Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

The application of the statutory derivative action in Pt 2F.1A of the Corporations Act to companies in liquidation under Ch 5 : Well oiled machine or is Pt 2F.1A in need of a tune up?

The statutory derivative action was introduced in Australia in 2000. This right of action has been debated in the literature and introduced in a number of other jurisdictions as well. However, it is by no means clear that all issues have been resolved despite its operation in Australia for over 10 years. This article considers the application of Pt 2F.1A of the Corporations Act to companies in liquidation under Ch 5. It demonstrates that the application involves consideration of not only proper statutory interpretation but also policy matters around the role and the supervision by the court of a liquidator once a company has entered liquidation.

Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Demonstration of closed-­loop airborne sense-­and-­avoid using machine vision

This paper describes a vision-based airborne collision avoidance system developed by the Australian Research Centre for Aerospace Automation (ARCAA) under its Dynamic Sense-and-Act (DSA) program. We outline the system architecture and the flight testing undertaken to validate the system performance under realistic collision course scenarios. The proposed system could be implemented in either manned or unmanned aircraft, and represents a step forward in the development of a “sense-and-avoid” capability equivalent to human “see-and-avoid”.

Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12 566 individuals

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10-5). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted. © The Author 2010. Published by Oxford University Press. All rights reserved.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem

In practice, parallel-machine job-shop scheduling (PMJSS) is very useful in the development of standard modelling approaches and generic solution techniques for many real-world scheduling problems. In this paper, based on the analysis of structural properties in an extended disjunctive graph model, a hybrid shifting bottleneck procedure (HSBP) algorithm combined with Tabu Search metaheuristic algorithm is developed to deal with the PMJSS problem. The original-version SBP algorithm for the job-shop scheduling (JSS) has been significantly improved to solve the PMJSS problem with four novelties: i) a topological-sequence algorithm is proposed to decompose the PMJSS problem into a set of single-machine scheduling (SMS) and/or parallel-machine scheduling (PMS) subproblems; ii) a modified Carlier algorithm based on the proposed lemmas and the proofs is developed to solve the SMS subproblem; iii) the Jackson rule is extended to solve the PMS subproblem; iv) a Tabu Search metaheuristic algorithm is embedded under the framework of SBP to optimise the JSS and PMJSS cases. The computational experiments show that the proposed HSBP is very efficient in solving the JSS and PMJSS problems.

Machine vision toolbox for MATLAB, release 3 [Software]

The Toolbox, combined with MATLAB ® and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. For modest image sizes the processing rate can be sufficiently ``real-time'' to allow for closed-loop control. Focus of attention methods such as dynamic windowing (not provided) can be used to increase the processing rate. With input from a firewire or web camera (support provided) and output to a robot (not provided) it would be possible to implement a visual servo system entirely in MATLAB. Provides many functions that are useful in machine vision and vision-based control. Useful for photometry, photogrammetry, colorimetry. It includes over 100 functions spanning operations such as image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration and color space conversion.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Impact of the introduction of machine gaming in Queensland on minor and major bingo

Material for this paper comes from as report commissioned by the Department of Family Services, Aboriginal and Islander Affairs. The report is the result of a multi strategy research project designed to assess the impact of gaming machines on the fundraising capacity of charitable and community organisations in Queensland. The study was conducted during the 1993 calendar year. The first Queensland gaming machine was commissioned on the 11 February, 1992 at 11.30 am in Brisbane at the Kedron Wavell Services Club. Eighteen more clubs followed that week. Six months later there were gaming machines in 335 clubs, and 250 hotels and taverns, representing a state wide total of 7,974 machines in operation. The 10,000 gaming machine was commissioned on the 18 March, 1993 and the 1,000 operational gaming machine site was opened on 18th February, 1994.