled inductors is also slightly improved without significantly changing their inductance. The LNA is fabricated in a 0.13- $\mu \text{m}$ SiGe BiCMOS technology, and it occupies a core area of 0.13 mm2. The LNA achieves a peak gain of 22.2 dB with the 3dB bandwidth of 22–47 GHz (fractional bandwidth up to 72.5%). The measured NF varies between 3.0 and 4.3 dB from 22 to 47 GHz. The measured input 1-dB gain compression point is stable from −23.9 to −22.6 dBm in the entire 3-dB gain bandwidth. The chip consumes a total power of 9.5 mW from a 1.2 V supply. [ABSTRACT FROM AUTHOR]

• A deep neural network (DNN) is presented to model a 1000 MW USC unit. • Stacked denoising auto-encoder (SDAE) is used to obtain the intrinsic features of the USC unit from the input big data. • The LSTM network is incorporated to learn the long-term dependencies caused by the time delay existed in the USC unit. • A reasonable intensity of added noise is identified in the sense of Lyapunov stability method to guarantee the convergence of the network. The ultra-supercritical (USC) coal fired boiler-turbine unit is an advanced power generation system with low emissions and high efficiency. It is also a typical multivariable nonlinear system with great inertia. Generally, building an accurate analytic model using the conventional system identification methods are quite difficult. However, the big data generated by the monitoring system can reflect the USC unit's operation status and reveal the internal mechanism, if appropriate data analysis methods are developed. A deep neural network (DNN) is proposed in this paper to model a 1000 MW USC unit. In this DNN, stacked denoising auto-encoder is adopted to obtain the intrinsic features from the input data, while the long short-term memory network is in charge of outputting the expected normal behaviors of USC system along the time axis. Furthermore, to guarantee the convergence of this network, a reasonable intensity of added noise is identified via Lyapunov stability method. The DNN model is compared with the traditional multi-layer perception network, the stacked denoising auto-encoder, and two other random neural networks, to show the advantages in forecasting the dynamic behavior of USC unit. [ABSTRACT FROM AUTHOR]

Analog-to-digital and digital-to-analog conversion devices for signals with very large bandwidth are not always available due to technical or cost issues. This limits the realization of very high data-rate digital communication systems. In this paper, we propose a dual pulse shaping (DPS) transmission scheme, which can achieve full Nyquist rate transmission with only a half of the sampling rate for each of the two data streams. Two classes of ideal complementary Nyquist pulses are formulated assuming raised-cosine (RC) pulse shaping. The condition for cross-symbol interference (CSI) free transmission is derived and validated for the proposed pulses. Structures of the DPS transmitter and receiver are described and low-complexity equalization techniques tailored to DPS are proposed. With DPS, a millimeter wave system with commercially available and affordable data conversion devices is exemplified for achieving high-speed low-cost wireless communications. Simulation results with two sets of practical dual spectral shaping pulses are provided. The results verify the effectiveness of the proposed scheme, with comparison to the benchmark conventional Nyquist pulse shaping system. [ABSTRACT FROM AUTHOR]

In this paper, we mainly investigate two issues for sequence labeling, namely, label imbalance and noisy data that are commonly seen in the scenario of named entity recognition (NER) and are largely ignored in the existing works. To address these two issues, a new method termed robust sequence labeling (RoSeq) is proposed. Specifically, to handle the label imbalance issue, we first incorporate label statistics in a novel conditional random field (CRF) loss. In addition, we design an additional loss to reduce the weights of overwhelming easy tokens for augmenting the CRF loss. To address the noisy training data, we adopt an adversarial training strategy to improve model generalization. In experiments, the proposed RoSeq achieves the state-of-the-art performances on CoNLL and English Twitter NER—88.07% on CoNLL-2002 Dutch, 87.33% on CoNLL-2002 Spanish, 52.94% on WNUT-2016 Twitter, and 43.03% on WNUT-2017 Twitter without using the additional data. [ABSTRACT FROM AUTHOR]

