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In this study, the authors investigate the state response for continuous‐time antilinear dynamical systems. First, they propose the concept of matrix anti‐exponential function, and then derived some nice properties of this new function. With the matrix anti‐exponential function as an effective tool, they obtain a closed‐form expression for the state response of continuous‐time antilinear systems. Finally, they derived an expression with finite terms for the proposed matrix anti‐exponential function, which will be useful for numerical implementation. [ABSTRACT FROM AUTHOR]

Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional feature space with possible better discriminative structure. In this paper, we propose a supervised manifold learning approach called SubManifold Individuality LEarning (SMILE). In SMILE, the linear subspace derived from the principal component analysis based on data with the same label is named as 'the individual subspace', while the linear subspace learned from all data is defined as 'the global subspace'. For each data sample, the aim of SMILE is to enlarge the diversity between its reconstructed data from individual subspace and global subspace, respectively, so that the intrinsic character of each class can be stimulated in the feature space. SMILE also utilizes the Laplacian matrix to restrict the local structure of data in the low-dimensional feature space in order to preserve the locality of the high-dimensional data. The proposed method is validated in appearance-based face recognition problem on some typical facial image databases via extracting discriminative features. Experimental results show that the proposed approach can obtain the discriminative structure of facial manifold and extract better features for face recognition than other counterparts approaches. [ABSTRACT FROM AUTHOR]

This article proposes a novel modeling method for the stochastic nonlinear degradation process by using the relevance vector machine (RVM), which can describe the nonlinearity of degradation process more flexibly and accurately. Compared with the existing methods, where degradation processes are modeled as the Wiener process with a nonlinear drift function formulized as the power law or exponential law, this kind of modeling method can characterize degradation processes with more nonlinear behavior. Instead of modeling the drift coefficient of the Wiener process directly, the weighted combination of basis functions is utilized to express the increment of the Wiener process and the parameters are calculated by a sparse Bayesian learning algorithm. Based on the proposed model, a numerical approximation formula for the probability density function (PDF) of the remaining useful life (RUL) is derived. Finally, comparison studies, including a numerical simulation and a practical case, are provided to demonstrate the effectiveness and the accuracy of the proposed methods for RUL estimation. [ABSTRACT FROM AUTHOR]

Sparse subspace clustering (SSC) has been widely employed in machine learning and pattern recognition, but it still faces scalability challenges when dealing with large-scale datasets. Recently, stochastic SSC (SSSC) has emerged as an effective solution by leveraging the dropout technique. However, SSSC cannot robustly handle noise, especially non-Gaussian noise, leading to unsatisfactory clustering performance. To address the above issues, we propose a novel robust and stochastic method called stochastic sparse subspace clustering with the Huber function (S3CH). The key idea is to introduce the Huber surrogate to measure the loss of the stochastic self-expression framework, thus S3CH inherits the advantage of the stochastic framework for large-scale problems while mitigating sensitivity to non-Gaussian noise. In algorithms, an efficient proximal alternating minimization (PAM)-based optimization scheme is developed. In theory, the convergence of the generated sequence is rigorously proved. Extensive numerical experiments on synthetic and six real datasets validate the advantages of the proposed method in clustering accuracy, noise robustness, parameter sensitivity, post-hoc analysis, and model stability. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive dynamic event-triggered robust finite-horizon suboptimal control scheme for multi-player nonzero-sum game of nonlinear systems with input disturbances based on dynamic programming (ADP) and integral sliding mode (ISM) control techniques. Firstly, dynamic event-triggered adaptive ISM controllers are designed to handle input disturbances that can force the system state to remain on the sliding manifold and relax the known upper bounds condition of input disturbances. Corresponding event-triggered condition is employed for ISM controller of each player to reduce the update frequency. Then, utilizing the obtained ISM dynamics, we employ ADP-based single critic neural networks to approximate the time-varying solution of the Hamilton–Jacobi equations. Thus, the event-triggered finite-horizon suboptimal controllers can be obtained, and corresponding state-based dynamic event-triggered condition is designed to improve the resource utilization. The stability property of the closed-loop system is demonstrated through the Lyapunov technique. Finally, the effectiveness of the proposed control scheme is confirmed through two simulation examples. [ABSTRACT FROM AUTHOR]

