This article aims at investigating distributed interval estimation methods for continuous-time linear time-invariant (LTI) systems. By allying a robust observer design method and interval analysis techniques, we develop a novel two-step interval estimation method for LTI systems whose outputs are measured by a series of nodes connected via a given directed graph. First, a distributed observer formed by a group of local observers is designed via an $H_{\infty } $ approach to obtain an accurate point-valued estimation. This estimation is completed by a reliable interval-valued estimation achieved by a rigorous set-valued analysis of the estimation error dynamics. In order to further enhance the accuracy of the estimated intervals, an elimination by inconsistency technique is applied to characterize the smallest common interval containing the actual state vector of the system. Compared with the existing distributed interval observer approaches, the proposed method can effectively enhance the tightness of the estimated state intervals. Simulation results are shown to support the theoretical findings.

In the field of artificial intelligence and robotics, enabling robots to understand and execute complex tasks in unknown and open environments through natural language has become a frontier of current research. Traditional methods that rely on predefined action libraries face significant limitations when dealing with environmental diversity and task uncertainty. Accordingly, this study introduces a novel embodied intelligence framework named ‘Embodied Assistant’, which utilizes large language models (LLMs) for open-ended reasoning and adaptive task planning, can autonomously guide robots to flexibly complete challenging tasks in complex scenarios without relying on fixed action templates. By establishing effective multimodal LLM interaction pathways, this framework grants robots the ability to perceive and understand the task environment, concurrently allowing them to accurately interpret natural language instructions, and to independently plan action strategies along with robust robot trajectories. Moreover, the introduced multi-level task feedback mechanism effectively enhances the robots’ ability to self-correct and replan when they encounter planning failures. In extensive real-life testing scenarios, the proposed method achieved an 83.3% task success rate across various combined mobility and grasping tasks, significantly outperforming the most advanced baseline methods. A detailed analysis of robot fault recovery further demonstrates the substantial potential of this method in practical applications. Prompts and videos are provided at the project homepage: embodied-assistant.github.io

Hyperspectral (HS) and light detection and ranging (LiDAR) fusion for land-cover identification in multimodal tasks is always restricted owing to modality heterogeneity, especially when annotated samples are unavailable. Clustering techniques become pivotal in these unsupervised scenarios, yet their inability to offer interpretable fusion mechanisms poses challenges in pursuing enhanced identification performance. In this article, an Intuitive Assimilation Modality-driven Crossmodal Subspace Clustering (IamCSC) method is proposed to exploit intuitive crossmodal information. Different from existing multiview clustering approaches, our core innovation is that IamCSC distills an intuitive assimilation modality (IAM) with distinguishing geological characteristics of HS and LiDAR images simultaneously. The IAM with a small number of IAM channels still preserves the spectral similarity and diversity of neighboring samples. Meanwhile, it reflects sample elevations by peak regions along the IAM channel dimension. Specifically, the IAM connects and balances these two RS modalities to learn an assimilation cluster structure from modality-specific subspace representations. Several matrix-wise constraints are considered for capturing the optimal modality-shared representation. Given that IamCSC does not exhibit over-reliance on spatial neighborhood contributions, it achieves satisfactory performance particularly in image pairs with larger ground sampling distances (GSDs). Multiple experiments clearly prove the effectiveness of IamCSC over state-of-the-art techniques qualitatively and quantitatively. The codes are provided inhttps://github.com/GEOywb/IamCSC.

Accurate medical image segmentation is crucial for precise diagnosis and treatment in clinical pathology analysis and surgical navigation. While Convolutional Neural Network (CNN)-based approaches excel in capturing and analyzing local features, they often lose key global context. Transformers, utilizing self-attention mechanisms, address this issue but often overlook localized and multi-scale features while also requiring significant computational resources. To integrate the advantages of CNNs and Transformers to achieve efficient and precise medical image segmentation, we propose a segmentation framework based on multi-feature fusion CNN and Bi-level Routing Attention Transformer (MCBTNet). MCBTNet integrates CNNs and Transformers within a U-shaped encoder-decoder architecture. This configuration not only extracts multi-scale features via the U-shaped structure but also efficiently captures global contextual information through the dynamic sparsity of the Bi-Level Routing Attention Transformer. Our novel Frequency-Channel-Spatial multi-dimensional attention mechanism is implemented on skip connections, enhancing segmentation accuracy and speed by maximizing multi-scale feature utilization. Finally, MCBTNet obtains the segmentation result by fusing the predictions of different scales. Experimental results on five public datasets demonstrate that MCBTNet outperforms state-of-the-art methods in Dice and HD metrics, with lower computational and memory requirements.

