Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Vision-based estimation of airborne target pseudobearing rate using hidden Markov model filters

The problem of estimating pseudobearing rate information of an airborne target based on measurements from a vision sensor is considered. Novel image speed and heading angle estimators are presented that exploit image morphology, hidden Markov model (HMM) filtering, and relative entropy rate (RER) concepts to allow pseudobearing rate information to be determined before (or whilst) the target track is being estimated from vision information.

An activity and resource advisory system for manufacturing process chains selection at the early stage of product development

Designers need to consider both the functional and production process requirements at the early stage of product development. A variety of the research works found in the literature has been proposed to assist designers in selecting the most viable manufacturing process chain. However, they do not provide any assistance for designers to evaluate the processes according to the particular circumstances of their company. This paper describes a framework of an Activity and Resource Advisory System (ARAS) that generates advice about the required activities and the possible resources for various manufacturing process chains. The system provides more insight, more flexibility, and a more holistic and suitable approach for designers to evaluate and then select the most viable manufacturing process chain at the early stage of product development.

Effect of dangling chains on the structure and physical properties of a tightly crosslinked poly(ethylene glycol) network

Major imperfections in crosslinked polymers include loose or dangling chain ends that lower the crosslink d., thereby reducing elastic recovery and increasing the solvent swelling. These imperfections are hard to detect, quantify and control when the network is initiated by free radical reactions. As an alternative approach, the sol-​gel synthesis of a model poly(ethylene glycol) (PEG-​2000) network is described using controlled amts. of bis- and mono-​triethoxy silyl Pr urethane PEG precursors to give silsesquioxane (SSQ, R-​SiO1.5) structures as crosslink junctions with a controlled no. of dangling chains. The effect of the no. of dangling chains on the structure and connectivity of the dried SSQ networks has been detd. by step-​crystn. differential scanning calorimetry. The role that micelle formation plays in controlling the sol-​gel PEG network connectivity has been studied by dynamic light scattering of the bis- and mono-​triethoxy silyl precursors and the networks have been characterized by 29Si solid state NMR, sol fraction and swelling measurements. These show that the dangling chains will increase the mesh size and water uptake. Compared to other end-​linked PEG hydrogels, the SSQ-​crosslinked networks show a low sol fraction and high connectivity, which reduces solvent swelling, degree of crystallinity and the crystal transition temp. The increased degree of freedom in segment movement on the addn. of dangling chains in the SSQ-​crosslinked network facilitates the packing process in crystn. of the dry network and, in the hydrogel, helps to accommodate more water mols. before reaching equil.

Efficiency comparison of Markov Chain Monte Carlo simulation with subset simulation (MCMC/ss) to standard Monte Carlo Simulation (sMC) for extreme event scenarios

Standard Monte Carlo (sMC) simulation models have been widely used in AEC industry research to address system uncertainties. Although the benefits of probabilistic simulation analyses over deterministic methods are well documented, the sMC simulation technique is quite sensitive to the probability distributions of the input variables. This phenomenon becomes highly pronounced when the region of interest within the joint probability distribution (a function of the input variables) is small. In such cases, the standard Monte Carlo approach is often impractical from a computational standpoint. In this paper, a comparative analysis of standard Monte Carlo simulation to Markov Chain Monte Carlo with subset simulation (MCMC/ss) is presented. The MCMC/ss technique constitutes a more complex simulation method (relative to sMC), wherein a structured sampling algorithm is employed in place of completely randomized sampling. Consequently, gains in computational efficiency can be made. The two simulation methods are compared via theoretical case studies.

Compressed sensing using hidden Markov models with application to vision based aircraft tracking

This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Regulating supply chains to improve health and safety

The fragmentation of previously integrated systems of production and service delivery has been an important feature of organisational restructuring over the last three decades. This article highlights the adverse implications of this development for the health and safety of workers, examines the extent to which current British health and safety law provides an adequate framework for addressing these outcomes and explores whether its capacity to do so could be enhanced through the introduction of new statutory provisions on the regulation of supply chains. It concludes that, in terms of both structure and operation, the present framework of law is problematic. It further argues that recent international initiatives show that it is feasible to develop such statutory provisions and that existing evidence suggests that provisions of this type could usefully be introduced in respect of a number of areas of activity where the implications of the externalisation of production and service delivery seem particularly problematic.

