Modelling Denial of Service Attacks on JFK with Meadow's Cost-Based Framework

We present the first detailed application of Meadows’s cost-based modelling framework to the analysis of JFK, an Internet key agreement protocol. The analysis identifies two denial of service attacks against the protocol that are possible when an attacker is willing to reveal the source IP address. The first attack was identified through direct application of a cost-based modelling framework, while the second was only identified after considering coordinated attackers. Finally, we demonstrate how the inclusion of client puzzles in the protocol can improve denial of service resistance against both identified attacks.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

A cost-based optimization of a fiberboard pressing plant using Monte-Carlo simulation (a reliability program)

In this research the reliability and availability of fiberboard pressing plant is assessed and a cost-based optimization of the system using the Monte- Carlo simulation method is performed. The woodchip and pulp or engineered wood industry in Australia and around the world is a lucrative industry. One such industry is hardboard. The pressing system is the main system, as it converts the wet pulp to fiberboard. The assessment identified the pressing system has the highest downtime throughout the plant plus it represents the bottleneck in the process. A survey in the late nineties revealed there are over one thousand plants around the world, with the pressing system being a common system among these plants. No work has been done to assess or estimate the reliability of such a pressing system; therefore this assessment can be used for assessing any plant of this type.

Low-Cost vision-based AUV guidance system for reef navigation

Ensuring the long term viability of reef environments requires essential monitoring of many aspects of these ecosystems. However, the sheer size of these unstructured environments (for example Australia’s Great Barrier Reef pose a number of challenges for current monitoring platforms which are typically remote operated and required significant resources and infrastructure. Therefore, a primary objective of the CSIRO robotic reef monitoring project is to develop and deploy a large number of AUV teams to perform broadscale reef surveying. In order to achieve this, the platforms must be cheap, even possibly disposable. This paper presents the results of a preliminary investigation into the performance of a low-cost sensor suite and associated processing techniques for vision and inertial-based navigation within a highly unstructured reef environment.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

A ‘cost-per-wear’ model based on contact lens replacement frequency

Purpose: The aim was to construct and advise on the use of a cost-per-wear model based on contact lens replacement frequency, to form an equitable basis for cost comparison. ---------- Methods: The annual cost of professional fees, contact lenses and solutions when wearing daily, two-weekly and monthly replacement contact lenses is determined in the context of the Australian market for spherical, toric and multifocal prescription types. This annual cost is divided by the number of times lenses are worn per year, resulting in a ‘cost-per-wear’. The model is presented graphically as the cost-per-wear versus the number of times lenses are worn each week for daily replacement and reusable (two-weekly and monthly replacement) lenses.---------- Results: The cost-per-wear for two-weekly and monthly replacement spherical lenses is almost identical but decreases with increasing frequency of wear. The cost-per-wear of daily replacement spherical lenses is lower than for reusable spherical lenses, when worn from one to four days per week but higher when worn six or seven days per week. The point at which the cost-per-wear is virtually the same for all three spherical lens replacement frequencies (approximately AUD$3.00) is five days of lens wear per week. A similar but upwardly displaced (higher cost) pattern is observed for toric lenses, with the cross-over point occurring between three and four days of wear per week (AUD$4.80). Multifocal lenses have the highest price, with cross-over points for daily versus two-weekly replacement lenses at between four and five days of wear per week (AUD$5.00) and for daily versus monthly replacement lenses at three days per week (AUD$5.50).---------- Conclusions: This cost-per-wear model can be used to assist practitioners and patients in making an informed decision in relation to the cost of contact lens wear as one of many considerations that must be taken into account when deciding on the most suitable lens replacement modality.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Comparing cost uplift in infrastructure delivery methods : a case based approach

