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This paper focuses on studying the optimization problem of multi-agent systems (MAS) under undirected graph. To reduce the communication frequency among agents, a zero-gradient-sum (ZGS) algorithm based on dynamic event-triggered (DET) mechanism is investigated. The event-triggered condition of each agent only uses its own state information and the neighbor's state information at the previous triggering instants, without requiring continuous state information from the neighbor. In addition, the designed algorithm allows for the sampling period to be arbitrarily large. The Lyapunov method is utilized to derive the sufficient conditions that incorporate time delay and parameters. As the event is only checked at the periodic moment, zeno behavior can be directly excluded. Finally, numerical simulations demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches. [ABSTRACT FROM AUTHOR]

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers. [ABSTRACT FROM AUTHOR]

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. [ABSTRACT FROM AUTHOR]

Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata. [ABSTRACT FROM AUTHOR]

This paper analyzes the modeling of a computer-aided piano music automatic notation algorithm, combines the influence of music on psychological detachment, and designs the piano music automatic notation algorithm in psychological detachment model construction. This paper investigates the multiresolution time-frequency representation constant Q-transform (CQT), which is common in music signal analysis, and finds that although CQT has higher frequency resolution at low frequencies, it also leads to lower temporal resolution. The variable Q-transform is introduced as a tool for multibasic frequency estimation of the time-frequency representation of music signals, which has better temporal resolution than CQT at the exact frequency resolution and efficient coefficient calculation. The short-time Fourier transform and constant Q-transform time-frequency analysis methods are implemented, respectively, and note onset detection and multibasic tone detection are implemented based on CNN models. The network structure, training method, and postprocessing method of CNN are optimized. This paper proposes a temporal structure model for maintaining music coherence to avoid manual input and ensure interdependence between tracks in music generation. This paper also investigates and implements a method for generating discrete music events based on multiple channels, including a multitrack correlation model and a discretization process. In this paper, the automatic piano music notation algorithm can play an influential role in significantly enhancing the actual effect of psychological detoxification. [ABSTRACT FROM AUTHOR]

The stochastic subspace algorithm is one of the most widely used structural identification techniques, which is generally involved with the stabilization diagram for estimating modal parameters. However, the conventional stabilization diagram has an inherent problem: some spurious modes may be identified as stable results, resulting in adverse effects on structural modal identification. To address this critical issue, this paper proposes an improved stochastic subspace algorithm involving a Monte Carlo–based stabilization diagram. Through a numerical simulation study, the good performance of the Monte Carlo–based stabilization diagram for discriminating the poles denoting the physical modes from those representing spurious modes is demonstrated. The numerical simulation results show that the proposed method can estimate structural modal parameters with high accuracy and robustness. Moreover, the proposed method is applied to field measurements on a 600-m-high skyscraper during Super Typhoon Mangkhut, and the results verify the applicability and effectiveness of the proposed method to field measurements. This paper aims to provide an effective tool for accurate estimation of modal parameters of civil structures. [ABSTRACT FROM AUTHOR]

In this paper, we study consensus behavior based on the local application of the majority consensus algorithm (a generalization of the majority rule) over four-connected bi-dimensional networks. In this context, we characterize theoretically every four-vicinity network in its capacity to reach consensus (every individual at the same opinion) for any initial configuration of binary opinions. Theoretically, we determine all regular grids with four neighbors in which consensus is reached and in which ones not. In addition, in those instances in which consensus is not reached, we characterize statistically the proportion of configurations that reach spurious fixed points from an ensemble of random initial configurations. Using numerical simulations, we also analyze two observables of the system to characterize the algorithm: (1) the quality of the achieved consensus, that is if it respects the initial majority of the network; and (2) the consensus time, measured as the average amount of steps to reach convergence. [ABSTRACT FROM AUTHOR]

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm's efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir. [ABSTRACT FROM AUTHOR]

A review of the existing phenomenological models was carried out, which gives a deep understanding of the features and characteristics of the formation of spray and dispersion of a liquid jet. These models give insight into the mechanisms of primary and secondary atomization of a liquid jet by tracing the behavior of a single fuel droplet placed in a stationary gaseous medium. The paper uses the direct numerical simulation method to present the computer simulation results of the atomization, dispersion, and evaporation of octane and dodecane droplets. To describe the interfacial interactions and track the particle's shape and position in the dispersed phase, the volume of fluid (VOF) method was used, which belongs to the class of Eulerian methods and is a non-autonomous algorithm. According to the results of computational experiments on the study of the processes of spraying, dispersion, and evaporation of octane and dodecane, the optimal combustion mode was established. Obtained results in this work have scientific and practical applications in constructing the fundamental theory of combustion and designing the injection systems structure with optimal parameters. [ABSTRACT FROM AUTHOR]

This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part. Within the framework of this model, the constrained least squares estimation is investigated, and the alternating-direction multiplier method (ADMM) is used to solve the model. The convergence is proved under certain conditions. Finally, numerical simulations are performed and applied to workers' wage data from CPS85. The results show that the ADMM algorithm is very effective in solving high-dimensional partially linear models. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem with unfolded coprime linear array (UCLA) and propose a low computational complexity signal-subspace fitting (SF) algorithm. SF algorithm is able to achieve excellent DOA estimation performance while it requires global angular search (GAS). Especially in the several source signals situation, expensive complexity cost causes. To decrease computational complexity, we propose an initialized based SF (ISF) algorithm, which involves the several one dimensional (1D) partial angular search (PAS) instead of the multidimensional GAS. Consequently, the complexity is significantly decreased. Due to the full utilization of the array aperture, the proposed method in UCLA can attain better performance than general CLA (GCLA). In addition, as the SF is attractive in practical application, the proposed ISF algorithm lowers the computational cost, while achieving almost approximate estimation performance as traditional SF and noise subspace fitting (NF). Moreover, numerical simulations are provided and verify the effectiveness and the superiority of the proposed algorithm for the UCLA. [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

Background: The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.Methods: In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.Results: The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.Conclusion: Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of [Formula: see text] has the potential to be implemented in real time using the graphics processing unit. [ABSTRACT FROM AUTHOR]

In this paper, a symplectic algorithm is utilized to investigate constrained Hamiltonian systems. However, the symplectic method cannot be applied directly to the constrained Hamiltonian equations due to the non-canonicity. We firstly discuss the canonicalization method of the constrained Hamiltonian systems. The symplectic method is used to constrain Hamiltonian systems on the basis of the canonicalization, and then the numerical simulation of the system is carried out. An example is presented to illustrate the application of the results. By using the symplectic method of constrained Hamiltonian systems, one can solve the singular dynamic problems of nonconservative constrained mechanical systems, nonholonomic constrained mechanical systems as well as physical problems in quantum dynamics, and also available in many electromechanical coupled systems. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a novel modified supertwisting fast nonlinear sliding mode controller (MSTFNSMC) to stabilize a quadrotor system under time-varying disturbances. The suggested control strategy is based on a modified supertwisting controller with a fast nonlinear sliding surface to improve the tracking performance. The paper suggests a simple optimization tool built-in MATLAB/Simulink to tune the proposed controller parameters. Fast convergence of state variables is established by using a nonlinear sliding surface for rotational and translational subsystems. The modified supertwisting controller is developed to suppress the effect of chattering, reject disturbances, and ensure robustness against external disturbance effect. The stability of the proposed controller (MSTFNSMC) is proved using the Lyapunov theory. The performance of the proposed MSTFNSMC approach is compared with the supertwisting sliding mode controller (STSMC) by numerical simulations to verify its effectiveness. [ABSTRACT FROM AUTHOR]

Finite element method is an important method to solve mathematical problems in engineering. Many mathematical equations are difficult to solve, but it becomes very simple after using the finite element method. In this paper, the finite element method is applied to the calculation of gravity anomaly. First, the variational equation of gravity anomaly calculation is established, and then the gravity anomaly value ten times the distance away from the anomaly body is used as the boundary condition. By comparing the gravity anomaly obtained by solving the stiffness matrix with the analytical solution, it can be found that the method in this paper has high accuracy. Finally, the model of Jinchuan copper nickel deposit is used for calculation, and the calculated gravity anomaly field is inverted with Growth3D. It can be found that the inversion result is very close to the model, which verifies the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