This article presents a novel physics-constrained zonotopic Kalman filter method for online capacity prediction of lithium-ion batteries. To describe capacity degradation, a state-space formulation is devised using the autoregressive model and an indirect representation of capacity. The approach consists of three steps: First, a zonotopic Kalman filter is proposed to estimate model parameters and parameter intervals. Subsequently, considering the capacity regeneration phenomenon, a physics-based constraint term is presented to optimize parameters, which updates the estimated model parameters obtained by the zonotopic Kalman filter. Finally, parameters and interval estimation are utilized to predict the future short-term capacity. The case study demonstrates the validity of our approach. Moreover, comparisons with the ellipsoid-based extended Kalman filter and predictive maintenance toolbox suggest that our approach can obtain more precise capacity prediction and tighter capacity interval results.

Ultrasound high-resolution imaging is essential in clinic. The limited number of channels in portable ultrasound machines results in low-resolution images being acquired. Achieving high-resolution ultrasound images relies on machines with a substantial number of channels, which increases costs. Obtaining potential high-channel images from low-channel images can significantly reduce the cost of ultrasound machines. A novel physics-based deep learning method spatially-variant model (SV-Net) is proposed to deconvolve low-channel ultrasound images, yielding high-resolution outputs. SV-Net is structured with a multi-channel Wiener deconvolution layer placed before Convolutional Neural Network (CNN). The multi-channel Wiener deconvolution layer consists of various differentiable Wiener deconvolutions, which leverage knowledge of spatially-variant Point Spread Functions (PSFs) in ultrasound images. These PSFs are subsequently optimized through further training to obtain high-quality ultrasound images. Experiments show the method's efficacy in improving lateral resolution and achieving favorable performance metrics, including PSNR, SSIM, MAE, and FWHM.

Numerous deep learning (DL) studies have indicated that fusing hyperspectral (HS) and light detection and ranging (LiDAR) data is effective for land-cover classification. However, the sequential characteristics (seqCHAs) in the spatial domain are always ambiguous and neglected. In this letter, we propose a deep crossmodal sequential interaction network (CsiNet) for HS and LiDAR data joint classification. We aim to verify the contributions of crossmodal seqCHAs in multimodal joint classification tasks and present an effective crossmodal sequential flattening (SF) strategy. Specifically, CsiNet sorts the neighboring samples in terms of the spectral and 3-D spatial diversities between the corresponding samples and the central one. Notably, the 3-D spatial diversity considers the shared sample positions in both modalities and the sample elevations in LiDAR data simultaneously. CsiNet is capable of extracting crossmodal sequential features comprehensively by long and short-term memory (LSTM) layers and better simulating sequential properties of samples compared with convolutional layer based networks. Experiments conducted on the Muufl Gulfport (MUUFL) and Houston 2013 datasets prove that CsiNet outperforms several state-of-the-art techniques qualitatively and quantitatively. When using 1% training samples per category, the overall accuracies of CsiNet on both datasets achieve 90.27% and 92.41% and are increased by 0.27% and 0.17% than the best comparison technique, respectively. Ablation experiments verify the effectiveness of CsiNet by replacing the crossmodal SF strategy with several alternative ones. All codes are available athttps://github.com/GEOywb/CsiNet.

In this article, we propose a polynomial chaos expansion (PCE) approach to interval estimation for uncertain Takagi–Sugeno fuzzy systems by considering time-invariant parameter uncertainties. First, we design the observer to get the point-valued estimates, where the accuracy of interval estimation is ensured by optimizing the gain matrices of the designed observer. Second, to obtain the interval estimation results of real states, the error dynamics is split into a random process with stochastic uncertainties and a bounded process with set-based uncertainties. Different from the interval estimation methods presented in the literature, which use only zonotopic analysis for interval estimation, we use the PCE approach in conjunction with zonotopic analysis for interval estimation, where the random process is analyzed using PCE in conjunction with the zonotopic method, and the bounded process is studied using the zonotopic analysis method. Moreover, a numerical simulation is proposed, and its results illustrate that the presented approach can decrease the conservatism of interval estimation and facilitate more interval estimation accuracy compared to an advanced interval observer design approach. Finally, interval estimation is performed for a vehicle lateral dynamic model to illustrate the applicability and superiority of the presented approach.