Due to the complexity and uncertainty of the nonlinear autonomous underwater glider (AUG) system, the control algorithms for attitude tracking of the AUG system are very difficult to directly design. In this paper, a novel autotuning control algorithm (ATCA) based on relay feedback and adaptive neural network is proposed to effectively implement the attitude tracking of the AUG system. The proposed algorithm only utilizes the online input/output (I/O) data to achieve the AUG system attitude control, ignoring the mathematical system model. The ATCA control parameters are initialized by relay feedback and adjusted online based on gradient descent algorithm with the partial derivative of the AUG system provided by adaptive neural network. Besides, in the ATCA, the fast adaptive learning factor is employed to make the AUG system respond quickly to the evolving reference trajectory. Furthermore, the complete stability of the closed-loop AUG system with the ATCA has been proven via the Lyapunov stability theory. The simulation studies illustrate the correctness the proposed algorithm. Compared with three popular data driven control algorithms, the proposed algorithm has superiority in terms of system response time, integral squared error (ISE) and integral absolute error (IAE). © 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved. • A data-driven algorithm named ATCA is proposed to achieve the AUG attitude control without relying on AUG system model. • Relay feedback solves the problem of controller parameters initialization, and only one learning factor is manually set. • The complete stability of closed-loop AUG system with proposed ATCA has been proven via Lyapunov stability theory. • In comparation with MFAC, LM-PIDNN-RF and RFPID, the simulation results of the AUG indicate the superiority of the ATCA. [ABSTRACT FROM AUTHOR]

• An improved DBSCAN method is proposed for 3D intersecting-plane segmentation which can detect the correct boundary points. • An approach for automatic point selection is proposed for modeling a plane. • An adaptive coplanar threshold is designed for differentiating planes. This paper introduces a new DBSCAN-based method for boundary detection and plane segmentation for 3D point clouds. The proposed method is based on candidate samples selection in 3D space and plane validity detection via revising the classical DBSCAN clustering algorithm to obtain a valid fitting plane. Technically, a coplanar threshold is designed as an additional clustering condition to group 3D points whose distances to the fitting plane satisfy the constraint of the threshold as one cluster. The threshold value is automatically adjusted to fit the local distribution of samples in the input dataset, which is free of parameter tuning. Planar objects can be detected by the proposed method since a cluster contains only data points belonging to one plane, and the boundaries among different planes can be correctly detected. Experimental evaluations are performed on both synthetic and real point cloud datasets. Results show that the proposed approach is effective for planar segmentation and high-quality segmentation of intersection boundaries. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a new face recognition method based on two-dimensional locality preserving projections (2DLPP) in frequency domain. For this purpose, we first introduce the two-dimensional locality preserving projections. Then the 2DLPP in frequency domain is proposed for face recognition. In fact, two dimensional discrete cosine transform (2DDCT) is used as a pre-processing step and it transforms the face image signals from spatial domain into frequency domain aiming to reduce the effects of illumination and pose changes in face recognition. Then 2DLPP is applied on the upper left corner blocks of the 2DDCT transformed matrices, which represent main energy of each original image. For demonstration, the Olivetti Research Laboratory (ORL), YALE, FERET and YALE-B face datasets are used to compare the proposed approach with the conventional 2DLPP and 2DDCT approaches with the nearest neighborhood (NN) classifier. The experimental results show that the proposed 2DLPP in frequency domain is superior over the 2DLPP in spatial domain and 2DDCT itself in frequency domain. [Copyright &y& Elsevier]

Abstract: In this paper we propose two new operators for complex polynomial matrices. One is the conjugate product and the other is the Sylvester-conjugate sum. Then some important properties for these operators are proved. Based on these derived results, we propose a unified approach to solving a general class of Sylvester-polynomial-conjugate matrix equations, which include the Yakubovich-conjugate matrix equation as a special case. The complete solution of the Sylvester-polynomial-conjugate matrix equation is obtained in terms of the Sylvester-conjugate sum, and such a proposed solution can provide all the degrees of freedom with an arbitrarily chosen parameter matrix. [Copyright &y& Elsevier]

• Different from the point cloud representation of the Cartesian coordinate system, a novel rotation-independent auxiliary network is proposed with the aid of the spherical coordinate system. • In order to cope with the challenge of feature extraction caused by the disorder of point cloud data itself, a novel graph convolution network was proposed. • In view of the particularity of point cloud data, how to effectively extract its global and local features and how to deal with the training problem of point cloud unbalanced data is also considered in this study. Point cloud data can be produced by many depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras, and they are widely used in broad applications of robotic navigation and remote-sensing for the understanding of environment. Hence, new techniques for object representation and classification based on 3D point cloud are becoming increasingly in high demand. Due to the irregularity of the object shape, the point cloud-based object recognition is a very challenging task, especially the pose variances of a point cloud will impose many difficulties. In this paper, we tackle the challenge of pose variances in object classification based on point cloud by developing a novel end-to-end pose robust graph convolutional network. Technically, we first represent the point cloud using the spherical system instead of the traditional Cartesian system for simplicity of computation and representation. Then a pose auxiliary network is constructed with an aim to estimate the pose changes in terms of rotation angles. Finally, a graph convolutional network is constructed for object classification against the pose variations of point cloud. The experimental results show the new model outperforms the existing approaches (such as PointNet and PointNet++) on the classification task when conducting experiments on both the ModelNet40 and the ShapeNetCore dataset with a series of random rotations of a 3D point cloud. Specifically, we obtain 73.02% accuracy for classification task on the ModelNet40 with delaunay triangulation algorithm, which is much better than the state of the art algorithms, such as PointNet and PointCNN. [ABSTRACT FROM AUTHOR]

