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Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The use of a large-scale antenna array (LSAA) has become an important characteristic of multi-antenna communication systems to achieve beamforming gains such as in designing millimeter-wave (mmWave) systems to combat severe propagation losses. In such applications, each antenna element has to be driven by a radio frequency (RF) chain for the implementation of fully-digital beamformers, significantly increasing the hardware cost, complexity, and power consumption. Therefore, constant-modulus analog beamforming (CMAB) becomes a viable solution. In this paper, we consider the scaled analog beamforming (SAB) or constant-modulus analog beamforming (CMAB) architecture and design the system parameters by solving two variants of beampattern matching problem. In the first case, both the magnitude and phase of the beampattern are matched to the given desired beampattern whereas in the second case, only the magnitude of the beampattern is matched. Both the beampattern matching problems are cast as a variant of the constant-modulus least-squares (CLS) problem. We provide efficient algorithms based on the alternating majorization-minimization (AMM) framework that combines the alternating minimization and the MM frameworks and the conventional-cyclic coordinate descent (C-CCD) algorithms to solve the problem in each case. We also propose algorithms based on a new modified-CCD (M-CCD) based approach. For all the developed algorithms we prove convergence to a Karush-Kuhn-Tucker (KKT) point (or a stationary point). Numerical results demonstrate that the proposed algorithms converge faster than the state-of-the-art solutions. Among all the algorithms, the M-CCD-based algorithms have faster convergence when evaluated in terms of the number of iterations and the AMM-based algorithms offer lower complexity. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications. [ABSTRACT FROM AUTHOR]

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes. [ABSTRACT FROM AUTHOR]

Despite major advancements in nonlinear programming (NLP) and convex relaxations, most system operators around the world still predominantly use some form of linear programming (LP) approximation of the AC power flow equations. This is largely due to LP technology's superior reliability and computational efficiency, especially in real-time market applications, security-constrained applications, and extensions involving integer variables, in addition to its ability to readily generate locational marginal prices (LMP) for market applications. In the aim of leveraging the advantages of LP while retaining the accuracy of NLP interior-point methods (IPMs), this paper proposes a sequential linear programming (SLP) approach consisting of a sequence of carefully constructed supporting hyperplanes and halfspaces. The algorithm is numerically demonstrated to converge on 138 test cases with up the 3375 buses to feasible high-quality solutions (i) without AC feasibility restoration (i.e., using LP solvers exclusively), (ii) in computation times generally within the same order of magnitude as those from a state-of-the-art NLP solver, and (iii) with robustness against the choice of starting point. In particular, the (relative) optimality gaps and the mean constraint violations are on average around $10^{-3}$ % and $10^{-7}$ , respectively, under a single parameter setting for all the 138 test cases. To the best of our knowledge, the proposed SLP approach is the first to use LP exclusively to reach feasible and high-quality solutions to the nonconvex AC OPF in a reliable way, which paves the way for system and market operators to keep using their LP solvers but now with the ability to accurately capture transmission losses, price reactive power (Q-LMP), and obtain more accurate LMP. [ABSTRACT FROM AUTHOR]

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios. [ABSTRACT FROM AUTHOR]

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability. [ABSTRACT FROM AUTHOR]

In mobile edge computing, popular mobile applications, such as augmented reality, usually offload their tasks to resource-rich edge servers. The user experience can be considerably affected when many mobile users compete for the limited communication and computation resources. The key technical challenge in task offloading is to guarantee the Quality of Service (QoS) for such applications. Existing work on task offloading focus on deterministic QoS (delay) guarantee, which means that tasks have to complete before the given deadline with 100 percent. However, it is impractical to impose a deterministic QoS guarantee for tasks due to the high dynamics of the wireless environment when offloading to edge servers. In this paper, we focus on task offloading with statistical QoS guarantee (tasks are allowed to complete before a given deadline with a probability above the given threshold), which can further save more energy by loosing the QoS requirement. Specially, we first propose a statistical computation model and a statistical transmission model to quantify the correlation between the statistical QoS guarantee and task offloading strategy. Then, we formulate the task offloading problem as a mixed integer non-Linear programming problem with the statistical delay constraint. We transform the statistical delay constraint into the constraints on CPU cycle numbers and the delay exponent respectively. We propose an algorithm to provide the statistical QoS guarantee for tasks using convex optimization theory and Gibbs sampling method. Experiment results show that the proposed algorithm outperforms the three baselines. [ABSTRACT FROM AUTHOR]

