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This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments. [ABSTRACT FROM AUTHOR]

With the rapid development of wind farm worldwide, monitoring the status of numerous wind turbines becomes the essential work. Abnormal data in wind power curve (WPC) are quite important for wind farm operations and maintenances because they usually reveal wind turbine failures or some extreme conditions. This paper proposes a new algorithm of WPC abnormal data detection and cleaning by image thresholding based on minimization of dissimilarity-and-uncertainty-based energy (MDUE). The basic idea is to transform the scattered data into a digital image and the problem of data cleaning is turned into an image segmentation problem. For all data pixels, the confidences of being classified as normal class are computed and make up a grey level feature image. Then the optimum threshold is determined by searching through the energy space based on intensity-based class uncertainty and shape dissimilarity. Finally, the normal and three types of abnormal data are marked after applying image thresholding to the feature image. The algorithm is compared with several data-based algorithms and a recently published image-based algorithm. A large number of experiments conducted on real-world WPC data collected from 37 wind turbines in two wind farms verified the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE. [ABSTRACT FROM AUTHOR]

Massive devices connected through fifth-generation (5G) networks constitute a ubiquitous Internet of Things (IoT), providing diverse service applications in a smart city. A robust network topology structure against cyber-attacks is the foundation of highly reliable service quality, especially in next-generation networks or beyond 5G (B5G) networks. Existing methods apply neural networks with deep reinforcement learning methods to advance the network topology. However, the reduction of unique hardware resource constraints and the application of edge intelligent networking capability of terminal nodes are emerging challenges for robustness optimization of IoT with 5G and B5G networks. To address these problems, we design a distributed learning framework utilizing edge intelligence, improving smart terminal nodes' networking capability, which is deployed on ordinary computers instead of specialized hardware such as GPUs. The proposed framework leveraging multi-core CPU and intelligent edge methods decreases the training time and economic cost and takes full advantage of computer resources. Furthermore, the best performing framework considers the distributed communication model of edge computing and optimizes the network topology by taking advantage of smart terminal nodes' contributions. We show that the framework succeeds in various topologies and outperforms compared with other state-of-art algorithms in improving the robustness for IoT topology in smart cities. [ABSTRACT FROM AUTHOR]

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, and various computational models are presented for solving them under the hypothesis of short-time invariance. To eliminate the large lagging error in the solution of the inherently dynamic nonlinear optimization problem, the only way is to estimate the future unknown information by using the present and previous data during the solving process, which is termed the future dynamic nonlinear optimization (FDNO) problem. In this paper, to suppress noises and improve the accuracy in solving FDNO problems, a novel noise-tolerant neural (NTN) algorithm based on zeroing neural dynamics is proposed and investigated. In addition, for reducing algorithm complexity, the quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is employed to eliminate the intensively computational burden for matrix inversion, termed NTN-BFGS algorithm. Moreover, theoretical analyses are conducted, which show that the proposed algorithms are able to globally converge to a tiny error bound with or without the pollution of noises. Finally, numerical experiments are conducted to validate the superiority of the proposed NTN and NTN-BFGS algorithms for the online solution of FDNO problems. [ABSTRACT FROM AUTHOR]

Rapid urbanization, high city population growth rates, technological development, environmental impacts, and human wellbeing in urban areas have over the years become increasingly challenging concerns. These trends are related to the complex, multiple factors to be considered when evaluating “smart” cities, that is, municipalities that seek to improve residents’ quality of life by combining new technologies and environmentally sustainable practices. The above tendencies thus add to the myriad of indicators that need to be included in smart city assessments, whose intricacy hinders related decision-making processes. Based on the principles of multiple-criteria decision analysis, this paper seeks to integrate cognitive mapping and the Choquet integral to develop an assessment system that facilitates the evaluation of smart cities, which was named “SMART-C.” The research includes a panel of experts on smart cities who identifies the evaluation criteria and their respective interactions. The results of a real-life application of the assessment system developed in this paper are validated both by the panel members and a representative of the Lisboa E-Nova nonprofit association of the Lisbon City Council's Environment and Energy Department. These experts confirm that the proposed evaluation system facilitates making distinctions between cities according to how fully they develop “smartness.” The advantages and limitations of this assessment framework are also discussed. [ABSTRACT FROM AUTHOR]