This paper proposes a two-step interval estimation method for discrete-time Takagi-Sugeno fuzzy nonlinear systems with parameter uncertainties. First, a novel observer structure without redundant parameters is proposed to obtain point-valued estimation for the considered system. Compared with the T-NL observer structure recently presented in the literature, the proposed observer structure is more concise and can simplify the design process. To improve the estimation accuracy, an H∞ design method that can simultaneously optimize all design parameters by solving linear matrix inequalities is proposed to design the proposed observer. Second, interval estimation is achieved by zonotopic analysis on the error dynamics of the designed observer. Comparison studies show that the proposed method not only has broader application scopes but also can obtain more accurate estimation results than a state-of-the-art interval observer design method. Moreover, the proposed method is applied to a missile control system to estimate the intervals of the attack angle and pitch rate.

Rails play an essential role in railway transportation, supporting the movement of various types of trains. Due to the high-frequency and high-intensity loads, as well as harsh operating environment, rails are susceptible to cracking or even fracturing. Among existing rail structural health monitoring (RSHM) methods, the advanced ones often rely on signal-driven deep learning algorithms, necessitating substantial computational time and extensive preliminary information for effective model training. Moreover, crack-related signals with low amplitudes are easily submerged in the complex interference noise environments. Although some noise reduction solutions have been reported, the RSHM results often obtain certain discrepancies from the actual outcomes. To address above issues, this paper presents an alternative RSHM method based on electromagnetic acoustic emission (EMAE) technology. The proposed method uses a double-layer signal processing (DLSP) algorithm and a novel health monitoring index, called spectrum information entropy (SIE). It can monitor the degradation state of rails accurately and quantitatively. In this method, the DLSP algorithm is utilized to identify EMAE signals from the original dataset, which contains complex and diverse interference noise signals. In addition, the SIE is extracted from the obtained EMAE signals to perform the RSHM. Experimental results validate the accuracy and simplicity of the proposed method.

This article investigates the state interval estimation problem for discrete-time linear time-varying descriptor systems subject to unknown but bounded system uncertainties. We propose a zonotope-based interval estimation method in an optimization framework. First, we present a novel zonotope-based interval estimator structure, in which the estimated interval bounds of each state component have design parameters independent of those of the other state components. Then, the widths of the jointly estimated intervals enclosing every state component are simultaneously minimized by solving parallel$L_{1}$optimization problems via linear programming. Finally, a simulation study shows the effectiveness and higher accuracy of the proposed method compared with existing methods.

In this work, an actuator fault detection problem for discrete-time Takagi–Sugeno fuzzy systems is tackled in a bounded error context where both state disturbances and measurement noise are assumed to be unknown but bounded with known bounds. First, a peak-to-peak performance synthesis method is applied to design a robust residual generator against the considered process disturbances and measurement noise. Meanwhile, an improved zonotopic approach is proposed to compute tight adaptive thresholds for residual evaluation. Then, a reliable set-membership fault detection strategy with the aid of generated residual signals and adaptive thresholds is introduced. Finally, the viability of the proposed method is demonstrated via a numerical simulation. Then, an experimentation on a 3-D Crane system is performed to show its practicability.

