Data Extraction Algorithm Research Articles

Evaluation of Algorithms for Automatic Data Extraction from Digital Holographic Images of Particles

The procedures and algorithms providing a numerical implementation of the physical process of image reconstruction from in-line particle holograms are discussed. The methods are proposed which allow functioning of this process in an automatic mode and ensure data processing and extraction from digital particle holograms in a form convenient for subsequent physical interpretation. The experimental results on realization of these methods are presented, using digital holograms of plankton particles, air bubbles in water, and sand particles, obtained under laboratory conditions as examples.

Testing an Algorithm for Data Extraction from Digital Holograms

A technique for automatic calculation of concentrations of particles in a volume and determination of their size distribution from the data reconstructed from digital holograms is proposed. The experimental results of implementation of this technique, using settling particles as an example, are presented.

Pattern Based Orbit Identification for Scatterometer Level-0 Signal Images

Considering the raw data extraction algorithms designed for different sensor data this paper focuses on the SCATTEROMETER Level-0 data extracted from raw data products. An analysis of extracted signal images is shown. Then a very simple technique but a very important concept is described and implemented on such signal images. Paper shows the approach which can be help full for identification of number of orbits in to the raw data products. The main objective of this approach is to provide the statistics to the application specific user. The approach is based on analyzed behavior of signal images extracted from the raw product. This approach defines a very basic standard to identify number of orbits based on SCATTEROMETER signal images. An implementation algorithm with its time complexity is shown with the corresponding implementation in C language. The proposed approach is for signal images of Data Quality Evaluation Level-0 SCATTEROMETER signal images, but it can also be extended for other signal images. The assumption for the paper is, the scanning geometry of signal image has been established in terms of scans and pixels i.e. header or interpretation format of the signal data.

Modeling of wood-like cellular materials with a geometrical data extraction algorithm

An algorithm on geometrical data extraction, material reconstruction and numerical analysis is presented in order to reconstruct the actual wood-like cellular materials and investigate their linear elastic material behavior in the transverse plane under different loading conditions. The algorithm implemented by Mathematica technical computing software is used to read the pixel data of cellular material images with a wide range of material scales, e.g. from micro- to millimeter scale. As a result of this process, geometrical properties including cell wall thicknesses, cell connectivities, vertex and center coordinates are determined. Identified geometrical properties are transferred to Abaqus/CAE computer aided engineering software by using a Python script and also converted into stereolithography STL model format enabling prototype generation and visualization. As an application example, the reconstructed model by means of the algorithm was used to investigate the in-plane effective stiffness properties of Norway spruce earlywood specimens in the frameworks of homogenization and finite element analysis.

A Three-dimension Huge Data Extraction Algorithm for Visualization Based on Computational Meshes

In order to accelerate the visualization processes of huge engineering data based on computational meshes, the extraction of huge data are necessary. A data extraction algorithm of engineering visualization data based on meshes is presented. Based on ordinary four-node tetrahedron elements, ten -node tetrahedron elements, eight-node hexahedron elements, the algorithms are studied to extract and simplify data for huge engineering data visualization, which includes relationships judgment between objective points and elements and interpolation algorithm in elements. Experiments of huge data transportation are given here, which show that the algorithm is reliable, and the algorithm can be used to extract visualization data from huge data of engineering computational results.

A non-linear iterative method for multi-layer DOT sub-surface imaging system.

Diffuse Optical Tomography (DOT) has become an emerging non-invasive technology, and has been widely used in clinical diagnosis. Functional near-infrared (FNIR) is one of the important applications of DOT. However, FNIR is used to reconstruct two-dimensional (2D) images for the sake of good spatial and temporal resolution. In this paper we propose a multiple-input and multiple-output (MIMO) based data extraction algorithm method in order to increase the spatial and temporal resolution. The non-linear iterative method is used to reconstruct better resolution images layer by layer. In terms of theory, the simulation results and original images are nearly identical. The proposed reconstruction method performs good spatial resolution, and has a depth resolutions capacity of three layers.

