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As a classical machine learning model, support vector machine (SVM) has attracted much attention due to its rigorous theoretical foundation and powerful discriminative performance. The doubly regularized SVM (DRSVM) is an important variant of SVM based on elastic-net regularization, which considers both the sparsity and stability of the model. To tackle the problems of explosive increases in data dimensions and data volume, the alternating direction method of multipliers (ADMM) algorithm can be used to train the DRSVM model. ADMM is an effective iterative algorithm for solving convex optimization problems by decomposing a large issue into a series of solvable subproblems, which is also well suited for distributed computing. However, lack of guaranteed convergence and slow convergence rate are two critical limitations of ADMM. In this paper, a 3-block ADMM algorithm based on the over-relaxation technique is proposed to accelerate DRSVM training, namely, the over-relaxed DRSVM (O-RDRSVM). The main strategy of the over-relaxation technique is to further append the information from the previous iteration to the next iteration to improve the convergence of ADMM. We also propose a distributed version of O-RDRSVM to handle parallel and distributed computing faster, termed DO-RDRSVM. Moreover, we develop a fast O-RDRSVM algorithm (FO-RDRSVM) and a fast DO-RDRSVM algorithm (FDO-RDRSVM), which further reduce the computational cost of O-RDRSVM and DO-RDRSVM by employing the matrix inversion lemma. The convergence analyses ensure the effectiveness of our algorithms for DRSVM training. Finally, extensive experiments on public datasets demonstrate the advantages of our algorithms in terms of convergence rate and training time while maintaining accuracy and sparsity comparable to those of previous works. [ABSTRACT FROM AUTHOR]

From the control theory, the best control gain produces a balance between the trajectory tracking accuracy and control energy consumption. The random search of the bat algorithm is one alternative to find the best control gain. In this paper, (1) a bat algorithm based control is proposed to decrease the control energy consumption in robots, where a bat algorithm is used to find the best control gain; and (2) a modified bat algorithm based control is proposed to increase the trajectory tracking accuracy in robots, where a modified bat algorithm is used to find the best control gain. The comparison between the two proposed controls and the simplex based control is illustrated for the trajectory tracking accuracy and control energy consumption in two robots. • A bat algorithm based control is proposed to decrease the control energy consumption in robots, where a bat algorithm is used to find the best control gain. • A modified bat algorithm based control is proposed to increase the trajectory tracking accuracy in robots, where a modified bat algorithm is used to find the best control gain. • The comparison between the two proposed controls and the simplex based control is illustrated for the trajectory tracking accuracy and control energy consumption in two robots. [ABSTRACT FROM AUTHOR]

In the present paper, a new strategy of multigrid method is introduced to accelerate the convergence speed of numerical simulations via lattice Boltzmann method. Based on the popular V-cycle multigrid algorithm, a simplified multigrid algorithm is presented and validated through the simulations of the classic lid-driven cavity flow for steady states. The novelty of the present algorithm resides in the construction of the information transferring process, in which, for a full cycle, the numerical simulation starts on the coarse mesh, transferring to medium mesh and then streams to the fine mesh through a prolongation operator. Afterwards, instead of using a restriction operator, the fluid information jumps back directly from the fine mesh to the coarse mesh via an assignment operator. The accuracy and efficiency of the simplified new algorithm are validated by comparing the results obtained when employing the classic V-cycle multigrid algorithm and the traditional lattice Boltzmann method with uniform Cartesian grid. [ABSTRACT FROM AUTHOR]

The lack of partnerships between biomass generating producers is one of the main barriers to efficient biogas production. Partnerships can help producers properly dispose of waste, assist in the supply of biodigesters, allow the union of properties with different potential biomass generators into clusters, and optimize operating costs. In this sense, new measures must be adopted with regard to the establishment of strategic partnerships between producers. Therefore, in this paper, we propose a new clustering algorithm, BiogasCluster , to decide the optimal number of clusters for generating a renewable energy community. To test the proposed algorithm's applicability, we implemented the algorithm in R language. Experiments with real databases showed that the BiogasCluster proved to be efficient in generating stable clusters for all considered scenarios. It was also found that the filters and criteria used in the proposed algorithm have not been used in previous studies. This study provides important guidance to creating strategic partnerships within bioenergy generation in addition to obtaining a preliminary assessment of the possibility of establishing clusters of rural properties with the aim of achieving energy security and adding value to their businesses. [ABSTRACT FROM AUTHOR]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

Time-variant Yang-Baxter-like matrix equation (YBLME) with the disturbances of noises is the hotspot in various scientific disciplines. The existing research, either can not handle the noise, or rely on the build-in numerical algorithm provided by MATLAB. Therefore, it is necessary to further study the digital-computer discrete solution of this problem. In this paper, the authors propose a noise-handling ten-instant discrete solution for the time-variant YBLME. By defining the matrix-form error function, Zhang neural network (ZNN) is exploited to design the continuous-time noise-handling ZNN model. For potential digital hardware (i.e., digital computer) realization, a discrete-time ZNN-based noise-handling ten-instant (DTZNH10i) algorithm, which is capable of handling three types of noises, is proposed on the basis of a Zhang time discretization (ZTD) formula. The convergence and precision of the proposed DTZNH10i algorithm are investigated and discussed. Theoretical analyses show the correctness and effectiveness of the proposed algorithm. The paper offers three examples of time-variant YBLME with disturbances of constant, linear, and bounded random noises to illustrate the efficacy and convergence of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The process of aligning one image, in the coordinate system of another is called registration. Image registration is vital in the medical domain and is used for diagnosis, therapy planning, and treatment of diseases. Due to its huge applicability and importance, medical image registration has emerged to be a separate domain of research. Beginning from the invasive landmark based registration, innumerable algorithms have been proposed to register two images of the human physiology. However, a breakthrough in the literature occurred when an information theory based measure, mutual information was used to register two images. Since then a large number of new algorithms have developed, which use mutual information for fully automatic registration of medical images. This paper is a survey of these algorithms. Beginning from its development, it discusses about some of the major works done on mutual information based image registration. Some comparative studies with other algorithms have also been discussed. The paper ends with a discussion of the developments in medical image registration algorithms post mutual information, which primarily include deep neural network (DNN) based algorithms. [ABSTRACT FROM AUTHOR]

