Post-processing Strategy Research Articles

Heidelberg-mCT-Analyzer: a novel method for standardized microcomputed-tomography-guided evaluation of scaffold properties in bone and tissue research.

Bone tissue engineering and bone scaffold development represent two challenging fields in orthopaedic research. Micro-computed tomography (mCT) allows non-invasive measurement of these scaffolds’ properties in vivo. However, the lack of standardized mCT analysis protocols and, therefore, the protocols’ user-dependency make interpretation of the reported results difficult. To overcome these issues in scaffold research, we introduce the Heidelberg-mCT-Analyzer. For evaluation of our technique, we built 10 bone-inducing scaffolds, which underwent mCT acquisition before ectopic implantation (T0) in mice, and at explantation eight weeks thereafter (T1). The scaffolds’ three-dimensional reconstructions were automatically segmented using fuzzy clustering with fully automatic level-setting. The scaffold itself and its pores were then evaluated for T0 and T1. Analysing the scaffolds’ characteristic parameter set with our quantification method showed bone formation over time. We were able to demonstrate that our algorithm obtained the same results for basic scaffold parameters (e.g. scaffold volume, pore number and pore volume) as other established analysis methods. Furthermore, our algorithm was able to analyse more complex parameters, such as pore size range, tissue mineral density and scaffold surface. Our imaging and post-processing strategy enables standardized and user-independent analysis of scaffold properties, and therefore is able to improve the quantitative evaluations of scaffold-associated bone tissue-engineering projects.

Bigram Language Models and Reevaluation Strategy for Improved Recognition of Online Handwritten Tamil Words

This article describes a postprocessing strategy for online, handwritten, isolated Tamil words. Contributions have been made with regard to two issues hardly addressed in the online Indic word recognition literature, namely, use of (1) language models exploiting the idiosyncrasies of Indic scripts and (2) expert classifiers for the disambiguation of confused symbols. The input word is first segmented into its individual symbols, which are recognized using a primary support vector machine (SVM) classifier. Thereafter, we enhance the recognition accuracy by utilizing (i) a bigram language model at the symbol or character level and (ii) expert classifiers for reevaluating and disambiguating the different sets of confused symbols. The symbol-level bigram model is used in a traditional Viterbi framework. The concept of a character comprising multiple symbols is unique to Dravidian languages such as Tamil. This multi-symbol feature of Tamil characters has been exploited in proposing a novel, prefix-tree-based character-level bigram model that does not use Viterbi search; rather it reduces the search space for each input symbol based on its left context. For disambiguating confused symbols, a dynamic time-warping approach is proposed to automatically identify the parts of the online trace that discriminates between the confused classes. Fine classification of these regions by dedicated expert SVMs reduces the extent of confusions between such symbols. The integration of segmentation, prefix-tree-based language model and disambiguation of confused symbols is presented on a set of 15,000 handwritten isolated online Tamil words. Our results show recognition accuracies of 93.0% and 81.6% at the symbol and word level, respectively, as compared to the baseline classifier performance of 88.4% and 65.1%, respectively.

Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks

In this paper, a clustered wireless sensor network is considered that is modeled as a set of coupled Gaussian multiple-access channels. The objective of the network is not to reconstruct individual sensor readings at designated fusion centers but rather to reliably compute some functions thereof. Our particular attention is on real-valued functions that can be represented as a post-processed sum of pre-processed sensor readings. Such functions are called nomographic functions and their special structure permits the utilization of the interference property of the Gaussian multiple-access channel to reliably compute many linear and nonlinear functions at significantly higher rates than those achievable with standard schemes that combat interference. Motivated by this observation, a computation scheme is proposed that combines a suitable data pre- and post-processing strategy with a nested lattice code designed to protect the sum of pre-processed sensor readings against the channel noise. After analyzing its computation rate performance, it is shown that at the cost of a reduced rate, the scheme can be extended to compute every continuous function of the sensor readings in a finite succession of steps, where in each step a different nomographic function is computed. This demonstrates the fundamental role of nomographic representations.

Temporal evolution of ischemic lesions in nonhuman primates: a diffusion and perfusion MRI study.