Open innovation (OI) has captured increasing interest over recent decades as this approach is linked to higher level organizational ambidextrous strategy, and has become a prerequisite for achieving competitive advantages. Assessing firms’ propensity for OI has, however, turned out to be an increasingly challenging endeavor. This is particularly true for small and medium-sized enterprises (SMEs) due to the myriad of factors that influence these companies’ capability for innovation. To overcome this challenge, this paper sought to integrate two operational research/management science techniques—cognitive mapping and the Choquet integral (CI) (a nonadditive measure and information aggregator)—to identify and prioritize relevant criteria for evaluating SMEs’ propensity for OI, and improving their organizational ambidexterity. To facilitate a real-world application, information was first collected from SME managers and entrepreneurs who agreed to participate in face-to-face group meetings, allowing realism to be incorporated in the decision-making process. The results were validated by both the panel members and project director of COTEC Portugal—a leading think-and-action network for advancing technology diffusion and business innovation cooperation. The findings include that cognitive mapping facilitates the identification and understanding of cause-and-effect relationships between the determinants of OI in SMEs. The CI, in turn, introduces realism into the construction of value functions and the respective assessments of SMEs. The limitations and implications of the proposed system are also discussed. [ABSTRACT FROM AUTHOR]

This article presents a fully integrated flipped voltage follower (FVF) based low-dropout (LDO) regulator with enhanced full-spectrum power supply rejection (PSR) and unity-gain bandwidth over 400MHz for noise-sensitive circuits. Following the study of three types of FVF LDO's PSR performances, we propose a novel FVF LDO with a low-gain fast loop-1 and a high-gain slow loop-2. In prior FVF LDOs, their PSRs are either full-spectrum, or not, but with low PSR at low frequency. In this article, we fully utilize both dc gains of loop-1 and loop-2 for the low-frequency PSR, while the high-frequency PSR remains unchanged. In addition, we use dynamic compensation to push the loop-2's UGB to higher frequency for a better PSR bandwidth. This work, fabricated in 65 nm complementary metal oxide semiconductor (CMOS), with 1.2-V input and 1-V output, exhibits a measured quiescent current (IQ) varying from 27 to 82 μA for a load current ILOAD between 5 μA and 20 mA. The circuit achieves a low frequency PSR of –58 dB with the worst full-spectrum PSR of –9 dB in 20 mA ILOAD with a 300 pF on-chip output capacitor. Further, with an UGB over 400 MHz, the proposed FVF LDO reaches 0.9 ns response time when ILOAD changes between 100 μA and 20 mA with edge times less than 0.8 ns. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the major causes of blindness. It is of great significance to apply deep-learning techniques for DR recognition. However, deep-learning algorithms often depend on large amounts of labeled data, which is expensive and time-consuming to obtain in the medical imaging area. In addition, the DR features are inconspicuous and spread out over high-resolution fundus images. Therefore, it is a big challenge to learn the distribution of such DR features. This article proposes a multichannel-based generative adversarial network (MGAN) with semisupervision to grade DR. The multichannel generative model is developed to generate a series of subfundus images corresponding to the scattering DR features. By minimizing the dependence on labeled data, the proposed semisupervised MGAN can identify the inconspicuous lesion features by using high-resolution fundus images without compression. Experimental results on the public Messidor data set show that the proposed model can grade DR effectively. Note to Practitioners—This article is motivated by the challenging problem due to the inadequacy of labeled data in medical image analysis and the dispersion of efficient features in high-resolution medical images. As for the inadequacy of labeled data in medical image analysis, the reasons mainly include the followings: 1) the high-quality annotation of medical imaging sample depends heavily on scarce medical expertise which is very expensive and 2) comparing with natural issues, it is more difficult to collect medical images because of privacy issues. It is of great significance to apply deep-learning techniques for diabetic retinopathy (DR) recognition. In this article, the multichannel generative adversarial network (GAN) with semisupervision is developed for DR-aided diagnosis. The proposed model can deal with DR classification problem with inadequacy of labeled data in the following ways: 1) the multichannel generative scheme is proposed to generate a series of subfundus images corresponding to the scattering DR features and 2) the proposed multichannel-based GAN (MGAN) model with semisupervision can make full use of both labeled data and unlabeled data. The experimental results demonstrate that the proposed model outperforms the other representative models in terms of accuracy, area under ROC curve (AUC), sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results. [ABSTRACT FROM AUTHOR]

