In the study of electroencephalography (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs), neurorehabilitation technologies hold significant potential for recovering from intracerebral hemorrhage (ICH). However, the rehabilitation process is hindered as the clinical practicality of such systems is reduced considerably due to their lengthy setup procedures caused by excessive number of channels. Accordingly, this study proposes a channel selection method based on an adaptive recursive learning framework, which establishes a comprehensive evaluation metric by combining time-frequency domain features. Experimental results demonstrate that, upon using 37.50 % fewer channels, the average accuracy of MI classification increased from 65.44 % to 69.28 % in healthy subjects and from 65.00 % to 67.64 % in patients with ICH. This study presents the pioneering EEG-based MI BCI channel selection process specifically designed for ICH patients, paving the way for personalized rehabilitation protocols and facilitating the translation of neurotechnology into clinical practice.

All-day satellite observations are crucial for communications, navigation, and remote sensing. Current systems rely on large, long-focal-length telescopes that struggle with daytime surveillance. Compact, short-focal-length star trackers, which are designed for measuring stellar positions and determining satellite attitudes, offer flexibility and rapid tracking but typically cannot operate during the day. In this article, we present a novel optical beacon and a star tracker for all-day ground-based satellite observations. The parameters of the star tracker and the onboard beacon system are optimized by analyzing the signal-to-noise ratio (SNR) between the atmospheric background radiation and the flux from the illumination source. In experiments with a 70-W peak illumination power and a 15° divergence angle, all-day satellite observations were achieved for a satellite at 500 km using a 15° ground-based observation angle. At night, the brightest observed satellite magnitude reached 1.19 at a distance of 570 km, while during the daytime, the SNR reached 20.17 dB at a distance of 538 km. This study significantly enhances the all-day satellite observation capability, fills the gap in daytime monitoring of satellites using star trackers, and provides strong support for future satellite space applications.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

This article is devoted to designing a novel aperiodic sampled-data-based event-triggered control strategy for Takagi–Sugeno fuzzy systems. First, via taking the structural features of fuzzy subsystems and the available information of fuzzy membership functions into consideration, an improved fuzzy-dependent adaptive event-triggered mechanism, which designs different adaptive event-triggered mechanisms for corresponding fuzzy subsystems, is proposed to provide extra design flexibility and further optimize communication efficiency. Then, the two-side looped-functional method and dynamic partitioning approach are introduced in the construction of the novel Lyapunov–Krasovskii functional (LKF). These two methods contribute to deriving preferable stability criterion and stabilization approach via relaxing the positive definite constraint on LKF and fully utilizing the inner system state during the whole aperiodic sampling interval. Eventually, two simulation examples are introduced to verify the effectiveness of the proposed control strategy and its advantages in lightening communication frequency.

This article investigates the stability and stabilization problem of aperiodic sampled-data nonlinear systems in the framework of interval type-2 (IT-2) fuzzy models. First, by introducing two adjustable parameters and splitting the sampling intervals into four nonuniform intervals, a refined two-side looped-functional method is constructed to fully utilize inner state information during the whole aperiodic sampling interval. Simultaneously, the positive definiteness constraint for the individual matrix in Lyapunov–Krasovskii functional can be further relaxed. Then, via constructing a novel fuzzy Lyapunov–Krasovskii functional (FLKF) together with a fuzzy-dependent-switching scheme, the available features of fuzzy membership functions (FMFs) can be further taken into consideration to increase the design flexibility. Consequently, the stability condition and corresponding controller design approach for aperiodic sampled-data IT-2 fuzzy systems can be obtained with less design conservatism and larger sampling intervals. Finally, two simulation examples are employed to demonstrate the validity and superiority of the proposed method.

The precise attitude determination and imagery positioning is crucial for remote sensing satellites to accomplish diverse observation missions. However, the complex fluctuations of environmental conditions result in severe and time-variant camera misalignment, which degrades the inertial attitude determination precision of optical payloads greatly and further hinders the improvement of positioning accuracy. Here, we develop a high-accuracy real-time attitude determination and imagery positioning system. With multiple laser sources integrated on the camera focal plane and a retroreflector reflecting the laser into a star tracker, the active optical monitoring path between the camera and the star tracker is constructed and provides the ability to monitor the camera misalignment. Using a dichroic mirror, the star tracker can detect the stars and the laser simultaneously. Then, the camera attitude determination and imagery positioning model based on the “star-laser” joint detection is established, which can precisely monitor the camera misalignment by the laser imaging and determine the inertial camera attitude combining with the star imaging. The ground simulate experiment is conducted and the results demonstrate the accuracy and effectiveness of the proposed method in monitoring the camera misalignment. Therefore, the proposed method provides an innovative idea for high-accuracy positioning.

Abstract Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the onfocus detection in the wild (OFDIW). It consists of 20623 images in unconstrained capture conditions (thus called “in the wild”) and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

This article addresses the aperiodic sampled-data control problem for flexible spacecraft with stochastic actuator failures. Flexible spacecraft dynamics are approximated by a group of T-S fuzzy models due to strong nonlinearity, and the multi-stochastic failures of spacecraft are depicted by a time-continuous and state-discrete Markov chain. To reduce the design conservativeness, a membership-sampling-dependent Lyapunov-Krasovskii functional (MSDLKF) is introduced to utilize the information of fuzzy membership functions and aperiodic sampling modes. Furthermore, a number of reliable fuzzy controllers are designed to obtain the exponential attitude stabilization under the circumstances of stochastic failures. At the same time, disturbance attenuation is ensured. The solution of the fuzzy controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). In the end, an example of the practical flexible spacecraft system is given to illustrate the feasibility and validity of the proposed fuzzy control methods.