Background and PurposeDiffusion-weighted imaging (DWI) and perfusion MRI were used to examine the spatiotemporal evolution of stroke lesions in adult macaques with ischemic occlusion.MethodsPermanent MCA occlusion was induced with silk sutures through an interventional approach via the femoral artery in adult rhesus monkeys (n = 8, 10–21 years old). The stroke lesions were examined with high-resolution DWI and perfusion MRI, and T2-weighted imaging (T2W) on a clinical 3T scanner at 1–6, 48, and 96 hours post occlusion and validated with H&E staining.ResultsThe stroke infarct evolved via a natural logarithmic pattern with the mean infarct growth rate = 1.38 ± 1.32 ml per logarithmic time scale (hours) (n = 7) in the hyperacute phase (1–6 hours). The mean infarct volume after 6 hours post occlusion was 3.6±2.8 ml (n = 7, by DWI) and increased to 3.9±2.9 ml (n = 5, by T2W) after 48 hours, and to 4.7±2.2ml (n = 3, by T2W) after 96 hours post occlusion. The infarct volumes predicted by the natural logarithmic function were correlated significantly with the T2W-derived lesion volumes (n = 5, r = 0.92, p = 0.01) at 48 hours post occlusion. The final infarct volumes derived from T2W were correlated significantly with those from H&E staining (r = 0.999, p < 0.0001, n = 4). In addition, the diffusion-perfusion mismatch was visible generally at 6 hours but nearly diminished at 48 hours post occlusion.ConclusionThe infarct evolution follows a natural logarithmic pattern in the hyperacute phase of stroke. The logarithmic pattern of evolution could last up to 48 hours after stroke onset and may be used to predict the infarct volume growth during the acute phase of ischemic stroke. The nonhuman primate model, MRI protocols, and post data processing strategy may provide an excellent platform for characterizing the evolution of acute stroke lesion in mechanistic studies and therapeutic interventions of stroke disease.

A prior-based metal artifact reduction algorithm for x-ray CT.

In computed tomography (CT), metal objects in the scanning filed are accompanied by physical phenomenon that causes projections to be inconsistent. These inconsistencies produce bright and dark shadows or streaks in analytically reconstructed images. Interpolation-based metal artifact reduction (MAR) algorithms usually replace the inconsistent projection data by estimating surrogate data based on the surrounding uncorrupted projections. In such cases, secondary artifacts will be generated when the data estimates are inaccurate. Therefore, better projection estimation is critical. This paper proposes an image post-processing strategy to create an intermediate image, named the prior image and better estimates of the surrogate data by forward projecting this prior image. The proposed method consists of three steps based on the forward projection MAR framework. First, metallic implants in the uncorrected images are segmented using a Markov random field model (MRF). Then a prior image is generated via an edge-preserving filter and a recovery procedure of the adjacent anatomical structures. Finally, the projection is completed via forward projecting this prior image and the corrected image is reconstructed by the filtered backprojection (FBP) method. Studies on both phantom and clinical data are carried out to verify the performance of the proposed method. The comparisons with other previous MAR algorithms demonstrate that the proposed MAR method performs better in metal artifact suppression and anatomical structure preservation.

A Novel Semi-Supervised Method for Obtaining Finer Resolution Urban Extents Exploiting Coarser Resolution Maps

In this work, we present a new semi-supervised strategy for obtaining finer spatial resolution urban maps from coarser resolution satellite data. Our method first uses a coarse resolution map as a source of training data. Then, we use semi-supervised learning in order to refine the set of initial (labeled) training samples by the inclusion of additional (reliable) unlabeled samples at the finer resolution level, in fully automatic fashion. The new unlabeled samples are automatically generated by our proposed methodology, which only requires a limited number of initial labeled samples for initialization purposes. Then, we conduct land cover classification (at the finer spatial resolution level) using a probabilistic multinomial logistic regression (MLR) classifier-in both supervised and semi-supervised fashion-by considering different numbers of labeled and unlabeled samples. In order to exploit spatial information, we use a Markov random field (MRF)-based postprocessing strategy to refine the obtained classification results. In order to test our concept, we use a global dataset: the European Space Agency's GlobCover product, as the coarser resolution map (300-m spatial resolution). Our experimental evaluation is further conducted using Landsat data (30-m spatial resolution) collected over three different locations in the city of Sao Paulo, Brazil, and over two different locations in the city of Guangzhou, China. We obtain promising results in the generation of finer resolution urban extent maps using very limited training samples, derived in all cases from the GlobCover product. These experiments suggest the potential of GlobCover to provide reliable training data in order to support mapping of urban areas at a global scale.

Minimizing lipid signal bleed in brain (1) H chemical shift imaging by post-acquisition grid shifting.

Low spatial resolution in conventional 1H brain chemical shifting imaging (CSI) studies causes partial volume error (PVE) or signal "bleed" that is especially deleterious to voxels near the scalp. The standard spatial apodization approach adversely affects spatial resolution. Here, a novel automated post-processing strategy of partial volume correction employing grid shifting ("PANGS") is presented, which minimizes residual PVE without compromising spatial resolution. PANGS shifts the locations of the reconstruction coordinates in a designated region of image space-the scalp, to match the tissue "centers-of-mass" instead of the geometric centers of each voxel, by iteratively minimizing the PVE from the scalp into outside voxels. PANGS' performance was evaluated by numerical simulation, and in 3 Tesla 1H CSI human studies employing outer volume suppression and long echo times. PANGS reduced lipid contamination of cortical spectra by up to 86% (54% on average). Metabolite maps exhibited significantly less lipid artifact than conventional and spatially-filtered CSI. All methods generated quantitatively identical spectral peak areas from central brain locations, but spatial filtering increased spectral linewidths and reduced spatial resolution. PANGS significantly reduces lipid artifacts in 1H brain CSI spectra and metabolite maps, and improves metabolite detection in cortical regions without compromising resolution.

GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither modifies the original optimization problem for SVM training, nor introduces new hyper-parameters. Experimentation carried out on a high number of databases (23) shows GSVM obtaining the desired balanced accuracy between classes. Furthermore, its performance improves well-known cost-sensitive schemes for SVM, without adding complexity or computational cost.

Conforming Finite-Element Methods for Modeling Convection in an Incompressible Rock Matrix

Coupled heat transport and fluid flow in porous rocks play a role in many geological phenomena, including the formation of hydrothermal mineral deposits, the productivity of geothermal reservoirs and the reliability of geo-sequestration. Due to the low compressibility of the fluid and rock matrix and the long-time scales the fluid can be treated as incompressible. The solution of the incompressible Darcy flux problem and the advection-dominated heat transport both provide numerically challenging problems typically addressed using methods specialized for the individual equations. In order to avoid the usage of two different meshes and solution approximations for pressure, flux, and temperature we propose to use standard conforming finite-element methods on the same mesh for both problems. The heat transport equation is solved using a linearized finite-element flux corrected transport scheme which introduces minimum artificial diffusion based on the discretized transport problem. The Darcy flux calculation from pressure uses a global post-processing strategy which at the cost of an extra partial differential equation leads to highly accurate flux approximation. In the limit of zero element size the flux is in fact incompressible. We investigate the numerical performance of our proposed method on a test problem using the parallelized modeling environment escript. We also test the approach to simulate convection in geologically relevant scenarios.

Third order convergent time discretization for parabolic optimal control problems with control constraints

We consider a priori error analysis for a discretization of a linear quadratic parabolic optimal control problem with box constraints on the time-dependent control variable. For such problems one can show that a time-discrete solution with second order convergence can be obtained by a first order discontinuous Galerkin time discretization for the state variable and either the variational discretization approach or a post-processing strategy for the control variable. Here, by combining the two approaches for the control variable, we demonstrate that almost third order convergence with respect to the size of the time steps can be achieved.