With the increasing applications of Internet of Things (IoT), service quality becomes more and more important. The topology control of wireless sensor networks (WSN) is one of the key techniques to optimize the quality of service (QoS) of IoT. Game theory is an efficient approach of WSN topology control through global optimization, which resolves the conflicting objectives of self-regard sensor nodes. However, many existing topology control algorithms using game theory waste energy due to the transmission of redundant sensed data. In this study, we present a novel dynamic spatial-correlation-aware topology control method (TC-GSC) using game theory to reduce the redundant data. In TC-GSC, the monitoring region is divided into several spatial-correlation areas, and only one active node is periodically selected for each area. Based on the collected information of active nodes, the sink node computes the transmitting power level using game theory and generates the connected topology. Moreover, the WSN topology can be dynamically updated when the active node’s remaining energy becomes greatly uneven, or the active node’s working time exceeds the threshold. Extensive simulation results demonstrate that TC-GSC can effectively improve the energy balance of nodes and significantly reduce the energy waste, thus prolonging the lifetime of networks. [ABSTRACT FROM AUTHOR]

This letter demonstrates unipolar p-type MoS2 field-effect transistors (p-FETs). The p-FETs are fabricated using high work function Pt as the contact electrode and p-type MoS 2 film as the active channel. The p-FETs, with a channel length of 1- $\mu $ m, show an output current of $-10~\mu $ A/ $\mu $ m with a drain voltage of −1 V. The Pt electrode, formed by slow electron-beam evaporation, shows a contact barrier height of 0.13 eV. In comparison, a faster deposition rate results in a larger resistance and a higher contact barrier of the Pt electrode. Raman characterization provides certain support for the improved contact interface of the slowly deposited Pt electrode, which may be an essential factor in improving current drive. [ABSTRACT FROM AUTHOR]

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks. [ABSTRACT FROM AUTHOR]

In this paper, we study a multiuser mobile edge computing (MEC) network, where tasks from users can be partially offloaded to multiple computational access points (CAPs). We consider practical cases where task characteristics and computational capability at the CAPs may be time-varying, thus, creating a dynamic offloading problem. To deal with this problem, we first formulate it as a Markov decision process (MDP), and then introduce the state and action spaces. We further design a novel offloading strategy based on the deep Q network (DQN), where the users can dynamically fine-tune the offloading proportion in order to ensure the system performance measured by the latency and energy consumption. Simulation results are finally presented to verify the advantages of the proposed DQN-based offloading strategy over conventional ones. [ABSTRACT FROM AUTHOR]

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the results for model-independent control of uncertain ${n}$ -degree of freedom robotic manipulators in the presence of external disturbances and backlash hysteresis. In order to improve the response characteristics of the system and attenuate the uncertainties, the developed robust model-free controller incorporates time delay control (TDC) as well as adaptive terminal sliding mode control (ATSMC) methods. Particularly, the time delay estimation is designed to estimate the unknown dynamics of the robotic manipulators by adopting the theory of TDC. Further, the ATSMC is utilized to obtain the robustness, finite-time convergence, and an adaptive tuning is exploited to deal with bounded derivative of unknown dynamics. The overall system stability is investigated by the Lyapunov theorem application and computed the finite convergence rate thereafter. Finally, a simulation comparison with an existing adaptive fractional-order terminal sliding mode control under backlash hysteresis is provided to illustrate the effectiveness and the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Organizational ambidexterity balances exploitative and exploratory behaviors so that organizations are able to exploit their existing competencies while simultaneously exploring new opportunities. Similarly, ambidextrous cybersecurity (AMBI-CYBER) focuses on the protection of data, systems, and networks, while fostering the rapid introduction of new technologies within a company. This balance is linked to cybersecurity absorptive capacity and defines the “Cybersecurity Efficient Frontier.” In this paper, we view AMBI-CYBER as a combination of organizational as well as technological and cultural competences which rely upon a multifaceted, multimodal, multinodal, and multilevel set of skills and capabilities. We outline the anatomy of the AMBI-CYBER architecture adopting a balanced scorecard, multistage approach under a 7Ps stage gate model (Patient, Persistent, Persevering, Proactive, Predictive, Preventive, and Preemptive). Such an approach emphasizes the need to enable a complex, nonlinear, adaptive process of dynamic intangible organizational assets, resources, and capabilities across a performance frontier where we aim to optimize safety, security, and privacy effectiveness and efficiency. We also suggests that the Quadruple/Quintuple Helix Innovation model may promote AMBI-CYBER enabling a locus-centric and triple-bottom-line-centric entrepreneurial process of discovery followed by development, exploration, exploitation, and deployment. [ABSTRACT FROM AUTHOR]