The on-orbit calibration technology for internal and external parameters of remote sensing camera is the key to guaranteeing the imaging quality and positioning accuracy. Conventional landmark-based methods are not suitable for satellite missions where landmarks do not work nor for the on-orbit autonomous calibration of large remote sensing constellation. Hence, we introduced an on-orbit geometric calibration method based on star sources observation. By adjusting the satellite attitude, the camera and star sensor can simultaneously detect the star sources. Taking the star sources as control points, a high-accuracy “camera-star sensor” joint geometric calibration model integrating the space–time effects and optical imaging is established, which precisely decouples the deviations caused by the relativistic effects, the distortion of internal parameters, and the bias of external parameters. Furthermore, the procedures of the method are designed for on-orbit autonomous calibration. On-orbit experiments were carried out in several satellites, such as Jilin-1 ShiPin07 (JL-1 SP07). The results showed that the accuracy of internal calibration could reach 0.052” and the error of external installation matrix was within ±1.4”. Therefore, the method is attractive for actualizing on-orbit autonomous geometric calibration accurately and complements the conventional methods where the availability of landmarks is hard.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

This article considers the exponential stability and aperiodic sampled-data control problem for nonlinear systems based on a class of Takagi–Sugeno fuzzy models. The fuzzy-dependent-switching control strategy together with a novel time-varying sampled-data controller is proposed to deal with the exponential stabilization problem of such systems. Mixed-fuzzy dependent Lyapunov–Krasovskii functionals (MFDLKFs), which fully make use of available characteristics of the sampling patterns, the signs and the upper bounds of the time derivative of fuzzy membership functions, are constructed for the purpose of reducing the design conservatism. Based on the proposed MFDLKFs, a novel exponential stabilization criterion for the fuzzy systems with aperiodic sampling is established in terms of linear matrix inequalities, which is less conservative and obtains a larger sampling interval compared with existing results. Finally, a simulation example is employed to demonstrate the effectiveness and superiority of the proposed fuzzy-dependent-switching control scheme.

Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical planning of liver diseases. In clinical practice, liver vessels are normally manual annotated by clinicians on each slice of CT images, which is extremely laborious. Several deep learning methods exist for liver vessel segmentation, however, promoting the performance of segmentation remains a major challenge due to the large variations and complex structure of liver vessels. Previous methods mainly using existing UNet architecture, but not all features of the encoder are useful for segmentation and some even cause interferences. To overcome this problem, we propose a novel deep neural network for liver vessel segmentation, called LVSNet, which employs special designs to obtain the accurate structure of the liver vessel. Specifically, we design Attention-Guided Concatenation (AGC) module to adaptively select the useful context features from low-level features guided by high-level features. The proposed AGC module focuses on capturing rich complemented information to obtain more details. In addition, we introduce an innovative multi-scale fusion block by constructing hierarchical residual-like connections within one single residual block, which is of great importance for effectively linking the local blood vessel fragments together. Furthermore, we construct a new dataset containing 40 thin thickness cases (0.625 mm) which consist of CT volumes and annotated vessels. To evaluate the effectiveness of the method with minor vessels, we also propose an automatic stratification method to split major and minor liver vessels. Extensive experimental results demonstrate that the proposed LVSNet outperforms previous methods on liver vessel segmentation datasets. Additionally, we conduct a series of ablation studies that comprehensively support the superiority of the underlying concepts.

How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and the quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (total about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature, and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.

The study of distributed remote sensing attracts much attention in the development of space exploration. The key issue towards this field is to study the system property of spacecraft cluster composed of multiple nano/micro modules. In addition, the formation of multiple nano/micro spacecraft is maintained and rebuilt for different application tasks. Due to the fact that micro/nano spacecraft is often lack of fuel, to maintain the stability of distributed remote sensing formation of nano/micro spacecraft for a relatively long period of time under the natural formation is very important. In this paper, a direct parameter method is proposed under the tree communication topology constraints for collaborative observation mode of distributed remote sensing spacecraft cluster. The orbit parameters of the N followers are obtained directly by this method. This paper also establishes a natural evolution model of spacecraft cluster with the advantages of simplicity, intuition and clear physical meaning. According to the assignment of national key research and development plan “distributed reconfigurable remote sensing technology based on nano/micro spacecraft”, we verify the correctness of the proposed method using the pre-given orbit parameters. Also, the numerical simulations of the formation flying are performed through STK and the simulation results show that the proposed approach has the advantages of easy implementation, high precision and robustness.

In this paper, time-domain (Lyapunov theorems) and frequency-domain (the Nyquist stability criterion) approaches are used to study leaderless and leader-following consensus algorithms with communication and input delays under a directed network topology. We consider both the first-order and second-order cases and present stability or boundedness conditions. Several interesting phenomena are analyzed and explained. Simulation results are presented to support the theoretical results.

To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine(SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples can be acquired, semi-supervised learning is desired to make full use of unlabeled samples. According to the restrict limitation in energy and bandwidth, the proposed algorithm incrementally implement classifier learning with the selected features from multiple sensor nodes, where rough set based feature reduction is used for retaining most of the intrinsic information. Furthermore, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. Experimental results demonstrate that the collaborative statistical learning algorithm can effectively implement target classification in WMSN. With the rough set based feature reduction, the proposed algorithm has outstanding performance in energy efficiency and time cost.

A finite-horizon robust Kalman filtering approach for discrete time-varying uncertain systems with additive uncertain-covariance white noises is presented. The system under consideration is subject to uncertainties in both the state and output matrices. The state and gain matrices of the filter are optimized to give a minimal upper bound on the state estimation error covariance for all admissible uncertainties