Generating Series for Interconnected Nonlinear Systems and the Formal Laplace-Borel Transform

Formal power series methods provide effective tools for nonlinear system analysis. For a broad range of analytic nonlinear systems, their input-output mapping can be described by a Fliess operator associated with a formal power series. In this dissertation, the inter connection of two Fliess operators is characterized by the generating series of the composite system. In addition, the formal Laplace-Borel transform of a Fliess operator is defined and its fundamental properties are presented. The formal Laplace-Borel transform produces an elegant description of system interconnections in a purely algebraic context. Specifically, four basic interconnections of Fliess operators are addressed: the parallel, product, cascade and feedback connections. For each interconnection, the generating series of the overall system is given, and a growth condition is developed, which guarantees the convergence property of the output of the corresponding Fliess operator. Motivated by the relationship between operations on formal power series and system interconnections, and following the idea of the classical integral Laplace-Borel transform, a new formal Laplace-Borel transform of a Fliess operator is proposed. The properties of this Laplace-Borel transform are provided, and in particular, a fundamental semigroup isomorphism is identified between the set of all locally convergent power series and the set of all well-defined Fliess operators. A software package was produced in Maple based on the ACE package developed by the ACE group in Université de Marne-la Vallée led by Sébastien Veigneau. The ACE package provided the binary operations of addition, concatenation and shuffle product on the free monoid of formal polynomials. In this dissertation, the operations of composition, modified composition, chronological products and the evaluation of Fliess operators are implemented in software. The package was used to demonstrate various aspects of the new interconnection theory

Foreground Detection in Camouflaged Scenes

Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected once a good background model is obtained. However, there are many situations where this is not the case. Of particular interest in video surveillance is the camouflage case. For example, an active attacker camouflages by intentionally wearing clothes that are visually similar to the background. In such cases, even given a decent background model, it is not trivial to detect foreground objects. This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes. The proposed method employs the stationary wavelet transform to decompose the image into frequency bands. We show that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands. To make the final foreground decision, a weighted voting scheme is developed based on intensity and texture of all the wavelet bands with weights carefully designed. Experimental results demonstrate that the proposed method achieves superior performance compared to the current state-of-the-art results.Comment: IEEE International Conference on Image Processing, 201

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI

Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers

In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are employed as the activation function and there is a normalized operation in the hidden layer, the modification of the scale factor and translation factor in the wavelet functions are ignored to avoid the fluctuations of the error curves. When there are 30 neurons in the hidden layer, to achieve the same square sum error (SSE) $\epsilon_{min}=10^{-3}$, EWNN needs 31 iteration steps, while the basic Elman neural network (BENN) model needs 86 steps. The Volterra-Laguerre model has 605 parameters to be estimated but still can't achieve the same magnitude accuracy of EWNN. Simulation results show that the proposed approach of EWNN model has fewer parameters and higher accuracy than the Volterra-Laguerre model and its convergence rate is much faster than the BENN model

A study on the significance of anti-endothelial cell antibodies in chronic obstructive pulmonary disease and the effect of methylprednisolone intervention: DOI: 10.14800/ics.1210

Objective: The purpose was to confirm the significance of Anti-endothelial cell antibodies(AECA) in chronic obstructive pulmonary disease (COPD) and validate the effect of methylprednisolone intervention. Methods: We recruited 40 patients with stable COPD, 40 patients with an acute exacerbation of COPD, and 20 healthy volunteers from March 2019 to August 2021. The healthy volunteers constituted the healthy control group. All patients with stable COPD were divided into the mild-moderate COPD group and the severe-very severe COPD group, for whom AECA and vascular endothelial growth factor (VEGF) in peripheral blood were detected by ELISA. The patients with acute exacerbation of COPD were divided into routine treatment group and methylprednisolone group by random number method. The routine group received routine treatment, and the methylprednisolone group was treated with methylprednisolone on the basis of routine treatment, and the course of treatment was 1 week, respectively. The AECA and VEGF in the peripheral blood of the two groups of patients before and after treatment were detected by ELISA. Results: Compared to control group, the AECA concentration was significantly elevated as the condition of COPD got serious between mild-moderate COPD group and the severe-very severe COPD group(P<0.05). And VEGF concentration was significantly lower as the condition of COPD got serious(P<0.05). AECA concentration was significantly lower after methylprednisolone treatment than before in patients with COPD exacerbation, and significantly lower than patients receiving the routine treatment (P<0.05). Besides, VEGF concentration was significantly elevated in patients with COPD exacerbation after methylprednisolone treatment than before, and considerably higher than patients receiving the routine treatment (P<0.05).   Conclusion: AECAs may be involved in the occurrence and development of COPD and related to its severity. Methylprednisolone can help reduce AECA expression while promoting VEGF expression

The Formal Laplace-Borel Transform of Fliess Operators and the Composition Product

The formal Laplace-Borel transform of an analytic integral operator, known as a Fliess operator, is defined and developed. Then, in conjunction with the composition product over formal power series, the formal Laplace-Borel transform is shown to provide an isomorphism between the semigroup of all Fliess operators under operator composition and the semigroup of all locally convergent formal power series under the composition product. Finally, the formal Laplace-Borel transform is applied in a systems theory setting to explicitly derive the relationship between the formal Laplace transform of the input and output functions of a Fliess operator. This gives a compact interpretation of the operational calculus of Fliess for computing the output response of an analytic nonlinear system. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved

Cross Cultural Comparative Research of Online Consumer Reviews Intentions

A detailed literature review on motives of knowledge sharing in virtual communities is conducted. Moreover, the comparative study on intentions of consumer reviews between Amazon.com and Amazon.cn, based on an online open-ended survey through content analysis, is done. The analytical results show that users contribute their consumption experiences are mainly depended upon social motives, psychological motives and economic motives, which are deeply correlated and influenced by their national culture dimensions