To get rid of plug-in/out actions for automatic wharves, a switchable-LCL-circuit (SLC) based inductive power transfer system is proposed to improve efficiency for reefer container wireless charging. As we knew, the power requirement of a reefer container only hits a few operation points, and each operation point is in corresponding to an individual resistance point (IRP). Therefore, the system efficiency can be improved by altering the IRPs into the optimal load resistance (OLR). The idea of this letter is to use the SLC to create multiple efficiency-load curves, then to regulate the peak points of the curves approaching the IRPs by reconfiguring the compensation parameters. Finally, a prototype was built to validate the performance of the proposed approach. The experimental results show that with the SLC, both the IRPs 20 and 160 Ω can be transformed into the OLR 55 Ω; the proposed system can gain the system efficiency >92.4% at the IRPs (20 Ω, 259.2 W) and (160 Ω, 32.4 W). [ABSTRACT FROM AUTHOR]

The noninverting buck–boost (NIBB) converter is a step-up and step-down dc–dc converter suitable for applications with a wide input voltage range. However, this circuit will face the problem of operating mode switching when the input voltage is close to the output voltage. Considering this problem, the principles of the NIBB converter are analyzed in this article. The duty cycle combinations of an NIBB converter are analyzed, and an analysis method for the mode transition problem from a geometric perspective is proposed. Using the two-edge pulsewidth modulation method, a novel bumpless mode transition control method is proposed. A nonlinear feedforward controller with hysteresis is designed according to the state equation of the circuit. Moreover, the inductor current reference value is compensated when the mode is switched so that the mode transition problem of the NIBB circuit is effectively solved. In addition, a small-signal analysis of the proposed method is performed. The analysis shows that the proposed method has good stability and dynamic performance in various typical conditions. A 10-kW NIBB prototype is built in this article, and the corresponding experimental scenarios are designed. The experimental results show that compared with the results of unified PI control during the mode transition, the transient oscillation of the output voltage is smaller, and the duration of the transient process is shorter. [ABSTRACT FROM AUTHOR]

Exiting from highways in crowded dynamic traffic is an important path planning task for autonomous vehicles (AVs). This task can be challenging because of the uncertain motion of surrounding vehicles and limited sensing/observing window. Conventional path planning methods usually compute a mandatory lane change (MLC) command, but the lane change behavior (e.g., vehicle speed and gap acceptance) should also adapt to traffic conditions and the urgency for exiting. In this paper, we propose a reinforcement learning-enhanced highway-exit planner. The learning-based strategy learns from past failures and adjusts the vehicle motion when the AV fails to exit. The reinforcement learning is based on the Monte Carlo tree search (MCTS) approach. The proposed learning-enhanced highway-exit planner is tested 6000 times in stochastic simulations. The results indicate that the proposed planner achieves a higher probability of successful highway exiting than a benchmark MLC planner. [ABSTRACT FROM AUTHOR]

In this paper, we propose a specular highlight image enhancement algorithm based on a dark channel prior to solve the problem of information loss in specular highlight images in real scenes. First, the algorithm is based on the dark channel prior algorithm, and a moving window minimum filter is used to estimate the global illumination component. Then, a weighted function based on the local pixel color difference is introduced to solve the halo artifacts in the image. Then, the improved guided filter algorithm is used to optimize the transmittance, which improves the computational efficiency of the algorithm. Finally, the brightness of the image is adjusted by the CLAHE algorithm to enhance the local details of the image. Experimental results show that this method can effectively enhance the information in specular highlight images, and its processing results are obviously better than those of other algorithms. [ABSTRACT FROM AUTHOR]

This article is devoted to event-triggered stabilization for a class of interval type-2 (IT2) fuzzy systems with aperiodic sampling. First, the IT2 Takagi–Sugeno fuzzy model and sampled-data controllers are established subject to mismatched membership functions. Second, considering a nonuniform sampling case, a refined adaptive event-triggered communication scheme is proposed in a hierarchy form to dynamically adjust the direction and rate of the event-triggered threshold parameter by state changing trend and relative state error, respectively. Thus, a complete dual-directional regulating mechanism with sensitivity to state variation is reasonably created to give extra flexibility, which is beneficial for a preferable tradeoff between control performance and network resource. Third, considering the practical behaviors on the sampling interval, a novel integral type of time-dependent Lyapunov function is constructed. Then, the stability criterion and the controller design approach are derived. Finally, the numerical examples are provided to demonstrate the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

