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Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future. • The trajectory/propulsion integrated design optimization is conducted. • The two-phase guidance strategy for lunar lander descent is proposed. • Based on the strategy, the analytical target cascading-decomposed framework is set. • The influence of trajectory and propulsion coupling on overall design is revealed. • The optimization effectiveness, convergence rate, and robustness of the ATC is shown. [ABSTRACT FROM AUTHOR]

This work investigates the less conservative stability conditions for linear systems with a time-varying delay. At first, augmented Lyapunov–Krasovskii functionals(LKFs) are constructed with state vectors that have not been utilized in the existing works, and an augmented zero equality that can be derived according to the augmented vector is proposed. By utilizing them, a stability condition is proposed in the form of a linear matrix inequality. And, by using novel delay-dependent LKFs and the introduced ones, improved results are obtained than the previous result. The addition of the delay-dependent LKFs increases the number of decision variables in the results. Therefore, any vectors of integral inequalities utilized in the proposed criterion are appropriately adjusted to reduce computational complexity. To check the excellence and validity of the proposed results, several numerical examples are applied. • The improved stability conditions for the linear system with a time-varying delay are proposed. • The proposed Lyapunov-Krasovskii functionals(LKFs) have more extended augmented vectors to obtain less conservative results. And LKFs based on an integral inequality (Lee et al., 2023) is utilized. • The novel LKFs which are the delay-dependent forms are proposed. The superiority of the results is checked according to the presence or absence of the forms. • The computational complexity is reduced by adjusting any vector of the integral inequality utilized in the proposed criteria. [ABSTRACT FROM AUTHOR]

The Diffusion Peterlin Viscoelastic Fluid (DPVF) model describes the movement of specific incompressible polymeric fluids. In this paper, we introduce and evaluate a new low-complexity linear-time filter finite element (FE) method for the DPVF model. In order to avoid the value at time t = − Δ t , the proposed time filter method consists of three steps, including a post-processing step. Firstly, a first-order Euler backward nonlinear fully discrete mixed FE scheme is employed to compute the numerical solutions at time t 1 = Δ t. For n ≥ 1 , we obtain the intermediate values ( u ˜ h n + 1 , p ˜ h n + 1 , d ˜ h n + 1) in Step II using a fully implicit backward Euler scheme. At the same time level, we proceed with these intermediate values ( u ˜ h n + 1 , p ˜ h n + 1 , d ˜ h n + 1) using the linear time filters. The linear time filters step does not significantly increase computational complexity. However, it can enhance temporal convergence accuracy from first order to second order for backward Euler time filter (BE time filter), and from second order to three order for BDF2 time filter. We demonstrate the almost unconditional stability of the scheme. Error estimates for the time filter method are derived and presented. Several numerical experiments are conducted to validate the theoretical findings and showcase the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

We propose a novel and simple spectral method based on the semi-discrete Fourier transforms to discretize the fractional Laplacian (− Δ) α 2 . Numerical analysis and experiments are provided to study its performance. Our method has the same symbol | ξ | α as the fractional Laplacian (− Δ) α 2 at the discrete level, and thus it can be viewed as the exact discrete analogue of the fractional Laplacian. This unique feature distinguishes our method from other existing methods for the fractional Laplacian. Note that our method is different from the Fourier pseudospectral methods in the literature which are usually limited to periodic boundary conditions (see Remark 1.1). Numerical analysis shows that our method can achieve a spectral accuracy. The stability and convergence of our method in solving the fractional Poisson equations were analyzed. Our scheme yields a multilevel Toeplitz stiffness matrix, and thus fast algorithms can be developed for efficient matrix-vector multiplications. The computational complexity is O (2 N log ⁡ (2 N)) , and the memory storage is O (N) with N the total number of points. Extensive numerical experiments verify our analytical results and demonstrate the effectiveness of our method in solving various problems. [ABSTRACT FROM AUTHOR]

This paper proposes a low-cost, penalty parameter-free, and pressure-robust Stokes solver based on the enriched Galerkin (EG) method with a discontinuous velocity enrichment function. The EG method employs the interior penalty discontinuous Galerkin (IPDG) formulation to weakly impose the continuity of the velocity function. However, despite its advantage of symmetry, the symmetric IPDG formulation requires a lot of computational effort to choose an optimal penalty parameter and compute different trace terms. To reduce such effort, we replace the derivatives of the velocity function with its weak derivatives computed by the geometric data of elements. Therefore, our modified EG (mEG) method is a penalty parameter-free numerical scheme that has reduced computational complexity and conserves the optimal convergence orders. Moreover, we achieve pressure robustness for the mEG method by employing a velocity reconstruction operator on the load vector on the right-hand side of the discrete system. The theoretical results are confirmed through numerical experiments with two- and three-dimensional examples. [ABSTRACT FROM AUTHOR]

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites' attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics. [ABSTRACT FROM AUTHOR]

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user's received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability. • Maximizes the LEO system throughput while ensuring the GEO system service quality. • Builds a PPO algorithm based deep reinforcement learning framework. • Constructs two benchmark schemes: improved FO (IFO) and the DNN-accelerated IFO. • Achieves the highest LEO system throughput with the lowest computational complexity. • Yields the best system stability compared with the benchmarks. [ABSTRACT FROM AUTHOR]