The traditional robust principal component analysis (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications in computer vision. However, the reconstructed image experiences serious interference by Gaussian noise, resulting in the degradation of image quality during the denoising process. Thus, a novel manifold constrained joint sparse learning (MCJSL) via non-convex regularization approach is proposed in this paper. Morelly, the manifold constraint is adopted to preserve the local geometric structures and the non-convex joint sparsity is introduced to capture the global row-wise sparse structures. To solve MCJSL, an efficient optimization algorithm using the manifold alternating direction method of multipliers (MADMM) is designed with closed-form solutions and it achieves a fast and convergent procedure. Moreover, the convergence is analyzed mathematically and numerically. Comparisons among the proposed MCJSL and some state-of-the-art solvers, on several accessible datasets, are presented to demonstrate its superiority in image denoising and background subtraction. The results indicate the importance to incorporate the manifold learning and non-convex joint sparse regularization into a general RPCA framework. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is an efficient dimension reduction technique, which has been extensively used in the fields, such as image processing, automatic control, and machine learning. The application to fault detection (FD) is still not investigated sufficiently. To improve the performance of NMF-based FD approaches, this article proposes a novel FD approach using the structured joint sparse NMF (SJSNMF) for non-Gaussian processes. The basic idea of SJSNMF is to incorporate the graph Laplacian to preserve the relationship between process variables and operation units and introduce the joint sparsity to exploit row-wise sparsity of the latent variables. Technically, an optimization algorithm based on the alternating direction method of multipliers (ADMM) is established. To detect the fault, two test statistical metrics are adopted and the kernel density estimation (KDE) is calculated to estimate the control limit. The effectiveness of the proposed SJSNMF is verified on the benchmark Tennessee Eastman process (TEP) and the cylinder–piston assembly of diesel engines. [ABSTRACT FROM AUTHOR]

Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research. • Summarize different methods in automatic retinal arteriovenous classification. • Compile the public datasets and their annotation improvement processes. • Summarize the flaws of existing evaluation systems and propose improvements. • Identify research future trends through qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

• A modular cable-driven snake-like manipulator (MCDSM) is designed. • The modified kinematic model and dynamic model of the MCDSM is developed. • A self-calibration method using redundant cable length is proposed. • An optimization method for cable tension in joint space is proposed. • A force control and compliant control method for the MCDSM is presented. The high flexibility of cable-driven snake-like manipulators (CDSM) makes them well-suited for tasks in narrow and unstructured spaces. However, ensuring both stiffness and load capacity while maintaining good compliance for safe human-robot interaction is challenging. We design a modular cable-driven snake-like manipulator (MCDSM) and improve its active compliance through impedance control. Firstly, the detachable arm segments of the MCDSM can adapt to multiple tasks. The modular drive units and plug-in drive boxes facilitate quick installation and maintenance. Furthermore, the modified kinematic model considers the torsion angle information of the detachable arm segments. Based on this model, a self-calibration method using redundant cable length is proposed for cable space to joint space. Moreover, a dynamic model for the MCDSM is established. An optimization method for cable tension in joint space is proposed to eliminate the coupling phenomenon during tension control. Finally, a compliant control method for the MCDSM is presented by combining the inner loop for the cable tension control. The feasibility of the proposed methods is demonstrated through simulations and experiments with a prototype. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the "robust" comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA. • To the best of author's knowledge, we are the first to model PCA in such a sparse and hypergraph Laplacian regularized framework. • An efficient alternating direction method of multipliers (ADMM) with convergence analysis is developed to optimize LRPCA. • Experiments on a simulation example and the TE practical process are conducted to illustrate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE: To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS: We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity. To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS: Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS: Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models. [ABSTRACT FROM AUTHOR]

One of the most significant challenges for an unmanned aerial vehicle (UAV) is to autonomously navigate in complex environments, as the signals from the global positioning system (GPS) are subject to disturbance and interference. To improve the autonomy and availability of the UAV navigation system without GPS, we design a new autonomous navigation system and implement it for real applications in this paper, in which one integrates the inertial measurement unit (IMU), the bionic polarization sensor (BPS), and the air data system (ADS). The BPS can provide effective heading angle measurement, and the ADS is used to output information for continuous velocity and height. The combination of BPS and ADS is a solution the inertial error drift. Kalman filter is selected to estimate the error state of the integrated navigation system based on the measurements from the BPS and ADS, and then the estimation is used to correct the navigation system error in real time. The simulation and experimental results have shown that the new integrated navigation system can perform with high precision and autonomy without GPS signal. [ABSTRACT FROM AUTHOR]