The alliance project delivery method is used for approximately one third of all Australian government infrastructure projects representing $8-$10 billion per annum. Despite its widespread use, little is known about the differences between estimated project cost and actual cost over the project lifecycle. This paper presents the findings of research into 14 Australian government alliance case studies investigating the observed cost uplift over each project’s lifecycle. I find that significant cost uplift is likely and that this uplift is greater than that afflicting traditional delivery methods. Furthermore, most of the cost uplift occurs at a different place in the project lifecycle, namely between Business Case and Contractual Commitment.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Integrating the resource-based view and transaction cost economics in immigrant business performance

This paper presents a new integrated framework that integrates the resource-based view and transaction cost economics to explain the phenomenon of immigrant entrepreneurship. We extend the existing literature on immigrant entrepreneurship by identifying different types of ethnic network resources and demonstrating how these resources interact with transaction costs in the context of Chinese immigrants. Thus, our study contributes to the literature by providing a theoretical framework which identifies mechanisms immigrant entrepreneurs use to strategically deploy resources to minimize costs and maximize performance outcomes.

Security analysis and enhancement of one-way hash based low-cost authentication protocol (OHLCAP)

Choi et al. recently proposed an efficient RFID authentication protocol for a ubiquitous computing environment, OHLCAP(One-Way Hash based Low-Cost Authentication Protocol). However, this paper reveals that the protocol has several security weaknesses : 1) traceability based on the leakage of counter information, 2) vulnerability to an impersonation attack by maliciously updating a random number, and 3) traceability based on a physically-attacked tag. Finally, a security enhanced group-based authentication protocol is presented.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Maximising the manufacturer performance value through lean initiatives using cost based model

Numerous tools and techniques have been developed to eliminate or reduce waste and carry out lean concepts in the manufacturing environment. However, appropriate lean tools need to be selected and implemented in order to fulfil the manufacturer needs within their budgetary constraints. As a result, it is important to identify manufacturer needs and implement only those tools, which contribute maximum benefit to their needs. In this research a mathematical model is proposed for maximising the perceived value of manufacturer needs and developed a step-by-step methodology to select best performance metrics along with appropriate lean strategies within the budgetary constraints. With the help of a case study, the proposed model and method have been demonstrated.

Investigating the cost-effectiveness of video telephone based support for newly diagnosed paediatric oncology patients and their families: design of a randomised controlled trial

Background Providing ongoing family centred support is an integral part of childhood cancer care. For families living in regional and remote areas, opportunities to receive specialist support are limited by the availability of health care professionals and accessibility, which is often reduced due to distance, time, cost and transport. The primary aim of this work is to investigate the cost-effectiveness of videotelephony to support regional and remote families returning home for the first time with a child newly diagnosed with cancer Methods/design We will recruit 162 paediatric oncology patients and their families to a single centre randomised controlled trial. Patients from regional and remote areas, classified by Accessibility/Remoteness Index of Australia (ARIA+) greater than 0.2, will be randomised to a videotelephone support intervention or a usual support control group. Metropolitan families (ARIA+ ≤ 0.2) will be recruited as an additional usual support control group. Families allocated to the videotelephone support intervention will have access to usual support plus education, communication, counselling and monitoring with specialist multidisciplinary team members via a videotelephone service for a 12-week period following first discharge home. Families in the usual support control group will receive standard care i.e., specialist multidisciplinary team members provide support either face-to-face during inpatient stays, outpatient clinic visits or home visits, or via telephone for families who live far away from the hospital. The primary outcome measure is parental health related quality of life as measured using the Medical Outcome Survey (MOS) Short Form SF-12 measured at baseline, 4 weeks, 8 weeks and 12 weeks. The secondary outcome measures are: parental informational and emotional support; parental perceived stress, parent reported patient quality of life and parent reported sibling quality of life, parental satisfaction with care, cost of providing improved support, health care utilisation and financial burden for families. Discussion This investigation will establish the feasibility, acceptability and cost-effectiveness of using videotelephony to improve the clinical and psychosocial support provided to regional and remote paediatric oncology patients and their families.

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor-intensive, time consuming and expensive. Remote sensing technologies represent an attractive and cost-effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection, and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments, we call this approach PTAGS. Improved object recognition is achieved by fusing information from multi-sensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches.