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.

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.

Poly[di-mu-aqua-bis{2-amino-4-nitrobenzoato)]-dirubidium

In the structure of title compound [Rb2(C7H5N2O4)2(H2O)2]n the asymmetric unit comprises two independent and different seven-coordinate Rb centres, one RbO7, the other RbO6N, with both having irregular stereochemistry. The RbO7 coordination comprises bridging oxygen donors from two water molecules, three carboxylate groups, and a nitro group, with one doubly bridging. The RbO6N coordination comprises the two bridging water molecules, one monodentate amine N donor, one carboxyl O donor and three O donors from nitro groups (one from the chelate bridge). The extension of the dinuclear unit gives a three-dimensional polymeric structure which is stabilized by both intra- and intermolecular amine N-H...O and water O-H...O hydrogen bonds to carboxyl and water O-atom acceptors, as well as a number of inter-ring \p--\p interactions [minimum ring centroid separation, 3.364(2) \%A]. This complex is both isostructural with the analogous Cs -nitroanthranilate monohydrate complex.

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.

Poly(2,5-bis(2-octyldodecyl)-3,6-di(furan-2-yl)-2,5-dihydro-pyrrolo[3,4-c] pyrrole-1,4-dione-co-thieno[3,2-b]thiophene): A high performance polymer semiconductor for both organic thin film transistors and organic photovoltaics

A new diketopyrrolopyrrole (DPP)-containing donor-acceptor polymer, poly(2,5-bis(2-octyldodecyl)-3,6-di(furan-2-yl)-2,5-dihydro-pyrrolo[3,4-c] pyrrole-1,4-dione-co-thieno[3,2-b]thiophene) (PDBF-co-TT), is synthesized and studied as a semiconductor in organic thin film transistors (OTFTs) and organic photovoltaics (OPVs). High hole mobility of up to 0.53 cm 2 V -1 s -1 in bottom-gate, top-contact OTFT devices is achieved owing to the ordered polymer chain packing and favoured chain orientation, strong intermolecular interactions, as well as uniform film morphology of PDBF-co-TT. The optimum band gap of 1.39 eV and high hole mobility make this polymer a promising donor semiconductor for the solar cell application. When paired with a fullerene acceptor, PC 71BM, the resulting OPV devices show a high power conversion efficiency of up to 4.38% under simulated standard AM1.5 solar illumination.

3,6-Di(furan-2-yl)pyrrolo[3,4-c]pyrrole-1,4(2H,5H)-dione and bithiophene copolymer with rather disordered chain orientation showing high mobility in organic thin film transistors

Pyrrolo[3,4-c]pyrrole-1,4(2H,5H)-dione or diketopyrrolopyrrole (DPP) is a useful electron-withdrawing fused aromatic moiety for the preparation of donor-acceptor polymers as active semiconductors for organic electronics. This study uses a DPP-furan-containing building block, 3,6-di(furan-2-yl)pyrrolo[3,4- c]pyrrole-1,4(2H,5H)-dione (DBF), to couple with a 2,2′-bithiophene unit, forming a new donor-acceptor copolymer, PDBFBT. Compared to its structural analogue, 3,6-di(thiophen-2-yl)pyrrolo[3,4-c]pyrrole-1,4(2H,5H)-dione (DBT), DBF is found to cause blue shifts of the absorption spectra both in solution and in thin films and a slight reduction of the highest occupied molecular orbital (HOMO) energy level of the resulting PDBFBT. Despite the fact that its thin films are less crystalline and have a rather disordered chain orientation in the crystalline domains, PDBFBT shows very high hole mobility up to 1.54 cm 2 V-1 s-1 in bottom-gate, top-contact organic thin film transistors.

Solution processable poly(2,5-dialkyl-2,5-dihydro-3,6-di-2-thienyl- pyrrolo[3,4-c]pyrrole-1,4-dione) for ambipolar organic thin film transistors

Solution processable diketopyrrolopyrrole (DPP)-bithiophene polymers (PDBT) with long branched alkyl side chains on the DPP unit are synthesized. These polymers have favourable highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels for the injection and transport of both holes and electrons. Organic thin film transistors (OTFTs) using these polymers as semiconductors and gold as source/drain electrodes show typical ambipolar characteristics with very well balanced high hole and electron mobilities (μ h = 0.024 cm 2 V -1 s -1 and μ e = 0.056 cm 2 V -1 s -1). These simple and high-performing polymers are promising materials for ambipolar organic thin film transistors for low-cost CMOS-like logic circuits.

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.