This paper proposes a penalty-based adaptive secure estimation method for multi-area power systems under false data injection (FDI) attacks, the adaptive secure estimation method specifically takes the characteristics of FDI attacks into account. Firstly, a new measurement modeling is delicately constructed for subarea of power systems, in which both the state and FDI attack information are well considered. Secondly, for convenient solving of constructed mixed variational inequality (MVI), series virtual nodes are effectively introduced to transfer the multi-area power systems with boundary nodes to a boundary system. Then, a penalty-based distributed estimation method is proposed to estimate the state of multi-area power systems under FDI attacks, where the penalty parameter can be adaptively adjusted based on the dynamic internal error and boundary error. Compared with some existing methods, the efficiency and accuracy of proposed method are improved, and the state and attack signals can be estimated simultaneously. Finally, a case study shows the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with the $H_{\infty }$ fault detection (FD) problem for a class of buck converter with the transmission-induced multiplicative noises and the event-triggered scheme under the network environment. The model of buck converter is approximated by a nonlinear model, and it is further analyzed based on the electricity knowledge. Due to the dynamic characteristics of network transmission, multiplicative noises need to be considered, which are described as a product of some uncorrelated standardized normal distribution and the transmitted signal. Meanwhile, an event-triggered mechanism is utilized for the purpose of reducing the energy consumption, and an FD observer is designed to detect the fault presented in buck converter. Some novel sufficient conditions are given in terms of linear matrix inequalities to guarantee the robust stability of the FD system, and the $H_{\infty }$ optimal fault tracking performance index is also achieved. Finally, an illustrative example is provided to show the validity and merit of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A comparative study on cold supercritical water (SCW) passing over a hot sphere is carried out using the particle-resolved direct numerical simulation (PR-DNS) to examine the wall effects. Different working conditions are considered to obtain the drag coefficient (Cd) and average Nusselt number (Nu) of hot spheres in SCW whose physical properties significantly change with temperature and pressure. The effects of wall factors on Cd and Nu under two different boundary conditions, namely a stationary one for the fluidized sphere problem and a moving one for the settling sphere problem, are compared and discussed. Numerical results show that Cd and Nu of a sphere in SCW cannot fit the previous correlations for conventional fluids well. Therefore, these correlations are improved for SCW, which are good tools to understand the basic knowledge of SCW. [ABSTRACT FROM AUTHOR]

This paper considers the tracking synchronization problem for a class of coupled reaction–diffusion neural networks (CRDNNs) with undirected topology. For the case where the tracking trajectory has identical individual dynamic as that of the network nodes, the edge-based and vertex-based adaptive strategies on coupling strengths as well as adaptive controllers, which demand merely the local neighbor information, are proposed to synchronize the CRDNNs to the tracking trajectory. To reduce the control costs, an adaptive pinning control technique is employed. For the case where the tracking trajectory has different individual dynamic from that of the network nodes, the vertex-based adaptive strategy is proposed to drive the synchronization error to a relatively small area, which is adjustable according to the parameters of the adaptive strategy. This kind of adaptive design can enhance the robustness of the network against the external disturbance posed on the tracking trajectory. The obtained theoretical results are verified by two representative examples. [ABSTRACT FROM AUTHOR]