• An adaptive backstepping controller based on nonlinear filter Lyapunov function is designed. • Integration of body control objectives and hard constraints into one control variable. • An adaptive backstepping bottom controller based on nonlinear disturbance observer is designed. • Conduct stability analyses of zero-power systems to ensure that safety performance indicators are bounded. • Backstepping dynamic surface control is used to avoid the derivative explosion phenomenon. In order to improve the ride comfort and handling stability of the electro-hydraulic active suspension system, a hierarchical control strategy is proposed. For the active suspension system with body mass uncertainty and safety constraints, an enhanced constraint adaptive backstepping controller is designed to generate the target force of body vertical motion. When dealing with constraints, nonlinear filters are introduced, backstepping technology and quadratic Lyapunov function are used to combine the control target and domain constraints, and the vertical displacement of the body and the suspension deflection control index are synthesized into a single controlled variable, so that the control variable converging to zero meets the suspension dynamic displacement limit. The ride comfort and safety of the active suspension system are improved. Secondly, stability analysis is conducted on the zero dynamic error system to ensure that all safety performance indicators are bounded. The effects of external disturbances, parameter uncertainties and modelling errors on the force tracking accuracy of asymmetric cylinder electrohydraulic systems are addressed. A backstepping dynamic surface control method based on a nonlinear perturbation observer is proposed, which estimates the composite perturbation terms such as modelling error and external perturbation, and uses the estimated value of the nonlinear observer to design a feedforward compensating control term to offset the effect of the composite perturbation on the system performance. In addition, the dynamic surface control method is used to calculate the derivative of the target force input, which avoids the derivative explosion phenomenon and reduces the computational complexity of the system. Simulation test results show that this method reduces the computational complexity of the system and improves the control performance. And under random and bumpy road conditions, the root-mean-square value of sprung mass acceleration performance index is reduced by 48.354 % and 72.677 % compared with passive suspension, which improves the ride comfort of the vehicle. [ABSTRACT FROM AUTHOR]

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by taking full use of the special structure of coefficient matrix arising from complex Helmholtz equations, two types of optimized PU (OPU) methods are discussed theoretically and experimentally. Specifically, the convergence factors of these two OPU methods are less than 0.172, and the optimal result of first OPU method can reach 0.0396 with σ 1 ≥ σ 2 > 0 , which is currently the best theoretical result in the literatures. Moreover, the second OPU method has better computational advantages compared with the first OPU method as it avoids the inverse computation of W at each step, indicating the less CPU time will be costed by the second OPU method. In addition, the optimal parameters involved in our algorithms consist of constants, reducing the computational complexity associated with parameter selection. Finally, numerical results are given, not only show the effectiveness of OPU methods, but also confirm the rationality of theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data. • It studies a generalized HMM where observation depends on two-step states. • Two-filter smoothing formula and its generalization are established. • Auxiliary variable sampling technique is applied to draw samples. • It proposes two particle smoothing algorithms by using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]

Recently, there has been a strong interest in the minimum error entropy (MEE) criterion derived from information theoretic learning, which is effective in dealing with the multimodal non-Gaussian noise case. However, the kernel function is shift invariant resulting in the MEE criterion being insensitive to the error location. An existing solution is to combine the maximum correntropy (MC) with MEE criteria, leading to the MEE criterion with fiducial points (MEEF). Nevertheless, the algorithms based on the MEEF criterion usually require higher computational complexity. To remedy this problem, an improved MEEF (IMEEF) criterion is devised, aiming to avoid repetitive calculations of the a p o s t e r i o r i error, and an adaptive filtering algorithm based on gradient descent (GD) method is proposed, namely, GD-based IMEEF (IMEEF-GD) algorithm. In addition, we provide the convergence condition in terms of mean sense, along with an analysis of the steady-state and transient behaviors of IMEEF-GD in the mean-square sense. Its computational complexity is also analyzed. Simulation results demonstrate that the computational requirement of our algorithm does not vary significantly with the error sample number and the derived theoretical model is highly consistent with the learning curve. Ultimately, we employ the IMEEF-GD algorithm in tasks such as system identification, wind signal magnitude prediction, temperature prediction, and acoustic echo cancellation (AEC) to validate the effectiveness of the IMEEF-GD algorithm. • We propose an IMEEF criterion for the high computational complexity of the MEEF. • Based on the IMEEF criterion, we derive a gradient-based algorithm. • We analyze the performance of the proposed algorithm based on statistical properties. • The experimental results show that the proposed algorithm has excellent performance. [ABSTRACT FROM AUTHOR]

Image processing is a vital research domain essential in day-to-day digital activities. Image compression reduces memory space, transmission bandwidth requirement, transmission time, and computational complexity. This article aims to construct and compare hybrid image compression schemes using DWT, PCA, and K-means clustering techniques. This research proposes an image compression scheme based on optimized K-means clustering and higher-level decomposed DWT (K-DWT). Also, we have developed the hybrid image compression schemes based on PCA and DWT (P-DWT), PCA, K-means, and DWT (P-K-DWT) and analyzed the performance. Initially, we implemented compression algorithms using K-means Clustering, PCA, and DWT. Further, using the python platform, K-DWT, P-DWT, and P-K-DWT compression models have been executed on the images of the kodak dataset. The research objective is to obtain good image quality and better compression algorithms. The image quality and compression efficiency are evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Compression Ratio (CR). The K-DWT methods have achieved a higher compression ratio, i.e., 15.3870, compared with other methods. Also, the outcomes show that the K-DWT method has provided the required values of PSNR and SSIM. [ABSTRACT FROM AUTHOR]