Novel features of arc plasma devices with thermionic and thermo-chemical cathodes and development of associated thermal plasma process reactors are investigated from device design and application point of view. Different types of plasma torches and open arcs, their fundamental differences in architecture, jet behavior, operational constraints, and evolution of interesting electromagnetic forces inside them have been probed. Investigation of instabilities in arcs through direct real-time imaging of arc path using fast photography and impact of powder loading on such instabilities are presented. Development of low power air plasma torches delivering large volume high-temperature plasma jet with long penetration depth has been investigated together with an estimation of hazardous NOx, produced from such devices during operation. Interesting attributes of various types of thermal plasma reactors including plasma spray chemical vapor deposition (CVD) reactors for thick and dense coating, reactors for generation of H-OH plasma, radio frequency (RF) thermal plasma reactors for the synthesis of nanostructures, and air plasma gasifier reactors for environment-friendly mitigation of municipal solid waste (MSW) and electronic wastes have been presented. Coke bed air plasma gasifiers are specially investigated for their important technological aspects. Experimental results are presented with simulation supports for enhanced understanding, wherever necessary. [ABSTRACT FROM AUTHOR]

One of the main challenges in wireless powered networks (WPNs) is the doubly near-far problem, i.e., the twofold degradation of the users’ performance due to different path-loss values that affects both the energy harvesting and the information transmission efficiency. To this end, we propose and optimize the application of decentralized power transfer and radio access in WPNs, which is implemented by using multiple remote radio heads (RRHs) with the capability to both transfer energy and receive information. More specifically, the use of non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) is investigated, while two novel schemes are proposed, hereinafter termed as partially and fully asynchronous transmission TDMA (AT-TDMA). According to the proposed schemes, the users harvest energy and transmit information to the RRHs in different portions of time. Furthermore, the sum and minimum throughput among users are jointly maximized by obtaining the Pareto optimal solutions for the scheduling of power transfer and information transmission. To evaluate the performance of the decentralized architecture compared to the centralized one, we solve the aforementioned optimization problem for both architectures, taking also into account the circuit power consumption. Simulations show that the use of multiple RRHs improves spectral efficiency compared to the centralized implementation, since it can tackle more efficiently the doubly near-far problem. In addition, the proposed AT-TDMA schemes increase the achievable data rate. [ABSTRACT FROM AUTHOR]

This article designed a soft bending actuator (SBA) and presented a neural-network-based tracking control strategy for such an actuator. To achieve high-precision control, a visual feedback system was established by using a high-speed camera to dynamically compute the corresponding central angle which described the curvature of the SBA. Considering its nonlinear features, the echo state Gaussian process, a fusion of echo state network and Gaussian process, was used to approximate the SBA's dynamics with a nonlinear autoregressive exogenous model. On this basis, a single-layer neural network was trained to calculate the control signal in light of the idea of predictive control, due to its simplicity and good approximation capabilities. The stability of the closed-loop system was guaranteed, and experimental results indicated that the proposed strategy showed relatively high tracking accuracy under various reference trajectories. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami- $m$ distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami- $m$ fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption. [ABSTRACT FROM AUTHOR]

This letter proposes a novel primary-side parameter estimation method for bidirectional inductive power transfer (BD-IPT) systems. This method can estimate mutual inductance at startup and monitor secondary-side phasor information continuously during operation. The proposed method can be used to simplify existing optimal control methods in BD-IPT systems by providing necessary feedback information without a wireless communication link. Besides, it helps to implement new control methods to further improve system performance. A current decoupling concept is utilized to estimate both the mutual inductance and secondary phasor information. A circuit model based on the superposition principle is presented to describe the current decoupling concept. The validity and accuracy of the proposed estimation method are demonstrated using the measured results of a 1-kW prototype BD-IPT system. [ABSTRACT FROM AUTHOR]

Side-scan sonar is widely utilized in the field of underwater exploration. Accurate segmentation of the side-scan sonar images is an essential part of sonar image processing. To improve the accuracy of image segmentation and reduce the occupancy of computing resources, a new active contour model for image segmentation is proposed in this article. Firstly, we make the energy function of each pixel determined by itself and its neighbors. This article embeds the local texture neighborhood region system and define its structure to against the noise and object boundary pollution of the image. Further, we introduce a Bayesian framework that embeds the Markov Random Field model and local texture information to counteract the problem of intensity inhomogeneity, improve the accuracy of segmentation, and reduce computational cost. Finally, according to this model, we customize a new multi-class energy function which applied to the level set function. By minimizing this energy equation, we can divide the image into three classes: object, shadow, and seabed background. The experimental results show that this method has a good effect on the synthesized high-noise sonar images and real sonar images, and also have significantly increased the accurate recognition of underwater targets. [ABSTRACT FROM AUTHOR]