Using centrifuge tests data to identify the dynamic soil properties: Application to Fontainebleau sand

Knowledge of the dynamic properties of the soil is of great importance as the dynamic shear modulus and damping ratio are necessary input data in finite element modeling programs. This paper presents a post-processing strategy to identify the shear modulus and damping ratio vs. shear strain curves using the experimental results of a dynamic centrifuge program. Application is presented for the Fontainebleau sand. The proposed methodology is fast, robust and able to capture the nonlinear hysteretic behavior of the material. Based on the results, specific parameters for the Fontainebleau sand are identified for the empirical equation of shear modulus and damping ratio proposed by Ishibashi and Zhang [1]. It is found that confining pressure has an important influence on both shear modulus evolution and damping ratio.

Processing terminology for the translation pipeline

Companies must translate their content if they want to operate multinationally. Both quality and speed of translation are key factors in determining market share in the target market. Proactively managing terminology, including pre-translating key terms for a translation project, has beneficial effects on these factors. However, in commercial environments, the volumes of content and subsequently of the required terminology are typically large. Therefore, integrating terminology into the translation pipeline requires a process that is as automated as possible. Term extraction is the cornerstone of this process, but to maximize efficiency it requires a post-processing strategy that repurposes existing lexical resources. Terms extracted from corpora and subsequently translated should be channeled into the company termbase so that they can be leveraged for other purposes. These and other effective practices for processing extracted terms are discussed, based on the author’s experiences in one large company.

Detection of Correct and Incorrect Measurements in Real-Time Continuous Glucose Monitoring Systems by Applying a Postprocessing Support Vector Machine

Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices

Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of the input matrix belongs to the cone spanned by a small subset of these columns). Since then, several algorithms have been designed to handle this subclass of NMF problems. In particular, Bittorf, Recht, R\'e and Tropp (`Factoring nonnegative matrices with linear programs', NIPS 2012) proposed a linear programming model, referred to as Hottopixx. In this paper, we provide a new and more general robustness analysis of their method. In particular, we design a provably more robust variant using a post-processing strategy which allows us to deal with duplicates and near duplicates in the dataset.

WE-G-BRB-01: Per-Frame Analysis of MatriXX Data.

One of the most widely used IMRT QA devices (MatriXX) is an array of ionization chambers which are periodically read during plan delivery. Although the ionization chambers are not expected to exhibit strong angular dependence, the measured dose distribution is often found to significantly differ from the planned dose distribution. We identify the origin of all factors that affect the measurement accuracy of the MatriXX and develop a per-frame post-processing strategy that reduces their impact on the passing rate of IMRT and VMAT plans. We developed software that reads the dose frame sequence recorded by the MatriXX and applies a number of correction factors to each frame. Angular correction factors are computed as ratios of measured dose at the isocenter of the phantom and planned dose at the same location for all clinically used photon energies. For every clinical case, the recorded movie file is read and the dose for every frame is corrected according to the angle of the beam. In addition, the background evolution is tracked in the 'beam-off' frames which are subsequently subtracted from the 'beam-on' frames according to a predictive model. Machine output correction is also implemented, which significantly improves the absolute dose measurements. The IMRT effective plane of measurement of the MatriXX was identified and found not to coincide with the isocentric plane. The clinical passing rates are significantly improved when the per-frame analysis software was introduced in our IMRT QA procedure. For a group of 800 patients with no corrections the average passing rate was 93.6%, while for the first 300 cases with per frame corrections the average passing rate was 97.3%. We identify all factors that impact the measurement accuracy of the MatriXX (angular effects, background evolution, machine output, plane of measurement) and propose a strategy for their elimination.

Facile fabrication of graphene devices through metalloporphyrin induced photocatalytic reduction

Solution processed graphene oxide (GO) sheets are electronically insulating and are generally reduced by chemical treatment or heat treatment in a reducing environment to recover their electronic properties, forming reduced graphene oxide (rGO). Here, GO sheets were photocatalytically reduced using 5-(4-hydroxyphenyl)-10,15,20-tri(p-tolyl) zinc(II) porphyrin (Zn(II)TTPOH) under ambient conditions. After illumination in the presence of a hole scavenger and Zn(II)TTPOH, the formation of rGO was confirmed through optical absorption measurements and monitoring of the D/G peak ratios from Raman measurements. The resultant rGO formed stable aqueous suspensions with the Zn(II)TTPOH. The electron transfer from photoexcited porphyrin to GO was studied through photoluminescence measurements. Utilising this photoreduction process as a post processing strategy, an increase in conductivity and an ambipolar field effect transistor (FET) behaviour were demonstrated on prepatterned GO devices. We have also confirmed the photoreduction process at low energy wavelengths (588 nm), indicating the versatility of using metalloporphyrins as photocatalysts for graphene oxide reduction.