[Display omitted] • A real-time digital meter reading recognition system in natural scenes is proposed; • A YOLO-style corner point detection method for reading area is proposed; • A YOLO-style end-to-end reading recognition method is proposed; • The digital meter reading recognition method has high accuracy and practicality; • A challenging dataset for digital meter readings was produced. Natural scenes with variable illumination, variable target scale and angular tilt pose significant challenges to the autonomous recognition of digital meter readings. Based on this, this paper proposes a deep learning-based autonomous real-time digital meter reading recognition method for natural scenes. First, the YOLO-style corner point detection method (YOLO-CPDM) for the reading area is proposed by reconstructing the detection heads and incorporating the corner detection loss function. Its localization accuracy is further refined by embedding attention mechanism module, implementing dynamic loss function and enhancing training data diversity through offline augmentation techniques like image rotation and flipping. Then, the detected corner points are used to geometrically correct the distorted reading area by perspective transformation to mitigate the interference caused by the shooting angle. Next, the YOLO-style end-to-end reading recognition method (YOLO-EERRM) is proposed to accurately extract the characters in the reading area. Finally, the validity of the YOLO-CPDM and YOLO-EERRM was verified on a produced dataset named SYSU-DM and 2 public datasets. Compared with the State of the arts (SOTA) keypoint detection model, the mean Average Precision @ 50:95 scores of the YOLO-CPDM improved by 2.8, 4.1, and 1.1 points, respectively, while the inference latency was only 5.3 ms, and YOLO-EERRM achieved 100 % accuracy and 3.1 ms inference latency on the SYSU-DM dataset. Statistically, the complete digital meter reading recognition method has 99.6 % accuracy and 8.6 ms inference latency, indicating that the system has high recognition accuracy and practicality. [ABSTRACT FROM AUTHOR]

Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods. [ABSTRACT FROM AUTHOR]

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702. [ABSTRACT FROM AUTHOR]

This paper proposes a new semantic concept extraction method to choose the salient features for representing multi-ethnic face characteristics based on axiomatic fuzzy set (AFS) theory. It has two advantages, one is that it could well convert the facial features to semantic concepts by bridging the semantic gap between image features and interpretable concepts; the other is that it could be considered as a dimension reduction method to preserve salient features for describing ethnic groups. Firstly, We build facial features to describe face with the landmarks of facial components, such as eyes, mouth and face contour, etc. , and then transform these facial features into semantic concepts. Secondly, a new approach is proposed to obtain the complex semantic concept sets of each ethnic group through clustering simple semantic concept based on AFS framework, and construct an optimal criterion to obtain valid semantic concepts of each ethnic group. Thirdly, we select the typical facial features which are corresponding to the semantic concepts to represent the ethnical face characteristic. Finally, we conduct experiments on Chinese Ethnic Face Database (CEFD), FEI and CK+ database to verify the effectiveness of our method. The K-means and fuzzy c-means (FCM) are used to verify the performance for describing multi-ethnic facial characteristics with the salient facial features. Specially, the obtained results demonstrate the efficacy of our approach, as the semantic concepts generated by optimal model can have an excellent interpretability and comprehension for the facial features. In addition, there is a comparative analysis between our method and other feature selection methods. [ABSTRACT FROM AUTHOR]

• A new similarity measure is proposed based on point-to-plane distance. This metric is able to give high and low similarities between coplanar and non-coplanar points, and reconstructing point clouds in 3D space can simplify the segmentation problem. • Using the nearest neighbor weighted method to estimate the local density, the obtained density distribution of the reconstructed point cloud is more accurate. • In order to overcome the difficulty associated with the selection of DBSCAN parameters due to high sensitivity in the existing work, an adaptive method is developed to cluster reconstructed point clouds. Experiments show that it can achieve the same or even better performance. This paper proposes an effective measure for the planar segmentation problem based on the clustering method. It uses the distance from a point to the local plane as a metric to characterize the relationship between data. As a result, the data points of the coplanar have a high similarity to distinguish each plane. A dissimilarity matrix of the input point cloud can be evaluated, and multidimensional scaling analysis is performed to reconstruct the correlation information between data points in the 3D Euclidean space. The obtained reconstructed point cloud shows the separation between different planes. An adaptive DBSCAN clustering method based on density stratification is developed to perform cluster segmentation on the reconstructed point cloud. Experimental results show that the proposed method can effectively solve the over-segmentation problem, and at the same time provide high segmentation accuracy. [ABSTRACT FROM AUTHOR]

