A low-order linear functional observer for time delay systems

This paper presents an efficient technique to design low order state function observers for linear time-delay systems. Assuming the existence of a linear state feedback controller to achieve stability or some control performance criteria of the time-delay system, a design procedure is proposed for
reconstruction of the state feedback control action. The procedure involves solving an optimisation problem with the objective to generate a matrix that is as close as possible to the given feedback gain of the required feedback controller. A condition for robust stability of the time-delay system using the observer-based control scheme is given. The attractive features of the proposed design procedure are that the resulted linear functional state observer is of a very low order and it requires information of a small number of outputs. Numerical examples are given to demonstrate the design procedure and its merits.

Towards optimization of a real-world robotic-sensor system of systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.

Power and politics in a system implementation

The central concern of this study is to identify the role of power and politics in systems implementation. The current literature on systems implementation is typically divided into two areas, process modelling and factor based studies. Process modelling classifies the implementation into a linear process, whereas factor based studies have argued that in order to ‘successfully’ implement a system, particular critical factors are required. This literature misses the complexities involved in systems implementation through the human factors and political nature of systems implementation and is simplistic in its nature and essentially de-contextualises the implementation process. Literature has investigated some aspects of human factors in systems implementation. However, it is believed that these studies have taken a simplistic view of power and politics. It is argued in this thesis that human factors in systems implementation are constantly changing and essentially operating in a dynamic relationship affecting the implementation process. The concept of power relations, as proposed by Foucault (1976, 1977, 1978, 1980, 1982), have been utilised in order to identify the dynamic nature of power and politics. Foucault (1978) argued that power is a dynamic set of relationships constantly changing from one point in time to the next. It is this recognition that is lacking from information systems. Furthermore, these power relations are created through the use of discourse. Discourse represents meaning and social relationships, forming both subjectivity and power relations. Discourses are also the practices of talk, text and argument that continuously form that which actors speak. A post-structuralist view of power as both an obvious and hidden concept has provided the researcher a lens through which the selection and implementation of an enterprise-wide learning management system can be observed. The framework aimed to identify the obvious process of system selection implementation, and then deconstruct that process to expose the hegemonic nature of policy, the reproduction of organisational culture, the emancipation within discourse, and the nature of resistance and power relations. A critical case study of the selection and implementation of an enterprise-wide learning management system at the University of Australia was presented providing an in-depth investigation of the implementation of an enterprise-wide learning management system, spanning five years. This critical case study was analysed using social dramas to distinguish between the front stage issues of power and the hidden discourses underpinning the front stage dramas. The enterprise-wide learning management system implemented in the University of Australia in 2003 is a system which enables academic staff to manage learners, the students, by keeping track of their progress and performance across all types of training activities. Through telling the story of the selection and implementation of an enterprise-wide learning management system at the University of Australia discourses emerged. The key findings from this study have indicated that the system selection and implementation works at two levels. The low level is the selection and implementation process, which operates for the period of the project. The high level is the arena of power and politics, which runs simultaneously to the selection and implementation process. Challenges for power are acted out in the front stage, or public forums between various actors. The social dramas, as they have been described here, are superfluous to the discourse underpinning the front stage. It is the discourse that remains the same throughout the system selection and implementation process, but it is through various social dramas that reflect those discourses. Furthermore, the enactment of policy legitimises power and establishes the discourse, limiting resistance. Additionally, this research has identified the role of the ‘State’ and its influence at the organisational level, which had been previously suggested in education literature (Ball, 1990).

Encoding, ensemble and accelerating strategies for linear causal model discovery

This thesis made outstanding contribution in automating the discovery of linear causal models. It introduced a highly efficient discovery algorithm, which implements new encoding, ensemble and accelerating strategies. Theoretic research and experimental work showed that this new discovery algorithm outperforms the previous system in both accuracy and efficiency.

A state space based approach in non-linear hemodynamic response modeling with fMRI data

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.

Blue whale habitat selection and within-season distribution in a regional upwelling system off southern Australia

Blue whales Balaenoptera musculus aggregate to feed in a regional upwelling system during November–May between the Great Australian Bight (GAB) and Bass Strait. We analysed sightings from aerial surveys over 6 upwelling seasons (2001–02 to 2006–07) to assess within-season patterns of blue whale habitat selection, distribution, and relative abundance. Habitat variables were modelled using a general linear model (GLM) that ranked sea surface temperature (SST) and sea surface chlorophyll (SSC) of equal importance, followed by depth, distance to shore, SSC gradient, distance to shelf break, and SST gradient. Further discrimination by hierarchical partitioning indicated that SST accounted for 84.4% of variation in blue whale presence explained by the model, and that probability of sightings increased with increasing SST. The large study area was resolved into 3 zones showing diversity of habitat from the shallow narrow shelf and associated surface upwelling of the central zone, to the relatively deep upper slope waters, broad shelf and variable upwelling of the western zone, and the intermediate features of the eastern zone. Density kernel estimation showed a trend in distribution from the west during November–December, spreading south-eastward along the shelf throughout the central and eastern zones during January–April, with the central zone most consistently utilised. Encounter rates in central and eastern zones peaked in February, coinciding with peak upwelling intensity and primary productivity. Blue whales avoided inshore upwelling centres, selecting SST ~1°C cooler than remotely sensed ambient SST. Whales selected significantly higher SSC in the central and eastern zones than the western zone, where relative abundance was extremely variable. Most animals departed from the feeding ground by late April.