MD1-2 Simultaneous spatial resolved measurement of vapor mass fraction and temperature of an evaporating fuel spray under DI-SI-engine conditions with planar laser-induced fluorescence(MD: Measurement and Diagnostics,General Session Papers)

The reduction of fuel consumption and pollutant emission is the main objective in development of modern IC-engines. A promising method to achieve this is the use of DI-SI engines with a spray-guided mixture formation. One advantage in efficiency of direct injection is the evaporation cooling of the fuel, which allows a higher compression. Although the compression temperature inside the engine can be calculated easily from the boundary conditions, the resulting mixture is not homogeneous through the combustion chamber. Thus, a spatial and temporal resolved measurement of the temperature and fuel concentration during injection is required to locally resolve evaporation rate, mixing and cooling in the spray. An approved method to measure temperature and concentration with a high temporal and spatial resolution under engine relevant conditions is planar laser-induced fluorescence (PLIF). For measurements in the spray, 3-pentanone as fluorescence tracer is added to a non-fluorescent surrogate fuel. The spray of a multi-hole injector is excited quasi-simultaneously by two different excimer lasers (2-line LIF), the temperature and the vapor mass fraction of the fuel can then be calculated from the two signals. The measurements are conducted in a high temperature/pressure cell, where engine relevant conditions can be simulated. To quantify temperature and concentration correctly, a precise fluorescence calibration of the tracer in a flow cell as well as an optimized post processing strategy of the raw images is presented. The results of the measurement are compared to a CFD-simulation under same conditions.

Carbon Nanotube-Based CMOS Gas Sensor IC: Monolithic Integration of Pd Decorated Carbon Nanotube Network on a CMOS Chip and Its Hydrogen Sensing

The integration of carbon nanotube (CNT)-based sensor and readout complementary metal-oxide-semiconductor integrated chip (CMOS IC) to detect hydrogen gas in a single chip is presented. First, we have fabricated the CMOS IC using the standard 0.35-μm CMOS process. Then, we have built 8 × 8 CNT-based sensor cells on it using a proposed tractable postprocessing strategy and judicious electrode scheme. The fabricated sensor IC can operate down to 10-ppm concentration of hydrogen in air as a hydrogen sensor. This paper is expected to have a major impact upon the integration of the CNT technology with CMOS technology and be extended to the development of CMOS IC integrated with various nanomaterials.

Parallel algorithm for training multiclass proximal Support Vector Machines

In this paper we describe a proximal Support Vector Machine algorithm for multiclassification problem by one-vs-all scheme. The computational requirement for the new algorithm is almost the same as training one of its element binary proximal Support Vector Machines. Low rank approximation is taken to reduce computational costs when the kernel matrix is too large. An error bound estimation for the approximated solution is given, which is used as a stopping criteria for low rank approximation. A post-processing strategy is developed to overcome the difficulty arising from unbalanced data and to improve the classification accuracy. A parallel implementation of the algorithm using standard MPI communication routines is provided to handle large-scale problems and to accelerate the training process. Experiment results on several public datasets validate the effectiveness of our proposed algorithm.

A new approach to plate theory based on through‐thickness homogenization

AbstractA new approach to linear plate theory for layered structures is introduced. The new theory distances itself from the classical strategy where through the thickness displacement functions are assumed a priori. Instead, the homogenization is based on a single assumption that the plate response is a superposition of fundamental states. An equivalent single‐layer (ESL) formulation is presented, which, for the first time, allows accurate three‐dimensional stress and strain fields to be recovered through a postprocessing strategy that is consistent with the theory's assumptions. Copyright © 2010 John Wiley &amp; Sons, Ltd.