Grid data extraction algorithm for ship routing

With the aim of extracting environmental data around routes, as the basis of ship routing optimization and other related studies, this paper, taking wind grid data as an example, proposes an algorithm that can effectively extract the grid data around rhumb lines. According to different ship courses, the algorithm calculates the wind grid index values in eight different situations, and a common computational formula is summarised. The wind grids around a ship route can be classified into ‘best-fitting’ grids and ‘additional’ grids, which are stored in such a way that, for example, when the data has a high-spacing resolution, only the ‘best-fitting’ grids around ship routes are extracted. Finally, the algorithm was implemented and simulated with MATLAB programming. As the simulation results indicate, the algorithm designed in this paper achieved wind grid data extraction in different situations and further resolved the extraction problem of meteorological and hydrogeological field grids around ship routes efficiently. Thus, it can provide a great support for optimal ship routing related to meteorological factors.

Analysis of new designer drugs and common drugs of abuse in urine by a combined targeted and untargeted LC-HR-QTOFMS approach.

The development of a liquid chromatography high-resolution mass spectrometry quadrupole-time-of-flight (LC-HRMS-QTOF) method for the analysis of new stimulant designer drugs (e.g. phenethylamine, amphetamine, cathinone and piperazine derivatives) and common drugs of abuse (e.g. ketamine and ritalinic acid) in urine is reported. Sample preparation was carried out by a fast and convenient salting-out liquid-liquid extraction (SALLE) procedure. The data was generated by a preferred target list combined with untargeted data-dependent acquisition recording additional sample information (i.e. not listed metabolites of target compounds or not database-stored drugs). The identification is realised by a fully automated data extraction algorithm, taking into account accurate mass spectra, fragment masses and retention times. Method validation comprised selectivity, linearity, accuracy, stability, determination of the limit of detection (LOD) and limit of quantification (LOQ) and evaluation of matrix effects and recoveries for a total set of 39 compounds. Acceptable quantitative results were obtained for 35 of the 39 analytes. Exemplarily, application of the additional untargeted data-dependent acquisition mode enabled the identification of metabolites of the preferred target list compounds ketamine and methylenedioxypyrovalerone (MDPV) without use of reference standards. Therefore, improvement of the database is feasible with every positive library hit. The approach presented here provides a very useful tool for the combined targeted and untargeted analysis of drugs of abuse in biological matrices such as urine.

Heterogeneous Web Data Extraction Algorithm Based On Modified Hidden Conditional Random Fields

As it is of great importance to extract useful information from heterogeneous Web data, in this paper, we propose a novel heterogeneous Web data extraction algorithm using a modified hidden conditional random fields model. Considering the traditional linear chain based conditional random fields can not effectively solve the problem of complex and heterogeneous Web data extraction, we modify the standard hidden conditional random fields in three aspects, which are 1) Using the hidden Markov model to calculate the hidden variables, 2) Modifying the standard hidden conditional random fields through two stages. In the first stage, each training data sequence is learned using hidden Markov model, and then implicit variables can be visible. In the second stage, parameters can be learned for a given sequence. (3) The objective functions of hidden conditional random fields are revised, and the heterogeneous Web data are extracted by maximizing the posterior probability of the modified hidden conditional random fields. Finally, experiments are conducted to make performance evaluation on two standard datasets-EData dataset and Research Papers dataset. Compared with the existing Web data extraction methods, it can be seen that the proposed algorithm can extract useful information from heterogeneous Web data effectively and efficiently.