In this paper, a means of generating residuals based on a fault isolation observer (FIO) and evaluating them using a support vector machine (SVM) is proposed. The proposed FIO generates the isolated residual signals and they shows robust performance regardless of unknown road surface conditions. This FIO is designed using a linear time-invariant quarter-car model. While quarter-car models have the form of a bilinear system, in this study the authors convert this bilinear model to a linear model with model uncertainty based on the assumption that the control input is limited. Therefore, the proposed FIO can be used regardless of the type of damper or controller. Furthermore, the SVM based residual evaluator without empirically set thresholds is used to evaluate the generated residuals. The proposed fault diagnosis algorithm is expected to reduce the effort required in the design procedure and it can also detect a small amount of sensor fault that cannot be detected by traditional limit-checking method. The proposed fault diagnosis algorithm is verified using low cost production accelerometers and a quarter-car test rig. Consequently, the fault diagnosis algorithm proposed in this paper can detect the faults of a sprung mass accelerometer and an unsprung mass accelerometer independently, and this algorithm can reduce the effort required in designing the diagnosis algorithm greatly. [ABSTRACT FROM AUTHOR]

Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower complexity because of its sparsity. In this paper, based on deterministic message passing algorithm (DMPA), algorithmic simplifications such as domain changing and probability approximation are applied for SCMA decoding. Early termination, adaptive decoding, and initial noise reduction are also employed for faster convergence and better performance. Numerical results show that the proposed optimizations benefit both decoding complexity and speed. Furthermore, efficient hardware architectures based on folding and retiming are proposed. VLSI implementation is also given in this paper. Comparison with the state-of-the-art have shown the proposed decoder's advantages in both latency and throughput (multi-Gbps). [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art approaches which are mainly geometrical in nature and restricted to sensors’ noise being uncorrelated, the proposed algorithm gives the exact orientations of the sensors and can easily deal with the cases of correlated noise. The proposed algorithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design parameters. Uniform passivity throughout the parameter space is a fundamental requirement of parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of nonpassive models to generate energy. In this paper, we propose the first available algorithm that, starting from a generic parameter-dependent state-space model, identifies the regions in the frequency-parameter space where the model behaves locally as a nonpassive system. The approach we pursue is based on an adaptive sampling scheme in the parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable skew-Hamiltonian/Hamiltonian pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples. [ABSTRACT FROM AUTHOR]

In network optimization problems, from traffic engineering to network monitoring, the routing model is typically considered as something given and frozen. This paper is motivated by the fundamental question how the ability to change and optimize the routing model itself influences the efficiency at which communication networks can be operated. To this end, we identify two main dimensions of a routing model: consistency (of a single route) and coherence (of sets of routes). We present analytical results on the impact of the routing model on the achievable route diversity as well as on the runtime of solving optimization problems underlying different case studies. We also uncover that it can sometimes be beneficial to artificially restrict the routing model, to significantly reduce the computational complexity without negatively affecting the route diversity much. [ABSTRACT FROM AUTHOR]

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task. [ABSTRACT FROM AUTHOR]

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently attracted great attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. In this paper, we propose a modified federated averaging (FedAvg) algorithm by introducing the local learning rates and present the convergence analysis. To combat the distortion, the local learning rate is optimized to adapt the fading channel, which is termed as dynamic learning rate (DLR). We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has a closed-form solution. Our studies are extended to a more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. We also present the asymptotic analysis and give a near-optimal and closed-form receive beamforming solution when the number of antennas approaches infinity. Extensive simulation results demonstrate the effectiveness of the proposed DLR scheme in reducing the aggregate distortion and guaranteeing the testing accuracy on the MNIST and CIFAR10 datasets. In addition, the asymptotic analysis and the close-form solution are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