Deep learning models have achieved high performance across different domains, such as medical decision-making, autonomous vehicles, decision support systems, among many others. However, despite this success, the inner mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This opacity makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that make deep learning models more transparent and explainable to humans. Some researchers recently argued that for a machine to achieve human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing literature on counterfactuals and causability for explainable artificial intelligence (AI). We performed a Latent Dirichlet topic modelling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles. This analysis yielded a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications to real-world data. Our research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Furthermore, our findings suggest that the explanations derived from popular algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. Thus, this paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable AI. • A survey on model-agnostic counterfactual approaches for XAI. • A novel taxonomy for model-agnostic counterfactual approaches for XAI. • A set of properties for causability systems for XAI. • Opportunities and challenges for causability systems based on modelagnostic counterfactual approaches. [ABSTRACT FROM AUTHOR]

• We add CUDA verification support to the VerCors verifier. • We verify data race-freedom and functional correctness of CUDA implementations of two parallel prefix sum algorithms. • We verify data race-freedom and functional correctness of CUDA implementations of stream compaction algorithm. GPUs are an important part of any High Performance Computing (HPC) architecture. To make optimal use of the specifics of a GPU architecture, we need programming models that naturally support the parallel execution model of a GPU. CUDA and OpenCL are two widely used examples of such programming models. Furthermore, we also need to redesign algorithms such that they adhere to this parallel programming model, and we need to be able to prove the correctness of these redesigned algorithms. In this paper we study two examples of such parallelized algorithms, and we discuss how to prove their correctness (data race freedom and (partial) functional correctness) using the VerCors program verifier. First of all, we prove the correctness of two parallel algorithms solving the prefix sum problem. Second, we show how such a prefix sum algorithm is used as a basic block in a stream compaction algorithm, and we prove correctness of this stream compaction algorithm, taking advantage of the earlier correctness proof for the prefix sum algorithm. The proofs as described in this paper are developed over the CUDA implementations of these algorithms. In earlier work, we had already shown correctness of a more high-level version of the algorithm. This paper discusses how we add support to reason about CUDA programs in VerCors, and it then shows how we can redo the verification at the level of the CUDA code. We also discuss some practical challenges that we had to address to prove correctness of the actual CUDA-level verifications. [ABSTRACT FROM AUTHOR]

We consider estimating a factor model for high-dimensional time series that contains structural breaks in the factor loading space at unknown time points. We first study the case when there is one change point in factor loadings, and propose a consistent estimator for the structural break location, whose convergence rate is shown to depend on an interplay between the dimension of the observed time series and the strength of the underlying factor structure. Our results reveal that the asymptotic behavior of the proposed estimator can be asymmetric in the sense that a larger estimation error can occur toward the regime with weaker factor strength. Based on the proposed estimator for the structural break location, we also consider the problem of estimating the factor loading spaces before and after the structural break. We show that the proposed estimators for change-point location and loading spaces are consistent when the numbers of factors are correctly estimated or overestimated. The algorithm for multiple change-point detection is also developed in the paper. Compared with existing results on change-point factor analyses of high-dimensional time series, a distinguished feature of the current paper is that the noise process is not necessarily assumed to be idiosyncratic and as a result we allow the noise process with potentially strong cross-sectional dependence. Another advantage for the proposed method is that it is specifically designed for the changes in the factor loading space and the stationarity assumption is not imposed on either the factor or noise process, while most existing methods for change-point detection of high-dimensional time series with/without a factor structure require the data to be stationary or 'close' to a stationary process between two change points, which is rather restrictive. Numerical experiments including a Monte Carlo simulation and a real data application are presented to illustrate the proposed estimators perform well. • Factor models for high-dimensional time series with structural breaks are studied. • The noise process in the setting can exhibit a strong notion of cross-sectional dependence. • The factor and noise processes are allowed to be non-stationary between change points. • The methods for change-point detection and estimation are proposed. • The asymptotic properties of proposed estimators are investigated. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

• Gait, the 6th vital sign, needs multi-modal methodologies for robust measurement. • Contemporary wearables enable multi-modal patient assessment. • A novel gait fusion approach is presented for use in multiple environments. • This can inform holistic gait assessment beyond the clinic during free-living. Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

