Time cumulative complexity modeling and analysis for space-based networks

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

Partial state estimation of time-delay systems.

This thesis broadly studies three crucial and rigorous inter-related control theoretical subjects: (i) Partial state estimation of linear systems; (ii) Stability analysis of time-delay systems with interval time-varying delays; and (iii) Functional observer design for time-delay systems.

Exponential stabilization of non-autonomous delayed neural networks via Riccati equations

This paper concerns with the problem of exponential stabilization for a class of non-autonomous neural networks with mixed discrete and distributed time-varying delays. Two cases of discrete time-varying delay, namely (i) slowly time-varying; and (ii) fast time-varying, are considered. By constructing an appropriate Lyapunov-Krasovskii functional in case (i) and utilizing the Razumikhin technique in case (ii), we establish some new delay-dependent conditions for designing a memoryless state feedback controller which stabilizes the system with an exponential convergence of the resulting closed-loop system. The proposed conditions are derived through solutions of some types of Riccati differential equations. Applications to control a class of autonomous neural networks with mixed time-varying delays are also discussed in this paper. Some numerical examples are provided to illustrate the effectiveness of the obtained results.

Technical efficiency in Australian textile and clothing firms: evidence from the business longitudinal survey

This paper estimates technical efficiency of Australian textile and clothing firms based on the data obtained from the Business Longitudinal Survey (BLS) conducted by the Australian Bureau of Statistics (ABS). Using a Cobb Douglas stochastic production frontier the paper examines firm level technical efficiency in the time varying inefficiency effect model with technical inefficiency effects assumed as an independently distributed truncated normal variable. Estimates of the production frontier revealed significant but small elasticities of labour and capital for textile and clothing firms, respectively, and a negative (but insignificant) Hicks neutral technical change for both. Estimated coefficients of the explanatory variables for inefficiency effects indicated that technical efficiency varied significantly according to firms’ age, size, capital intensity, proportion of non-production to total workers and type of legal status. Predicted firm specific efficiency varied from 16 per cent to 95 per cent and mean efficiency ranged between 30 to 70 per cent. In view of these results policies have been suggested to improve technical efficiency of the firms as well as productivity growth of the sub sectors.

Precoder design for multi-antenna systems with temporally and spatially correlated fading channels

This paper addresses the problem of the design of a precoder for multiple transmit antenna communication systems with spatially and temporally correlated fading channels. Using the theories of matrix differential calculus, the paper derives a precoder for unitary space-time codes that can exploit the spatio-temporal correlation in the time-varying fading channels. The design criterion is based on minimizing the mean square error of the channel estimates. Computer simulation results show that a significant performance gain can be achieved by using the designed precoder.

State and input simultaneous estimation for a class of nonlinear systems

This paper addresses the problem of estimating simultaneously the state and input of a class of nonlinear systems. Here, the systems nonlinear part comprises a Lipschitz nonlinear function with respect to the state and input, and a state-dependent unknown function including additive disturbance as well as uncertain/nonlinear/time-varying terms. Upon satisfying some conditions, the observer design problem can be solved via a Riccati inequality or a LMI-based technique with asymptotic estimation guaranteed. A numerical example is included for illustration.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

A stable MCA learning algorithm

Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.

Power control in CDMA systems based on channel identification

Power control design is a critical aspect of CDMA cellular systems design. This paper develops an adaptive power controller design method for CDMA systems. The key to the power control design is on the recursive identification of the underlying wireless stochastic channel model parameters. The identification process guarantees the power controller to work well for systems in unknown or time varying network environment.

Atopic disease and breastfeeding - cause or consequence?

Background

A number of studies have observed an association between breast-feeding and increased risk of development of asthma and eczema. It has been proposed that these results might be due to early signs of atopic disease in the infant causing mothers to prolong breast-feeding.

Objective

We sought to determine whether early symptoms of atopic disease (eczema, food reaction, or asthma) or positive skin prick test responses reduce the likelihood of ceasing breast-feeding.

