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Multiplexing technology creates several orthogonal data channels and dimensions for high‐density information encoding and is irreplaceable in large‐capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin‐multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self‐developed laser‐induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high‐dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra‐high‐capacity storage and real‐time encryption. [ABSTRACT FROM AUTHOR]

A wide range of practical control systems such as robot manipulators and spacecrafts can be modeled by Euler–Lagrange systems. On one hand, the requirements for system control accuracy, response speed, and robustness are ever increasing. On the other hand, the control performances are inevitably degraded by multiple disturbances. In this paper, a novel finite‐time composite control law is proposed for a class of Euler–Lagrange systems subject to multiple heterogeneous disturbances including a neutrally stable disturbance and a norm‐bounded disturbance. More specifically, a refined sliding mode disturbance observer (RSMDO) is proposed to estimate and reject the neutrally stable disturbance in the feedforward channel, while an adaptive nonsingular backstepping control (ANBC) is designed in the feedback channel to stabilize the system and attenuate the observer error as well as the norm‐bounded disturbance. The proposed RSMDO can not only fully utilize the disturbance information, but also quantify the convergence time and steady‐state accuracy of the estimation error. The finite‐time stability analysis is carried out for the closed‐loop system. Finally, an example of spacecraft attitude control system is given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m‐phase GaTe0.5Se0.5 with wide‐spectral photoresponses (250–1064 nm) and stack it with WSe2 are utilizes to construct a two‐dimensional van der Waals heterojunction (2D‐vdWH), enabling the design of a gate‐tunable wide‐spectral photodetector. By utilizing the multi‐photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof‐of‐concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non‐tunable photoresponse (71.17%). Artificially designed gate‐tunable wide‐spectral 2D‐vdWHs GaTe0.5Se0.5/WSe2‐based photodetector present a promising pathway for the development of miniaturized and cost‐effective remote sensing classification technology. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic. [ABSTRACT FROM AUTHOR]

The performance of the gimbal servo system in control moment gyro (CMG), which includes precision, lifespan etc., is one of the crucial factors of spacecraft attitude control. The various practical disturbances, however, will not only deteriorate the velocity‐tracking accuracy but will also cause the abnormal gimbal velocity problem (especially peak phenomenon). To this end, this paper proposes a refined anti‐disturbance control method to deal with velocity output constraints and multiple disturbances. Starting with fully understanding the prior information of multiple disturbances, a refined disturbance observer with a low conservativeness is designed to accurately estimate disturbances. The disturbance‐estimation error is analyzed in detail to ensure convergence to a bounded region. Subsequently, a novel barrier Lyapunov function‐based backstepping controller is proposed that considers the residuals of disturbance estimation to simultaneously achieve multiple disturbances attenuation and compensation, and handle velocity output constraints. Notably, the gimbal's maximum velocity is precisely limited to a pre‐specified low range, which benefits the CMG's lifespan and performance. Finally, both simulation and experimental results show that the proposed method performs better in disturbance estimation, velocity tracking, and robustness. [ABSTRACT FROM AUTHOR]

This article studies the leader‐following attitude coordination problem of a group of rigid spacecraft subject to communication constraint, disturbances and uncertain control coefficient matrices. A fully distributed adaptive anti‐disturbance attitude coordinated control scheme with event‐triggering mechanism is developed. First, the event‐triggered adaptive distributed observer is designed for each follower to estimate the leader's information without continuous communication requirement. Based on the estimated information, the adaptive anti‐disturbance attitude tracking controller is designed such that asymptotic coordinated tracking can be achieved under additive disturbances and actuators' partial loss of efficiency. The proposed control scheme ensures that all the closed‐loop signals are bounded and the positive lower bound of inter‐event times exists in each subsystem. Simulation results illustrate the effectiveness and flexibility of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Multiple disturbances are the main constraints that hinder the high-performance velocity tracking of the gimbal servo system in control moment gyro. This article presents a non-cascade structured velocity-tracking controller based on a refined disturbance observer for handling multiple disturbances and improve the tracking performance. Specifically, a disturbance observer is designed to estimate the imbalance disturbance, which can be represented by an exogenous system. The disturbances that cannot be modeled a priori are treated as a lumped term and estimated via extended state observer. By resorting to disturbance estimation, a novel sliding mode controller based on the disturbance estimation values is developed to handle the mismatched problem and chattering issue. Finally, experimental tests are conducted to verify the effectiveness of the proposed anti-disturbance control scheme. [ABSTRACT FROM AUTHOR]