In this paper, some new approaches for stability and stabilizability determination as well as state feedback stabilization controllers of linear time-invariant (LTI) interval systems are proposed. The presented stability conditions are less conservative than those of Kharitonov's theorem, and Gerschgorin's disc theorem methods. Moreover, some of the proposed stability, stabilizability, and feedback stabilization control methods for LTI interval systems are proved to be sufficient and necessary conditions. Compared with some traditional stability analysis and feedback stabilization design methods for LTI interval systems, these new approaches have lower computational complexity because of a special form of parameter vertex matrices developed in this work. Some numerical and practical examples are given to demonstrate the effectiveness and advantages of the proposed methods. • The proposed stability conditions are less conservative compared to traditional methods. • One of the state feedback stabilization control methods is proven to be both a sufficient and necessary condition. • The computational complexity of the proposed strategies is substantially reduced, courtesy of the KCV matrix. [ABSTRACT FROM AUTHOR]

In this paper, several local and parallel finite element algorithms are proposed and analyzed for the 2D/3D stationary inductionless incompressible magnetohydrodynamic (MHD) equations. The core concept is to guarantee the charge-conservation property by choosing mixed finite element spaces in H 0 (div , Ω) × L 0 2 (Ω) to approximate (J , ϕ) , meantime combining the idea of domain decomposition method to realize parallel operation. The characteristic of the proposed algorithms is that the computational complexity is greatly reduced while ensuring the accuracy of the numerical simulation. With the local a prior estimate as the technical means of theoretical analysis, we give the error estimates of the algorithms. Finally, several numerical experiments are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This article presents a simple, efficient, and high-order numerical scheme for the approximation of the convolution integral of the form (1) ∫ R 2 ∇ x ′ Φ (x , x ′) ⋅ U (x ′) d x ′ , where Φ (x , x ′) = i 4 H 0 (1) (κ | x − x ′ |) and U is smooth and compactly supported vector function on R 2. Moreover, we employ this integration scheme to obtain a Nyström solver for the numerical solution of the electromagnetic wave scattering problem by inhomogeneous orthotropic media, that is, media with refractive index of the form [ n 11 (x) n 12 (x) 0 n 21 (x) n 22 (x) 0 0 0 n 33 (x) ] , where n i j (x 1 , x 2 , x 3) are independent of x 3 coordinate. On a uniform grid of size n × n , the suggested approach has computational complexity O (n 2 log ⁡ n) and displays high-order convergence when the material properties are smooth. When the material attributes are real analytic, the approach actually converges exponentially. We demonstrate the performance of our integration scheme and the Nyström solver through several numerical experiments. • A simple, efficient, and high-order numerical scheme for the approximation of the convolution integral is devoloped. • This numerical Integration scheme is implemented for the solution of the electromagnetic wave scattering problem by inhomogeneous orthotropic media. • The proposed scheme has computational complexity O (N log ⁡ N) on a uniform grid of size N , and exhibits high-order convergence. • Numerical results demonstrate the accuracy and effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Industry 4.0, and the accompanying digitalization of industrial processes, lead to a continuously increasing amount of data. One reason is the installation of more sensors on machines and production lines in combination with shorter measurement intervals. The rising amount of data requires elaborate methods to remove redundant and uninformative data to reduce time complexity and required computational resources. This could be achieved by feature selection methods in combination with the right preprocessing and enhancement methods to generate and identify important features. Feature selection methods are mostly described in a methodological way without knowing how data is handled prior to their application, assuming a high-quality dataset. To fill this gap, we define a process for feature selection, focusing on time-series industrial data. The definition consists of three phases for the data adaptation: Preprocessing, Amplification and Selection. Each phase fulfills a specific purpose, ranging from handling missing and censored data to the enhancement of the dataset by higher-order data and aggregations and the selection itself. Our experiments show that the application of these phases on three different industrial use-cases lead to better accuracy results in machine learning models. [ABSTRACT FROM AUTHOR]

We develop the contour integral method for numerically solving the Feynman-Kac equation with two internal states Xu and Deng (2018) [23] , describing the functional distribution of particle's internal states. The striking benefits are obtained, including spectral accuracy, low computational complexity, small memory requirement, etc. We perform the error estimates and stability analyses, which are confirmed by numerical experiments. [ABSTRACT FROM AUTHOR]

An observer-based control method with parallel Q-filter is proposed to reject unknown multiple narrow-band disturbances for line of sight stabilization in telescope systems. In general, wide-band or multiple narrow-band disturbances rejection of observer-based control can be transformed into designing Q-filter, which could be too high-order to achieve fully-accuracy digital implementation, resulting in degrading the closed-loop performance or even causing instability. Therefore, a new parallel type of Q-filters is proposed to flexibly adjust the number of Q-filter in the sensitivity function and effectively reduce the computational complexity. A sufficient condition of the effectiveness for the parallel Q-filter and the closed-loop stability is derived and verified through simulations. Furthermore, disturbance frequencies can be on-line identified to adaptively optimize the Q-filter parameters. The effectiveness of the proposed method is validated on the image-based stabilization subsystem of a segmented lightweight large-scaled diffractive telescope. • Complex disturbances rejections can be transformed into designing cascade Q-filter. • Parallel form of Q-filter could effectively reduce the computational complexity. • Parallel Q filters are flexible to shape the error attenuation function. • Q-filter parameters can be on-line identified through frequencies identification. [ABSTRACT FROM AUTHOR]