In multimodal data fusion and land-cover interpretation tasks, the fusion interpretability between hyperspectral image (HSI) and light detection and ranging (LiDAR) data is always nontrivial to be clarified. Furthermore, the heterogeneous sample and distribution variances of these two remote sensing (RS) modalities impede the joint classification performance. In this paper, a Hyperspectral Intrinsic Image Decomposition guided Data Fusion Network (HI2D2FNet) is proposed. Generally, classic hyperspectral intrinsic image decomposition (HIID) performs well in image enhancement and shadow removal. It decomposes one HSI into one reflectance component and one shading component. Inspired by the core mechanism of HIID, our motivation is to preliminarily exploit its potential for multimodal RS data fusion and explore the inherent modality connection between HSI and LiDAR data from the intrinsic perspective. Specifically, compared with existing techniques, HI2D2FNet is capable of fusing the horizontal geometry information in the shading component with the vertical geometry information in the LiDAR data from both sample and distribution perspectives in the spatial domain. The generated cross-modal geometry feature contributes to guiding the reflectance stream optimization. This unique fusion framework connects both modalities with respect to geometry information and enhances the specific fusion interpretability. The decomposition and fusion modules in HI2D2FNet are optimized simultaneously in a novel alternative optimization pattern. Furthermore, several unique cross-modal constraints in terms of prior RS properties are presented. Experiments conducted on three widely available datasets prove the superiority of HI2D2FNet over state-of-the-art techniques. The source codes will be available at https://github.com/GEOywb/HI2D2FNet.

Remote sensing (RS) modalities from multi-sensor platforms, including hyperspectral (HS) and light detection and ranging (LiDAR) data, have been garnering increasing attention in overcoming the lack of information diversity. However, the specific modality correlation is always weakened and neglected. By reducing the spectral uncertainty, hyperspectral intrinsic image decomposition (HIID) has been proven to be effective in enhancing the HS image quality. It is an ill-posed problem and is challenged to capture the inherent modality connection. This paper proposes a shadow mask driven multimodal intrinsic image decomposition (smMIID) for HS and LiDAR data fusion. Notably, smMIID creatively clarifies and activates the potential of the crossmodal information in RS modalities and builds on the strength of the LiDAR elevation information when constructing HIID constraints. Our motivation is to overcome the deficiency of information diversity and modality correlation in existing IID based frameworks for better data fusion performance. Classic IID methods decompose the HS image into one reflectance component (RC) and one shading component (SC). There are three constraints in smMIID: 1) the fundamental constraint on RC and SC, 2) the HS-LiDAR hybrid gradient based constraint on RC and 3) the LiDAR gradient based constraint on SC. A shadow mask is obtained and utilized to guarantee spectral consistency when combining neighboring samples. Experiments on HS and LiDAR datasets prove that smMIID outperforms other techniques in terms of visualization and classification. The analyses are provided theoretically and experimentally to demonstrate that smMIID is robust to shadow mask generation.

This article studies the security synthesis of cyber–physical systems subject to stealthy attacks via zonotopic set theory. The set is used to quantify the effect of potential stealthy attacks on systems. Control performance and security level are characterized using$L_{\infty }$performance index and the radius of the attack-induced state set, respectively. Sufficient design conditions are given to optimize the security while guaranteeing a prescribed level of control performance. Simulation examples are conducted to demonstrate the effectiveness of the proposed method.

Hyperspectral image processing is faced with difficulties considering its redundant features and complex information. Studies on hyperspectral feature extraction in the deep learning domain have become increasingly popular. The mainstream techniques fully consider the spatial information in local neighborhoods when extracting spectral features by constructing deep neural networks. Deep generative models simulate the intrinsic structure of samples by adequately training, showing their potential values for signal processing. In this article, a convolutional two-stream network (cs2GAN-FE) based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial–spectral feature extraction. The improved WGAN is composed of one generator and one discriminator; the former perceives real data distributions, and the latter determines the attribution of generated data. The designed two-stream strategy is not a simple extension of a one-stream strategy and considers both the static spectral–spatial information and the dynamic spectral reflectance variation in multiple bands. Intrinsic spatial–spectral features are extracted by the trained discriminator considering sample distributions and feature relationships. The loss function is also improved for the unique structure of cs2GAN-FE. Various state-of-the-art techniques are chosen for comparison. Experimental results show the feasibility and potential of this network. Besides, experiments with the random split and the disjointed split both show that the proposed method can outperform other comparison techniques.