Federated learning (FL) involves training machine learning models over distributed edge nodes (i.e., workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the parameter server has to wait for the slowest workers, leading to significant waiting time due to edge heterogeneity. Though asynchronous FL can well tackle the edge heterogeneity, it requires frequent model transfers, resulting in massive communication resource consumption. Moreover, the different relative frequency of workers participating in asynchronous updating may seriously hurt training accuracy, especially on Non-IID data. In this paper, we propose a semi-asynchronous federated learning mechanism (FedSA), where the parameter server aggregates a certain number of local models by their arrival order in each round. We theoretically analyze the quantitative relationship between the convergence bound of FedSA and different factors, e.g., the number of participating workers in each round, the degree of data Non-IID and edge heterogeneity. Based on the convergence bound, we present an efficient algorithm to determine the number of participating workers to minimize the training completion time. To further improve the training accuracy on Non-IID data, FedSA deploys adaptive learning rates for workers by their relative participation frequency. We extend our proposed mechanism to the dynamic and multiple learning tasks scenarios. Experimental results on the testbed show that our proposed mechanism and algorithms address the three challenges more effectively than the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of $\mathcal {O}(1/t)$ while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is a common and serious disease in infants. Hip landmark detection plays a critical role in diagnosing the development of neonatal hip in the ultrasound image. However, the local confusion and the regional weakening make this task challenging. To solve these challenges, we explore the stable hip structure and the distinguishable local features to provide dependencies for hip landmark detection. In this paper, we propose a novel architecture named Dependency Mining ResNet (DM-ResNet), which investigates end-to-end dependency mining for more accurate and much faster hip landmark detection. First of all, we convert the landmark detection to the heatmap estimation by ResNet to build a strong baseline architecture for fast and accurate detection. Secondly, a dependency mining module is explored to mine the dependencies and leverage both the local and global information to decline the local confusion and strengthen the weakening region. Thirdly, we propose a simple but effective local voting algorithm (LVA) that seeks trade-off between long-range and short-range dependencies in the hip ultrasound image. Besides, a dataset with 2000 annotated hip ultrasound images is constructed in our work. It is the first public hip ultrasound dataset for open research. Experimental results show that our method achieves excellent precision in hip landmark detection (average point error of 0.719mm and successful detection rate within 1mm of 79.9%). [ABSTRACT FROM AUTHOR]

Deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, most of these methods are driven only by the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR cine images is not explored, which may limit further improvements in dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operator is proposed to explore low-rank priors in dynamic MR imaging to obtain improved reconstruction results. In particular, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed as SLR-Net. SLR-Net is defined over a deep network flow graph, which is unrolled from the iterative procedures in the iterative shrinkage-thresholding algorithm (ISTA) for optimizing a sparse and LR-based dynamic MRI model. Experimental results on a single-coil scenario show that the proposed SLR-Net can further improve the state-of-the-art compressed sensing (CS) methods and sparsity-driven deep learning-based methods with strong robustness to different undersampling patterns, both qualitatively and quantitatively. Besides, SLR-Net has been extended to a multi-coil scenario, and achieved excellent reconstruction results compared with a sparsity-driven multi-coil deep learning-based method under a high acceleration. Prospective reconstruction results on an open real-time dataset further demonstrate the capability and flexibility of the proposed method on real-time scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose three new compressive sensing-based algorithms for channel estimation in millimeter wave hybrid MIMO systems over doubly (time and frequency) selective channels. Utilizing the basis expansion model (BEM) for effectively representing doubly selective channels, we first propose a BEM-based block orthogonal matching pursuit (BBOMP) algorithm, which can work with any training sequence structure. Next, we present the second algorithm to reduce the complexity of the BBOMP method by employing a special training sequence which results in a computationally efficient block sparse sensing matrix. Finally, in order to further decrease the computational complexity, we propose the third algorithm, where the channel estimation is split into two separate steps of tap detection and gain estimation. The proposed algorithms exploit the entire available training sequence to estimate all channel parameters, and can capture channel variations across the entire training frames without requiring a feedback channel. The Cramer-Rao lower bound and the computational complexity of the proposed algorithms are also addressed. Intensive computer simulations are conducted to evaluate the accuracy of the proposed approaches, showing that they can significantly improve the mean squared error performance compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems. [ABSTRACT FROM AUTHOR]