This paper concerns about the safe approaching in rendezvous with spherical envelops of spacecraft. Based on the nonnegative characteristic of positive systems, a novel strategy of controlling the relative distance positive and convergent is proposed to guarantee no‐collision approaching. The dynamics of the relative distance is decoupled from that of the relative angles. A nonlinear disturbance observer is designed, and two anti‐disturbance controllers are constructed to reject the effect of disturbances for the subsystems of relative distance and angles. Under the controllers, the closed‐loop subsystem of relative distance is a positive and stable system, while that of relative angles is a general stable system satisfying the given time‐varying constraints. Particularly, a theorem is proposed to present the conditions of controllers to ensure the system positivity under a class of external inputs without nonnegativity default. Finally, simulation results are presented to illustrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

Introduction: The Amplatzer and Watchman left atrial appendage closure (LAAC) devices are the two most frequently used devices for LAAC devices worldwide. This meta-analysis aimed to compare the safety and efficacy of the two devices. Methods: We searched the PubMed, EMBASE, and the Cochrane Library for studies up to February 6, 2022 that compared the safety and efficacy of the Amplatzer and Watchman devices. Results: Fifteen studies including 2,150 patients in randomized controlled trials and 2,526 patients in observational studies were included in the meta-analysis. Amplatzer device was associated with higher rates of major procedure-related complications (odds ratio [OR]: 1.99, 95% confidence interval [CI]: 1.45–2.74, p < 0.0001) and device embolization (OR: 1.99, 95% CI: 1.09–3.64, p = 0.03). However, Amplatzer device had lower rates of total peridevice leak (PDL) (OR: 0.48, 95% CI: 0.27–0.83, p = 0.009), significant PDL (OR: 0.27, 95% CI: 0.12–0.57, p = 0.0007) and device-related thrombus (DRT) (OR: 0.67, 95% CI: 0.48–0.95, p = 0.02). No statistical differences were observed between the two devices in other safety and efficacy endpoints, such as pericardial effusion, cardiac tamponade, air embolism, vascular complications, ischemic stroke/transient ischemic attack (TIA), hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding. Conclusions: Amplatzer LAAC device was associated with higher rates of major procedure-related complications, especially in device embolization. Watchman LAAC device was associated with higher rates of PDL and DRT. There were no significant differences between two devices in ischemic stroke/TIA, hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding. [ABSTRACT FROM AUTHOR]

In this article, a composite anti-disturbance control of disturbance rejection and attenuation for a class of nonlinear positive systems is proposed for the first time to achieve both positivity and stability. The disturbance observer-based control (DOBC) method is utilized to compensate for the effects of the modeled disturbances, while the $L_{1}$ control strategy is adopted to suppress the effects of the disturbances which are not compensable to satisfy the $L_{1}$ index performance. Besides, two analysis schemes of the closed-loop system are presented. The first scheme is commonly used and is easy for analysis, but it has some conservativeness. The second scheme is proposed novelly with less conservativeness, which releases the positivity constraint of disturbance estimation errors. The corresponding theorems and algorithms are proposed for necessary conditions and solutions. Finally, two examples are presented to illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation–maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies. [ABSTRACT FROM AUTHOR]

This paper investigates the velocity-tracking problem of the gimbal system in a single gimbal control moment gyro (SGCMG) suffering from high-frequency disturbance. In order to accurately and quickly reject the high-frequency disturbance resulted from nonuniform mass distribution and manufacturing imperfection, a finite-time harmonic disturbance observer (FTHDO) is constructed according to the available information about the high-frequency disturbance. Then, a composite controller is developed to achieve simultaneous disturbance rejection and attenuation. The nominal control performance can be recovered under the high-frequency disturbance. It has been demonstrated that the velocity-tracking error can be stabilized by the proposed composite controller within finite time. Finally, numerical simulation results are presented to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition. [ABSTRACT FROM AUTHOR]

