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A letter to the editor discussing primary small cell carcinoma of the urinary bladder is presented.

A new formula for the gain of an open-ended rectangular waveguide is presented. The gain is simulated using commercial electromagnetic software and curve-fitted to derive a new formula. The proposed formula includes the effect of waveguide wall thickness. The accuracy of the proposed formula is compared with existing formulas and measured data. [ABSTRACT FROM AUTHOR]

This paper considers the problem of delay-dependent state estimation for neural networks with time-varying delays and stochastic parameter uncertainties. It is assumed that the parameter uncertainties are affected by the environment which is changed with randomly real situation, and its stochastic information such as mean and variance is utilized in the proposed method. By constructing a newly augmented Lyapunov-Krasovskii functional, a designing method of estimator for neural networks is introduced with the framework of linear matrix inequalities (LMIs) and a neural networks model with stochastic parameter uncertainties which have not been introduced yet. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of ℋ∞ performance and stability analysis for linear systems with interval time-varying delays. It is assumed that the parameter uncertainties are of stochastic properties to represent random change of various environments. By constructing a newly augmented Lyapunov-Krasovskii functional, less conservative criteria of the concerned systems are introduced with the framework of linear matrix inequalities (LMIs). Four numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stability analysis for neural networks with time-varying delays. By utilizing the Wirtinger based integral inequality and constructing a suitable augmented Lyapunov-Krasovskii functional, two less conservative delay dependent criteria to guarantee the asymptotic stability of the concerned networks are derived in terms of linear matrix inequalities (LMIs). Three numerical examples are included to explain the superiority of the proposed methods by comparing maximum delay bounds with the recent results published in other literature. [ABSTRACT FROM AUTHOR]

The problem of passivity analysis for neural networks with time-varying delays and parameter uncertainties is considered. By the consideration of newly constructed Lyapunov-Krasovskii functionals, improved sufficient conditions to guarantee the passivity of the concerned networks are proposed with the framework of linear matrix inequalities (LMIs), which can be solved easily by various efficient convex optimization algorithms. The enhancement of the feasible region of the proposed criteria is shown via two numerical examples by the comparison of maximum allowable delay bounds. [ABSTRACT FROM AUTHOR]

The problem of stability analysis and H∞ output tracking control for linear systems with time-varying delays is studied. First, by construction of a newly augmented Lyapunov-Krasovskii functional, a delay-dependent stability criterion for nominal systems with time-varying delays is established in terms of linear matrix inequalities (LMIs). Second, based on the H∞ sense, the proposed method is extended to solve the problem of designing an H∞ output tracking controller to track the output of a given reference model. Finally, three examples are included to show the validity and effectiveness of the presented delay-dependent stability and the H∞ output tracking controller design. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of ℋ∞ synchronization for chaotic neural networks with time-varying delays. A new model of the networks with disturbances in both master and slave systems is presented. By constructing a suitable Lyapunov—Krasovskii functional and using a reciprocally convex approach, a novel ℋ∞ synchronization criterion for the networks concerned is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate a delay-dependent robust synchronization analysis for coupled stochastic discrete-time neural networks with interval time-varying delays in networks coupling, a time delay in leakage term, and parameter uncertainties. Based on the Lyapunov method, a new delay-dependent criterion for the synchronization of the networks is derived in terms of linear matrix inequalities (LMIs) by constructing a suitable Lyapunov-Krasovskii's functional and utilizing Finsler's lemma without free-weighting matrices. Two numerical examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper designs a delay-dependent leader-following protocol for multiagent systems with both communication delay and randomly occurring self-delay. Based on the randomly occurring mode of self-delay, a new model of switched multi-agent systems which have not been introduced yet is constructed. By constructing a newly piecewise Lyapunov-Krasovskii functional, a leaderfollowing stability criterion of the switched multi-agent systems is derived by the framework of linear matrix inequalities (LMIs) and the average dwell time with the randomly occurring mode. Based on the result of the derived stability criterion, a designing leader-following protocol for the system will be proposed. One numerical example is included to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We consider multi-agent systems with time-varying delays and switching interconnection topologies. By constructing a suitable Lyapunov-Krasovskii functional and using the reciprocally convex approach, new delay-dependent consensus criteria for the systems are established in terms of linear matrix inequalities (LMIs), which can be easily solved by using various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This study is concerned with the stability problem for uncertain discrete-time systems with interval time-varying delays. By construction of an augmented Lyapunov-Krasovskii's functional and utilisation of new zero equalities, improved delay-dependent criteria for the stability of the systems are derived for guaranteeing the asymptotic stability of the concerned systems. The effectiveness and the reduced conservatism of the derived results are demonstrated by three illustrative examples. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delaydependent criteria for the stability of the networks are derived in terms of linear matrix inequalities LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]