In routine laparoscopic surgeries, the fatigue operation and improper chief-assistant coordination of the assistant surgeon both reduce the stability of the endoscopic field of view (EFOV) and the surgical efficiency. In contrast, the assistant endoscope-holding robot (AEHR) can stably hold the endoscope and adjust the pose of endoscopic image sensor. However, the traditional method simplifies the tracking of surgical instrument tips (SITs) to the tracking of feature points, and lacks the depth optimization of monocular endoscopic image senor. Based on them, this paper proposes a surgical instrument tracking control method based on visual tracking features (VTFs) and hand-eye coordination (HEC) with feedback of multiple sensors, which can control the depth and axial rotational degrees of freedom (DOFs) of endoscope while tracking SITs. Firstly, the motion mapping Jacobian matrix between the endoscopic image sensor and the endoscope-holding manipulator (EHM) end-effector is constructed with joint encoder feedbacks, and the VTFs of endoscopic camera feedback are extracted based on the deep segmentation model of SITs. Secondly, the position adjustment model based on VTFs and the attitude adjustment model based on HEC are established. Thirdly, a complete set of visual servo control (VSC) software and hardware system for AEHRs is designed. Finally, the correctness, effectiveness and operability of the proposed method are comprehensively verified through the simulation and experiment of surgical instrument tracking. The visual tracking distance and radius error of endoscopic image sensor frames are reduced by no less than 34.6% and 42.59% under different experimental backgrounds, respectively. [Display omitted] • An endoscopic pose control method based on VTFs and HEC is established. • An endoscopic position adjustment model is derived. • A VSC experimental platform and simulation system for AEHR is reconstructed. • An open-source semantic segmentation data set for surgical instrument tips is provided. • The system modeling and instrument tracking performance are verified. [ABSTRACT FROM AUTHOR]

Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. It requires robust and appropriate affinity graphs as input in order to form clusters with desired structures. Constructing such affinity graphs is a nontrivial task due to the ambiguity and uncertainty inherent in the raw data. Most existing spectral clustering methods typically adopt Gaussian kernel as the similarity measure, and employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, especially when the number of features is large. In this paper, we propose a novel unsupervised approach, named Axiomatic Fuzzy Set-based Spectral Clustering (AFSSC), to generate more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces to more accurately reveal the latent data distribution and thereby lead to improved data clustering. We demonstrate the efficacy of the proposed approach on different kinds of data. The results have shown the superiority of the proposed approach compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper we propose a robust face recognition algorithm for low resolution RGB-D Kinect data. Many techniques are proposed for image preprocessing due to the noisy depth data. First, facial symmetry is exploited based on the 3D point cloud to obtain a canonical frontal view image irrespective of the initial pose and then depth data is converted to XYZ normal maps. Secondly, multi-channel Discriminant Transforms are then used to project RGB to DCS (Discriminant Color Space) and normal maps to DNM (Discriminant Normal Maps). Finally, a Multi-channel Robust Sparse Coding method is proposed that codes the multiple channels (DCS or DNM) of a test image as a sparse combination of training samples with different pixel weighting. Weights are calculated dynamically in an iterative process to achieve robustness against variations in pose, illumination, facial expressions and disguise. In contrast to existing techniques, our multi-channel approach is more robust to variations. Reconstruction errors of the test image (DCS and DNM) are normalized and fused to decide its identity. The proposed algorithm is evaluated on four public databases. It achieves 98.4% identification rate on CurtinFaces, a Kinect database with 4784 RGB-D images of 52 subjects. Using a first versus all protocol on the Bosphorus, CASIA and FRGC v2 databases, the proposed algorithm achieves 97.6%, 95.6% and 95.2% identification rates respectively. To the best of our knowledge, these are the highest identification rates reported so far for the first three databases. [ABSTRACT FROM AUTHOR]

The problem of finding a sparse solution for linear equations has been investigated extensively in recent years. This is an NP-hard combinatorial problem, and one popular method is to relax such combinatorial requirement into an approximated convex problem, which can avoid the computational complexity. Recently, it is shown that a sparser solution than the approximated convex solution can be obtained by solving its non-convex relaxation rather than by solving its convex relaxation. However, solving the non-convex relaxation is usually very costive due to the non-convexity and non-Lipschitz continuity of the original problem. This difficulty limits its applications and possible extensions. In this paper, we will consider the non-convex relaxation problem with the nonnegative constraint, which has many applications in signal processing with such reasonable requirement. First, this optimization problem is formulated and equivalently transformed into a Lipschitz continuous problem, which can be solved by many existing optimization methods. This reduces the computational complexity of the original problem significantly. Second, we solve the transformed problem by using an efficient and classical limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Finally, some numerical results show that the proposed method can effectively find a nonnegative sparse solution for the given linear equations with very low computational cost. [ABSTRACT FROM AUTHOR]

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, ‘shared information’ may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ 1 - norm from SRC and ℓ 2 , 1 - norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ 1 - norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition. [ABSTRACT FROM AUTHOR]