Low-level wind shear is a vital weather process affecting aircraft safety while taking off and landing and is known as the "aircraft killer" in the aviation industry. As a result, effective monitoring and warning are required. Several ramps detection algorithms for low-level wind shear based on glide path scanning of lidar have been developed, including double and simple ramp detection, with the ramp length extension and contraction strategies corresponding to the algorithm. However, current algorithms must be improved to determine the maximum shear value and location. In this paper, a new efficient algorithm based on the shear intensity factor value is presented, in which wind speed changes and distance are both considered when calculating wind shear. Simultaneously, the effectiveness of the improved algorithm has been validated through numerical simulation experiments. Results reveal that the improved algorithm can determine the maximum intensity value and wind shear location more accurately than the traditional algorithm. In addition, the new algorithm improved the detection ability of lidar for weak wind shear. [ABSTRACT FROM AUTHOR]

Angular random walk and rate random walk are two dominant random noise components which are inherent in almost gyroscopes. Therefore, modelling these noise components accurately is very important. Here, a modified algorithm is proposed for estimating the two random noise components accurately. Up to now, the statistical modelling of the two random noise components has been conducted by the best linear unbiased estimator, which is based on the formulae for mean, variance and covariance of non‐overlapping Allan variance of them, that is, angular random walk and rate random walk. The modified algorithm is developed by using the best linear unbiased estimator, based on formulae for mean, variance and covariance of fully‐overlapping Allan variance which are newly derived in this paper. Efficiency of the algorithm is evaluated by simulation and experiment. [ABSTRACT FROM AUTHOR]

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively. [ABSTRACT FROM AUTHOR]

The C-RAN architecture has been prevailing in mobile networks for 5G and beyond. Along with the significant increase in the amount of mobile data, network operators must consider the spatio-temporal pattern of traffi c demand due to human mobility and lifestyles. It has been an important problem to efficiently deploy mobile networks avoiding the underutilization of cell capacities in off -peak times. To address this problem, in this paper we propose a concept of an adaptive C-RAN architecture toward the beyond 5G era. The proposed architecture enables flexible reconfi guration of the radio unit (RU) states and movable DU locations considering the changes in demand distribution. The DUs and RUs compose wireless relay fronthaul networks to forward fronthaul streams satisfying the strict latency requirements. This paper also proposes the joint scheduling algorithm to activate RUs, relocate DUs, and determine forwarding paths of fronthaul streams. It was confi rmed with computer simulations that mobile networks can be efficiently reconstructed with the proposed idea by activating/deactivating RUs and relocating/ migrating DUs. [ABSTRACT FROM AUTHOR]

This paper is concerned with the development and application of optimally efficient numerical methods for the simulation of vascular tumour growth. This model used involves the flow and interaction of four different, but coupled, phases which are each treated as incompressible fluids, Hubbard and Byrne (2013). A finite volume scheme is used to approximate mass conservation, with conforming finite element schemes to approximate momentum conservation and an associated equation. The principal contribution of this paper is the development of a novel block preconditioner for solving the linear systems arising from the discrete momentum equations at each time step. In particular, the preconditioned system has both a solution time and a memory requirement that is shown to scale almost linearly with the problem size. [ABSTRACT FROM AUTHOR]

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works. [ABSTRACT FROM AUTHOR]

The popularity of diverse IoT-based applications and services continuously generating tremendous amount of data has revealed the significance of data compression (DC). Principal component analysis (PCA) is one of the most commonly employed algorithms for DC. However, when dealing with large-scale matrices, the standard PCA takes a very long time and requires a lot of memory. Therefore, this paper presents a novel distributed stochastic PCA algorithm (DSPCA) for hierarchical sensor network based on gradient-based adaptive PCA (GA-PCA), where the standard PCA is reformulated as a single-pass stochastic setting to find the direction of approximate maximal variance. The step-size in each iteration is obtained by incorporating the stabilized Barzilai-Borwein method with the gradient optimization. This enables DSPCA to be processed with low computational complexity while maintaining a high convergence speed. Computer simulation with two types of datasets displays that the proposed scheme consistently outperforms the representative DC schemes in terms of reconstruction accuracy of original data and explained variance. [ABSTRACT FROM AUTHOR]