For the task of detecting marine targets, numerous machine-learning methods have been suggested, which can achieve comparable accuracy. Despite the successful application of deep learning in the field of marine target detection in recent years, existing detection methods face challenges due to the significant interference of sea clutter. As artificial intelligence technology advances, the Swin Transformer can serve as an effective backbone for extracting discriminative features. However, it has not yet been utilized for target detection, and the combination of Swin Transformer and YOLO architecture has not been applied to similar missions. In light of this, we propose a novel method, YOLO-SWFormer, which combines the Swin Transformer and YOLO framework for target detection. Our method can extract discriminative features from plan-position indicator (PPI) images despite the interference of sea clutter, thereby reducing computational complexity and enhancing target detection accuracy. Experimental results on a Sea Clutter Database demonstrates that our method surpasses existing methods in terms of accuracy, indicating its potential as a promising solution for marine target detection tasks. [ABSTRACT FROM AUTHOR]

This article discusses self-triggering algorithm using distributed model predictive control (DMPC) to achieve dynamic consensus in linear multi-agent systems (MASs). The iterative computations and communications required at each time step in traditional consensus algorithms cause escalation of the energy consumption and shorten the life span of the MAS. An attempt to solve this problem is made by proposing a sequential self-triggering consensus algorithm, where each agent computes its own triggering instants. A Laguerre based DMPC design is adopted that notably reduces the computational complexity of conventional DMPC. The proposed self-triggered DMPC algorithm optimizes the control input and triggering interval while guaranteeing the dynamic consensus of the agents. By virtue of the Laguerre function based control architecture, the additional computations owing to the self-triggered algorithm do not impose stress on the controller; yet reduce the load on communication resources. The equality constraint on the terminal state of the agents is utilized along with Lyapunov criteria to establish the closed loop stability of the MAS. The proposed scheme achieves a considerable drop in controller design computations as well as data transmissions among agents, and the same is established by comparing these traits of existing schemes while achieving comparable performance. The proposed algorithm is verified through simulation of platoon configuration of vehicles, each of which is modeled as a linear multi-input multi-output (MIMO) system. • A discrete Laguerre based self triggered control design is proposed for dynamic consensus of multi-agent system • The proposed design directly computes the optimal control input and triggering instants for each agent • The design achieves dynamic consensus with reduced data transmissions for multi-agents [ABSTRACT FROM AUTHOR]

Collaborative teams pursue common goals, completing tasks and making decisions with various levels of interdependence. A shared mental model (SMM) is a foundational structure in high performing, production teams and aids humans in predicting their teammate's goals and intentions. Advice teams utilize a transactive memory system (TMS) that integrate sources of knowledge and the source's credibility. SMMs and TMSs elevate human performance when the nature of emergence complements the associated team type. However, project and action teams require both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) for machine agents that unifies SMM and TMS characteristics. The HCM enables anytime selection over the two cognitive representations with the computational complexity of a single model. Furthermore, the pliant nature of credibility modeling in TMSs can represent expertise, thoroughness, or trust simultaneously in the team context. Results in a multi-agent project domain demonstrate the HCM's efficacy for machine agent teams and potential for applications in human–machine teams. [ABSTRACT FROM AUTHOR]

Among the stereo matching algorithms, the semi-global matching (SGM) is an efficient and high-accuracy method. However, its huge demand for memory access and high computational complexity makes it difficult to achieve a real-time and efficient processing on hardware. Based on the spatial redundancy found in the matching cost, we propose some effective techniques to reduce the requirement of on-chip and off-chip memory, while simultaneously greatly lower the computational complexity. Experimental results present that the proposed SGM algorithm reduces the computational complexity by 71%–74% and has almost the same quality of disparity map compared with the original 8-path SGM. The proposed 3-path fully-pipelined architecture is implemented on the Xilinx VCU-106 with a throughput of 1920 × 1080/54 fps. We also synthesize and layout it with TSMC 40 nm standard library, leading to an area of 8.1 mm 2 with throughput of 1920 × 1080/192 fps. The million disparity estimation per second (MDE/s) of the proposed design reaches up to 50,960, which outperforms conventional ASIC implementations. • We propose two SGM designs using the four-cycle time-sharing structure. • One is 4-path SGM, which has almost the same quality as the original 8-path SGM. • One is 3-path SGM, which completely eliminates the requirement of off-chip memory. • We aimed to lower the hardware complexity and reduce memory access requirements. [ABSTRACT FROM AUTHOR]

During the last few years, many deep learning (DL) methods have been proposed for the classification of hyperspectral images (HSI), and the final results show that these models have great potential. However, most existing deep models only consider the original spectral HSI datacube, excluding other sources of information such as textural and geometrical features. In addition to their computational complexity, most DL approaches cannot effectively consider local nonlinear relationships between features. In order to address these issues, this paper develops a new DL approach called MLKD-CNN, which uses multifeature local kernel descriptors (MLKDs) and convolutional neural networks (CNN) to classify HSIs. This method produces local kernel descriptors (LKDs) of spectral, extended morphological profiles, and Gabor features in the feature generation step. A simple CNN is then used to classify MLKD generated from spatial and spectral features. The proposed MLKD considers local nonlinear relationships of both spectral and spectral features, making them capable of achieving high accuracy levels while reducing the computational burden of deep methods. The experiments on four benchmark HSIs show that the proposed MLKD-CNN outperforms some recent HSI classification methods in terms of accuracy and computation time. [ABSTRACT FROM AUTHOR]