Methods

A prospective birth cohort of 620 infants from Melbourne, Australia, was used. Telephone interviews every 4 weeks were conducted until 64 weeks and then again at 78 and 104 weeks to determine duration of breast-feeding (both exclusive and total) and evidence of atopic disease. Because of the varying time of onset of atopic symptoms, they were modeled as time-varying covariates in Cox models.
Results

Only 52 (8.4%) infants did not establish breast-feeding, whereas an additional 103 (25.0%) did not establish exclusive breast-feeding. Early signs of atopic disease or sensitization were independently associated with an approximately 28% reduction in risk of ceasing exclusive breast-feeding (adjusted hazard ratio, 0.72; 95% CI, 0.53-0.97); P = .029), but there was no evidence for a relationship with risk of ceasing breast-feeding completely (adjusted hazard ratio, 1.12; 95% CI, 0.92-1.37; P = .262).
Conclusion

Early signs of atopic disease might prolong the duration of exclusive breast-feeding. This could mask a protective effect of breast-feeding or even result in breast-feeding appearing to be a risk factor for the development of atopic diseases. Future investigation of the relationship between breast-feeding and atopic diseases should consider this possibility.

A unified learning algorithm to extract principal and minor components

Recently, many unified learning algorithms have been developed to solve the task of principal component analysis (PCA) and minor component analysis (MCA). These unified algorithms can be used to extract principal component and if altered simply by the sign, it can also serve as a minor component extractor. This is of practical significance in the implementations of algorithms. Convergence of the existing unified algorithms is guaranteed only under the condition that the learning rates of algorithms approach zero, which is impractical in many practical applications. In this paper, we propose a unified PCA & MCA algorithm with a constant learning rate, and derive the sufficient conditions to guarantee convergence via analyzing the discrete-time dynamics of the proposed algorithm. The achieved theoretical results lay a solid foundation for the applications of our proposed algorithm.

Continuous corrosion rate measurement by noise resistance calculation

A commonly employed method of corrosion rate measurement is to determine the electrochemical impedance of the corroding material. One technique for estimating the impedance is the noise impedance calculation. While it has been shown to yield useful results, there are a number of problems that require attention. This paper identifies some of those problems-specifically, those of detrending and resolution-and provides a solution that allows continuous, time-varying noise resistance and noise impedance calculations to be performed with known time, frequency, and magnitude resolutions. Applications to synthetic and experimental data are included as illustrations

Dynamic currency hedging for international stock portfolios

The paper studies dynamic currency risk hedging of international stock portfolios using a currency overlay. A dynamic conditional correlation (DCC) multivariate GARCH model is employed to estimate time-varying covariance among stock market returns and currency returns. The conditional covariance is then used in the estimation of risk-minimizing conditional hedge ratios. The study considers seven developed economies over the period January 2002 to April 2010 and estimates daily conditional hedge ratios for portfolios of various stock market combinations. Conditional hedging is shown to dominate traditional static hedging and unconditional hedging in terms of risk reduction both in-sample and out-of-sample, especially during the recent global financial crisis. Conditional hedging also proves to consistently reduce portfolio risk for various levels of foreign investments.

A generalised data analysis approach for baggage handling systems simulation

Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.

Resisting web proxy-based HTTP attacks by temporal and spatial locality behavior

A novel server-side defense scheme is proposed to resist the Web proxy-based distributed denial of service attack. The approach utilizes the temporal and spatial locality to extract the behavior features of the proxy-to-server traffic, which makes the scheme independent of the traffic intensity and frequently varying Web contents. A nonlinear mapping function is introduced to protect weak signals from the interference of infrequent large values. Then, a new hidden semi-Markov model parameterized by Gaussian-mixture and Gamma distributions is proposed to describe the time-varying traffic behavior of Web proxies. The new method reduces the number of parameters to be estimated, and can characterize the dynamic evolution of the proxy-to-server traffic rather than the static statistics. Two diagnosis approaches at different scales are introduced to meet the requirement of both fine-grained and coarse-grained detection. Soft control is a novel attack response method proposed in this work. It converts a suspicious traffic into a relatively normal one by behavior reshaping rather than rudely discarding. This measure can protect the quality of services of legitimate users. The experiments confirm the effectiveness of the proposed scheme.