Abstract: By two recently proposed operations with respect to complex matrices, a simple explicit solution to the Sylvester-conjugate matrix equation is given in a finite series form. The obtained solution can also be equivalently expressed in terms of the so-called controllability-like matrix and observability-like matrix. The proposed solution can provide all the degrees of freedom which is represented by a free parameter matrix. An illustrative example is employed to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: Some concepts, such as divisibility, coprimeness, in the framework of ordinary polynomial product are extended to the framework of conjugate product. Euclidean algorithm for obtaining greatest common divisors in the framework of conjugate product is also established. Some criteria for coprimeness are established. [Copyright &y& Elsevier]

Abstract: Two operations are introduced for complex matrices. In terms of these two operations an infinite series expression is obtained for the unique solution of the Kalman–Yakubovich-conjugate matrix equation. Based on the obtained explicit solution, some iterative algorithms are given for solving this class of matrix equations. Convergence properties of the proposed algorithms are also analyzed by using some properties of the proposed operations for complex matrices. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of complementarity problems with non-Lipschitzian functions arising from the reaction and diffusion problems. We develop a monotone iterative algorithm for this class of complementarity problems. The sequence generated by the proposed algorithm is monotonically decreasing and converges to the solution of the complementarity problems. Finally, numerical results are reported to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• This paper proposes densely connected convolutional networks (DenseNets) for SHM. • DenseNets is applied to perform vibration based structural damage identification. • Dense block is used to alleviate the gradient vanishing and strengthen feature flow. • DenseNets is developed as an efficient feature and robust feature extractor. • Numerical and experimental studies are conducted to validate the proposed approach. Vibration-based damage identification has been a challenging task in structural health monitoring. The main difficulty lies on the reliable correlation between the measured vibration characteristics and damage states (e.g., stiffness reductions) of structures. Such states can ideally indicate the presence, locations, and severities of structural damages. The procedure is considered as a feature extraction process from the input measurement, mapping the selected features to damage states. Time domain vibration responses, e.g., acceleration responses, are used in this study for damage identification. To address this pattern recognition problem, many methods have been developed including various neural networks in recent years. This paper proposes a novel approach based on densely connected convolutional networks (DenseNets), which is one of the major breakthroughs in the computer vision community, for vibration based structural damage identification. It implements dense connectivity in the convolutional neural network architecture, which fits well for this study using acceleration responses. Both low-level and high-level features are learned and reused during training. It not only eases the information flow during training, but also preserves all levels of features and tends to be more effective for damage identification. Besides, the dense connectivity alleviates the gradient vanishing problem and strengthens feature propagation through the network. In the meantime, these designs substantially reduce the number of parameters, making the network easy to train. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Both modelling uncertainties and measurement noises are considered in numerical studies. The results from numerical and experimental studies demonstrate that the damage localization and quantification are achieved with high accuracies (e.g., Regression value ≥ 96.0% on numerical datasets, and ≥ 94.9% on experimental datasets) and good robustness. [ABSTRACT FROM AUTHOR]

Total variation (TV) is broadly utilized in image processing because it is able to preserve sharp edges and object boundaries, which are usually the most important parts of an image. Recently, the non-convex functions such as the smoothly clipped absolute deviation, the minimax concave penalty, the capped ℓ 1 -norm penalty and the ℓ p quasi-norm with 0 < p < 1 have been shown remarkable advantages in sparse learning due to the fact that they can overcome the over-penalization associated with the ℓ 1 -norm. In this paper, an efficient non-convex total variation approach for image deblurring and denoising model has been proposed, which combines a non-convex regularization term and a non-convex data fitting term perfectly. Firstly, the non-convex functions are employed into the regularization term and the fidelity term for enhancing the sensitivity to sharp edges and object boundaries. Secondly, the optimizing minimization method based on the alternating direction method of multipliers (ADMM) is proposed to solve the non-convex total variation optimization problem. The resulting subproblems either have closed-form solutions or can be solved by fast solvers, which makes the ADMM particularly efficient. In theory, with the help of the smoothing technique and Kurdyka-Lojasiewicz function, we prove that the sequence generated by the ADMM converges to a stationary point when the penalty parameter is above a computable threshold. The numerical experiments illustrate that our proposed non-convex total variation model outperforms the existing convex and non-convex total variation models. [ABSTRACT FROM AUTHOR]

• We are the first to solve the dual formulation rather than itself. This is very important in high-dimensional problems. For example, XTX and YYY are of size p × p and q × q. For the dual optimization problem, we only need XXT and YYT of size n × n. Thus, the cost is reduced from O (p 3) and O (q 3) to O (n 3)(n ≪ max (p, q)). • We develop a fast two-stage semi-smooth Newton augmented Lagrangian method (tSSNALM) to optimize SCCA model, and the resulting subproblems either have closed-form solutions or can be solved by fast solvers. In addition, we prove that the sequence generated by tSSNALM converges to a local optimum. • We conduct a variety of simulated examples to demonstrate that our proposed tSSNALM can achieve better performance than the existing state-of-the-art solvers CoLaR and AMA. Canonical correlation analysis (CCA) is a very useful tool for measuring the linear relationship between two multidimensional variables. However, it often fails to extract meaningful features in high-dimensional settings. This motivates the sparse CCA problem, in which ℓ 1 constraints are applied to the canonical vectors. Although some sparse CCA solvers exist in the literature, we found that none of them is efficient. We propose a fast two-stage semi-smooth Newton augmented Lagrangian method (tSSNALM) to solve sparse CCA problems, and we provide convergence analysis. Numerical comparisons between our approach and a number of state-of-the-art solvers, on simulated data sets, are presented to demonstrate its efficiency. To the best of our knowledge, this is the first time that duality has been integrated with a semi-smooth Newton method for solving sparse CCA. [ABSTRACT FROM AUTHOR]