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms. • Multi-detection multi-target tracking using OTHR is studied by dimension descent. • The detection of new-born targets with unknown prior knowledge is considered. • The convergence of the novel multi-target tracking algorithm is analyzed. • The labeled GMPHD is proposed to reduce the computational burden. [ABSTRACT FROM AUTHOR]

Neural material representations are becoming a popular way to represent materials for rendering. They are more expressive than analytic models and occupy less memory than tabulated BTFs. However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained. While this is practical when there is no need to edit the material, it can become very limiting when the fragment of the material used for training is too small or not tileable, which frequently happens when the material has been captured with a gonioreflectometer. In this paper, we propose a novel neural material representation which jointly tackles the problems of BTF compression, tiling, and extrapolation. At test time, our method uses a guidance image as input to condition the neural BTF to the structural features of this input image. Then, the neural BTF can be queried as a regular BTF using UVs, camera, and light vectors. Every component in our framework is purposefully designed to maximize BTF encoding quality at minimal parameter count and computational complexity, achieving competitive compression rates compared with previous work. We demonstrate the results of our method on a variety of synthetic and captured materials, showing its generality and capacity to learn to represent many optical properties. • Neural BTF representations allow for efficient reflectance encoding. • Autoencoder models trained with data augmentation allow for material propagation. • Neural reflectance propagation enables the creation of tileable BTF. [ABSTRACT FROM AUTHOR]

Multi-view semi-supervised classification is inherently a challenging task in multi-view learning due to the lack of label information. Existing methods generally suffer from insufficient data fusion, expensive computation cost in the solution procedure and fail in tackling unseen samples directly, intensively limiting their applicability and efficiency in real scenarios. To address these issues, we propose an adaptive collaborative fusion method, seeking for an appropriate representation and fusion for multi-view data. The main advantage of the proposed method is that it simultaneously fuses both multiple feature projections and similarity graphs to learn a joint projection subspace as well as a unified similarity graph that fully preserve the correlation and distinction among views. Meanwhile, our method can coalesce different views in an adaptive-weighting manner, making the learned subspace more discriminative and facilitating label propagation on the fused graph. Furthermore, an acceleration strategy has been designed to reduce the computational complexity, thereby making the proposed method scalable to relatively large-scale data. Finally, an alternating optimization has been adopted to solve the formulated objective function. Extensive experiments on synthetic and real-world datasets are conducted to demonstrate the effectiveness and superiority of our proposed method. • Propose a collaborative fusion method for multi-view semi-supervised learning. • Adaptively fuse different views to learn a joint projection and a unified graph. • The proposed method can efficiently deal with large-scale multi-view data. • Experiments on various datasets validate the method effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. • Reputation-based control has a fault detection and isolation mechanism. • W-MSR and RCA-T use a fault-resilient control that make microgrids flexible. • The reputation-based algorithm tolerates switching topologies of communication graph. • The W-MSR and RCA-T algorithms do not require computational complexity on DG level. • The performance is evaluated via simulations in addition to theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by introducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol-level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given (n , k) Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when compared to other symbol-level iterative decoders while working at a relatively lower complexity. [ABSTRACT FROM AUTHOR]

This paper presents a distributed filtering problem for three-dimensional angle-only target tracking (AOTT) in sensor (i.e., observer) networks. An instrumental variable-based pseudo-linear information filter (IVIF) is firstly derived on the basis of the designed bias-compensated pseudo-linear information filtering, with the help of summation forms of information quantities and bias compensation in a centralized fusion manner. Then, the distributed IVIF (DIVIF) is put forward by using finite-time average consensus to obtain the arithmetic means of defined information quantities and compensated bias in observer networks, which ensures that the filtering result of every observer is consistent with the centralized one. Finally, the iteratively DIVIF is proposed via gradually approaching the true values of relative distance and the corresponding angles between the target and every observer to get the filtering parameters more and more accurately, in order to achieve higher filtering precision. In addition, the computational complexity of the proposed method is also analyzed. The advantages of filtering precision of the proposed method over the existing pseudo-linear Kalman filter and its variants are demonstrated by an AOTT example in observer networks in terms of iteration steps, different levels of process noises and observer's accuracy. • A distributed filtering problem is presented for angle-only target tracking. • An instrumental variable-based pseudo-linear information filter is derived. • The distributed implementation is designed through finite-time average consensus. • An iterative estimation is adopted to further improve filtering precision. • The computational complexity of the proposed method is analyzed. [ABSTRACT FROM AUTHOR]

Abrupt-motion tracking is challenging due to the target's unpredictable action. Although particle filter (PF) is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. This paper proposed a quantum-inspired particle filter for abrupt-motion tracking. We apply the concept of quantum superposition to transform classical particles into quantum particles. Quantum representation and corresponding quantum operations are addressed to utilize quantum particles. The superposition property of quantum particles avoids the concerns of particle impoverishment and sample-size dependency. The proposed diversity-preserving quantum-enhanced particle filter (DQPF) obtains better accuracy and stability with fewer particles. A smaller sample size also helps to reduce computational complexity. Moreover, it has significant advantages for abrupt-motion tracking. The quantum particles are propagated at the prediction stage. They will exist at possible places when abrupt motion occurs, which reduces the tracking delay and enhances the tracking accuracy. This paper conducted experiments compared to state-of-the-art particle filter algorithms. The numerical results demonstrate that the DQPF is not susceptible to motion mode and particle number. Meanwhile, DQPF maintains excellent accuracy and stability. • We transformed classical particles into quantum ones. • We addressed quantum representation and corresponding quantum operations. • The proposed DQPF avoids particle impoverishment and sample-size dependency. • The proposed DQPF has significant advantages for abruptmotion tracking. [ABSTRACT FROM AUTHOR]