Regulating supply-chains to address the occupational health and safety problems associated with precarious employment: The case of home-based clothing workers in Australia

Over the past 20 years the labour market, workforce and work organisation of most if not all industrialised countries have been significantly refashioned by the increased use of more flexible work arrangements, variously labelled as precarious employment or contingent work. There is now a substantial and growing body of international evidence that many of these arrangements are associated with a significant deterioration in occupational health and safety (OHS), using a range of measures such as injury rates, disease, hazard exposures and work-related stress. Moreover, there is an emerging body of evidence that these arrangements pose particular problems for conventional regulatory regimes. Recognition of these problems has aroused the concern of policy makers - especially in Europe, North America and Australia - and a number of responses have been adopted in terms of modifying legislation, producing new guidance material and codes of practice and revised enforcement practices. This article describes one such in itiative in Australia with regard to home-based clothing workers. The regulatory strategy developed in one Australian jurisdiction (and now being ‘exported’ into others) seeks to counter this process via contractual tracking mechanisms to follow the work, tie in liability and shift overarching legal responsibility to the top of the supply chain. The process also entails the integration of minimum standards relating to wages, hours and working conditions; OHS and access to workers’ compensation. While home-based clothing manufacture represents a very old type of ‘flexible’ work arrangement, it is one that regulators have found especially difficult to address. Further, the elaborate multi-tiered subcont racting and diffuse work locations found in this industry are also characteristic of newer forms of contingent work in other industries (such as some telework) and the regulatory challenges they pose (such as the tendency of elaborate supply chains to attenuate and fracture statutory responsibilities, at least in terms of the attitudes and behaviour of those involved).

An exploration of NGO and media efforts to influence workplace practices and associated accountability within global supply chains

This paper investigates how social and environmental non-government organisations (NGOs) use the news media in an endeavour to create changes in the social performance and associated accountabilities of multinational buying companies’ (MBCs’) supply chains located in the developing country of Bangladesh. In this research, we explicitly seek the views of senior officers from global and local NGOs operating in Bangladesh, as well as the views of journalists from major global and local news media organisations. Our results show that social and environmental NGOs strategically use the news media in an effort to effect changes in corporate labour practices and related disclosure practices. More particularly, both the NGOs and the news media representatives stated that NGOs would be relatively powerless to create change in corporate without media coverage. This is the first known study to specifically address the joint and complementary role of NGOs and the news media in potentially creating changes in the social and environmental operating and disclosure practices of supply chains emanating from a developing country.

The new supply chains : the Multichannel Networks

Digital innovation is transforming the media and entertainment industries. The professionalization of YouTube’s platform is paradigmatic of that change. The 100 original channel initiative launched in late 2011 was designed to transform YouTube’s brand through production of a high volume of quality premium video content that would more deeply engage its audience base and in the process attract big advertisers. An unanticipated by-product has been the rapid growth of a wave of aspiring next-generation digital media companies from within the YouTube ecosystem. Fuelled by early venture capital some have ambitious goals to become global media corporations in the online video space. A number of larger MCNs (Multi-Channel Networks) - BigFrame, Machinima, Fullscreen, AwesomenessTV, Maker Studios , Revision3 and DanceOn - have attracted interest from media incumbents like Warner Brothers, DreamWorks, Discovery, Bertlesmann, Comcast and AMC, and two larger MCNs Alloy and Break Media have merged. This indicates that a shakeout is underway in these new online supply chains, after rapid initial growth. The higher profile MCNs seek to rapidly develop scale economies in online distribution and facilitate audience growth for their member channels, helping channels optimize monetization, develop sustainable business models and to facilitate producer-collaboration within a growing online community of like-minded content creators. Some MCNs already attract far larger online audiences than any national TV network. The speed with which these developments have occurred is reminiscent of the 1910s, when Hollywood studios first emerged and within only a few years replaced the incumbent film studios as the dominant force within the film industry.

Enhanced interfacial thermal transport across graphene–polymer interfaces by grafting polymer chains

Thermal transport in graphene-polymer nanocomposite is complicated and has not been well understood. The interfacial thermal transport between graphene nanofiller and polymer matrix is expected to play a key role in controlling the overall thermal performance of graphene-polymer nanocomposite. In this work, we investigated the thermal transport across graphene-polymer interfaces functionalized with end-grafted polymer chains using molecular dynamics simulations. The effects of grafting density, chain length and initial morphology on the interfacial thermal transport were systematically investigated. It was found that end-grafted polymer chains could significantly enhance interfacial thermal transport and the underlying mechanism was considered to be the enhanced vibration coupling between graphene and polymer. In addition, a theoretical model based on effective medium theory was established to predict the thermal conductivity in graphene-polymer nanocomposites.