Computational ghost imaging (CGI) captures images via the correlation between a set of illumination patterns and the transmitted/reflected signal of an object. It has been widely studied in many fields and has advanced from experimental verification to practical applications. However, there will be some motion blur in the results when a moving object is imaged with an insufficiently fast detector. To eliminate this blurring, we present here a tracking compensation method for CGI, in which a series of patterns illuminate the object according to the motion of the object, and the signal intensity is collected synchronously by the bucket detector. The principle of this compensation for moving and rotating objects is explained in detail. Both simulation and experimental results show that this method can effectively eliminate the motion blur and provide high quality reconstruction, outperforming conventional CGI, broad potential applications in object tracking, remote sensing and real-time imaging. [ABSTRACT FROM AUTHOR]

Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks. [ABSTRACT FROM AUTHOR]

As the global environmental pollution and energy crisis become more serious, lithium-ion batteries (LIB) received an increasing research interest due to their low self-discharge rates, long cycle life, and high power density for the transportation applications. Battery management system (BMS) plays an essential part for the LIB system as it can guarantee an efficient and stable operation through LIB state of charge (SOC) estimation. In this article, a nonlinear observer with terminal voltage feedback injection (VFNO) is designed based on the electrochemical single particle (SP) model to monitor the SOC of LIB. The convergence of the SP-VFNO system in consideration of measurement error is proved in terms of Lyapunov stability theory. The current, the terminal voltage, and the SOC reference value are measured in the battery testing system. Besides, the extended Kalman filter (EKF) based on both the SP model and the second-order resistor-capacitor (SORC) model to estimate SOC is adopted for comparison. The experimental results indicate that the proposed SP-VFNO method has superiority with a faster convergence rate and a higher estimation precision, which can help the accurate SOC estimation for BMS in practical application. [ABSTRACT FROM AUTHOR]

It is well known that the interaction between cascaded individually designed power conversion systems can cause instability. To overcome this issue, a Hamiltonian energy control scheme is proposed, which is based on passivity control theory and port-controlled Hamiltonian framework. A complementary PI adjustment term is also included in the control algorithm to eliminate the steady-state output voltage error caused by the parameter uncertainty. The proposed control approach is applied to three different cascade structures. First, the cascade structure between dc/dc converters is considered, and the detailed controller design is given. Second, the cascade connection of a single converter and its LC filter is studied. By placing the LC filter into the Hamiltonian model of the controlled converter system, the dynamic and potential instability caused by the filter can be adjusted. Finally, the cascade structure between subsystems including filters and converters, which are common in microgrids, is studied. By using the Hamiltonian function (storage function) as the Lyapunov function candidate, the large-signal stability of each controlled converter system is proved. When the cascade structure contains multiple controlled converter systems, the stability of the entire cascaded system is guaranteed by the superposition of multiple Lyapunov functions. A 3.5 kW 220−270−350 V test bench is built in the laboratory to demonstrate the application of the proposed control approach to these three cascade structures. [ABSTRACT FROM AUTHOR]

Automatic detection of gait phases through supervised learning is a feasible approach that takes advantage of the consistency of the gait cycle among healthy subjects. However, gait patterns among subjects with impairments are not as consistent and most of the existing algorithms have limited performance detecting phases during impaired gait. In this paper, we proposed one algorithm that used linear classifiers to detect in real-time the transition between consecutive gait phases. Our approach is a generalization of the rule- and threshold-based algorithms for event detection. Linear classifiers are parametric models that require appropriate values in order to perform correct classification of the gait phases. We introduced a modified Support Vector Machine (SVM) to compute such sub-optimal combinations of those values, and a further optimization with a hybrid meta-heuristic approach that integrates a Genetic and a Simulated Annealing Algorithm. We tested our approach on data collected by a single-IMU foot-mounted wearable device during overground and treadmill walking for two groups: one with healthy and one with Parkinson’s Disease subjects. The ${F}_1$ -scores were 0.987 and 0.953 for the two groups, which were comparable with our previously developed threshold-based method, which obtained 0.988 and 0.974, respectively. Our proposed approach achieved similar performance as the threshold-based scheme, with the advantage of not relying on any prior knowledge of specific features for any particular inertial signal. [ABSTRACT FROM AUTHOR]