Abstract Hyperspectral unmixing is an important technique which attempts to acquire pure spectra of distinct substances (endmembers) and estimate fractional abundances from highly mixed pixels. This paper proposed a novel deep network‐based framework for unmixing problem. It contains two parts: a three‐dimensional convolutional autoencoder for hyperspectral denoising (denoising 3D CAE) which aims to recover data from highly noised input imagery through an unsupervised manner, and a restrictive non‐negative sparse autoencoder which extracts endmembers and abundances from the scene simultaneously. The proposed denoising 3D CAE network integrates 3D operations in each layer, which allows manipulating volumetric representation of the image data directly and facilitates hierarchical exploration for latent information. Being trained with corrupted hyperspectral image data, the denoising 3D CAE network has strong capacity of capturing the principle and robust local features in spatial and spectral domains efficiently, and it shows superior performance for image recovery with high noise disturbance. Moreover, a part‐based non‐negative autoencoder is concatenated, and the ‐norm penalty is imposed for sparsity enhancement of the solution. Comparative experiments are conducted both on synthetic and real‐world hyperspectral data, which demonstrate the applicability and effectiveness of the proposed unmixing framework.

Hyperspectral image with high dimensionality always increases the computational consumption, which challenges image processing. Deep learning models have achieved extraordinary success in various image processing domains, which are effective to improve classification performance. There remain considerable challenges in fully extracting abundant spectral information, such as the combination of spatial and spectral information. In this article, a novel unsupervised hyperspectral feature extraction architecture based on spatial revising variational autoencoder (AE) (UHfeSRVAE) is proposed. The core concept of this method is extracting spatial features via designed networks from multiple aspects for the revision of the obtained spectral features. Multilayer encoder extracts spectral features, and then, latent space vectors are generated from the obtained means and standard deviations. Spatial features based on local sensing and sequential sensing are extracted using multilayer convolutional neural networks and long short-term memory networks, respectively, which can revise the obtained mean vectors. Besides, the proposed loss function guarantees the consistency of the probability distributions of various latent spatial features, which obtained from the same neighbor region. Several experiments are conducted on three publicly available hyperspectral data sets, and the experimental results show that UHfeSRVAE achieves better classification results compared with comparison methods. The combination of spatial feature extraction models and deep AE models is designed based on the unique characteristics of hyperspectral images, which contributes to the performance of this method.

This article deals with the problem of fault detection for discrete-time Lipschitz nonlinear systems subject to a class of restricted frequency-domain specifications. We present a novel observer structure with more design parameters, which can be applied to enhance the observer performance. The performances of fault sensitivity and disturbance robustness are characterized using finite-frequency$H_{-}$and$H_{\infty }$indices, respectively. Less restrictive design conditions are obtained based on a reformulated Lipschitz property. Moreover, to detect faults timely, a novel dynamic threshold is synthesized based on zonotopic set-membership techniques. Simulation examples are conducted to demonstrate the viability and validity of the presented method.

Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high‐frequency noise and signal. A stop condition of the decomposition is proposed based on the k‐means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state‐of‐the‐art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods’ results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.

This paper proposes a novel interval observer design method for discrete-time Takagi-Sugeno fuzzy systems with parametric uncertainty, disturbances, and measurement noise. We present a new structure of interval observer with more design parameters, which can be used to broaden the application scope of interval observer design. For improving the accuracy of interval estimation, an L∞norm-based approach is used in the design of interval observer to attenuate the effect of the unknown disturbances, noise, and parametric uncertainty. Furthermore, the design conditions are formulated into a set of linear matrix inequalities, which can be efficiently solved. Numerical simulations are given to illustrate the effectiveness of the proposed method.

Hyperspectral technology is a huge leap of remote sensing technology, however, how to make full use of its rich information is a difficult problem. A novel hyperspectral band selection method based on 3D empirical mode decomposition (3D-EMD) is proposed by constructing the upper approximation and the lower approximation of the whole image. The hyperspectral image (HSI) is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and bands are selected from the HSI using each TIMF based on optimum index factor (OIF). These bands are combined into a band combination. Experimental results demonstrate that the proposed method yields improved decomposition performance on the HSI and increases classification accuracy.

Hyperspectral data with high dimensionality always needs more storage space and increases the computational consumption, manifold learning is extensively used in dimensionality reduction. A novel dimensionality reduction method based on manifold learning is proposed through learning a stable local manifold representation. Four adjacency graphs are constructed to model the interclass similarity, interclass diversity, intraclass similarity and intraclass diversity respectively, and then merge these graphs into the discriminant objective function for linear dimensionality reduction. The classification results in the use of different methods are compared with and experimental results show that the proposed method is effective and it is superior to comparison methods.