• Momentum and heat transfer of spheroids in supercritical water were studied. • Effects of incident angle, particle shape and Reynolds number were discussed. • Correlations for Cd and Nu of spheroids in SCW were established. This paper studies the effect of the incident angle (θ) on momentum and heat transfer of spheroids in supercritical water (SCW) based on numerical simulations. Both prolate and oblate spheroids are considered with the aspect ratio (Ar) ranging from 0.5 to 2.5. Numerical results show that both the drag coefficient (Cd) and average Nusselt number (Nu) of the oblate spheroid (Ar < 1) in SCW decrease with the increase of θ. Opposite trend is found for the prolate spheroid (Ar > 1). When θ ≤ 45° , both Cd and Nu of the two kinds of spheroids decrease with the increase of Ar. When θ > 45° , both Cd and Nu of the prolate spheroid increases with Ar. Nu of the oblate spheroid also increases with Ar but its Cd is dependent on the competition of friction and pressure. The effect of θ on Nu can be enhanced by either lifting Re or ∣ Ar-1 ∣. At last, the effect of θ is directly considered in the correlations of Cd and Nu for spheroids in SCW which can improve the accuracy of the multi-phase flow modellings of SCW. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative output regulation problem for heterogeneous multiagent systems (MASs) under switching topology. Two novel distributed event-triggered adaptive control strategies based on state feedback and output feedback are developed, which can avoid using the minimal nonzero eigenvalue of Laplacian matrix associated with global system topologies. It is shown that under the proposed control protocols, MASs could achieve asymptotic tracking and disturbance rejection, and meanwhile, the amount of transmission data and communication cost among agents can be reduced. Then, the leader-following consensus problem of MASs is given as an application of our main results. Finally, an example is presented to verify the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization issue of coupled reaction–diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results. [ABSTRACT FROM PUBLISHER]

Noncontact vital sign detection is widely used in finding victims in post-disaster search and rescue, through-wall surveillance (TWS), and medical diagnosis and monitoring. Human subject can be remotely sensed by extracting life activities such as respiration and heartbeat. However, the signal to noise ratio (SNR) is often low, particularly in complex environments, which results in errors in both range and respiration frequency (RF) estimation. To improve the accuracy, an improved system for the vital sign detection is presented which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the short-time Fourier transform (STFT) of the standard deviation of the received signals. Further, the ensemble empirical mode decomposition (EEMD) based frequency accumulation (FA) technique is presented to determine RF. Performance results are presented which show that the proposed method is superior to several well-known techniques. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensus problem for multi-agent systems with switching topology. A novel distributed control strategy is proposed to reduce the frequency of controller update and save network resources. Based on communication uncertainty of practical networks, partial information exchange and switching topology which subjects to a heterogeneous Markov chain are considered in controller design. An H ∞ consensus criterion is derived by using linear matrix inequality and Lyapunov methods. According to this consensus criterion, a sufficient condition on designing H ∞ state-feedback controller is presented. Finally, a simulation example is given to illustrate the effectiveness of the proposed event-based control strategy. [ABSTRACT FROM AUTHOR]

The end-to-end speech translation (ST) model usually adopts the encoder-decoder structure, which takes the speech of the source language as input and directly outputs its translation result in the target language. Since the model performs cross-modal translation, it needs to extract semantically rich representations from speech. Compared with text, speech is fine-grained and contains more noise, which puts a great burden on the encoder of the ST model. The modal gap between speech and text usually causes the ST model to perform inferior to the corresponding machine translation (MT) model. To bridge the cross-modal gap, this paper proposes to use adversarial training to relieve the burden on the ST encoder by providing internal supervision signals. In this approach, the encoder in the ST model can extract representations that contain rich semantics, which greatly improves performance. Experiments on the datasets of Augmented Librispeech English-French and MuST-C English-German show the effectiveness of our method. Further analysis indicates that our proposed method can perform well in low-resource conditions compared to strong baselines. [ABSTRACT FROM AUTHOR]

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this study, parallelization of a Discrete Element Method (DEM) code titled Trubal was carried out based on the CPU–GPU heterogeneous architecture where both two- and three-dimensional cases were assessed. In Trubal, the particle–particle/wall interaction rules are governed by the theoretical contact mechanics which enable the direct use of real physical material properties in the calculation. We reconstructed Trubal in two steps: reconstruction of the static storage structure; essential parallelism on the relative newer version code. Numerical simulations were implemented to present the benefits of this research. Firstly, two simulations of die filling with a moving shoe involving 6000 and 60,000 two-dimensional particles were conducted under (i) NVIDIA Tesla C2050 card together with Intel Core-Duo 2.93 GHz CPU and (ii) NVIDIA Tesla K40c card along with Intel Xeon 3.00 GHz CPU. Average speedups of (i) 4.69 and 12.78 as well as (ii) 6.52 and 18.60 in computational time were obtained, respectively. Then, a simulation of die filling with a stationary shoe containing 20,000 three-dimensional particles was carried out under the same conditions where average speedups of (i) 12.90 and (ii) 19.66 in computational time were obtained, respectively. It is shown that the final version parallel code gave a substantial acceleration on the original Trubal. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probabilities are used to establish the relationship between abovementioned two concepts. The jump parameters are governed by the hidden semi-Markov process, which can better describe the asynchronous information between the controller modes and the system modes. Besides, time delays are considered to be time-varying and dependent on the hidden modes. By employing some mathematical transformation and constructing a novel Lyapunov–Krasovskii functional, some new parameter-dependent sufficient stabilization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods. [ABSTRACT FROM AUTHOR]