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a method for estimating the time of arrival (ToA) of long term evolution (LTE) signals received on multiple separate transmission bands by the same base station (BS) mast. By exploiting the overall bandwidth occupied by the different signals and the correlation between the corresponding channel impulse responses, a higher precision is achieved with respect to the usually adopted single-band approach whenever the time-correlation among the bands is sufficiently high. The ToA estimation is carried out by generalizing the space-alternating generalized expectation-maximization (SAGE) algorithm to the multi-band context, proving that the availability of multiple bands provides a reduced standard deviation for the estimated ToA, with a limited increase of the computational cost. The main analyzed issue consists in the management of the asynchrony between transmitters belonging to distinct cellular operators, which is addressed by developing a suitable method to combine the contributions provided by the different bands. The method is validated by simulations in dual- and tri-band scenarios, and is further applied to real dual-band signals measured through a portable setup and experimentally acquired from an LTE BS mast covering multiple cells. [ABSTRACT FROM AUTHOR]

This paper presents a new single-source three-phase seven-level inverter with an inherent three-time voltage boosting capability for medium-voltage applications. The proposed topology is comprised of series connection of two switched-capacitor (SC) networks with an added half-bridge module per phase. Each of such integrated SC networks requires a single capacitor associated with three active power switches. The three-time voltage boosting feature of the proposed topology can reduce the dc-link voltage requirements by 50% in comparison to the traditional neutral point clamped (NPC), flying capacitors, active NPC (ANPC), hybrid clamped ANPC, and cascaded H-bridge topologies, and 75% to advanced ANPC topologies. It can also reduce the number of required switches and capacitors as well as their voltage stresses compared to those state-of-the-art topologies reported in the literature. By integrating “n” number of added SC networks in the proposed seven-level basic topology, the number of output voltage levels can be extended to 2n+3 per phase. A finite control set model predictive control algorithm is also derived to control the converter in both the active and reactive power exchanges modes without distorting the generated grid current quality. The capacitor voltage balancing is inherent in the proposed topology, and thus, there is no need for any additional voltage balancing circuit, which further reduces the control complexity. The performance of the proposed topology and its control algorithm are validated by several measurement results. [ABSTRACT FROM AUTHOR]

This paper proposes a direct speed and flux control system for Dual Stator Winding Induction Machines (DSWIMs) based on Super Twisting Sliding Mode Control (STSMC). The proposed nonlinear controller which has finite time convergence of error to zero, is designed such that the Lyapunov stability condition is satisfied. Furthermore, a novel torque-sharing algorithm is introduced for DSWIMs. This algorithm enables them to operate in a wide speed region, including zero speed, such that the required electromagnetic torque is supplied by two winding sets based on their power ratings. In addition, a Sliding Mode Control (SMC) based full order observer is proposed for DSWIMs. This observer, which is able to estimate the winding fluxes, flux angles and rotor speed with high-accuracy, guarantees the optimal flux condition. The functionality of the proposed sensor-less DSWIM drive scheme is evaluated through some experimental tests performed on a 3.3 kW DSP-based DSWIM drive system. The experimental results confirm the effectiveness of the proposed control method, torque sharing algorithm and the full order observer in various speed regions. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

Capon’s minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

In a human–robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners—This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality. [ABSTRACT FROM AUTHOR]