In order to overcome the noise impact of hyperspectral images during the acquisition process due to target movement and environmental interference, this paper proposes a spectrum elaboration method based on empirical mode decomposition (EMD) for visible and near-infrared hyperspectral image data. The difference with the prior arts is that this method obtains a spectral curve by processing a series of hyperspectral data cubes. The experiments show that this method can obtain the vector of spectral signature that reflects the essential features for the moving object, as the results of spectral angle indicate that discrepancies between non-congeners are greater and similar congeners are more similar, which compared to the gaussian filtering method and the direct averaging method. has a higher accuracy than the traditional method without feature enhancement and has good innovative and practical value.

A novel deep learning classification method for hyperspectral data based on convolutional neural network is proposed in this paper. Deep learning means bringing multiple layers instead of one to the structure. Through convolution layers and pooling layers, the features in different layers are extracted from original spectral feature images. The key of this method is to restructure spectral feature images and choose convolution filters with a reasonable size, so that the spectral features of different land coverings in high dimensions can be extracted properly. In our experiments, proposed method was applied for hyperspectral data in several different situations, and preferable classification performance were obtained through relative parameters adjustment, which were given recommended scope during our comparative experiments.

Manifold learning based unsupervised classification methods will be unable to obtain satisfactory results because of the lack of training samples. The employment of training samples' information makes manifold learning based classification become supervised, and thus brings the improvement on classification accuracy. In order to make full use of this information, we emphatically consider the hyperspectral data distribute by clusters. A novel supervised manifold learning method termed class encoding is proposed for hyperspectral data classification. The experimental results show that this algorithm has better classification performance than the existing supervised manifold learning algorithm.

To address the multi-classification problems of hyperspectral dataset, a new method with weighted kernel function based on Chernoff distance is proposed. Chernoff distance utilizes the information between categories and strengthens the separability of original dataset. The adjustable parameter in Chernoff distance can fit the hyperspectral dataset well compared with other least upper bounds. Pairwise decision tree reduces the number of subclassifiers that the dataset requires and improves the classification accuracy. The guidance of the weighed subclassifiers is global separability metric computed by Chernoff distance. Weighted subclassifiers highlight bands with more useful information and reduce accumulative error. Comparative experiment shows the effectiveness of the proposed method.

In this paper, we proposed a novel texture-mapping method for buildings using remote sensing images and digital surface model. For generating better 3D map, boring manually or semi-automatic texture-mapping of buildings is always needed. However, only with remote sensing images and digital surface model, it is difficult to generate `sides' of buildings, and corresponding relation between triangular mesh and texture image is also hard to be found. Inspired by building extraction result, we found that roof-contour could guide interpolation of `sides' of building, and generate triangular mesh, and an interactive frame of texture mapping is introduced for processing the generated multi-sides and corresponding texture-images of buildings. Experiments show that texture-mapping of buildings using remote sensing resource is easily realized with our frame, and excellent results could be obtained.

Recently, many researchers have attempted to exploit spectral–spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral–spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral–spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples.

This letter introduces a novel algorithm to construct sensing and measurement dictionaries in compressive sensing using alternating projection method. The cumulative and mutual cross coherence of the constructed dictionaries are lower than those of Gaussian random dictionary. The concept of General Restricted Isometry Constant (GRIC) is introduced. Low cumulative cross coherence puts bound on GRIC and small GRIC improves successful recovery rate of OMP algorithm. Experiments demonstrate that OMP algorithm performs better using dictionaries constructed by the proposed algorithm than Gaussian random dictionaries and those constructed by Schnass' algorithm.

Usually, effects of a system under PID control with considerable time delay are not satisfied. However, model-based predictive control is an advanced control strategy that uses a move optimization method for achieving satisfactory closed-loop dynamic responses of complex systems. This paper describes a real time scheme that utilizes DMC algorithm to deal with a large pure time delay in process and adopts nuclear radiation to measure thickness of battery separator. The system has been designed and implemented in one of the battery separator plants of China. And the method of measure and control has wide applicability for similar continuous thin material process.