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original image. Meanwhile, the direction information of the origin was calculated and embedded in the RHFM domain. During normal watermark embedding, the image-embedded direction information was transformed by a one-level DWT and the low-frequency sub-band was divided into 8 × 8 non-overlapping blocks. Each block was transformed by DCT, and two low-frequency coefficients were selected to embed the encrypted watermark. Experimental results demonstrated that the proposed algorithm is robust against rotation and other attacks. The analysis also showed that the proposed algorithm satisfies the invisibility of watermarking algorithms and has good security performance. [ABSTRACT FROM AUTHOR]

Due to hardware limitations, it is challenging for sensors to acquire images of high resolution in both spatial and spectral domains, which arouses a trend that utilizing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to fuse an HR-HSI in an unsupervised manner. Considering the fact that most existing methods are restricted by using linear spectral unmixing, we propose a nonlinear variational probabilistic generative model (NVPGM) for the unsupervised fusion task based on nonlinear unmixing. We model the joint full likelihood of the observed pixels in an LR-HSI and an HR-MSI, both of which are assumed to be generated from the corresponding latent representations, i.e., the abundance vectors. The sufficient statistics of the generative conditional distributions are nonlinear functions with respect to the latent variable, realized by neural networks, which results in a nonlinear spectral mixture model. For scalability and efficiency, we construct two recognition models to infer the latent representations, which are parameterized by neural networks as well. Simultaneously inferring the latent representations and optimizing the parameters are achieved using stochastic gradient variational inference, after which the target HR-HSI is retrieved via feedforward mapping. Though without supervised information about the HR-HSI, NVPGM still can be trained based on extra LR-HSI and HR-MSI data sets in advance unsupervisedly and processes the images at the test phase in real time. Three commonly used data sets are used to evaluate the effectiveness and efficiency of NVPGM, illustrating the outperformance of NVPGM in the unsupervised LR-HSI and HR-MSI fusion task. [ABSTRACT FROM AUTHOR]