Eddy current based approaches have been investigated for a wide range of inspection applications. Dodd-Deeds model and the truncated region eigenfunction expansion (TREE) method are widely applied in various occasions, mostly for the cases that the sample is relatively larger than the radius of the sensor coil. The TREE method converts the integral expressions to the summation of many terms in the truncated region. In a recent work, the impedance of the co-axial air-cored sensor due to a plate of finite radius was calculated by the modified Dodd-Deeds analytical approach proposed by authors. In this paper, combining the modified analytical solution and the TREE method, a new finite region eigenfunction expansion (FREE) method is proposed. This method involves modifying its initial summation point from the first zero of the Bessel function to a value related to the radius of the plate, therefore makes it suitable for plate with finite dimensions. Experiments and simulations have been carried out and compared for the verification of the proposed method. Further, the planar size measurements of the metallic circular plate can be achieved by utilising the measured peak frequency feature. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fixed-time convergent continuous controller designed to solve a stock management problem with the objective to drive stock and supply chain levels at the reference values, subject to loss rate disturbances whose bounds are unknown. The only measurable state of the supply chain is the inventory retailer stock level, whereas the supply line inventory level should be estimated. The designed controller includes a fixed-time convergent differentiator, an adaptive fixed-time convergent disturbance observer, and a fixed-time convergent regulator. The adaptive fixed-time convergent observer is used to estimate a disturbance without excessively increasing the controller gains. The controller design is validated in a case study of stock management. The calculated upper estimate for the total settling (convergence) time and the obtained simulation results confirm the fixed-time convergence and the robustness of the designed controller. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

Gearboxes are widely used in many industrial fields, and their faults may bring serious economic losses or even casualties. Therefore, two new indexes, namely, cyclic harmonic ratio (CHR) and log-CHR, are proposed in this article for gearbox fault diagnosis, which can effectively measure the degree of specific second-order cyclostationary components from squared envelope spectrum and log-envelope spectrum, respectively. CHR and log-CHR are the improved forms of indicator of second-order cyclostationary (ICS2) and log-cycligram (LC) index, respectively. They are also the simplified form of discolored CHR (DCHR). The proposed indexes are more robust than the available ICS2 and LC index, and the relevant comparisons are also conducted by using an acoustic emission signal of the planetary gearbox and a vibration signal of the fixed-axis gearbox. In addition, a noise resistance study is conducted to evaluate the robustness of the proposed methods. As a result, the proposed methods are proved to have better performances than ICS2gram and LC, and they can be regarded as effective methods for gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

For inductive power transfer (IPT) systems, loads and system input voltages are subject to change, which affects system efficiency and stability. This article presents a perturbation and observation (P&O) method for maximum energy efficiency tracking (MEET) with a model predictive control (MPC) scheme for improving the dynamic performance of series–series compensated IPT systems. In the IPT system, the inverter at the primary side incorporates the P&O method and phase shift modulation (PSM) to minimize system input power. Meanwhile, the rectifier at the secondary side is controlled by MPC control based PSM to improve the dynamic response of the output voltage. Simulated and experimental results show that, compared to the PI controller, the MPC controller, based on a simple but accurate mathematical model, has a better dynamic response to load and input voltage variations. With the MPC controller, the settling time of the output voltage is reduced by 85.7%, which indicates a particularly stable power supply to the load. Furthermore, MEET adopting the P&O method in the IPT system can promote the system efficiency by 1.85% on average when the output voltage is regulated by the MPC controller. [ABSTRACT FROM AUTHOR]

The surface mass balance (SMB) of West Antarctica is an important glaciological input to understanding polar climate and sea-level rise but with historically poor in situ data coverage. Previous studies demonstrate the utility of frequency-modulated continuous-wave radar to image subsurface layering in ice sheets, providing an additional source of data with which to estimate SMB. Traditional methods, however, require time-intensive manual oversight. Here, we present a probabilistic, fully automated approach to estimate annual SMB and uncertainties from radar echograms using successive peak-finding and weighted neighborhood search algorithms with the Monte Carlo simulations based on annual-layer likelihood scores. We apply this method to ground-based and airborne radar in a 175-km transect of the West Antarctic interior and compare the results to traditional manual methods and independent estimates from firn cores. The method demonstrates an automated estimation of SMB across a range of accumulation rates (100–450-mm water equivalent per year) and layer gradients up to 2 m/km. Based on likelihood-weighted F-scores, automated layer picks have a success rate between 64% and 84.6% compared with manually picked layers for three validation sites dispersed across the region. Comparisons between the automated SMB estimates and independent firn cores show a bias of 24 ± 70-mm water equivalent per year (12% ± 35% water equivalent of the in situ mean accumulation rate) although individual core site biases differ. This new approach permits the fully automated extraction of annual SMB rates and should be broadly and readily applicable to previously collected and ongoing radar data sets across polar regions. [ABSTRACT FROM AUTHOR]