One of the main aims of the paper is to develop the mesh geometry technique for corank-two edge-bipartite graphs Δ with n + 2 ≥ 3 vertices, and the mesh algorithms introduced in [30,33] and successfully studied in our recent article [42]. We introduce and study the concept of a self-duality of mesh geometries Γ ( R ˆ Δ , Φ Δ) viewed as Φ Δ -mesh translation quivers. We show how self-dualities of mesh geometries Γ ( R ˆ Δ , Φ Δ) and the mesh geometry technique is applied to an affirmative algorithmic solution of so called Horn-Sergeichuk type problem [9, Problem 4.3] on the self-congruency of square integer matrices A ∈ M n + 2 (Z) , for the class of non-symmetric Gram matrices A = G ˇ Δ of corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. More precisely, we show that each of the mesh geometries Γ ( R ˆ Δ , Φ Δ) is self-dual, we construct its dual form Γ ( R ˆ Δ , Φ Δ) o p = Γ ( R ˆ Δ , Φ Δ − 1) isomorphic with Γ ( R ˆ Δ , Φ Δ) , and we construct a canonical self-duality isomorphism f Δ : Γ ( R ˆ Δ , Φ Δ) → Γ ( R ˆ Δ , Φ Δ) o p of mesh translation quivers. Using the self-duality f Δ we construct combinatorial algorithms such that, given a square Gram matrix A = G ˇ Δ ∈ M n + 2 (Z) of Δ lying in this class, they are able to compute a Z -invertible matrix B ∈ M n + 2 (Z) that coincide with its inverse B − 1 and defines the congruence of A with A t r , i.e., the equation A t r = B t r ⋅ A ⋅ B is satisfied. An idea of our solution is outlined in Section 8 of our recent article [42] , where among others two of our 13 algorithms solving the problem are constructed. The remaining 11 algorithms are constructed in the present article. We do it by means of the structure of the standard self-dual Φ Δ -mesh translation quiver Γ ( R ˆ Δ , Φ Δ) (called a geometry) canonically associated with Δ, consisting of Φ Δ -meshes of Φ Δ -orbits O Δ (w) of vectors w ∈ R ˆ Δ ⊆ Z n + 2 , where Φ Δ : Z n + 2 → Z n + 2 is the Coxeter transformation of Δ. We construct in the paper such self-dual Φ Δ -mesh geometry Γ ( R ˆ Δ , Φ Δ) , for each of the corank-two loop-free edge-bipartite graphs Δ, with n + 2 ≤ 6 vertices. [ABSTRACT FROM AUTHOR]

In this paper, the design and implementation of low power and high-efficiency filterbank for MPEG-2/4 AAC system is presented. Since filterbank represents the most computation-intensive kernel of AAC codec, we design it with algorithm and architecture aspects. We supply the dedicated algorithm for filterbank. The derived algorithm and the hardware shared engine (HSE) we proposed for AAC can reduce the computation power and implement as a codec used in encoder and decoder. We also optimize the performance, hardware resources, and power consumption thoroughly. It is designed as an intellectual property (IP) to construct the overall decoder in an embedded system. The hardware cost is with 16.1 k logic gates, 2 k-word local memory, and 1 K-word coefficient ROM. The proposed design has a real-time operation at only 1.25 MHz with a sampling rate of 48 kHz. It can achieve 0.70 mW power consumption in TSMC 0.18 μm CMOS technology. Furthermore, we use a programmable chip (SOPC) platform which includes the software and hardware engine. Throughout the computation analysis, the bitstream parser and lower complexity part are performed by a software solution, and the higher complexity part is computed by a hardware solution. Several design techniques are needed including the wrapper design, embedded CPU, and IP to construct the system. Based on this co-design approach, a whole AAC audio decoder is also established. • This paper presents the algorithm and hardware design for MPEG-2/4 AAC. • Real demonstration is constructed in the embedded system. • VLSI design of the proposed chip is performed with various power-saving techniques. • The proposed design outperforms other designs on gate-count usage and power consumption. [ABSTRACT FROM AUTHOR]

The Two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for the study of many modern systems applied for security and biometrics. The adoption of this algorithm, which is a compute intensive task, is limited due to its hardware design complexity. The first objective of this paper is to underline the effect of the hardware/software co-design (Hw/Sw co-design) for the reduction of the processing time and power consumption. Secondly, we propose an innovative architecture for the 2D-FFT algorithm tested on Zynq Soc, which requires less processing time and memory compared to the traditional algorithm. Three implementations (one software and two Hw/Sw co-designs) of the 2D-FFT algorithm using the Zynq SoC are presented in this paper. The first is based on ARM processor. A speedup of 29x is obtained compared to the original implementation thanks to many optimizations. The second is a Hw/Sw co-design solution of the traditional 2D-FFT algorithm introduced on a hybrid platform combining an ARM Cortex-A9 processor with an FPGA. The third is also a Hw/Sw co-design solution using our optimized 2D-FFT algorithm to reach the real-time contraints for high-resolution images (1920 × 1080). It provides a speedup of 1.13x, 3.31x and 96.21x faster than the Hw/Sw co-design implementation of the traditional RC algorithm, the pure software implementations with and without optimizations, respectively. • Underline the effect of the Hw/Sw co-design of the 2D-FFT algorithm for the reduction of the processing time and power consumption. • Innovative architecture for the 2D-FFT algorithm tested on Zynq Soc respecting real-time constraints. • A comparative study between four existing architectures are used to implement the FFT IP to select the best one. • Study the impact of directives on resource usage and performance. [ABSTRACT FROM AUTHOR]