This article aims to point out an error in the concerned paper and a possible correction to address the error. The correction is achieved by several revisions made on the corresponding assumption, event-triggering function, adaptive laws, and event-triggered control law. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of the static output-feedback control for a class of discrete-time linear semi-Markov jump systems (SMJSs). Through a mode-dependent resilient control scheme and an invertible linear transformation, a resulting equivalent closed-loop system can be obtained. The embedded Markov chain (EMC) is piecewise homogeneous, which leads to incomplete semi-Markov kernel is variable in the finite interval. A novel class of multivariate dependent Lyapunov function is constructed, which is mode-dependent, elapsed-time-dependent, and variation-dependent. Numerically testable stabilization criteria are established for discrete-time linear SMJSs via abovementioned Lyapunov function. Under bound sojourn time, a desired stabilizing controller is designed such that the closed-loop system is mean-square stable. Finally, the theoretical results are applied to a practical RLC circuit system to show the effectiveness and applicability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict ($\Upsilon _{1}, \Upsilon _{2}, \Upsilon _{3}$)- $\gamma $ -dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua’s circuit and quadruple-tank process system (QTPS). [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) have been proven to be very effective in tackling low-dimensional expensive problems. However, it remains a challenge to solve high-dimensional expensive problems (HEPs) with the curse of dimensionality. Therefore, this paper proposes a surrogate-assisted improved multioperator differential evolution (SA-IMODE) algorithm to address HEPs up to 2000 dimensions. Specifically, this paper proposes a novel relationship classification model-based environment selection strategy (RCES), in which a classification model is used to distinguish between "good" and "bad" solutions to assist in environment selection. By doing this, the "unpromising" solutions are thrown away directly without evaluation to reduce the number of expensive fitness evaluations. Moreover, the solution-improvement data point and error samples are added to construct the training dataset to improve the model's prediction accuracy. Two coordinate systems are utilized to produce five types of DE operators to suit different fitness landscapes. An adaptive strategy is also used to select suitable DE operators to generate promising offspring. Furthermore, a local search mechanism is applied to refine the best solution in the current population to accelerate the algorithm's convergence. The systematic experiment results show SA-IMODE has a significant advantage over thirteen state-of-the-art algorithms on benchmark problems. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive event-triggered predictive control (AETPC) for finite time microgrid is considered and the AETPC design takes the presence of the packet loss into account in the communication network when the state information is transmitted between the microgrid and the large power grid. In order to solve this issue on packet loss phenomenon, a prediction model is proposed to predict the packet loss. An event-triggered mechanism with adaptive adjustment parameters is also proposed to save network resources. The control performance with finite time is studied to ensure that the large power grid can obtain the state information of the microgrid in a finite time. Finally, the effectiveness of the proposed algorithm is verified by the simulation example of the microgrid. [ABSTRACT FROM AUTHOR]

Never before has data sharing been more convenient with the rapid popularity and wide adoption of cloud computing. However, mobile access and flexible search with security for outsourced data in heterogeneous systems are two major obstacles prior to practical deployment of cloud computing. To meet the requirements in secure mobile access and flexible search to sensitive data, this paper, for the first time, proposes a versatile primitive referred to as an identity based proxy re-encryption system with outsourced equality test (IBPRE-ET). This primitive allows a data owner in an identity based broadcast encryption system (IBBE) seamlessly to share his/her data with a data sharer in an identity based encryption system (IBE). Moreover, it supports a data sharer in an IBBE system to perform search functionality on ciphertexts related to a data sharer in an IBE system. We also formally prove that the proposed IBPRE-ET system is selective secure using a random oracle model. Meanwhile, the theoretic evaluation and experimental result demonstrate our scheme is practical in the heterogeneous system. Finally, we show an application of our IBPRE-ET to the collaborative office automation system. [ABSTRACT FROM AUTHOR]

In this paper, we study a new problem in the scenario of sequences labeling. To be exact, we consider that the training data are with annotation of various degrees, namely, fully labeled, unlabeled, and partially labeled sequences. The learning with fully un/labeled sequence refers to the standard setting in traditional un/supervised learning, and the proposed partially labeling specifies the subject that the element does not belong to. The partially labeled data are cheaper to obtain compared with the fully labeled data though it is less informative, especially when the tasks require a lot of domain knowledge. To solve such a practical challenge, we propose a novel deep conditional random field (CRF) model which utilizes an end-to-end learning manner to smoothly handle fully/un/partially labeled sequences within a unified framework. To the best of our knowledge, this could be one of the first works to utilize the partially labeled instance for sequence labeling, and the proposed algorithm unifies the deep learning and CRF in an end-to-end framework. Extensive experiments show that our method achieves state-of-the-art performance in two sequence labeling tasks on some popular data sets. [ABSTRACT FROM AUTHOR]