This paper establishes an approximate optimal critic learning algorithm based on single neural network (NN) policy iteration (PI) aiming at solving for continuous-time (CT) 2-player zero-sum games (ZSGs). In fact, we have to face the problem that the errors will disturb the dynamics and in turn identifying dynamics will generate errors. In order to prevent the effect of errors, in this paper, a single NN-based online PI algorithm is developed for the CT system, which is disturbed nonlinear ZSG. With plenty of online data, the Hamilton–Jacobi–Isaacs equation can be solved without complete dynamics. Then by the least-squares method, we can obtain the NN weights. Moreover, in the process of dealing with the undisturbed system, we find the way that obtains NN weights in this paper is equal to the way that obtains the optimal solution by the Gauss–Newton method. Based on the convergence of the Gauss–Newton method, we can efficiently obtain the optimal controller for the undisturbed system by utilizing online data. After getting the controller of the undisturbed system, it is time to take disturbance into consideration, so that we design a robust control pair to overcome the disturbance. In order to demonstrate the effectiveness of this algorithm, we design a set of simulations. The results verify that we can solve the disturbed nonlinear ZSG by this algorithm. [ABSTRACT FROM AUTHOR]

This paper concerns the deployment problem of wireless sensor networks (WSNs) with mobile robotic sensor nodes for spatiotemporal monitoring. The proposed approach, deep reinforced learning tree (DRLT), utilizes deep reinforcement learning (DRL) to improve the efficiency of searching the most informative sampling locations. The parameterized sampling locations in an infinite horizon space are modeled according to their spatiotemporal correlations and subject to various constraints, including field estimation error and information gain. And the model-based information gain can be calculated efficiently over an infinite horizon. In this manner, the effectiveness of the sampling locations is learned through DRLT during the exploration by the robotic sensors. Then DRLT can instruct the robotic sensors to avoid unnecessary sampling locations in future iterations. Also, it is proved in this paper that the proposed algorithm is capable of searching for the near-optimal sampling locations effectively and guaranteeing a minimum field estimation error. Simulation based on national oceanic and atmospheric administration (NOAA) datasets is presented, which demonstrates the significant enhancements made by the proposed algorithm. Compared with the traditional approaches, such as the information theory-based greedy approach or random sampling, the proposed method shows a superior performance with regard to both estimation error and planning efficiency. [ABSTRACT FROM AUTHOR]

Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influenced by noises. The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new objective function instead of the ${L} _{2}$ -norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative algorithm. The desirable performance of the QMEE-based CSP method, namely CSP-QMEE, is demonstrated with a toy example and two real EEG datasets, including Dataset IIb of the brain–computer interfaces (BCIs) Competition IV (three channels) and Dataset IIIa of the BCI Competition III (60 channels). The new method can achieve satisfactory performance compared to existing methods on all datasets. The promising results in this paper suggest that the CSP-QMEE may become a powerful tool for BCIs. [ABSTRACT FROM AUTHOR]

To increase market transparency, independent system operators (ISOs) have been working on minimizing uplift payments based on convex hull pricing theorems. Along this direction, in this paper, based on the analysis of specific generator features in the Midcontinent ISO (MISO) system, besides reviewing integral formulations for several special cases, we develop two integral formulations of a single generator that can capture these features. We then build a compact convex hull pricing formulation based on these integral formulations, which guarantees to obtain an exact convex hull price by solving a large-scale linear program. Meanwhile, to address the computational challenges caused by the large-scale MISO system, we propose innovative iterative algorithms with convergence properties, plus a complementary algorithm, to obtain an approximated convex hull price. The case studies on MISO instances with and without transmission constraints indicate that our algorithms lead to an exact convex hull price for all numerical studies and, the solutions can be obtained within 20 minutes. [ABSTRACT FROM AUTHOR]

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/. [ABSTRACT FROM AUTHOR]

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks. [ABSTRACT FROM AUTHOR]

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior. [ABSTRACT FROM AUTHOR]

Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramér–Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works. [ABSTRACT FROM AUTHOR]

The integration of aerial base stations carried by drones in cellular networks offers promising opportunities to enhance the connectivity enjoyed by ground users. In this paper, we propose an optimization framework for the 3–D placement and repositioning of a fleet of drones with a realistic inter-drone interference model and drone connectivity constraints. We show how to maximize network coverage by means of an extremal-optimization algorithm. The design of our algorithm is based on a mixed-integer non-convex program formulation for a coverage problem that is NP-Complete, as we prove in the paper. We not only optimize drone positions in a 3–D space in polynomial time, but also assign flight routes solving an assignment problem and using a strong geometrical tool, namely Bézier curves, which are extremely useful for non-uniform and realistic topologies. Specifically, we propose to fly drones following Bézier curves to seek the chance of approaching to clusters of ground users. This enhances coverage over time while users and drones move. We assess the performance of our proposal for synthetic scenarios as well as realistic maps extracted from the topology of a capital city. We demonstrate that our framework is near-optimal and using Bézier curves increases coverage up to 47 percent while drones move. [ABSTRACT FROM AUTHOR]

With the rapid spread of mobile devices, many traditional online social network services, like academic applications, start to develop mobile apps and improve their service quality through mobile sensing. A rich friendship among users can provide support for such applications, e.g., help to delegate sensing tasks in mobile crowd sensing. Therefore, studying friend recommendation that offers users’ suggestions on who to connect to, so as to enrich the user's friendship, is necessary to Mobile Social Networks (MSNs). Most existing strategies utilize user's relationship, or similarity, to make the recommendation, which overlooks the existence of multi-type connections among users, e.g., common paper based and common topic based connections among authors in academic networks. To overcome such limitation, we characterize each type of connections by a corresponding network layer and then propose a novel algorithm for joint recommendations in multilayer MSNs. Particularly, two types of results are presented in the paper. (i) Our proposed algorithm, named as Cross-layer 2-hop Path (C2P) algorithm, implements the joint recommendation by suggesting a user establish connections to his cross-layer two-hop neighbors, i.e., those who link to the user by two-hop paths with the two hops belonging to two different layers, respectively. In doing so, each produced recommendation item is a combination of user relationships in both two layers and therefore can better meet user demands. (ii) By analytical derivations, along with further empirical validation on real datasets, we give the performance evaluation on our proposed algorithm. First, we prove that the algorithm is efficiently implementable with a constant complexity of each recommendation in most cases. Then, we evaluate its recommendation performance by two metrics, i.e., accuracy and diversity, where the former metric measures recommendation accuracy and the latter one measures an algorithm's capability to provide diverse recommendation items. Our results show that C2P algorithm is optimal in terms of accuracy and for diversity, its performance is no less than the algorithm that is applied in single layers. And finally, the effectiveness of the proposed algorithm is validated on both synthetic and large-scale real datasets, where it outperforms the baseline algorithms with an up to 32 percent accuracy gain and obtains an approximately 0.5 ratio of the algorithm's diversity to the theoretical upperbound. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed continuous-time optimization problem, which consists of a group of agents with variant local cost functions. An adaptive consensus-based algorithm with event triggering communications is introduced, which can drive the participating agents to minimize the global cost function and exclude the Zeno behavior. Compared to the existing results, the proposed event-based algorithm is independent of the parameters of the cost functions, using only the relative information of neighboring agents, and hence is fully distributed. Furthermore, the constraints of the convexity of the cost functions are relaxed. [ABSTRACT FROM AUTHOR]

This paper considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The paper addresses this problem for homogeneous Markovian wireless channel models. An online energy-optimal computation offloading algorithm, OnOpt, is proposed. Its energy optimality is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian processes, and the Gilbert-Elliott channel model is used to show how the particular structure of the Markov chain can be exploited in computing optimal offload initiation times more efficiently. It is shown that job completion time probabilities can be computed recursively, which leads to a significant reduction in the computational complexity of OnOpt. The performance of the proposed algorithm is compared to three others, namely, Immediate Offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines. [ABSTRACT FROM AUTHOR]