Due to the complexity of process operation, industrial process data are often nonlinear and nonstationary, high dimensional, and multivariate with complex interactions between multiple outputs. To address all these issues, this paper proposes a novel industrial predictive model that integrates deep feature extraction and fast online adaptation, and can effectively deal with multiple process outputs. Specifically, a multi-output gradient radial basis function network (MGRBF) with excellent predictive capacity of nonstationary data is first used to provide preliminary prediction of target outputs. This prior quality information is combined with the original process input for deep feature learning and dimensional reduction. Through layer-wise feature extraction by the stacked autoencoder (SAE), deep quality-enhanced features can be obtained, which is further fed into a MGRBF tracker for online prediction. In order to timely capture the fast-changing process characteristics, the first two modules, namely, preliminary MGRBF predictor and SAE feature extractor are frozen after training, while the structure and parameters of the MGRBF tracker are updated online in an efficient manner. Two industrial case studies demonstrate that the proposed adaptive deep MGRBF network outperforms existing state-of-the-art online modeling approaches as well as deep learning models, in terms of both multi-output modeling accuracy and online computational complexity. • A quality-relevant feature extraction strategy integrating MGRBF predictor and SAE feature extractor. • An efficient online adaptation strategy is designed to tune the adaptive part of the deep model. • Evaluation with two industrial case studies. • Outperforming SOTA approaches with better prediction accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider and analyze the stability of three implicit/explicit (IMEX) schemes for the time-dependent natural convection problem, these considered numerical schemes contain the first order backward Euler scheme, second order Crank-Nicolson IMEX scheme and BDF2-AB2 combination. All numerical schemes deal with the linear terms implicitly and the nonlinear terms explicitly. Then the original nonlinear problem is split into two linearized subproblems with constant coefficient matrixes, which reduces the computational complexity and saves a lot of storage. The biggest feature of this paper is to establish the unconditional stability of three IMEX schemes for incompressible flows, which improves and supplements the published theoretical findings. Finally, some numerical examples are provided to show the performances of the considered numerical schemes. [ABSTRACT FROM AUTHOR]

• The dynamic equation of rigid-flexible coupled robotic systems with large beam deflections is modeled for the first time. • A novel minimal-learning-parameter neural network-based fixedtime sliding mode control scheme is proposed. • The fixed time stability of tracking errors is achievable for two angular positions. • The computational complexity of neural networks will be greatly reduced by resorting to minimal-learning-parameter method. The rigid-flexible coupled robotic mechanisms possess several desired properties for real mechanical applications only relying on the elastic deformation of the compliant beams/flexures. In this paper, a comprehensive modeling methodology, based on the virtual work principle, has been developed to characterize the rigid-flexible coupled robotic mechanisms with large deformations. Simultaneously, the neural network fixed-time slide mode trajectory controller is designed to adjust the desired angular positions of rigid-flexible coupled robotic mechanisms. The minimum-learning-parameter-based neural network is adopted to approximate the unknown dynamics of rigid-flexible coupled robotic mechanisms. Additionally, it is shown that the tracking errors enjoy practical fixed-time stability via the developed scheme. Finally, two numerical cases are implemented to verify the efficacy and superiority of the developed minimum-learning-parameter-based neural network-based fixed-time slide mode trajectory control method. [ABSTRACT FROM AUTHOR]

• We combine the domian knowledge of HSI with deep learning. • We develop an efficient unfolding network (EUNet) for HSI SR. • We design a lightweight HSI SR prior network with disentangled spatial-spectral representation. • Our EUNet can achieve comparable results in different scenarios with limited computing resources. Hyperspectral image super-resolution (HSI SR) is dramatically impacted by high spectral dimensionality, insufficient spatial resolution, and limited availability of training samples. Current approaches mainly rely on complex data-driven models to address some of these challenges, and the characteristics of HSI are not fully considered in the model design. In this paper, we propose an efficient unfolding network with disentangled spatial-spectral representation (EUNet) for HSI SR by combining domain knowledge (i.e., spectral correlation, degradation model, and structure prior) with deep learning. Specifically, the optimization process of the super-resolution prior-driven Maximum A Posterior (MAP) framework is unfolded into an interpretable multi-stage network, which inherits the advantages of deep learning-based image super-resolution (e.g., feature extraction in low-resolution space) and explicitly imposes the degradation model constraint. To well incorporate the structure prior of HSI, spatial and spectral feature extraction is disentangled by a variant of depthwise separable convolution, and spectral correlation is embedded by a lightweight spectral attention mechanism, so that the difficulty and computational complexity of feature learning are greatly reduced. Experiments on benchmark datasets with different degradation models demonstrate the feasibility and superiority of the proposed EUNet over other state-of-the-art methods in terms of evaluation metrics and computational complexity. The source code is available at https://github.com/denghong-liu/EUNet. [ABSTRACT FROM AUTHOR]