Residual blocks have been widely used in deep learning networks. However, information may be lost in residual blocks due to the relinquishment of information in rectifier linear units (ReLUs). To address this issue, invertible residual networks have been proposed recently but are generally under strict restrictions which limit their applications. In this brief, we investigate the conditions under which a residual block is invertible. A sufficient and necessary condition is presented for the invertibility of residual blocks with one layer of ReLU inside the block. In particular, for widely used residual blocks with convolutions, we show that such residual blocks are invertible under weak conditions if the convolution is implemented with certain zero-padding methods. Inverse algorithms are also proposed, and experiments are conducted to show the effectiveness of the proposed inverse algorithms and prove the correctness of the theoretical results.

The canonical correlation analysis (CCA) has attracted wide attention in fault detection (FD). To improve the detection performance, we propose a new joint sparse constrained CCA (JSCCCA) model that integrates the l 2,0 -norm joint sparse constraints into classical CCA. The key idea is that JSCCCA can fully exploit the joint sparse structure to determine the number of extracted variables. We then develop an efficient alternating minimization algorithm using the improved iterative hard thresholding and manifold constrained gradient descent method. More importantly, we establish the convergence guarantee with detailed analysis. Finally, we provide extensive numerical studies on the simulated dataset, the benchmark Tennessee Eastman process, and a practical cylinder-piston process. In some cases, the computing time is reduced by 600 times, and the FD rate is increased by 12.62% compared with classical CCA. The results suggest that the proposed approach is efficient and fast.

Background: Colon adenocarcinoma (COAD) is a prevalent malignancy worldwide, yet, its underlying pathogenesis and genetic characteristics are still unclear. Previous studies have suggested that NADH dehydrogenase 1 alpha subcomplex subunit 4-like 2 (NDUFA4L2) may affect tumor progression across various cancers. However, this effect on COAD has rarely been reported. Thus, this study investigated NDUFA4L2's prognostic and diagnostic relevance and explored its potential connection with immune cell infiltration in COAD., Methods: To achieve this, RNA sequencing data from Cancer Genome Atlas (TCGA) was analyzed to assess NDUFA4L2's prognostic value in COAD, and factors relevant to the prognosis of COAD, including NDUFA4L2, were scrutinized using Kaplan-Meier analyses as well as univariate and multivariate Cox regression. A nomogram model was created to project prognosis based on the results of multivariate Cox analysis. Furthermore, gene set enrichment analysis (GSEA) was employed to pinpoint key NDUFA4L2-related pathways, and single-sample GSEA (ssGSEA) on TCGA data was employed to investigate the connections of NDUFA4L2 with cancer immune infiltrations., Results: Our findings revealed significant associations of high NDUFA4L2 expression with poor overall survival, progression-free interval, and disease-specific survival of COAD patients. GSEA indicated close links of NDUFA4L2 with several signaling pathways implicated in tumorigenesis, including extracellular matrix receptor interaction, the intestinal immune network for immunoglobulin A production, natural killer (NK) cell-mediated cytotoxicity, pathways in cancer, cell adhesion molecules, mitogen-activated protein kinase signaling pathway, Hedgehog signaling pathway, transforming growth factor beta signaling pathway, and chemokine signaling pathway. Additionally, ssGSEA identified a positive link between increased NDUFA4L2 expression and higher infiltration degree of various immune cells, such as immature dendritic cells, macrophages, NK cells and dendritic cells., Conclusions: Collectively, our findings demonstrate the association of increased NDUFA4L2 expression with adverse prognosis and heightened immune cell infiltration in COAD patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This paper presents a novel approach to multi-view graph learning that combines weight learning and graph learning in an alternating optimization framework. Multi-view graph learning refers to the problem of constructing a unified affinity graph using heterogeneous sources of data representation, which is a popular technique in many learning systems where no prior knowledge of data distribution is available. Our approach is based on a fusion-and-diffusion strategy, in which multiple affinity graphs are fused together via a weight learning scheme based on the unsupervised graph smoothness and utilised as a consensus prior to the diffusion. We propose a novel multi-view diffusion process that learns a manifold-aware affinity graph by propagating affinities on tensor product graphs, leveraging high-order contextual information to enhance pairwise affinities. In contrast to existing multi-view graph learning approaches, our approach is not limited by the quality of initial graphs or the assumption of a latent common subspace among multiple views. Instead, our approach is able to identify the consistency among views and fuse multiple graphs adaptively. We formulate both weight learning and diffusion-based affinity learning in a unified framework and propose an alternating optimization solver that is guaranteed to converge. The proposed approach is applied to image retrieval and clustering tasks on 16 real-world datasets. Extensive experimental results demonstrate that our approach outperforms state-of-the-art methods for both retrieval and clustering on 13 out of 16 datasets.