Vehicle Tracking System based on Videotaping Data

The relevance of microscopic traffic simulation models for the analysis of traffic operations and safety is based on their ability to reproduce real time traffic operations at a given location. Accordingly, one of the major steps in the development and application of these models is a good calibration of the model input parameters based on a thorough comparison of simulated and observed vehicle trajectories. Videotaping provides a low cost nonintrusive procedure for capturing individual vehicle operations over time, and as such, provides a useful tool for obtaining observational data for calibration and validation of traffic simulation models. Before videotaped data can be used, however, vehicle tracks or trajectories will need to be extracted from a frame by frame analysis of vehicle progression measurements concerning longitudinal and lateral position, speed, and acceleration over time. A system for tracking moving vehicles is presented that overcomes many of the practical limitations of current videotaping applications usually resulting from traffic and site conditions for the road segment being videotaped. The data extraction algorithm proposed in this paper provides a more flexible (less restrictive) method for videotaping of vehicle in the traffic stream, which attempts to overcome many of the limitations imposed by other more restricting taping methods currently in use. However, the accuracy of the vehicle tracking system needs to be assessed. This necessitates a comparison of individual vehicle trajectories as extracted from the video with the corresponding profiles obtained from baseline ground control points (GCP) referenced values for the same trajectories. This study has two objectives: 1) to introduce and describe the video extraction algorithm that allows vehicle segmentation and tracking, and the computation of traffic parameters from the tracking data, 2) to assess the accuracy of this algorithm with respect to changes in the horizontal viewing angle, and the number and placement of GCP along a given road segment. Preliminary results obtained from a case study shown that the number of GCP and the deflection angle from the perpendicular camera sightline to the roadway have a significant effect on the accuracy of the detected vehicle trajectories. Furthermore, the placement of GCP along the road segment has a significant effect on error, especially as it affects the scale of pixels at the edge of the video angle.

Measurement of mutation-specific survival as a real-time cancer care quality indicator.

30 Background: As targeted therapies become widespread in the treatment of cancer, tracking clinical effectiveness as a function of mutation status, outside of the research setting, is needed. We evaluated whether mutation-specific survival statistics could be derived in a completely automated fashion from electronic medical record data sources, for quality assurance and purposes preparatory to research. Methods: Patients with cancer mutation analysis obtained for clinical care at Vanderbilt University up to June 2013 were included. Informatics algorithms were developed to automatically extract tumor mutation status, cancer type, date of diagnosis, and date of death or date of last contact using multiple structured and unstructured clinical data sources including billing codes, narrative pathology reports, and the Social Security Death Index. Survival was modeled using Cox proportional hazards, stratified by mutation type; mutations occurring less than 10 times were aggregated into an “Other” category. Results: 2,636 out of 3,115,904 patients had sufficient data for inclusion. Date of death was recorded for 32% of patients; overall median follow-up was 19 months. The median mutation-specific survival for lung cancer and melanoma patients is shown in the Table. For lung cancer patients, EGFR mutation was associated with superior survival (HR 0.7, 95% CI 0.5-1, p = 0.05). For melanoma patients, GNAQ mutation was associated with inferior survival (HR 2.2, 95% CI 1.3-3.8, p = 0.006) whereas BRAF mutation was not statistically significant (p = 0.32). Conclusions: It is feasible to create survival curves based on cancer mutation status in a fully automated fashion for quality assurance and purposes preparatory to research. Further iterative improvements in the data extraction algorithms are continuing; updates will be presented. Future work will enable stratification by mutation subtype, treatment exposure, staging, and demographics. [Table: see text]

Efficient Data Extraction Method for Near-Infrared Spectroscopy (NIRS) Systems With High Spatial and Temporal Resolution

An hardware-efficient method for the extraction of hemodynamic responses in near-infrared spectroscopy systems is proposed to increase the spatial and temporal resolution. The performance improvement is attributed to high signal-to-noise ratio receivers, a modulation scheme, and a multi-input-multi-output based data extraction algorithm. The proposed system shows more than twofold improvement in the figure of merit compared to conventional designs. Experimental results support the validity of the proposed system.

Measurement of isothermal diffusion coefficients in ternary mixtures using counter flow diffusion cell

A new diffusion cell and an interferometer have been developed for determination of isothermal diffusion coefficients in ternary mixtures. Two interferometer schemes have been proposed and tested for the measurements. The classical Mach–Zehnder interferometer can be reconfigured into a common-path interferometer to resolve high gradients of the refractive index that are typical of diffusion experiments. Formulations, methodology and algorithms for data extraction are presented for the example of a ternary mixture with an equal mass fraction of all components (1:1:1) in the system of tetralin–isobutylbenzene–dodecane. The selection of the fitting procedure is very important for obtaining reliable results.