With continuously decreasing circuit scale, more and more test patterns (test contents) are added to test set to achieve acceptable defect levels, which seriously affect test time and, consequently, test cost. Theoretically, dropping invalid (pattern that can make the test pass) test patterns can reduce test cost. In fact, When the valid (pattern that can make the test fail) and invalid patterns overlap seriously, identifying effective patterns performs poorly, which will not achieve the expected results. This paper proposes a kind of eliminate redundancy method, so that this LDA-MRMR algorithm which can provide test cost reduction without increasing the defect level obviously. Experimental results demonstrate that the proposed method sacrifices 3% of the predictive accuracy in exchange for 3.7 times time saving compared with traditional methods. In addition, the algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process. • This paper provides test cost reduction without increasing the defect level obviously. • We find the optimal solution between test cost and test quality. • Mathematical model was proposed to make it is possessed of stronger applicability and generality. • Test time and test quality have been greatly improved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast time-frequency mask technique that relies on the sparseness of source signals for blind source separation (BSS) to separate a mixture of two input sounds in a single signal automatically. Due to the sparseness of source signals, the signal can be distinguished when it is transformed into the time-frequency domain. Most previous methods did not mention the effect of different angles on accuracy. To overcome such problems, we first define two features which are normalized level-ratio and phase-difference. Next, we use our method to decrease the variance of Direction of Arrival (DOA). This can reduce the variance of features so that it can reduce the iterations of k -means. Finally, a mask is generated according to the clustered features. Our method does not require any prior information or parameter estimation. The motivation of the proposed design is to incorporate the BSS system with some smart voice appliances. In the application scenario, all the non-human voices may appear and regard as interference. We use Signal to Distortion Ratio (SDR) and Signal to Interference Ratio (SIR) to make some comparison. Based on the proposed system, then we present a hardware design. We use the TSMC 90-nm CMOS process. As a cost-effective result, it consumes about 120 K gates and executes with a frequency of 10 MHz. The power consumption is only 2.92 mW with low power design considerations. • This paper presents the algorithm and hardware design for blind source separation. • The experimental results with various mixtures are simulated in real environments to verify the effectiveness. • Real demonstration is constructed in the embedded system. • VLSI design of the proposed chip is performed. [ABSTRACT FROM AUTHOR]

• The accuracy is greatly improved after replacing SCS module with the CSC module in Shift-GCN. • Inspired by shift CNNs, we replace shift space convolution in Shift-GCN with a sparse shift and named SparseShift-GCN. • The accuracy of algorithm was further improved after introduction of OHEM into SparseShift-GCN. Skeleton-based action recognition is widely used due to its advantages of lightweight and strong anti-interference. Recently, graph convolutional networks (GCNs) have been applied to action recognition and have made breakthrough progress. The shift convolution operator can effectively replace the spatial convolution and greatly reduce the computational complexity of the algorithm. This article first applies the Conv-Shift-Conv (CSC) module and the Shift-Conv-Shift-Conv (SC2) module to replace the Shift-Conv-Shift (SCS) module in spatial graph convolution of Shift-GCN respectively. This design can reorder the shifted channels more effectively. The experimental results show that the CSC module has the best effect and effectively improves accuracy of model. After that, this article proposes to replace the shift module in the original Shift-GCN with a sparse shift module and named SparseShift-GCN. This structure can reduce the redundancy of features, prevent overfitting and improve the generality of the model. Based on the improvement in the previous step, better results have been achieved. Finally, this paper uses OHEM Loss and Weighted Loss to carefully design the loss function of the model and introduces it into the model proposed in this paper. Experimental results show that OHEM Loss further improves the accuracy of algorithm. After a series of improvements, our proposed model has improved the accuracy of 4 different streams to varying degrees, which improves the overall performance of the network. [ABSTRACT FROM AUTHOR]

In this paper, a subgradient-based neurodynamic algorithm is presented to solve the nonsmooth nonconvex interval-valued optimization problem with both partial order and linear equality constraints, where the interval-valued objective function is nonconvex, and interval-valued partial order constraint functions are convex. The designed neurodynamic system is constructed by a differential inclusion with upper semicontinuous right-hand side, whose calculation load is reduced by relieving penalty parameters estimation and complex matrix inversion. Based on nonsmooth analysis and the extension theorem of the solution of differential inclusion, it is obtained that the global existence and boundedness of state solution of neurodynamic system, as well as the asymptotic convergence of state solution to the feasible region and the set of L U -critical points of interval-valued nonconvex optimization problem. Several numerical experiments and the applications to emergency supplies distribution and nondeterministic fractional continuous static games are solved to illustrate the applicability of the proposed neurodynamic algorithm. [ABSTRACT FROM AUTHOR]

Orbit determination (OD) for spacecraft with unknown maneuver is always a challenging task. This paper proposes a novel framework for efficiently solving continuously maneuvering spacecraft OD problems by merging the Long Short-Term Memory (LSTM) neural network and the filter algorithms. A polynomial-representation fitted the unknown continuous maneuver is first proposed. Rather than directly output the estimated maneuver, the LSTM is trained to detect the unknown maneuver and then estimate the coefficients of the polynomial-representation. Then a fusion is designed to combine the prediction of the LSTM and the estimation of the filter for a more accurate estimation. The proposed LSTM-based framework is successfully applied to solve a Low-Earth-orbit OD problem and a Middle-Earth-orbit OD problem. The continuously maneuvering target is well tracked and the unknown maneuver is accurately estimated. Numerical simulations show that the LSTM trained based on one training dataset can also be applied to other scenarios that share some common features with the training dataset. • A framework for solving maneuvering spacecraft OD problems is proposed. • A polynomial-representation is used to fit the unknown maneuver. • A fusion strategy is designed to combine the predictions of the LSTM and estimations of the filter. [ABSTRACT FROM AUTHOR]

This paper aims to address the material and spatial uncertainties in the stochastic electric field problem by proposing two efficient algorithms based on the stable nodal integration method (SNIM). The random variables/fields are used to describe the material and spatial uncertainties and a generalized stochastic perturbation-SNIM (GS-SNIM) method is developed to address the random-variable scenario while a random field-SNIM (RF-SNIM) method is developed to address the random-field scenario. Numerical examples are studied to fully verify the accuracy and efficiency of the proposed GS/RF-SNIM. The analysis results demonstrate that (1) the GS-SNIM is able to solve the random-variable based uncertainties through different orders expansion; (2) the RF-SNIM can calculate the expected values and variances of the stochastic electric field problem; and (3) the present methods produce accurate stochastic analysis results and significantly reduce the computational cost. • GS/RF-SNIM is proposed for stochastic electric field problems. • Both material uncertainty and spatial uncertainly are considered. • The GS/RF-SNIM can achieve high level of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we relax the equality constraints of the diffeomorphic image registration in Han and Wang (2020) [9] to equality and inequality constraints. Then, the mesh folding is eliminated. Moreover, a numerical algorithm for the proposed model is presented. Numerical tests are also performed to show that the proposed algorithm is competitive compared with some published image registration models. [ABSTRACT FROM AUTHOR]