Background: Cerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP., Objective: This paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data., Methods: Custom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective., Results: Upon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years., Conclusions: Anecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day., Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367422., (©Siavash Khaksar, Huizhu Pan, Bita Borazjani, Iain Murray, Himanshu Agrawal, Wanquan Liu, Catherine Elliott, Christine Imms, Amity Campbell, Corrin Walmsley. Originally published in JMIR Rehabilitation and Assistive Technology (https://rehab.jmir.org), 20.10.2021.)

Spermidine (Spd), spermine (Spm), and putrescine (Put), which are the most widely distributed cellular polyamines, are essential for normal growth and multiplication of both eukaryotic and prokaryotic cells. In this study, we identified the only putative polyamine transport system PotABCD in Streptococcus suis, a worldwide zoonotic Gram-positive pathogen causing lethal infections in humans and pigs. It was discovered that S. suis could uptake polyamines preferably Spd and Spm. By constructing a potA deleted mutant, we confirmed that PotABCD was responsible for polyamine uptake, and PotD bound to the protein of polyamines. The four PotABCD genes were co-transcribed with murB, a gene involved in peptidoglycan (PG) synthesis. Furthermore the roles of polyamine transport system in maintaining the PG structure were detected to understand the biological significance of this co-transcription. In contrast to the wild type, the mutant ΔpotA exhibited elongated chain length and abnormal cell division morphology. Phenotypic changes were attributed to be the up-regulation of genes involved in PG synthesis and hydrolysis in ΔpotA. Additionally, polyamines functioned not only as feedback regulators of PotA by inhibiting PotA activity but also as regulators on potABCD and genes involved in PG synthesis. This study reveals the functions of PotABCD in polyamine transport and the regulatory roles of polyamines in PG synthesis. Results provide new insights into the machineries contributing to normal growth and cell division of S. suis., (Copyright © 2017 Elsevier GmbH. All rights reserved.)

Like eukaryotes, bacteria express one or more serine/threonine kinases (STKs) that initiate diverse signaling networks. The STK from Streptococcus suis is encoded by a single-copy stk gene, which is crucial in stress response and virulence. To further understand the regulatory mechanism of STK in S. suis , a stk deletion strain (Δ stk ) and its complementary strain (CΔ stk ) were constructed to systematically decode STK characteristics by applying whole transcriptome RNA sequencing (RNA-Seq) and phosphoproteomic analysis. Numerous genes were differentially expressed in Δ stk compared with the wild-type parental strain SC-19, including 320 up-regulated and 219 down-regulated genes. Particularly, 32 virulence-associated genes (VAGs) were significantly down-regulated in Δ stk . Seven metabolic pathways relevant to bacterial central metabolism and translation are significantly repressed in Δ stk . Phosphoproteomic analysis further identified 12 phosphoproteins that exhibit differential phosphorylation in Δ stk . These proteins are associated with cell growth and division, glycolysis, and translation. Consistently, phenotypic assays confirmed that the Δ stk strain displayed deficient growth and attenuated pathogenicity. Thus, STK is a central regulator that plays an important role in cell growth and division, as well as S. suis metabolism.

Glucose-inhibited division protein (GidA), is a tRNA modification enzyme functioning together with MnmE in the addition of a carboxymethylaminomethyl group to position 5 of the anticodon wobble uridine of tRNA. Here, we report a GidA homolog from a Chinese isolate SC-19 of the zoonotic Streptococcus suis serotype 2 (SS2). gidA disruption led to a defective growth, increased capsule thickness, and reduced hemolytic activity. Moreover, the gidA deletion mutant (ΔgidA) displayed reduced mortality and bacterial loads in mice, reduced ability of adhesion to and invasion in epithelial cells, and increased sensitivity to phagocytosis. The iTRAQ analysis identified 372 differentially expressed (182 up- and 190 down-regulated) proteins in ΔgidA and SC-19. Numerous DNA replication, cell division, and virulence associated proteins were downregulated, whereas many capsule synthesis enzymes were upregulated by gidA disruption. This is consistent with the phenotypes of the mutant. Thus, GidA is a translational regulator that plays an important role in the growth, cell division, capsule biosynthesis, and virulence of SS2. Our findings provide new insight into the regulatory function of GidA in bacterial pathogens.