Degraded document binarization has received keen attention due to its vital influence on subsequent document analysis tasks. In this study, we propose a novel Degraded Document Binarization model through the vision transFormer framework, termed D2BFormer. Thanks to its end-to-end trainable fashion, the D2BFormer model is able to autonomously optimize its parameterized configuration of the entire learning pipeline without incurring the intensity-to-binary value conversion phase, resulting in an improved binarization quality. In addition, we propose a novel dual-branched encoding feature fusion module, which combines architectural components from the vision transformer framework and deep convolutional neural networks. The resulting encoding module can extract features from an input document that are sensitive to both global and local characteristics. Meanwhile, the proposed encoding feature extraction module can operate internally at a much lower spatial resolution than that of a raw input document, leading to reduced computational complexity. Furthermore, we propose a novel progressively merged decoding feature fusion module through carefully introduced skip connections both inside and outside the decoding network. The resulting decoding module progressively combines counterpart features derived from the corresponding layers of the encoding network with comparable spatial resolutions and up-sampled features generated from previous layers in the decoding network. Finally, the experiments conducted on ten public datasets demonstrate that the proposed D2BFormer model gains promising performance in terms of four metrics. • An end-to-end binarization model through vision transformer was proposed firstly. • A novel dual-branched encoding feature fusion module was designed. • A progressively merged decoding feature fusion module was introduced. [ABSTRACT FROM AUTHOR]

Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has already been applied to a wide range of fields. However, the shape of error entropy cannot be changed freely since its kernel function is the Gaussian kernel function, which causes the error entropy-based algorithm to handle only some specific kinds of noises. Benefiting from the property that the generalized Gaussian kernel function is free to adjust its shape, a novel Kalman-type filter algorithm based on the generalized minimum error entropy (GMEEKF) criterion is derived. Moreover, the mean error behavior, mean square error behavior, and computational complexity of the GMEEKF algorithm are analyzed. Finally, several simulations and experiments are performed to demonstrate the performance of the GMEEKF algorithm in comparison with the existing Kalman-type filter algorithms. • A novel Kalman-type filter algorithm based on the GMEE criterion is derived. • Several theoretical analysis of the GMEEKF is given. • Some simulations are given to the performation of the GMEEKF algorithm. • The state estimation of the elastic actuator is given. [ABSTRACT FROM AUTHOR]

In this paper, an efficient dimension splitting method is applied to the two-dimensional (2D) and three-dimensional (3D) Cahn-Hilliard type equations with significant applications in physical, biological and computer science. The proposed method can greatly reduce the storage requirements and computational complexity in two and three dimensions, and preserve the high precision and mass conservation. The dimension splitting method is based on auxiliary variables for spatial derivative terms and the operator splitting approach. It converts the 2D or 3D problem into a series of one-dimensional (1D) problems which can be solved by using multi-thread. The stability and accuracy of the proposed method are improved by a local stabilizing approach and extrapolation. The analyses of discrete energy and discrete mass conservation property are shown. Numerical examples confirm the advantages of proposed method. And a variety of simulations, such as the phase separations, curvature driven flows, diblock copolymers, tumor growth and volume restoration, are performed with respect to practical applications. • Dimension splitting method for Cahn-Hilliard type equations. • The global stability is achieved by the local stability of solving each sub-problem. • Fourth-order mass conservative scheme based on the compact differencing and extrapolation. • A large number of numerical examples for practical applications. [ABSTRACT FROM AUTHOR]

The dichromatic number of a digraph D is the minimum number of colors of a vertex coloring of D such that D has no monochromatic cycles, the dichromatic number is an extension of the chromatic number to the class of digraphs. The Hajós join is a tool to obtain r -chromatic graphs, using the dichromatic number, J. Bang-Jensen et. al. extended the Hajós join to digraphs, and thus obtained a tool to obtain r-dichromatic digraphs. J. Bang-Jensen et. al. posed in 2020 the problem of how to obtain the symmetric cycle of length 5 from symmetric cycles of length 3. We recently solved this problem by applying a genetic algorithm. In this article, we generalize the construction of the symmetric cycle of length 5 and determine the computational complexity as Θ(n ln(n)). [ABSTRACT FROM AUTHOR]

The concept of mutual-visibility in graphs has been recently introduced. If X is a subset of vertices of a graph G, then vertices u and v are X- visible if there exists a shortest u , v-path P such that V(P) ∩ X ⊆ { u , v}. If every two vertices from X are X- visible, then X is a mutual-visibility set. The mutual-visibility number of G is the cardinality of a largest mutual-visibility set of G. It is known that computing the mutual-visibility number of a graph is NP-complete, whereas it has been shown that there are exact formulas for special graph classes like paths, cycles, blocks, cographs, and grids. In this paper, we study the mutual-visibility in distance-hereditary graphs and show that the mutual-visibility number can be computed in linear time for this class. [ABSTRACT FROM AUTHOR]

Person image generation is a challenging task aimed to transfer the person of the source image from a source pose to a target pose while preserving its style. In this paper, we proposed a Generative Adversarial Network based on Decoupled Semantic Attention Transfer (DSAT-GAN), focusing on that local semantic representations of different image styles and contents cannot be accurately decoupled and transferred. This architecture employs a novel Multi-scale Semantic Mapping Generation Network (Ms-SMGN), driven by two network modules with different semantic attention mechanism, aiming to accurately align and transfer the representations of local semantics at different spatial scales. Then, a channel-separated convolution is applied in the encoding networks instead of the traditional channel fully-connected operation, which reduces computational complexity while realizing channel semantic decoupling. Moreover, a Gram matrix-based global style loss is introduced to further enhance the consistency of high-level semantic between generated and target images. Experiments on Market-1501 and DeepFashion datasets show that DSAT-GAN has superior performance compared with other recent baselines. Additionally, this architecture can be extended to the data enhancement scenes to significantly improve the accuracy of person Re-identification. [Display omitted] • Our model alleviates the loss of semantic features of complex alignment regions. • Encoders learn feature vectors with channel-separated convolution and attention. • Decoders can fuse and decode feature vectors and generate integrated person details. • Global style loss constrain the high-level semantic of generated person images. • The experimental results on the datasets demonstrate the superiority of our model. [ABSTRACT FROM AUTHOR]

Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cram é r-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations. [ABSTRACT FROM AUTHOR]

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead. [ABSTRACT FROM AUTHOR]

Neural network pruning plays an important role in the deployment on resource-constrained devices by reducing the scale of the network and the computational complexity. Different from existing pruning methods that only consider the amount of information filters contain, we think the distribution of information is more important to the model accuracy. Then we use visualization of feature map to consider the impact of the attention area on the network decision. In order to directly relate the attention area with confidence score, we propose HScore to use interpolation and normalization to visualize the attention area of feature map and calculate the confidence score by putting only the attention area into the model. The principle behind is that if the feature map focuses on an area which can achieve a higher classification confidence, then the corresponding channel is important to keep the accuracy of model. The confidence score of attention area represents the importance of the channel to the model and filters with smaller confidences would be pruned. The experiment results prove that HScore can prune more redundant filters while maintaining better accuracy. Notably, our method can reduce 68.6% parameters and 73.0% FLOPs of ResNet-110 even with 0.14% top-1 accuracy increase on CIFAR-10. With ResNet-50, we achieve a 55% FLOPs reduction with only a loss of 1.03% in the top-1 accuracy on LSVRC-2012, which has advanced the state-of-the-art. [ABSTRACT FROM AUTHOR]

The exponential growth in wireless communication has resulted in a high demand for novel wireless services within the frequency bands that can deal with the challenge pertaining to the current spectrum shortage. Thus, a 5G-MIMO based CR communication system has been developed to enhance the spectrum by employing a spectrum sensing (SS) algorithm. This recommended SS algorithm, based on the hybrid filter detection (HFD) method, employs the Cosine law for filtering the traffic signal, and then enables segmentation by applying the Welch algorithm. Then, the Hann algorithm is employed to window the traffic signal, and facilitates damping the MIMO impact for different waveforms. This also includes universal filtered multi-carrier (UFMC), filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and Generalized Frequency Division Multiplexing (GFDM) waveforms. Also, evaluation of the operating parameters such as SNR, the antenna count, the signal span, and power as well as computational complexity was done. Based on the simulation results, a notable achievement was seen for the parameters that were below 0 dB of SNR, <0.09 % global system error probability, >96 % global detection probability, <0.06 % global false alarm probability, and possessing simple complexity. The recommended system's parameters were assessed, which demonstrated its superiority versus other previously developed systems. This algorithm was found to identify all types of 5G signals and not just one type of 5G. [ABSTRACT FROM AUTHOR]

Nowadays, predictive current control (PCC) strategy has been recognized as a promising option for the control of electric drives for their control flexibility, fast dynamic response and weighting factorless structure of objective function. However, such kinds of conventional control strategies make drives to suffer from the ingress of significant harmonics in stator current, increasing further the steady state flux ripple followed by a noticeable torque ripple. To ameliorate these aforementioned shortcomings of conventional scheme, the present work employs the concept of virtual voltage vector (V 3) in PCC strategy. The mathematical steps of the proposed V 3 PCC make the scheme more frontward in delay compensation, and also less parameter sensitive than the conventional one. As a baseline for the performance analysis, the present V 3 PCC is compared with a weighting factorless V 3 based predictive flux control (PFC) scheme in induction motor (IM) drive. A speed adaptive flux observer is considered for the speed estimation purpose in both the control schemes. The performance of both the sensorless schemes for IM drive are analysed in MATLAB/Simulink, and also experimentally verified in a 2.2 kW IM drive test bench. • The proposed scheme uses only twelve V3s, and provides a satisfactory results without much computational complexity. • The proposed scheme considers delay compensation in reference current waveform and avoids the appearance of large ripple in controlled variables. • The required rotor flux is computed from the proposed control structure. Therefore, the scheme is less affected by parameter mismatch. • Single objective function based feature motivates to consider V3PFC as the benchmark to evaluate the performance of the proposed V3PCC scheme for IM drive. [ABSTRACT FROM AUTHOR]

The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification by processing the electrocardiogram signals. The solution is based on an effective hybridization of the multirate processing, QRS selection, variational mode decomposition, features mining from Modes, Metaheuristic optimization based features selection, and machine learning algorithms. The MIT-BIH dataset is used for experimentation. Performance of the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms is investigated for features selection. A multi-subjects and multi-class dataset is used for testing the performance of classification by following the 10-fold cross validation strategy. The multirate processing with QRS selection and Metaheuristic optimization dependent features selection bring compression and aptitude for the processing and data transmission efficiencies. The system efficiently incorporates the multirate processing while securing an effective signal reconstruction. The respective compression gains and classification accuracies for the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms are 27-fold, 29.45-fold & 46.29-fold and 99.14%, 99.08% & 98.65%. [ABSTRACT FROM AUTHOR]