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This paper studies global output feedback stabilization (OFS) control for non-strict feedback nonlinear systems with unknown functions (UFs). In order to deal with the UFs problem, a Lemma is proposed. This Lemma not only avoids the disadvantages of approximation methods, but also avoids the Assumptions of UFs. Therefore, the algorithm in the paper reduces the conservatism. Then, a control algorithm is proposed to solve the global OFS control problem of closed-loop systems. At the same time, the "explosion of terms" problem of backstepping is also solved. Finally, the algorithm is applied to Duffing system and Chua's oscillator system to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract This paper is concerned with the observer-based control problem for Markovian jump delay systems with parameter uncertainties using quantized measurements. The parameter uncertainties are assumed to be norm bounded. The aim is to design a suitable observer-based controller which guarantees the stochastic stability of the resulting closed-loop system with a prescribed mixed passivity and H ∞ performance index. A novel stability criterion is obtained by constructing a mode-dependent Lyapunov–Krasovskii functional based on the delay-partitioning technique. Then, with the novel stability criterion, sufficient conditions for the solvability of the presented observer-based controller design problem are derived. All the results obtained in this paper can be tackled by a feasibility problem in terms of linear matrix inequalities. Finally, three numerical examples are provided to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper revisits the issue of the stabilization for neutral type descriptor hybrid systems (NTDHSs) with time-varying delays. By a novel Lyapunov–Krasovskii functional (LKF) we constructed, a mode dependent and delay dependent criterion is acquired for the unforced NTDHSs to be stochastically admissible. On the basis of the criterion, a mode dependent state feedback controller (SFC) is designed such that the resulting closed-loop system is regular, impulse free and stochastically stable. By singular-value decomposition method, sufficient conditions are established via strict linear matrix inequality (LMI). And the gain of the SFC can be derived. It is worth noting that our results generalize the corresponding results published in latest literatures to some degree. The superiority and validity of our methods are displayed via two numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: In general, a system with time delay may have multiple stable delay intervals. Especially, a stable delay interval does not always contain zero. Asymptotically accurate stability conditions such as discretized Lyapunov–Krasovskii functional (DLF) method and sum-of-square (SOS) method are especially effective for such systems. In this article, a DLF-based method is proposed to estimate the maximal stable delay interval accurately without using bisection when one point in this interval is given. The method is formulated as a generalized eigenvalue problem (GEVP) of linear matrix inequalities (LMIs), and an accurate estimate may be reached by iteration either in a finite number of steps or asymptotically. The coupled differential–difference equation formulation is used to illustrate the method. However, the idea can be easily adapted to the traditional differential–difference equation setting. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the problem of exponential filter design for linear systems simultaneously with distributed delays, Markovian jumping parameters and norm-bounded parametric uncertainties. The purpose is to design full-order mode-dependent filters such that the filtering error system is not only mean-square robustly exponentially stable with a specified decay rate but also satisfies an performance requirement. First, sufficient conditions for the stability and performance analysis of the filtering error system are derived based on a novel version of mode-dependent Lyapunov–Krasovskii functional. Then, delay-dependent and decay-rate-dependent conditions for the existence of desired filters are obtained in terms of linear matrix inequalities (LMIs). The filter coefficients can be computed by using feasible solutions of the presented LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed design method. [Copyright &y& Elsevier]

Abstract: We are often faced with the problem of distinguishing between visually similar objects that share the same general appearance characteristics. As opposed to object categorization, this task is focused on capturing fine image differences in a pose-dependent fashion. Our research addresses this particular family of problems and is centered around the concept of learning from example pairs. Formally, we construct a parameterized visual similarity function optimally separating the pairs of images that depict the objects of the same class or identity from the pairs representing different object classes/identities. It combines various image distances that are quantified by comparing local descriptor responses at the corresponding locations in both paired images. To find the best combinations, we train ensembles of so-called Kernel Regression Trees which model the desired similarity function as a hierarchy of fuzzy decision stumps. The obtained function is then used within a k-NN-like framework to address complex multi-classification problems. Through the experiments with several image datasets we demonstrate the numerous advantages of the proposed approach: high classification accuracy, scalability, ease of interpretation and the independence of the feature representation. [Copyright &y& Elsevier]

Abstract: In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in the scene which is first appropriately filtered to extract the relevant colour information and establish the regions of interest. The tracker predicts the position and the scale of the detected sign candidate over time to reduce computation. The classifier compares a discrete-colour image of the observed sign with the model images with respect to the class-specific sets of discriminative local regions. They are learned off-line from the idealised template sign images, in accordance with the principle of one-vs-all dissimilarity maximisation. This dissimilarity is defined based on the so-called Colour Distance Transform which enables robust discrete-colour image comparisons. It is shown that compared to the well-established feature selection techniques, such as Principal Component Analysis or AdaBoost, our approach offers a more adequate description of signs and involves effortless training. Upon this description we have managed to build an efficient road sign recognition system which, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, demonstrates a competitive performance in the experiments involving real traffic video. [Copyright &y& Elsevier]

Abstract: This paper addresses the set-membership filtering problem for a class of discrete time-varying systems with sensor saturation in the presence of unknown-but-bounded process and measurement noises. A sufficient condition for the existence of set-membership filter is derived. A convex optimisation method is proposed to determine a state estimation ellipsoid that is a set of states compatible with sensor saturation and unknown-but-bounded process and measurement noises. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state by solving a time-varying linear matrix inequality. Simulation results are provided to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: We report on a high-efficiency 532nm green light conversion from an external cavity-enhanced second harmonic generation of a home-made 1064nm single-frequency Nd:YVO4 laser with a periodically poled KTP crystal. A stable green power of 60mW with a conversion efficiency of 75% was measured. Meantime, we investigate the quadrature amplitude noise of the green light at the same experimental setup and 0.6dB green light squeezing was experimentally observed (taking into account the total detection efficiency of 58%, the squeezing should be 1.1dB). The squeezing as a function of input power was also studied and we found qualitative agreement with theoretical prediction. [Copyright &y& Elsevier]