While yielding satisfactory segmentation results for images with low SNR and poor contrast, one-dimensional (1-D) and two-dimensional (2-D) Otsu’s thresholding methods have the downside of high computational complexity. So far, three-dimensional (3-D) Otsu method has been based on histogram, which has only probability distribution of pixels as an object of interest. Histogram-based segmentation methods do not consider the contextual information which is significant to enrich the quality of segmented image. In this paper, a context-based 3-D Otsu algorithm has been proposed that considers the pixel intensity values as well as spatial information along with same properties of histogram. The proposed method is evaluated comprehensively with respect to quality and a detailed analysis is presented to compare the results of histogram-based 1-D, 2-D, and 3-D Otsu and energy-based 1-D, 2-D, and 3-D Otsu method, respectively. Experimental outcomes demonstrate the superiority of energy-based 3-D Otsu algorithm compared to histogram-based methods in terms of improved performance metrics, including mean error (ME), mean square error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), and entropy. Experiments on standard daily life color images have been carried out to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations. [ABSTRACT FROM AUTHOR]

For a hybrid ac/dc power system incorporating voltage source converter-based multi-terminal direct current (VSC-MTDC) grids, it is essential to solve the power flow problems efficiently for reliable operation. This paper presents an efficient ac/dc power flow algorithm considering the complementary constraints of VSC's reactive power limit and ac node voltage. It can be distinguished from the existing works in terms of both modeling and algorithm aspects. First, the VSC model is formulated, in which the interactions between the reactive power limit and the ac node voltage of VSC are viewed as complementary relations. The complementary relations are expressed using a smooth Fischer-Burmeister function. Then, power flow models of the ac grid and dc grid with a constant Jacobian matrix are formulated based on the VSC model, respectively. To efficiently solve the power flow of an ac/dc grid, the structure characteristic of the Jacobian matrix of ac/dc power flow computation is analyzed. It shows that the Jacobian matrix can be approximated as a block-upper triangular one. Based on this characteristic, a decoupled ac/dc power flow computation algorithm is provided. Test results on two hybrid ac/dc systems under different scenarios validate the correctness, convergence, and efficiency of the proposed power flow algorithm. [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]

This paper studies the event-triggered containment control (CC) problem of multiagent systems with a directed graph, where the followers’ communication graph is an undirected graph. A novel event-triggered CC protocol is presented to schedule communications between agents, which depends on both local states and control inputs. Different from traditional approaches using broadcasts, a unique property of the proposed protocol is that it enables agent-to-agent data transmission. To do so, each communication link is associated with a specific local event. The estimated relative interagent states are transmitted over a specific link only when the related input-based event is detected. We develop sufficient conditions to solve CC problem under this protocol and extend it to the adaptive event-triggered protocol that does not require the global knowledge on the smallest positive eigenvalue of the Laplacian. For both protocols, we derive positive low bounds on the interevent time intervals generated by individual events, which eliminate the “Zeno phenomenon.” Feasibility of the proposed algorithm is verified by an example. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption. [ABSTRACT FROM AUTHOR]

Autonomous vehicles will generate data from the variety of sensors they employ to track the surrounding environment. This data is inherently valuable, as it gives algorithm designers the potential to leverage prior experience in order to improve driving performance over time. This paper uses the lens of autonomous racing to provide an example of how data from previous iterations of driving can be used to improve quantitative metrics of performance. Two complementary algorithms are demonstrated in this paper. The first algorithm uses iterative learning control (ILC) to simultaneously improve lateral and longitudinal tracking of the desired racing trajectory over multiple laps, while the second algorithm is focused on altering the trajectory itself using a search method. When driven experimentally at the limits of handling, the result is a reduction in lap time of nearly 1.4 seconds, a major improvement. [ABSTRACT FROM AUTHOR]