A new three-way filtering power divider (FPD) design based on multilayer substrate integrated waveguide (SIW) with equal/unequal power division is proposed in this brief. By elaborately integrating three same-type slotline-microstrip transition structures with a pair of TE101-mode resonant cavities, dual-mode three-way filtering power division response is attained in the design. Owing to the loading effect of the common coupling slot, one transmission zero is generated to improve lower passband selectivity. Furthermore, three-way equal and unequal power division can be achieved by flexibly changing the length of the microstrip line beyond the coupling slot. Simultaneously, favorable in-band port-to-port isolation is obtained by loading isolation resistors between the three output microstrip lines. For demonstration, two FPD prototypes with different power division ratios (1:1:1 and 1:1:1.5) operating at 11.8 GHz are designed, fabricated, and tested. The measured results agree well with the simulation results, verifying the presented design concept. [ABSTRACT FROM AUTHOR]

This brief is concerned with the problem of observed-based asynchronous control for a class of semi-Markov jump systems (SMJSs) in continuous-time domain. There exists an asynchronous phenomenon between the system modes and the controller modes due to the limitations of the actual environment. System modes and controller modes are usually described as hidden modes and observable modes, respectively. Their relationship can be exhibited by emission probabilities. In this brief, a hidden semi-Markov model is used to describe such phenomenon. A conventional approach is employed and an improved result is derived to solve observed-based control problem. Based on a common Lyapunov function, an observed-based asynchronous control law depending on the observable mode is synthesized to ensure the stochastic stability of the closed-loop system. Finally, a practical RLC circuit system example is provided to illustrate the effectiveness and merits of the proposed methods. [ABSTRACT FROM AUTHOR]

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson’s disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy (${F}_{{1}}$ -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages. [ABSTRACT FROM AUTHOR]

This article concerns the problem of improving ride comfort of a full-vehicle active suspension system through an observer-based decentralized event-triggered $H_{\infty }$ controller. The observer-based controller is adopted to cope with the situation that states information of vehicles are not available. To reduce the observer-based controller computing frequency, a decentralized event-triggered data transmission scheme is provided, and lower bounds on the minimum interevent time are guaranteed. Based on the proposed controller, the $H_\infty$ performance of an active full-vehicle suspension system can be obtained. Finally, simulations in the bump and random road conditions are shown to demonstrate the effectiveness of the derived algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-based security control problems for a set of discrete-time stochastic systems suffered from randomly occurred attacks, especially the denial-of-service attacks and the deception attacks. An attack model with compensation is established to describe combinations of attacks to the networked control systems. An event-triggered mechanism is employed to debase the communication load by transmitting measurement signals when a definite triggering condition is satisfied. A definition of security probability is proposed to describe the transient state of controller systems. The aim of this paper is to design a dynamic output feedback controller, thereupon the closed-loop system achieves the described security in probability. Some novel sufficient conditions are proposed to guarantee the input-to-state stability of the system in probability, and the controller gains are designed by solving a set of matrix inequalities. The upper bound of the quadratic cost function is also derived. Finally, simulation examples are applied to illustrate the effectiveness of the proposed design scheme. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of the switched system with bounded actuators and average dwell-time. We present the switching rules for the designed controllers based on two kinds of parametric algebraic Riccati equation. The exponential stability of the switched system under the average-dwell time is ensured by the designed controllers and the switching rules. The dynamic performance of the control system is enhanced greatly by the proposed method. The simulation results illustrate the effectiveness and the usefulness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) based on motor imagery (MI) translates human intentions into computer commands by recognizing the electroencephalogram (EEG) patterns of different imagination tasks. However, due to the scarcity of MI commands and the long calibration time, using the MI-based BCI system in practice is still challenging. Zero-shot learning (ZSL), which can recognize objects whose instances may not have been seen during training, has the potential to substantially reduce the calibration time. Thus, in this context, we first try to use a new type of motor imagery task, which is a combination of traditional tasks and propose a novel zero-shot learning model that can recognize both known and unknown categories of EEG signals. This is achieved by first learning a non-linear projection from EEG features to the target space and then applying a novelty detection method to differentiate unknown classes from known classes. Applications to a dataset collected from nine subjects confirm the possibility of identifying a new type of motor imagery only using already obtained motor imagery data. Results indicate that the classification accuracy of our zero-shot based method accounts for 91.81% of the traditional method which uses all categories of data. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs), and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents, and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number, and CPU time. [ABSTRACT FROM AUTHOR]