With the evolution of smart cities, images are used in a wide range of services such as smart healthcare and surveillance. How to ensure that images are transmitted and shared securely is of paramount importance for smart cities. To this end, a secure and efficient scheme for image transmission is proposed in this paper, which uses sparse signal transformation (SST) and parallel compressive sensing (CS). The primary employed techniques are sparse signal transformation (SST), parallel CS, and diffusion-permutation operation. The compression performance is achieved by parallel CS, whereas the encryption performance is derived from SST, parallel CS, and diffusion-permutation procedure. SST is exploited to change energy information before CS sampling and incorporated into diffusion-permutation framework, which not only balances the security and the efficiency of the algorithm, but also improves the transmission efficiency of the cipher image. We introduce chaotic system to generate the measurement matrix, SST matrix, and diffusion matrix to improve security. Furthermore, numerical simulation results and theoretical analyses confirm the security performances and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Reliability is a vital issue in the area of communication. In this paper, we particularly investigate the reliability issue for molecular communication based on drift diffusion (MCD2). Since molecules easily accumulate in the channel to produce strong internal symbol interference (ISI), the receiver nanomachine will generate high bit error rate (BER) for the process of decode information. Based on this problem, on the premise of considering channel diffusion noise and ISI noise, the expression of channel BER is deduced to analyze reliability. Then a ratio detection algorithm (RDA) is proposed to reduce BER to improve the reliability performance that enables the receiver nanomachine to adapt the channel condition. Furthermore, an expression of signal to interference plus noise ratio is defined in numerical simulation to verify our goal with different parameters, as well as with the adoption of RDA. The results indicate that the performance of RDA in reducing BER works well in general case in improving reliability performance for MCD2. [ABSTRACT FROM AUTHOR]

In this paper, a novel demapper for 2-D non-uniform constellations (2D-NUCs) is proposed, exploiting the characteristics of these constellations. It represents the combination of two underlying demapping techniques targeting the ATSC 3.0 compliant OFDM transceiver. On the one hand, for low code rates, we define a metric to perform condensed demapping. On the other hand, for high code rates, adaptive sub-region demapping is proposed. In this paper, a combination of both demapping methods is designed showing comparable performance to the classical ML demapper. The gap does not exceed 0.1 dB for all code rates of the ATSC 3.0 standard. Higher complexity reduction, from 79.2% to 95.4%, than state of art 2D-NUC demappers is obtained for 2D-256NUCs. These results are validated for ideal and non-ideal channel state information over additive white noise Gaussian and Rayleigh independently and identically distributed channels. Results are extended to 2D-1kNUCs and 2D-4kNUCs showing demapping complexity reduction from 96% to 99.7% with a negligible impact on performance. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive algorithm for active control of noise sources that are of impulsive nature. The impulsive type sources can be better modeled as a stable distribution than the Gaussian. However, for stable distributions, the variance (second order moment) is infinite. The standard adaptive filtering algorithms, which are based on minimizing variance and assuming Gaussian distribution, converge slowly or become even unstable for stable (impulsive) processes. In order to improve the performance of the standard filtered-x least mean square (FxLMS)-based impulsive active noise control (IANC) systems, we propose two enhancements in this paper. First, we propose employing modified tanh function-based nonlinear process in the reference and error paths of the standard FxLMS algorithm. The main idea is to automatically give an appropriate weight to the various samples in the process, i.e. appropriately threshold the very large values so that system remains stable, and give more weight to samples below threshold limit so that the convergence speed can be improved. A second proposal is to incorporate the fractional-gradient computation in the update vector of IANC adaptive filter. Computer simulations have been carried out using experimental data for the acoustic paths. The simulation results demonstrate that the proposed algorithm is very effective for IANC systems. [ABSTRACT FROM AUTHOR]