Abstract: In this paper, we show that two kinds of W-type entangled states can be generated from a non-degenerate two-crystal geometry parametric down-conversion and they are very useful for quantum teleportation and quantum cloning. We also show that quantum teleportation of unknown one-particle state from a sender to either one of three receivers can be realized probabilistically by using the four-photon entangled state directly from a degenerate two-crystal geometry parametric down-conversion. [Copyright &y& Elsevier]

Principal Component Analysis (PCA) has been of great interest in computer vision and pattern recognition. In particular, incrementally learning a PCA model, which is computationally efficient for large-scale problems as well as adaptable to reflect the variable state of a dynamic system, is an attractive research topic with numerous applications such as adaptive background modelling and active object recognition. In addition, the conventional PCA, in the sense of least mean squared error minimisation, is susceptible to outlying measurements. To address these two important issues, we present a novel algorithm of incremental PCA, and then extend it to robust PCA. Compared with the previous studies on robust PCA, our algorithm is computationally more efficient. We demonstrate the performance of these algorithms with experimental results on dynamic background modelling and multi-view face modelling. [Copyright &y& Elsevier]

Detecting faces across multiple views is more challenging than in a fixed view, e.g. frontal view, owing to the significant non-linear variation caused by rotation in depth, self-occlusion and self-shadowing. To address this problem, a novel approach is presented in this paper. The view sphere is separated into several small segments. On each segment, a face detector is constructed. We explicitly estimate the pose of an image regardless of whether or not it is a face. A pose estimator is constructed using Support Vector Regression. The pose information is used to choose the appropriate face detector to determine if it is a face. With this pose-estimation based method, considerable computational efficiency is achieved. Meanwhile, the detection accuracy is also improved since each detector is constructed on a small range of views. We developed a novel algorithm for face detection by combining the Eigenface and SVM methods which performs almost as fast as the Eigenface method but with a significant improved speed. Detailed experimental results are presented in this paper including tuning the parameters of the pose estimators and face detectors, performance evaluation, and applications to video based face detection and frontal-view face recognition. [Copyright &y& Elsevier]

We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the nonlinear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories. [Copyright &y& Elsevier]

In this paper, a partially mode-dependent dissipative filter design method is presented for uncertain Markovian jump systems with mixed delays and unknown transition rates. The uncertainty is assumed to be time-invariant which belongs to a ploytope. By constructing a mode-dependent and parameter-dependent Lyapunov–Krasovskii functional based on delay-partitioning approach, we propose a sufficient condition for the existence of a partially mode-dependent dissipative filter, which ensures that the resulting filtering error system is stochastically stable and dissipative. The filter parameters can be derived by using feasible solutions to a certain set of linear matrix inequalities. Finally, two simulation examples are provided to illustrate the effectiveness of the filter design technique. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization problem for neutral time-delay systems with actuator saturation. Different from the existing techniques, the auxiliary time-delay feedback is introduced for the first time in this paper. Based on such a technique, the saturation nonlinearity is represented as the convex combination of state feedback and auxiliary time-delay feedback. By employing free-weighting matrix technique and Jensen integral inequalities, and performing the accurate estimation of the lower bounds of L–K functionals, the improved delay-dependent local stabilization conditions are proposed in terms of linear matrix inequalities (LMIs). Numerical examples illustrate the reduced conservatism of the proposed conditions in this paper. [ABSTRACT FROM AUTHOR]

Malignant tumors and central nervous system (CNS) disorders are intricately linked to a process known as "aberrant cell cycle re-entry," which plays a critical role in the progression of these diseases. Addressing the dysregulation in cell cycles offers a promising therapeutic approach for cancers and CNS disorders. MicroRNAs (miRNAs) play a crucial role as regulators of gene expression in cell cycle transitions, presenting a promising therapeutic avenue for treating these disorders and their comorbidities. This review consolidates the progress made in the last three years regarding miRNA-based treatments for diseases associated with aberrant cell cycle re-entry. It encompasses exploring fundamental mechanisms and signaling pathways influenced by miRNAs in cancers and CNS disorders, particularly focusing on the therapeutic effects of exosome-derived miRNAs. The review also identifies specific miRNAs implicated in comorbidity of cancers and CNS disorders, discusses the future potential of miRNA reagents in managing cell cycle-related diseases. [Display omitted] • Linking miRNAs to cell cycle dysregulation in cancers and CNS disorders. • Highlighting exosome-derived miRNAs in the treatment of cancers and CNS disorders. • Revealing the shared mechanism of miRNAs-based interventions in cancers and CNS disorders. • Identifying specific miRNAs in comorbidity of cancers and CNS disorders. • Discussing the challenges of miRNA reagents in disease management. [ABSTRACT FROM AUTHOR]

Abstract: This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria. [Copyright &y& Elsevier]

Abstract: Two sufficient conditions, which ensure the existence and uniqueness of the equilibrium point and global exponential stability of bi-directional associative memory (BAM) neural networks with distributed delays and reaction–diffusion terms, are obtained by using the theory of topological degree, properties of M-matrix, Lyapunov functional, and analysis technique. The two sufficient conditions are independent of each other. The results remove the usual assumption that the activation functions are of bounded character. Exponential converging velocity index is estimated, which depends on the delay kernel functions and system parameters. Two numerical examples are given to show the correctness of our analysis. These results can be applied to design globally exponentially stable networks and thus have important significance in both theory and applications. [Copyright &y& Elsevier]