Observation-switching linear dynamic systems for tracking humans through unexpected partial occlusions by scene objects

This paper focuses on the problem of tracking people through occlusions by scene objects. Rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. The filter automatically switches between a foot-based measure of position (assuming z = Q) to a head-based position measure (given the person's height) when an occlusion of the person's lower body occurs. No knowledge of the scene or its occluding objects is used. Unlike similar research [2, 14], the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. The approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. Occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person's head is visible. Results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

DDoS discrimination by linear discriminant analysis (LDA)

In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet. Different attack methods and strategies are trying to challenge defence systems. Among the behaviours of attack sources, repeatable and predictable features differ from source of legitimate traffic. In addition, the DDoS defence systems lack the learning ability to fine-tune their accuracy. This paper analyses real trace traffic from publicly available datasets. Pearson's correlation coefficient and Shannon's entropy are deployed for extracting dependency and predictability of traffic data respectively. Then, LDA is used to train and classify legitimate and attack traffic flows. From the results of our experiment, we can confirm that the proposed discrimination system can differentiate DDoS attacks from legitimate traffic with a high rate of accuracy.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Rapid multi sample DNA amplification using rotary-linear polymerase chain reaction device (PCRDisc)

Multiple sample DNA amplification was done by using a novel rotary-linear motion polymerase chain reaction (PCR) device. A simple compact disc was used to create the stationary sample chambers which are individually temperature controlled. The PCR was performed by shuttling the samples to different temperature zones by using a combined rotary-linear movement of the disc. The device was successfully used to amplify up to 12 samples in less than 30 min with a sample volume of 5 μl. A simple spring loaded heater mechanism was introduced to enable good thermal contact between the samples and the heaters. Each of the heater temperatures are controlled by using a simple proportional–integral–derivative pulse width modulation control system. The results show a good improvement in the amplification rate and duration of the samples. The reagent volume used was reduced to nearly 25% of that used in conventional method.

Analysis of residues of prometryne and acetochlor in soil-water system by solid-phase extraction and gas chromatography/mass spectrometry

A highly sensitive and simple analytical method was developed for analyzing the binary mixed pesticides of prometryne and acetochlor in soil–water system by gas chromatography/mass spectrometry (GC/MS). The sample solution was first purified by C18 solid-phase extraction column, which was leached by acetone. The leachate was enriched to 1.0 mL by pressure blowing concentrator and then analyzed by GC/MS. The linear calibration curves were showed in the range of 1–15 μg/mL with a correlation coefficient of 0.9991. The average recoveries (n = 5) were between 95.3 and 115.7%, with relative standard deviations ranged from 1.71 and 7.95%. The limits of detection of Prometryne/Acetochlor were up to 0.06 and 0.17 μg/mL, respectively. This method provides a reliable approach to examine and evaluate the residues of prometryne and acetochlor in the soil–water system.

A pose-wise linear illumination manifold model for face recognition using video

The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illumination manifold-based normalization; (ii) pose robustness is achieved by decomposing each appearance manifold into semantic Gaussian pose clusters, comparing the corresponding clusters and fusing the results using an RBF network; (iii) a fully automatic recognition system based on the proposed method is described and extensively evaluated on 600 head motion video sequences with extreme illumination, pose and motion pattern variation. On this challenging data set our system consistently demonstrated a very high recognition rate (95% on average), significantly outperforming state-of-the-art methods from the literature.

An illumination invariant face recognition system for access control using video

Illumination and pose invariance are the most challenging aspects of face recognition. In this paper we describe a fully automatic face recognition system that uses video information to achieve illumination and pose robustness. In the proposed method, highly nonlinear manifolds of face motion are approximated using three Gaussian pose clusters. Pose robustness is achieved by comparing the corresponding pose clusters and probabilistically combining the results to derive a measure of similarity between two manifolds. Illumination is normalized on a per-pose basis. Region-based gamma intensity correction is used to correct for coarse illumination changes, while further refinement is achieved by combining a learnt linear manifold of illumination variation with constraints on face pattern distribution, derived from video. Comparative experimental evaluation is presented and the proposed method is shown to greatly outperform state-of-the-art algorithms. Consistent recognition rates of 94-100% are achieved across dramatic changes in illumination.