In ultra-high-field (UHF) magnetic resonance imaging (MRI) applications, the design and analysis of high-frequency radio frequency (RF) coils requires full-wave electromagnetic (EM) methods that can handle complex field-tissue interactions. Using a Huygens' equivalent surface, the Method of Moments (MoM) and the Finite-Difference Time-Domain (FDTD) algorithm can be combined to accurately model the high-frequency RF coils. In previous research, a uniform FDTD mesh structure was considered, providing a compromised solution for coil-tissue interactions. This paper proposes a hybrid FDTD/MoM algorithm with non-uniform meshes. The fine mesh domain is set at the Huygens' surface, and the other domain uses coarse meshes. The proposed algorithms are strictly validated, and their computational performance is compared against conventional methods. Results show that the new algorithm can improve the calculation efficiency without losing accuracy. Specifically, compared with the uniform FDTD method, the numerical difference between both hybrid methods remains at 3.2%. Still, the calculation time of the non-uniform grid algorithm is reduced by 64.2%, demonstrating the effectiveness of the new algorithm for modeling RF coils for UHF-MRI applications. [ABSTRACT FROM AUTHOR]

Recently, few studies have been made on the stereophonic acoustic echo cancellation (SAEC) with nonlinear systems. To identify such nonlinear model, the functional link artificial neural network (FLANN) and the widely linear model can provide an approach to explore the SAEC with complex random variable. In this paper, a class of augmented complex-value functional link network (ACFLN) adaptive algorithms is developed. Based on the augmented complex-value functional least-mean-square (ACFLMS) algorithm, we have proposed the recursive augmented complex-value functional least-mean-square (RACFLMS) algorithm designed by a recursive structure. To further reduce its computational complexity and enhance its performance, a novel inverse square root function is employed in its structure of the RACFLMS algorithm. The results of several experiments demonstrate that our approach can effectively model the nonlinear systems and verify the improvement of the proposed algorithms. [ABSTRACT FROM AUTHOR]

• Stock management in a supply chain integrated inventory model. • A solution procedure is developed to find optimal policy for the problems under vendors warehouse capacity constraint. • Algorithms for solutions of unconstrained and constrained optimization problems. Coordination between a single manufacturer (vendor) and a buyer (retailer), via the delivery schedule in joint economic lot–sizing problem for a two stage supply chain, is investigated. To satisfy the buyer's demands, the product is delivered in discrete equal size sub-batches from the vendor's stock to the buyer's stock. Under stock management (SM) policy, the vendor delivers each sub-batch as soon as the buyer's inventory reduces to zero. For consignment stock (CS) policy, in the case of buyer's warehouse space limitation, the maximum level of the buyer's, consequently of the vendor's, inventory position can be controlled by the vendor's just in time or delayed deliveries (CS-k policies). If a space limitation is attached to vendor's warehouse, then the maximum level of the vendor's, consequently of the buyer's, inventory position can be controlled by just in time, or advanced for the buyer deliveries (in our notation SM-v-policies). Relatively optimal SM-v-policies can be obtained as solutions of adequate constrained problems. A comparative study, done in this paper, shows that the SM-v-policies can perform better than policies obtained as solutions of the sharply constrained problem where the maximal vendor's inventory level is equal to the maximum attainable space limit. Numerical examples are presented with appropriate comments to the solutions obtained by the proposed algorithm [ABSTRACT FROM AUTHOR]

This paper studies a new variant of the vehicle routing problem with drones, i.e., the two-echelon vehicle routing problem with drones, where multiple vehicles and drones work collaboratively to serve customers. Drones can perform multiple back-and-forth trips when their paired vehicle stops at a customer node, forming a two-echelon network. Several practical constraints such as customers' delivery deadlines and drones' energy capacity are considered. Different from existing studies, we treat the number of drones taken by each vehicle as a decision variable instead of a given parameter, which provides more flexibility for planning vehicle and drone routes. We first formulate this problem as a mixed-integer linear programming model, which is solvable by off-the-shelf commercial solvers. To tackle instances more efficiently, we next construct a set-partitioning model. To solve it, an exact branch-and-price algorithm is proposed, where a bidirectional labeling algorithm is used to solve the pricing problem. To speed up the algorithm, a tabu search algorithm is first applied before the exact labeling algorithm for finding desired columns in each iteration of the column generation process. Extensive numerical tests show that our algorithm can solve most instances within 25 customers to optimality in a short time frame and some instances of 35 customers to optimality within a three-hour time limit. Results also demonstrate that the allocation decisions of drones can help save the duration of all routes by 3.44% on average for 25-customer instances, compared to the case of fixing the number of paired drones on each vehicle. In addition, sensitivity analyses show that multiple strategies, e.g., adopting batteries of a higher energy density and developing faster drones, can be applied to further improve the delivery efficiency of a truck–drone system. • A two-echelon vehicle routing problem with drones is studied. • The allocation decisions of drones to vehicles are considered. • An MILP model and a set-partitioning model are provided. • A branch-and-price algorithm is developed. • Results show the effectiveness of the algorithm and provide managerial insights. [ABSTRACT FROM AUTHOR]