This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed to exploit additional degrees of freedom and more information on extra states. Then, by constructing Lyapunov–Krasovskii functional using APFs and integrals of quadratic forms with high order scalar functions, a novel stability criterion is derived for DNNs, in which the benefits of the improved inequalities are fully integrated and the information on delay and its derivative is well reflected. By virtue of the advantages of APFs, more desirable performance is achieved through the proposed approach, which is demonstrated by the numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, the optimal DoS attack on cyber-physical systems is studied in Stackelberg game framework, which is manifested by energy allocation on communication channels. To give a realistic picture of DoS attack on a normal user launched by several hackers, a Stackelberg game between one defender and multiple attackers is considered. Compared with the existing literature, which mainly focus on equilibrium in static games, the dynamic process of the Stackelberg game is also reflected in this paper, which demonstrates the intelligence of attackers switching channels to allocate aggressive energy. In the solution of Stackelberg equilibrium, self-adaptive Particle Swarm Optimization (PSO) algorithm is utilized to cope with the nonlinearity of the reward function. Besides, for the purpose of better performance of both sides in choosing channels to allocate energy, an online computation algorithm is proposed. Finally, numerical examples are equipped to illustrate the theoretical results proved in this work. [ABSTRACT FROM AUTHOR]

This paper presents a crowd evacuation simulation approach that is based on navigation knowledge and two-layer control mechanism. In this approach, using the multi-population cultural algorithm framework, the control mechanism of the crowd evacuation simulation is divided into two parts, namely, the belief and population spaces. The population space is divided into groups (sub-populations), and a leader is selected in each group according to a fitness value. The belief space comprises multiple agents and a knowledge base. Each navigation agent corresponds to a group leader. A navigation agent obtains a leader's position through the acceptance function and later passes the information to the knowledge base. On the basis of the position, the obstacles, and the congestion situation provided by the navigation agent, the knowledge base management agent dynamically plans the path and provides the navigation agent the next position along the path. The navigation agent later passes the information to the leader through the affection function. The individuals in the group follow the leader through the social force model in moving to the location provided by the navigation agent. The entire process is repeated until the exit is reached. The path information that successfully reached the exit is recorded, and the knowledge base is updated. This method establishes the relationship between the population and the navigation agent with knowledge and transforms a blindly moving crowd into a guided evacuation as the mass evacuation simulation problem is decomposed into a sub-problem of moving blocks. This approach effectively solves the problem of microscopic models because each individual calculates the path and resolves the slow speed problem. The simulation results illustrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Spatial regression is widely used for modeling the relationship between a dependent variable and explanatory covariates. Oftentimes, the linear relationships vary across space, such that some covariates have location-specific effects on the response. One fundamental question is how to detect the systematic variation in the model and identify which locations share common regression coefficients and which do not. Only a correct model structure can assure unbiased estimation of coefficients and valid inferences. A new procedure is proposed, called Spatial Heterogeneity Automatic Detection and Estimation (SHADE), for automatically and simultaneously subgrouping and estimating covariate effects for spatial regression models. The SHADE employs a class of spatially-weighted fusion type penalty on all pairs of observations, with location-specific weight constructed using spatial information, to cluster coefficients into subgroups. Under certain regularity conditions, the SHADE is shown to be able to identify the true model structure with probability approaching one and estimate regression coefficients consistently. An alternating direction method of multiplier algorithm (ADMM) is developed to compute the SHADE. In numerical studies, the empirical performance of the SHADE is demonstrated by using different choices of weights and comparing their accuracy. The results suggest that spatial information can enhance subgroup structure analysis in challenging situations when the spatial variation among regression coefficients is small or the number of repeated measures is small. Finally, the SHADE is applied to find the relationship between a natural resource survey and a land cover data layer to identify spatially interpretable groups. [ABSTRACT FROM AUTHOR]

Clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this article, we propose a Bayesian nonparametric framework, deep nonparametric Bayes (DNB), for jointly learning image clusters and deep representations in a doubly unsupervised manner. In doubly unsupervised learning, we are dealing with the problem of “unknown unknowns,” where we estimate not only the unknown image labels but also the unknown number of labels as well. The proposed algorithm alternates between generating a potentially unbounded number of clusters in the forward pass and learning the deep networks in the backward pass. With the help of the Dirichlet process mixtures, the proposed method is able to partition the latent representations space without specifying the number of clusters a priori. An important feature of this work is that all the estimation is realized with an end-to-end solution, which is very different from the methods that rely on post hoc analysis to select the number of clusters. Another key idea in this article is to provide a principled solution to the problem of “trivial solution” for deep clustering, which has not been much studied in the current literature. With extensive experiments on benchmark datasets, we show that our doubly unsupervised method achieves good clustering performance and outperforms many other unsupervised image clustering methods. [ABSTRACT FROM AUTHOR]

