State and input simultaneous estimation for a class of time-delay systems with uncertainties

This brief addresses the problem of estimation of both the states and the unknown inputs of a class of systems that are subject to a time-varying delay in their state variables, to an unknown input, and also to an additive uncertain, nonlinear disturbance. Conditions are derived for the solvability of the design matrices of a reduced-order observer for state and input estimation, and for the stability of its dynamics. To improve computational efficiency, a delay-dependent asymptotic stability condition is then developed using the linear matrix inequality formulation. A design procedure is proposed and illustrated by a numerical example.

Vision based target tracking using robust linear filtering

The use of perspective projection in tracking a target from a video stream involves nonlinear observations. The target dynamics, however, are modeled in Cartesian coordinates and result in a linear system. In this paper we provide a robust version of a linear Kalman filter and perform a measurement conversion technique on the nonlinear optical measurements. We show that our linear robust filter significantly outperforms the Extended Kalman Filter. Moreover, we prove that the state estimation error is bounded in a probabilistic sense.

Antenna selection and channel estimation for hybrid relay systems

The research addressed performance issues for wireless signal transmission and has shown that performance improves with the help of relays due to increased diversity. Further, the areas of antenna selection and channel estimation and modelling has been investigated for improved cost and complexity and has shown to further enhance the performance of the wireless relay systems.

Safeguard information infrastructure against DDoS attacks: experiments and modeling

Nowadays Distributed Denial of Service (DDoS) attacks have made one of the most serious threats to the information infrastructure. In this paper we firstly present a new filtering approach, Mark-Aided Distributed Filtering (MADF), which is to find the network anomalies by using a back-propagation neural network, deploy the defense system at distributed routers, identify and filtering the attack packets before they can reach the victim; and secondly propose an analytical model for the interactions between DDoS attack party and defense party, which allows us to have a deep insight of the interactions between the attack and defense parties. According to the experimental results, we find that MADF can detect and filter DDoS attack packets with high sensitivity and accuracy, thus provide high legitimate traffic throughput and low attack traffic throughput. Through the comparison between experiments and numerical results, we also demonstrate the validity of the analytical model that can precisely estimate the effectiveness of a DDoS defense system before it encounters different attacks.

Ensemble parameter estimation for graphical models

Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

Mobility modelling and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.

On-line tool condition monitoring and control system in forging processes

Cold bulk metal forming has made large-scale production of small complex solid parts economically feasible. Tooling used in metal forming poses many uncertainties in the preliminary cost estimation and production process and continual tool replacement and maintenance dramatically reduces productivity and raises manufacturing cost. In order to tackle this, an on-line tool condition monitoring system using artificial neural network (ANN) to integrate information from multiple sensors for forging process has been developed. Together with the force, acoustic emission signals and process conditions, information developed from theoretical models is integrated into the ANN tool monitoring system to predict tool life and provide the maintenance schedule.

Robust multi-sensor data fusion

This research obtains the optimal estimation and data fusion for linear and nonlinear systems suffering from uncertain observations (missing measurements). The noise from the different data sources are considered to correlated. The derivation of the robust Kalman filter for systems subject to aditional uncertainties in the modelling parameters is presented.

Spam filtering for network traffic security on a multi-core environment

This paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

Cybernetics and the agility question

The ability to sense and respond effectively to fluctuations in an environment is the fundamental problem addressed by cybernetics. When applied to the context of the organizational IT function, agility denotes the capacity of the IT function to perceive "signals" from its internal and external environments, to interpret these, and respond appropriately. The processing of such signals requires the selection and filtering of information to drive decision-making for response in a timely way. The challenge for the IT function is processing an overwhelming collection of signals, in un-standardized formats, and from overlapping sources, that tends to overload decision-makers. Informed by a cybernetic model, we studied how the IT function enables agility. We found evidence (1) that the more mature the policy processes of the IT function, the more the IT function will create agility in information systems; (2) The more mature the intelligence processes of the IT function to look outside the organization, the more the IT function will create agility in information systems and; (3) The more mature the control processes of the IT function that focus on the current use of information systems, the more the IT function will create agility in information systems.

Finite element model updating using estimation of distribution algorithm

Finite Element (FE) model updating has been attracting research attentions in structural engineering fields for over 20 years. Its immense importance to the design, construction and maintenance of civil and mechanical structures has been highly recognised. However, many sources of uncertainties may affect the updating results. These uncertainties may be caused by FE modelling errors, measurement noises, signal processing techniques, and so on. Therefore, research efforts on model updating have been focusing on tackling with uncertainties for a long time. Recently, a new type of evolutionary algorithms has been developed to address uncertainty problems, known as Estimation of Distribution Algorithms (EDAs). EDAs are evolutionary algorithms based on estimation and sampling from probabilistic models and able to overcome some of the drawbacks exhibited by traditional genetic algorithms (GAs). In this paper, a numerical steel simple beam is constructed in commercial software ANSYS. The various damage scenarios are simulated and EDAs are employed to identify damages via FE model updating process. The results show that the performances of EDAs for model updating are efficient and reliable.

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system.

Robust extended Kalman filter based technique for location management in PCS networks

Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using Robust Extended Kalman Filter as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.

A review of current mobility prediction techniques for ad hoc networks

Next Generation Networks (3G & beyond) will support real-time multimedia applications through traditional wide-area networking concepts as well as hot-spot (WLAN) and ad hoc networking concepts. In order to fulfil the vision of Next Generation Networks a method of maintaining a real-time flow despite frequent topology changes and irregularity in user movement is required. Mobility Prediction has been identified as having applications in the areas of link availability estimation and pro-active routing in ad hoc networks. In this work we present an overview of current mobility prediction schemes that have been proposed. Simulation results are also presented.

Cross-national compartability of burden of disease estimates:the European disability weights project

Objective: To investigate the sources of cross-national variation in disability-adjusted life-years (DALYs) in the European Disability
Weights Project.

Methods: Disability weights for 15 disease stages were derived empirically in five countries by means of a standardized procedure and the cross-national differences in visual analogue scale (VAS) scores were analysed. For each country the burden of dementia in women, used as an illustrative example, was estimated in DALYs. An analysis was performed of the relative effects of cross-national variations in demography, epidemiology and disability weights on DALY estimates.

Findings: Cross-national comparison of VAS scores showed almost identical ranking orders. After standardization for population size and age structure of the populations, the DALY rates per 100 000 women ranged from 1050 in France to 1404 in the Netherlands. Because of uncertainties in the epidemiological data, the extent to which these differences reflected true variation between countries was difficult to estimate. The use of European rather than country-specific disability weights did not lead to a significant change in the burden of disease estimates for dementia.

Conclusions: Sound epidemiological data are the first requirement for burden of disease estimation and relevant between-countries comparisons. DALY estimates for dementia were relatively insensitive to differences in disability weights between European countries.