This paper presents the trajectory control strategies for the de-orbiting and re-entry phases of a micro satellite equipped with a deployable and controlled aero-brake. They are framed in the design of the Italian space mission MISTRAL (MIcro-SaTellite with Air-Launchable Re-entry capabilities) developed under the supervision of the DAC-Campanian Aerospace District. The proposed control approach makes use of two different control strategies for the de-orbiting and re-entry phases, respectively. The former is based on a linear quadratic optimal control, whereas the latter is based on a model predictive control approach. Numerical simulation results demonstrate the effectiveness of the proposed approach which guarantees a good compromise between stability and performance during the mission phases of interest. • A control strategy for the de-orbiting and re-entry phase of a micro-satellite using a deployable aero-brake is proposed. • An LQR-based approach with state feedback is used for the de-orbiting phase. • An MPC approach with a terminal cost on the prediction time horizon is used for the re-entry phase. • Performance is assessed numerically by modelling both the entire GNC system and the satellite dynamics. • The achieved tracking error is compatible with the required landing accuracy of 250 km. [ABSTRACT FROM AUTHOR]

• Adaptively allocate computing resource to input regions for better network inference. • Patch sampling algorithm reduces redundant calculation costs in overlapping regions. • Comparable performance is achieved on two datasets by comparing with SOTA methods. [Display omitted] In this paper, we propose an entropy-dynamic resolution detection (EDRdet) method for object detection in aerial images. Most conventional object detection methods usually detect each region in aerial images directly with a fixed resolution, so that the resolution of the hard-to-detect region in the whole aerial image is not enough and that of the easy-to-detect region is not necessary. We argue that different resolutions of regions are required for efficient and accurate object detection in aerial images. Our EDRdet dynamically adjusts the resolution of each region via measuring the challenge of the detection process, therefore, our model can achieve a good trade-off between the computational cost and the accuracy effectively. Specifically, our EDRdet searches hard-to-detect regions through low-resolution global context information, and inputs higher-resolution patches to supplement lost feature at low resolution for fine-grained detection. We apply the entropy to determine the regions in aerial images required to be detected at higher resolutions, and where the entropy represents the uncertainty of the detection result. Moreover, we design a patch sampling algorithm to make the selected regions sparse to further improve the efficiency of patch generation. Extensive experiments on the DOTA and Visdrone2019 datasets verify that our EDRdet can reduce the computational cost and improve the model accuracy effectively. [ABSTRACT FROM AUTHOR]

In this paper, we study an optimally accurate second-order time-stepping algorithm for nonstationary magneto-hydrodynamics equations. The algorithm deals with a linear treatment for the nonlinear terms in both momentum equations and magnetic equations, and adds some stabilization terms of the discrete solutions for the velocity, the pressure and the magnetic. We derive that this algorithm satisfies unconditionally stable and optimally accurate error estimate. Numerical tests are given to validate the predicted convergence rate. [ABSTRACT FROM AUTHOR]

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

The problems of recognizing series–parallel graphs, outerplanar graphs, and generalized series–parallel graphs have been studied separately in the past. Efficient algorithms have been presented. However, none of the algorithms are certifying. A certifying algorithm generates, in addition to its answer, a certificate that can be used by a checker (a separate algorithm) to verify the correctness of the answer. The certificate is positive if the answer is 'yes', and is negative if the answer is 'no'. In this paper, an O (| E | + | V |) -time certifying algorithm that simultaneously determines if a graph G = (V , E) is series–parallel, outerplanar, or generalized series–parallel is presented. The positive certificates are a construction sequence for constructing G if G is series–parallel, a generalized construction sequence for constructing G if G is generalized series–parallel but not series–parallel, and the edge set of the exterior boundary of an outerplanar embedding of G if G is outerplanar. The negative certificates are forbidden subgraphs or forbidden structures of G. All these certificates are generated by making only one pass over G after a preprocessing step decomposing G into its biconnected components. [ABSTRACT FROM AUTHOR]

An added edge to a graph is called an inset edge. Predicting k inset edges, which minimize the average distance of a graph, is known to be NP-Hard. However, when k = 1 , the complexity of the problem is polynomial. In this paper, some tools for a precise analysis of the effect of inset edges on the tree's average distance are established. Using the tools, one can avoid using the distance matrix for better performance of algorithms. Here, some applications of the tools and a tight bound for the change of average distance when an inset edge is added to a tree are presented. [ABSTRACT FROM AUTHOR]

Spectral clustering is one of the most important clustering approaches, often yielding performance superior to other clustering approaches. However, it is not scalable to large data sets in its original form due to the computational burden of the required large-matrix eigen-decomposition. In this paper, a two-step spectral clustering algorithm is introduced by extending recent advances of scalable spectral clustering based on low-rank affinity matrix using landmarks. In the first step, a scalable spectral clustering algorithm using raw landmark-based affinity matrix is adopted. In the second step, a novel low-rank affinity matrix is learnt via the probability density estimators, constructed from the estimated clusters as derived from the first step. Since the prior information on cluster labels can be utilised in the second step, this learnt affinity matrix reflects intrinsic pairwise data relationships much better. While the proposed more complicated algorithm results in a higher computational cost than the previous landmark-based spectral research, it can be shown that the associated computational cost still scales well with data size. It is demonstrated that the proposed algorithm is capable of achieving far superior performance than other state-of-the-art algorithms for several benchmark multi-class image data sets. [ABSTRACT FROM AUTHOR]

Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed optimization problem with closed convex set constraint based on multi-agent system under weight-balanced graph. Moreover, based on the gradient tracking and projection methods, the proposed distributed algorithm with gradient tracking can improve the convergence rate by introducing a projection error term and an auxiliary parameter. In contrast to some existing constrained distributed gradient algorithms, the proposed one can make the convergence faster and enhance the performance of convergence. The proposed algorithm is illustrated with two simulation examples to show its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

For multilayer perceptron (MLP), the initial weights will significantly influence its performance. Based on the enhanced fractional derivative extend from convex optimization, this paper proposes a fractional gradient descent (RFGD) algorithm robust to the initial weights of MLP. We analyze the effectiveness of the RFGD algorithm. The convergence of the RFGD algorithm is also analyzed. The computational complexity of the RFGD algorithm is generally larger than that of the gradient descent (GD) algorithm but smaller than that of the Adam, Padam, AdaBelief, and AdaDiff algorithms. Numerical experiments show that the RFGD algorithm has strong robustness to the order of fractional calculus which is the only added parameter compared to the GD algorithm. More importantly, compared to the GD, Adam, Padam, AdaBelief, and AdaDiff algorithms, the experimental results show that the RFGD algorithm has the best robust performance for the initial weights of MLP. Meanwhile, the correctness of the theoretical analysis is verified. [ABSTRACT FROM AUTHOR]

The cohesive subgraph k-core is a maximal connected subgraph with the minimum degree δ ≥ k of a simple graph, where integer k ≥ 0. Define the core number of a vertex w as the maximum k such that w is contained in a k -core. The core decomposition problem which is calculating the core numbers of all vertices in static graphs, and the core maintenance problem which is updating the core numbers in dynamic graphs are our main concern. Although, core numbers can be updated by the core decomposition algorithms, only a small part of vertices' core numbers have changed after the change of a graph. Thus, it is necessary to update core numbers locally to reduce the cost. In this paper, we study the core maintenance problem on edge-weighted graphs by using the vertex sequence k-order which is ordered by the order that the core decomposition algorithm removes vertices. We design the core maintenance algorithms for inserting one edge at a time and the method of updating the k -order, which reduce the searching range and the time cost evidently. For the removing case, we use the existing subcore algorithm to do the core maintenance and modify it with the method of updating k -order we design. Finally, we do extensive experiments to evaluate the effectiveness and the efficiency of our algorithms, which shows that the order based algorithm has a better performance than the existing for the core maintenance on edge-weighted graphs. • We solve the core maintenance problem on edge-weighted graphs with a tool named weighted k -order. • The weighted k -order, a vertex sequence, is defined on edge-weighted graphs for the first time. • We give the properties of vertices whose core numbers increase on the k-order for inserting case on edge-weighted graphs. • The order based core maintenance algorithm for inserting case on edge-weighted graphs is given, which is the best. • We give the methods to update the k-order after inserting or removing one edge for the next core maintenance. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new strategy for solving highly oscillatory two-dimensional Vlasov-Poisson systems by means of a specific version of the parareal algorithm. The novelty consists in using reduced models, obtained from the two-scale convergence theory, for the coarse solving. The reduced models are useful to approximate the original Vlasov-Poisson model at a low computational cost since they are free of high oscillations. Both models are numerically solved in a particle-in-cell framework. We illustrate this strategy with numerical experiments based on long time simulations of a charged beam in a focusing channel and under the influence of a rapidly oscillating external electric field. On the basis of computing times, we provide an analysis of the efficiency of the parareal algorithm in terms of speedup. [ABSTRACT FROM AUTHOR]

Automata based algorithms have always dominated the history of string matching, thanks to their intrinsic nature of recognizers. Among them, those based on the suffix automaton deserve a special mention, because its use has always led to elegant and very efficient solutions in practice. In this paper, we present two novel string matching algorithms based on the suffix automaton which are particularly suitable for very long strings. The first algorithm simulates the suffix automaton through the well known bit-parallelism approach, while the second makes use of an alternative non-standard simulation. We mainly focus on the second solution, which turns out to be more flexible in practice, and present some extensions of it. Experimental results suggest that both our solutions are very efficient in practical cases scaling much better when the length of the pattern increases. Moreover, our second approach is also competitive with the most effective solutions available for the exact string matching problem. • We present two novel suffix automata based algorithms specifically designed for very long strings. • We describe a generic non-standard approach for simulating the suffix automaton of a string. • Our first solution is a bit-parallel based extension of the BNDM algorithm which can be encoded using few bits. • Our second solution is related to the occurrence of unique factors in the pattern. • Experimental results suggest that both our solutions are very efficient in practical cases. [ABSTRACT FROM AUTHOR]

In the l -Path Vertex Cover problem (resp., the l -Component Order Connectivity problem) the input is an undirected graph G and an integer k. The goal is to decide whether there is a set of vertices of size at most k whose deletion from G results in a graph that does not contain a path with l vertices (resp., does not contain a connected component with at least l vertices). In this paper we give a parameterized algorithm for l -Path Vertex Cover when l = 5 , 6 , 7 , whose running times are O ⁎ (3.945 k) , O ⁎ (4.947 k) , and O ⁎ (5.951 k) , respectively. We also give an algorithm for l -Component Order Connectivity whose running time is O ⁎ ((l − 1 − ϵ l) k) for every l ≥ 4 , where ϵ l > 0 for every l. [ABSTRACT FROM AUTHOR]