In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the $m$ -waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations. [ABSTRACT FROM AUTHOR]

Single-pixel imaging (SPI) enables the visualization of objects with a single detector by using a sequence of spatially modulated illumination patterns. For natural images, the number of illumination patterns may be smaller than the number of pixels when compressed-sensing algorithms are used. Nonetheless, the sequential nature of the SPI measurement requires that the object remains static until the signals from all the required patterns have been collected. In this paper, we present a new approach to SPI that enables imaging scenarios in which the imaged object, or parts thereof, moves within the imaging plane during data acquisition. Our algorithms estimate the motion direction from inter-frame cross-correlations and incorporate it in the reconstruction model. Moreover, when the illumination pattern is cyclic, the motion may be estimated directly from the raw data, further increasing the numerical efficiency of the algorithm. A demonstration of our approach is presented for both numerically simulated and measured data. [ABSTRACT FROM AUTHOR]

Sparse subspace clustering (SSC) relies on sparse regression for accurate neighbor identification. Inspired by recent progress in compressive sensing, this paper proposes a new sparse regression scheme for SSC via two-step reweighted $\ell _{1} $ -minimization, which also generalizes a two-step $\ell _{1} $ -minimization algorithm introduced by E. J. Candès et al. in [The Annals of Statistics, vol. 42, no. 2, pp. 669–699, 2014] without incurring extra algorithmic complexity. To fully exploit the prior information offered by the computed sparse representation vector in the first step, our approach places a weight on each component of the regression vector, and solves a weighted LASSO in the second step. We propose a data weighting rule suitable for enhancing neighbor identification accuracy. Then, under the formulation of the dual problem of weighted LASSO, we study in depth the theoretical neighbor recovery rates of the proposed scheme. Specifically, an interesting connection between the locations of nonzeros of the optimal sparse solution to the weighted LASSO and the indexes of the active constraints of the dual problem is established. Afterwards, under the semi-random model, analytic probability lower/upper bounds for various neighbor recovery events are derived. Our analytic results confirm that, with the aid of data weighting and if the prior neighbor information is accurate enough, the proposed scheme with a higher probability can produce many correct neighbors and few incorrect neighbors as compared to the solution without data weighting. Computer simulations are provided to validate our analytic study and evidence the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker–Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs. • The distributed small fixed-wing UAV swarm system is designed. • The hybrid-flocking control algorithm is proposed by combining three control protocols. • The optimal mixing weight improves the mission performance. • Numerical simulations and flight experiments validate the hybrid-flocking algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new modified Single Block Zero‐Padding (mSBZP) Partial Correlation Method (PCM) Parallel Code Search (PCS) algorithm for effective acquisition of weak GNSS tiered signal using coherent processing of its secondary code (SC) component. Two problems are discussed: acquisition of primary codes with a longer period using FFT blocks of limited length, and the utilization of PCS in the presence of SC bit transition. The PCM and SC bit transition forms parasitic fragments in the Cross‐Ambiguity‐Function (CAF) to devaluate signal detection performance. A novel analysis of this mechanism and its impact is presented. A novel mSBZP‐PCM‐PCS algorithm is proposed, which does not degrade the CAF. Then, the algorithm is combined with SC bit transition removal schema and sequential search to construct an estimator for weak tiered signal acquisition. The performance of the method is demonstrated by analysis and computer simulation using Galileo E1C and GPS L1C‐P signals. [ABSTRACT FROM AUTHOR]

When the deviation between the actual pipelines and the ideal models cannot be neglected, the intersecting curve weld seam based on non-ideal models should be specially studied. This paper will introduce a novel method to quantify the deviation of the intersecting curve weld seam based on the non-ideal models. Weld location is a technical means for obtaining the actual position of weld seam, which may be used to obtain the location information of some key points on the intersecting curve. Combined with the information of weld location and the inherent characteristics of the intersecting curves, this paper analyzes the experimental results of actual intersecting curve welding, and all these works laid the foundation for the proposed algorithm. First of all, on the basis of previous studies, this paper introduces a kind of model for intersecting pipelines, which can cover most of the ways of coherence. Secondly, based on this model, the factors which may lead to the deviation of the theoretical intersecting curve from the actual intersecting curve are analyzed. Generally, there may be some connections or coupling between the sources of deviation. Against the above problem, the paper gives a solution to model each source of deviation and discuss the relationship among them, and then makes use of the information of weld location to quantify the main sources of deviation one by one, especially the quantification of the main pipes ovality. Finally, the correctness and flexibility of the algorithm are verified by the MATLAB simulation. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

Force estimation methods enable robots to interact with the environment or humans compliantly and safely without additional sensing device. In this paper, we present a novel method for estimating unknown contact forces exerted on a robot manipulator. The force estimation method is divided into two steps. The first step is to identify a robot dynamics model. A parametric model is derived first based on rigid-body dynamic (RBD) theory. To improve the model accuracy, a nonparametric compensator trained with multilayer perception (MLP) is added to compensate for errors of the RBD model. The result is a semiparametric model that provides better model accuracy than either the RBD model or the MLP model alone. The second step is to construct a force estimation observer. A novel estimation method called disturbance Kalman filter (DKF) is developed in this paper. The design of DKF based on a time-invariant composite system model is presented. DKF can take both manipulator's dynamics model and disturbance's dynamics model into account. As with Kalman filter, it can provide robust and accurate estimation against uncertainty. Simulation and experimental results, obtained using a six-degrees-of-freedom Kinova Jaco2 manipulator, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly. [ABSTRACT FROM AUTHOR]

This paper constructs a dynamic multiobjective location model; three objectives are considered: the first objective maximizes the total utility of relief supplies, the second objective minimizes the number of temporary facilities needed to operate, and the third objective maximizes the satisfaction for all demand points. We propose an improved NSGA-II to solve the optimization problem. The computational experiments are divided into two sections: In the first procedure, the numerical experiment is constructed by the classical functions ZDT1, ZDT2, and DTLZ2; the results show that the proposed algorithm generates the exact Pareto front, and the convergence and uniformity of the proposed algorithm are better than the NSGA-II and MOEA/D. In the second procedure, the simulation experiment is constructed by a case in emergency management; the results show that the proposed algorithm is more reasonable than the traditional algorithms NSGA-II and MOEA/D in terms of the three objectives. It is proved that the improved NSGA-II algorithm, which is proposed in this paper, has high precision application for the sudden disaster crisis and emergency management. [ABSTRACT FROM AUTHOR]

In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms. [ABSTRACT FROM AUTHOR]

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multitarget tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a Second-Order PHD (SO-PHD) filter, propagating the second-order moment (i.e., variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modeling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. This paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multiobject filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters. [ABSTRACT FROM PUBLISHER]

Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference for estimating the three-dimensional (3-D) location (azimuth angle, elevation angle, and range) of the OFDM signal. In the algorithm, considering that it is difficult to distinguish the frequency of the OFDM signal's subcarriers and the phase-based method is always affected by errors of the frequency estimation, this paper employs sparse representation (SR) to obtain the super-resolution frequencies and the corresponding phases of subcarriers. Further, as the phase differences of the adjacent sensors including azimuth angle, elevation angle and range parameters can be expressed as indefinite equations, the near-field OFDM signal's 3-D location is obtained by employing the least square method, where the phase differences are based on the average of the estimated subcarriers. Finally, the performance of the proposed algorithm is demonstrated by several simulations. [ABSTRACT FROM AUTHOR]

This paper proposes that fire parameter data of smoke, temperature, and CO is fused by sparse representation algorithm. It designs a kind of overcomplete dictionary and obtains the sparse solution of fire recognition through L1 norm, L3/4 norm, L1/2 norm, and L1/4 norm, respectively, in order to select more suitable norm type. A comprehensive classification method is proposed for fire identification. The simulation results show that L1 norm and L3/4 norm are used to obtain the solution with remarkable sparsity and high accuracy. The comprehensive classification method is more effective than minimum residual method and sum of weight coefficients method. This paper uses DSP TMS320F28022 as the core chip, TC72 as the temperature sensor, MQ-7 as the CO gas sensor, and MQ-9 as the smoke sensor to design the hardware of fire detection system. Code Composer Studio (CCS) software is used to compile and debug the program. Proteus software is used to load the program into the hardware circuit for joint simulation. The simulation results show that system design is feasible. [ABSTRACT FROM AUTHOR]

