Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

Investment decision framework for infrastructure asset management: a probability-based approach

Australia’s civil infrastructure assets of roads, bridges, railways, buildings and other structures are worth billions of dollars. Road assets alone are valued at around A$ 140 billion. As the condition of assets deteriorate over time, close to A$10 billion is spent annually in asset maintenance on Australia's roads, or the equivalent of A$27 million per day. To effectively manage road infrastructures, firstly, road agencies need to optimise the expenditure for asset data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. A procedure for assessing investment decision for road asset management has been developed. The procedure includes: • A methodology for optimising asset data collection; • A methodology for calibrating deterioration prediction models; • A methodology for assessing risk-adjusted estimates for life-cycle cost estimates. • A decision framework in the form of risk map

Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.

Bayesian decision procedures for binary and continuous bivariate dose-escalation studies

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included. Copyright (C) 2006 John Wiley & Sons, Ltd.

Multicollinearity and financial constraint in investment decisions: a bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Crop, Native Vegetation, and Biofuels: Response of White- Tailed Deer to Changing Management Priorities

The expansion of the cellulosic biofuels industry throughout the United States has broad-scale implications for wildlife management on public and private lands. Knowledge is limited on the effects of reverting agriculture to native grass, and vice versa, on size of home range and habitat use of white-tailed deer (Odocoileus virginianus). We followed 68 radio-collared female deer from 1991 through 2004 that were residents of DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska, USA. The refuge was undergoing conversion of vegetation out of row-crop agriculture and into native grass, forest, and emergent aquatic vegetation. Habitat in DNWR consisted of 30% crop in 1991 but removing crops to establish native grass and wetland habitat at DNWR resulted in a 44% reduction in crops by 2004. A decrease in the amount of crops on DNWR contributed to a decline in mean size of annual home range from 400 ha in 1991 to 200 ha in 2005 but percentage of crops in home ranges increased from 21% to 29%. Mean overlap for individuals was 77% between consecutive annual home ranges across 8 years, regardless of crop availability. Conversion of crop to native habitat will not likely result in home range abandonment but may impact disease transmission by increasing rates of contact between deer social groups that occupy adjacent areas. Future research on condition indices or changes in population parameters (e.g., recruitment) could be incorporated into the study design to assess impacts of habitat conversion for biofuel production.

A Hybrid Queue-based Bayesian Network Framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and Exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an $R^2$ goodness of fit of 0.9994 and 0.9982 respectively over a 10 hour test period. The utility of the framework is demonstrated on a number of usage scenarios including real time monitoring and `what-if' analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

A review of the potential role of greenhouse gas abatement in native vegetation management in Queensland's rangelands

Concern about the risk of harmful human-induced climate change has resulted in international efforts to reduce greenhouse gas emissions to the atmosphere. We review the international and national context for consideration of greenhouse abatement in native vegetation management and discuss potential options in Queensland. Queensland has large areas of productive or potentially productive land with native woody vegetation cover with approximately 76 million ha with woody cover remaining in 1991. High rates of tree clearing, predominantly to increase pasture productivity, continued throughout the 1990s with an average 345,000 ha/a estimated to have been cleared, including non-remnant (woody regrowth) as well as remnant vegetation. Estimates of greenhouse gas emissions associated with land clearing currently have a high uncertainty but clearing was reported to contribute a significant proportion of Australia's total greenhouse gas emissions from 1990 (21%) to 1999 (13%). In Queensland, greenhouse emissions from land clearing were estimated to have been 54.5 Mt CO(2)-e in 1999. Management of native vegetation for timber harvesting and the proliferation of woody vegetation (vegetation thickening) in the grazed woodlands also represent large carbon fluxes. Forestry (plantations and native forests) in Queensland was reported to be a 4.4 Mt CO(2)-e sink in 1999 but there are a lack of comprehensive data on timber harvesting in private hardwood forests. Vegetation thickening is reported for large areas of the c. 60 million ha grazed woodlands in Queensland. The magnitude of the carbon sink in 27 million ha grazed eucalypt woodlands has been estimated to be 66 Mt CO(2)-e/a but this sink is not currently included in Australia's inventory of anthropogenic greenhouse emissions. Improved understanding of the function and dynamics of natural and managed ecosystems is required to support management of native vegetation to preserve and enhance carbon stocks for greenhouse benefits while meeting objectives of sustainable and productive management and biodiversity protection.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.