The benefits of using crossover in crossing fitness gaps have been studied extensively in evolutionary computation. Recent runtime results show that majority-vote crossover is particularly efficient at optimizing the well-known Jump benchmark function that includes a fitness gap next to the global optimum. Also estimation-of-distribution algorithms (EDAs), which use an implicit crossover, are much more efficient on Jump than typical mutation-based algorithms. However, the allowed gap size for polynomial runtimes with EDAs is at most logarithmic in the problem dimension n. In this paper, we investigate variants of the Jump function where the gap is shifted and appears in the middle of the typical search trajectory. Such gaps can still be overcome efficiently in time O (n log ⁡ n) by majority-vote crossover and an estimation-of-distribution algorithm, even for gap sizes almost n. However, if the global optimum is located in the gap instead of the usual all-ones string, majority-vote crossover would nevertheless approach the all-ones string and be highly inefficient. In sharp contrast, an EDA can still find such a shifted optimum efficiently. Thanks to a general property called fair sampling , the EDA will with high probability sample from almost every fitness level of the function, including levels in the gap, and sample the global optimum even though the overall search trajectory points towards the all-ones string. Finally, we derive limits on the gap size allowing efficient runtimes for the EDA. • We study 2 algorithms using majority-vote crossover and an estimation-of-distribution algorithm on modified jump functions. • We derive theorems on the algorithms' runtime using rigorous mathematical analyses. • All 3 algorithms can overcome the fitness gap of the jump functions efficiently for moderate sizes of the gap. • All but the estimation-of-distribution algorithm usually fail to find an optimum located within the gap. • The estimation-of-distribution is efficient since it samples fairly on all fitness levels towards the optimum. [ABSTRACT FROM AUTHOR]

• Analyses general algorithms as sets of sequences (tasks) over a domain. • Defines an equivalence relation on sets of sequences to recover algorithmic tasks. • Characterises the execution trace sets of Blum-Shub-Smale machines. • Compares trace-based approaches to algorithms to iterator-based accounts. Traditional models of computation and algorithms take a constructive approach, essentially considering a transition function over a given state space. Yet algorithms may also be considered in terms of the observed behaviour they generate. This paper marries these perspectives with a domain-independent, trace based account of computational and algorithmic processes. We focus on the model of real computation due to Blum, Shub and Smale [3] and provide a precise characterisation of the execution trace sets generated by their machines. [ABSTRACT FROM AUTHOR]

Chaotic systems are suitable for image encryption due to their numerous intrinsic properties. However, the low-dimensional chaotic systems used in many existing chaos-based image encryption algorithms suffer from various drawbacks. To overcome such problems, a new four-dimensional multi-scroll hyperchaotic system is constructed in this paper. By studying the stability of equilibrium point, dissipation attractors, Lyapunov exponent spectrum, and bifurcation diagram of the system, these are proved that the system has rich dynamical behaviors. Then, based on the system, a new color image encryption algorithm is designed by using the classical structure of the 'scrambling-diffusion-scrambling' algorithm. In this image encryption algorithm, an optimized Arnold transform is used and the plaintext pixel sum is applied to the scrambling parameters so that different plaintext image pixels correspond to different scrambling parameters and are fully resistant to plaintext attacks. Then for the three Red, Green, Blue (RGB) channels, three different diffusion directions are adopted. Finally, the correlation between the pixels is further broken by an ascending permutation scrambling operation to get the final encrypted image. The experimental results show that the key space is up to 2207, the information entropy is up to 7.9998, the Number of Pixels Change Rate (NPCR) and the Unified Average Changing Intensity (UACI) values are close to the ideal values, the scheme can resist exhaustive and differential attacks, and the scheme has good robustness through noise attacks and crop attacks. • Design of hyperchaotic system based on multi-scroll and its encryption algorithm in color image. • The encryption algorithm uses a "scrambling-diffusion-scrambling" structure. • Experiments show that the algorithm is highly secure. [ABSTRACT FROM AUTHOR]

In this paper, GMV as a major AOCS (Attitude Orbit Control System)/GNC (Guidance, Navigation and Control) system provider presents its work on the development of a GNC system for a Kinetic Impact mission. Asteroid impacts on Earth are a known source of extinction and played an important role in the evolution of life on Earth. This comes as no surprise given the high number of objects orbiting in Earth's vicinity—asteroid impacts become a question of "when" rather than "if". This comes as no surprise given the high number of objects orbiting in Earth's vicinity—asteroid impacts become a question of "when" rather than "if". Under this motivation, thousands of potentially hazardous asteroids (PHAs) are monitored around the globe and naturally the question on the next step rose: What can we do when an incoming asteroid is identified as being on collision course with Earth? This is the motivation behind the field of Planetary Defence. Several mitigation strategies are being studied, but most of these have technology readiness levels still far off from the required. The one that comes closest is the kinetic impactor concept, which is the focus of this work. The concept is simple from a theoretical standpoint - a spacecraft (impactor) is sent into a collision course against the hazard NEO transferring enough momentum through kinetic energy to slightly change the NEO's trajectory and causes it to miss the Earth. The main component of kinetic impactors is the GNC, due to the necessity of a fully autonomous system. The major elements are the camera and the guidance, since they will allow detecting the target and modifying its trajectory to have a successful impact, respectively. This study includes the implementation of the required algorithms as well as an analysis of their performance in the impact scenario. The main variables that control the simulations have been examined in order to determine the design limits. The sensitivity analysis illustrates that the errors introduced in the model were chosen conservatively, inasmuch as the impact could be reached introducing higher uncertainties and errors in the systems considered. In general, even with the not excessively complex algorithms employed, the performance of the GNC is good. This study opens the door for the development of a kinetic impactor mission. • This research introduces the key parameters that shape a kinetic impactor. • Current state of the art and algorithms of the GNC system would allow the impact. • The performance is limited by the sensor employed and the image processing. • The study demonstrates that the use of an Unscented Kalman Filter is possible. • Results open the door for the development of a kinetic impact mission. [ABSTRACT FROM AUTHOR]