Compressive sensing theory has attracted widespread attention in recent years and sparse signal reconstruction has been widely used in signal processing and communication. This paper addresses the problem of sparse signal recovery especially with non-Gaussian noise. The main contribution of this paper is the proposal of an algorithm where the negentropy and reweighted schemes represent the core of an approach to the solution of the problem. The signal reconstruction problem is formalized as a constrained minimization problem, where the objective function is the sum of a measurement of error statistical characteristic term, the negentropy, and a sparse regularization term, ℓp-norm, for 0 < p < 1. The ℓp-norm, however, leads to a non-convex optimization problem which is difficult to solve efficiently. Herein we treat the ℓp-norm as a serious of weighted ℓ1-norms so that the sub-problems become convex. We propose an optimized algorithm that combines forward-backward splitting. The algorithm is fast and succeeds in exactly recovering sparse signals with Gaussian and non-Gaussian noise. Several numerical experiments and comparisons demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Accurate location information is essential for mobile systems such as wireless sensor networks. A location-aware sensor network generally includes two types of nodes: Sensors whose locations to be determined and anchors whose locations are known a priori. For range-based cooperative positioning, sensors’ locations are deduced from anchor-to-sensor and sensor-to-sensor range measurements. Positioning accuracy depends on the network parameters such as network connectivity and size. This paper provides a generalized theory that quantitatively characterizes such a relation between network parameters and positioning accuracy. We use the average degree as a connectivity metric and use geometric dilution of precision (DOP) to quantify positioning accuracy. Under the assumption that nodes are randomly deployed, we prove a novel lower bound on expectation of average geometric DOP (LB-E-AGDOP) and derives a closed-form formula that relates LB-E-AGDOP to only three parameters: Average anchor degree, average sensor degree, and number of sensor nodes. The formula shows that positioning accuracy is approximately inversely proportional to the average degree, and a higher ratio of average anchor degree to average sensor degree yields better positioning accuracy. Furthermore, the paper shows a strong connection between LB-E-AGDOP and the best achievable accuracy. Finally, we demonstrate the theory via numerical simulations with three different random graph models. [ABSTRACT FROM PUBLISHER]

Recent studies reveal that both phase aberration and reverberation play a major role in degrading ultrasound image quality. We previously developed an algorithm for suppressing clutter, but we have not yet tested it in the context of aberrated wavefronts. In this paper, we evaluate our previously reported algorithm, called aperture domain model image reconstruction (ADMIRE), in the presence of phase aberration and in the presence of multipath scattering and phase aberration. We use simulations to investigate phase aberration corruption and correction in the presence of reverberation. As part of this paper, we observed that ADMIRE leads to suppressed levels of aberration. In order to accurately characterize aberrated signals of interest, we introduced an adaptive component to ADMIRE to account for aberration, referred to as adaptive ADMIRE. We then use ADMIRE, adaptive ADMIRE, and conventional filtering methods to characterize aberration profiles on in vivo liver data. These in vivo results suggest that adaptive ADMIRE could be used to better characterize a wider range of aberrated wavefronts. The aberration profiles’ full-width at half-maximum of ADMIRE, adaptive ADMIRE, and postfiltered data with 0.4- mm^-1 spatial cutoff frequency are 4.0 ± 0.28 mm, 2.8 ± 1.3 mm, and 2.8 ± 0.57 mm, respectively, while the average root-mean square values in the same order are 16 ± 5.4 ns, 20 ± 6.3 ns, and 19 ± 3.9 ns, respectively. Finally, because ADMIRE suppresses aberration, we perform a limited evaluation of image quality using simulations and in vivo data to determine how ADMIRE and adaptive ADMIRE perform with and without aberration correction. [ABSTRACT FROM AUTHOR]

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development. [ABSTRACT FROM AUTHOR]