Singularly perturbed systems (SPSs) wildly exist in practical engineering practice; however, the disturbance rejection in SPSs is still a problem. As well-known disturbance rejection methods, composite hierarchical anti-disturbance control (CHADC) can effectively compensate and suppress disturbance, which improves the control performances of complex systems. To enhance the capability of anti-disturbance and control precision, this article proposes a novel composite hierarchical anti-disturbance (CHAD) dynamic surface control (DSC) for the fast subsystem considering highly dynamic disturbance, and CHAD proportion-integration (PI) control for the slow subsystem with slow-varying harmonic disturbance. The disturbances are supposed to be generated by an exogenous system based on partially known information with model uncertainty, and the stabilities of both fast and slow closed-loop subsystems are analyzed. Meanwhile, the compensability of disturbances is discussed. Finally, simulations for an F-8 aircraft are given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This article presents a single‐hidden‐layer feedforward network (SLFN) aided fault‐tolerant control (FTC) scheme for a class of nonlinear systems subject to actuator faults. First, the upper bounds of the norms of the unknown functions, which cover information of actuator faults and amplitude saturation degrees, are deployed. The upper bounds are approximated by SLFNs with adaptive techniques. Subsequently, a sliding mode control approach is presented to allow for prompt corrective reactions, with explicit consideration of multivariable conditions and control input constraints. Finally, simulation results based on a nonlinear Boeing 747 aircraft model are presented to demonstrate the effectiveness of the proposed FTC scheme. [ABSTRACT FROM AUTHOR]

Adversarial attack on neural networks has become an important problem restricting its security applications, and among adversarial attacks oriented towards the sample set, the universal perturbation design causing most sample output errors is critical to the study. This paper takes the neural network for image classification as the research object, summarizes the existing universal perturbation generation algorithm, proposes a universal perturbation generation algorithm combining batch stochastic gradient rise and spherical projection search, achieves loss function reduction through the iterative training of stochastic gradient rise in batch samples, and limits the universal perturbation search to a high-dimensional sphere with radius ε to reduce the search space of universal perturbation. Moreover, the regularized technology is introduced to improve the generation quality of universal perturbations. The experimental results show that compared with the baseline algorithm, the attack success rate increases by more than 10%, the solution efficiency of universal perturbation is improved by one order of magnitude, and the quality controllability of universal perturbation is better. [ABSTRACT FROM AUTHOR]

Neural network technology has achieved good results in many tasks, such as image classification. However, for some input examples of neural networks, after the addition of designed and imperceptible perturbations to the examples, these adversarial examples can change the output results of the original examples. For image classification problems, we derive low-dimensional attack perturbation solutions on multidimensional linear classifiers and extend them to multidimensional nonlinear neural networks. Based on this, a new adversarial example generation algorithm is designed to modify a specified number of pixels. The algorithm adopts a greedy iterative strategy, and gradually iteratively determines the importance and attack range of pixel points. Finally, experiments demonstrate that the algorithm-generated adversarial example is of good quality, and the effects of key parameters in the algorithm are also analyzed. [ABSTRACT FROM AUTHOR]

For a practical engineering system, disturbances usually have a negative impact on system performance. Especially, when the system physical constraints (e.g., actuator amplitude saturation and input channel constraint) are present, the study for antidisturbance controllability (ADC) is of paramount importance. This article presents the concepts of disturbance estimability, disturbance compensability, and an enhanced antidisturbance control (EADC). From the viewpoint of control system design, an example of spacecraft attitude control is given. The control constraints and multiple disturbances are explicitly analyzed for the sake of attitude control system design of flexible spacecraft. Subsequently, an EADC approach with integration of the disturbance observer-based control (DOBC) and active disturbance rejection control (ADRC) is proposed, based on the characteristics of disturbances exposed. Moreover, prescribed performance approach is incorporated into the spacecraft attitude tracking control. In consequence, the presented control can not only achieve superior antidisturbance capability than DOBC or ADRC, but also quantify the control performances. Numerical simulations and hardware-in-the-loop tests exemplify the applicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Precise attitude control of space manipulators plays an important role in advanced on-orbit assembly tasks. The vibration of flexible appendage and inertial uncertainties encountered in the operating process, however, may cause attitude error or even safety threats to the space manipulator system. In this article, a high-precision attitude control scheme of a space manipulator system is designed via a combination of disturbance observer (DO), prescribed performance-based ${H} _{\infty }$ control, and iterative learning control (ILC) techniques. The proposed control scheme consists of three portions: 1) a DO that estimates the vibration disturbance caused by flexible appendage of base satellite; 2) a robust ${H} _{\infty }$ controller with prescribed performance to attenuate the inertial uncertainties resulting from capture of an unknown object; and 3) an ILC for improving the transient and steady-state process in the presence of a repetitive on-orbit assembly task. This novel control scheme can not only handle the flexible vibration and inertial uncertainty of the space manipulator but also achieve satisfactory tracking performance. Both simulation and experimental results confirm the superiority of the proposed control strategy. [ABSTRACT FROM AUTHOR]