This paper presents two high-efficiency charge pumps (CPs) by utilizing the gate voltage boosting technique (GVBT). Unlike traditional bulk-CMOS and all-NMOS CPs, an input bias voltage is independently applied to the gate terminal, and it improves the current driving capability without the need of large pumping capacitors or high-frequency clocks. Meanwhile, the GVBT decouples the state of transistors from the clocks connected to the pumping capacitor, and it can eliminate the reversion loss in both main and auxiliary circuits. Fabricated in a 0.18- $\mu \text{m}$ standard CMOS technology, the single-stage all-NMOS CP achieved a maximum output voltage of 6.589 V and a peak power efficiency of 80.08%. We also implemented the bulk-CMOS CP with GBVT for comparison, the single-stage all-NMOS CP achieved $\sim 1.24\times $ more output voltage than the bulk-CMOS CP under a load current of 1.5 mA. The voltage errors of the all-NMOS CP between the analytical and the measured results are less than 7%. [ABSTRACT FROM AUTHOR]

[Display omitted] Surrogate models are widely used to mimic complex systems to reduce the high experimental cost. As the system becomes high-dimensional and complex, there is an increasing demand for building relatively simplified surrogate models that capture key variables and represent complex interactions. This study proposes the annealing combinable Gaussian process, an integrated solution for identifying key variables and constructing the high-precision surrogate model. Firstly, to identify redundant variables, this study optimises variables selection with a modified simulated annealing algorithm over the complete model space. This process is called the outer loop. Secondly, to improve the model accuracy and structure, this study constructs a non-parametric Gaussian process model with additive or multiplicative kernel, effectively extracting high-order interactions. Simultaneously, a Markov chain is proposed to sample the model space. The conditional entropy is used as the scoring rule for the simulated annealing algorithm of the outer loop. It is named the inner loop. This study also discusses the rationality of conditional entropy as a criterion. The annealing combinable Gaussian process performs well in various application scenarios, including regression and classification problems. Finally, our method is implemented with the particle-in-cell simulation to find out the key physics parameters. [ABSTRACT FROM AUTHOR]

High-speed railway systems face frequent cybersecurity threats targeting their information networks. Continuous operation of protection mechanisms on wireless sensors results in persistent energy consumption, which is undesirable. To address this, we propose an optimal defense strategy selection approach for High-speed Railway Wireless Sensor Networks (HSR-WSNs) using an enhanced signaling game model. We first establish the basic elements and overall structure of the model, considering the presence of incomplete information and dynamic interactions between players. This paper provides background on signaling games, including theoretical concepts of equilibrium strategies and locations. We then develop a system to quantify payoffs for offensive and defensive tactics, incorporating distance and usage frequency weights. Finally, Long Short-Term Memory (LSTM) and the public UNSW-NB15 dataset are leveraged to augment assumptions and reduce human subjectivity in results. Experiments demonstrate the model effectively identifies energy balance points for HSR-WSNs and determines appropriate protection strategies. The work contributes to enhancing the selection of effective defense techniques against network attacks in wireless sensor systems. This has significant practical implications in enabling optimized energy usage and functionality of secured sensor nodes. [ABSTRACT FROM AUTHOR]

Causal discovery is pivotal across numerous scientific and engineering disciplines. General causal discovery methods often falter in accuracy and efficiency when dealing with high-dimensional data due to the curse of dimensionality. While recent advancements in recursive constraint-based methods offer some improvements for high-dimensional causal discovery, their reliance on high-order conditional independence (CI) tests in causal decomposition hinders efficiency and accuracy. To address this challenge, we introduce a novel causal decomposition method based on spectral clustering, called CDSC, tailored for enhanced high-dimensional causal discovery. Concretely, CDSC first constructs the Laplacian matrix of the original variable set based on correlation distance. Then, CDSC decomposes the original variable set into multiple subsets without CI tests and recovers the causal structure of each subset. After determining these causal structures, CDSC merges these sub-results into a complete causal structure. A notable advantage of CDSC over other causal decomposition methods lies in its use of simplified spectral clustering. CDSC preserves the connectivity of the subsets to avoid destroying the causality in partitioned subsets. Moreover, we analyze the validity and time complexity of CDSC theoretically. Exclusive experiments on various real-world causal structures show that CDSC outperforms the state-of-the-arts both in accuracy and efficiency significantly. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips. [ABSTRACT FROM AUTHOR]

Dataflow architecture has native advantages in achieving high instruction parallelism and power efficiency for today's emerging applications such as high performance computing and deep neural network. In dataflow computing, the execution of instructions is driven by data, so the data transfer efficiency of the network on chip (NoC) is a key factor affecting performance. However, the NoC performance degrades due to the increasing use of multicast communications in many applications. The existing dataflow architecture instruction scheduling algorithms do not optimize multicast communication between the instruction and its successor instructions, so the routing paths of many multicast packets have forks which cause bandwidth waste and potential network congestion. We propose a sharing path awareness (SPA) algorithm to optimize multicast communication in the dataflow architecture. The algorithm shares the routing paths from the instruction to its child node to reduce the NoC bandwidth waste through the instruction scheduler. For applications using software iteration, we further extend the loop optimization to the SPA algorithm to sufficiently exploit instruction-level parallelism. Compared with the state-of-the-art algorithm, we show that the SPA algorithm achieves 20.21% average performance improvement and 15.11% energy consumption reduction for our experimental workloads. [ABSTRACT FROM AUTHOR]

Adversarial training methods are frequently-used empirical defense methods against adversarial examples. While many regularization techniques demonstrate effectiveness when combined with adversarial training, these methods typically work in the time domain. However, as the adversarial vulnerability can be considered a high-frequency phenomenon, it is crucial to regulate adversarially-trained neural network models in the frequency domain to capture low-frequency and high-frequency features. Neural networks must fully utilize the detailed local features extracted by their receptive field. To address these challenges, we conduct a theoretical analysis of the regularization properties of wavelets, which can enhance adversarial training. We propose a wavelet regularization method based on the Haar wavelet decomposition named Wavelet Average Pooling. This wavelet regularization module is integrated into a wide residual neural network to form a new model called WideWaveletResNet. On the CIFAR-10 and CIFAR-100 datasets, our proposed Adversarial Wavelet Training method demonstrates considerable robustness against different types of attacks. It confirms our assumption that our wavelet regularization method can enhance adversarial robustness, particularly in deep and wide neural networks. We present a detailed comparison of different wavelet base functions and conduct visualization experiments of the Frequency Principle (F-Principle) and interpretability to demonstrate the effectiveness of our method. The code is available on the open-source website: https://github.com/momo1986/AdversarialWaveletTraining. [ABSTRACT FROM AUTHOR]

Zero-watermarking algorithms provide the means of effective, lossless copyright protection, but the existing ones can only resist a limited variety of geometric attacks. To overcome this limitation, a novel zero-watermarking algorithm resistant to an extensive variety of geometric attacks is suggested, in which fractional-order Exponent-Fourier moments (FrEFMs) and discrete Fourier transform (DFT) are used as complementary geometric invariant features to construct zero-watermarks. Relying on the rotation, scaling, aspect ratio changing and cropping invariance of FrEFMs amplitude and the perfect translation invariance of DFT amplitude, our algorithm can effectively resist a wider variety of geometric and common image processing attacks, which has been experimentally illustrated in this work. Therefore, the proposed zero-watermarking algorithm is superior to other cutting-edge ones. [ABSTRACT FROM AUTHOR]