Identification of rheumatoid arthritis patients using an administrative database: A Veterans Affairs study

The accuracy of the diagnosis is vital when administrative databases are used for pharmacoepidemiologic and outcome studies. Data pertaining to the utility of databases for rheumatoid arthritis (RA) are sparse and variable. We assessed the utility of various diagnostic algorithms to identify RA patients within the Veterans Health Administration (VHA) databases. Using the International Classification of Diseases, Ninth Revision code for RA at 2 visits at least 6 months apart, we identified 1,779 patients between October 1, 1998 and September 30, 2009 in our local Veterans Affairs Medical Center (VAMC) administrative database. Disease-modifying antirheumatic drug (DMARD) use was ascertained from the pharmacy database. Cases were analyzed based on DMARD therapy and RA codes at clinic visits. A total of 543 patients' medical records, selected by stratification and random selection on the basis of their visits, were reviewed to ascertain the clinicians' diagnoses and clinical criteria documentation. Positive predictive values (PPVs) were calculated for various database case identification algorithms using diagnosis of RA by medical record review as the gold standard. The PPV for identification of RA with 2 RA codes 6 months apart was 30.9%. Addition of DMARD therapy increased the PPV to 60.4%. The PPV further increased to 91.4% when having an RA code at the last VAMC rheumatology clinic visit criterion was added. An algorithm using only 2 administrative RA codes 6 months apart had a low PPV for correctly identifying patients with RA in the VHA database. Including DMARD therapy and requiring an RA code at the last visit with a rheumatologist increased the performance of the data extraction algorithm.

MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing

Metabolomics Ion-based Data Extraction Algorithm (MET-IDEA) is a computer program for processing large-scale metabolomics data. MET-IDEA utilizes network Common Data Form (netCDF) data files available from a diversity of chromatographically coupled mass spectrometry (MS) systems, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized data. This article reports on recent improvements to MET-IDEA which include new visualization of peak integrations, display of mass spectra associated with integrated peaks, and optional manual peak integration. The computational performance of MET-IDEA has also been improved to avoid memory overflow during the processing of large data sets and the software made compatible with 64 bit CPUs and operating systems. These new functions improve the performance of MET-IDEA, and they allow users to visualize peak integrations and curate the results through manual integration if desired. The improved version of MET-IDEA better facilitates the quantitative analysis of complex MS-based metabolomics data. MET-IDEA is freely available for academic and non commercial use at ( http://bioinfo.noble.org/gateway/index.php?option=com_wrapper&Itemid=57 ). Commercial use is available via licensing agreement.

条纹管成像激光雷达条纹数据提取算法

条纹管成像激光雷达条纹数据提取算法

Automated Identification and Extraction of Horizontal Curve Information from Geographic Information System Roadway Maps

Roadway horizontal alignment has long been recognized as one of the most significant contributing factors to lane departure crashes. Knowledge of the location and geometric information of horizontal curves can greatly facilitate the development of appropriate countermeasures. When curve information is unavailable, obtaining curve data in a cost-effective way is of great interest to practitioners and researchers. To date, many approaches have been developed to extract curve information from commercial satellite imagery, Global Positioning System survey data, laser-scanning data, and AutoCAD digital maps. As geographic information system (GIS) roadway maps become more accessible and more widely used, they become another cost-effective source for extraction of curve data. This paper presents a fully automated method for the extraction of horizontal curve data from GIS roadway maps. A specific curve data–extraction algorithm was developed and implemented as a customized add-in tool in ArcMap. With this tool, horizontal curves could be automatically identified from GIS roadway maps. The length, radius, and central angle of the curves were also computed automatically. The only input parameter of the proposed algorithm was calibrated to have the least curve identification errors. Finally, algorithm validation was conducted through a comparison of the algorithm-extracted curve data with the ground truth curve data for 76 curves that were obtained from Bing aerial maps. The validation results indicated that the proposed algorithm was very effective and that it identified completely 96.7% of curves and computed accurately their geometric information.

Electronic Health Record Use to Classify Patients with Newly Diagnosed versus Preexisting Type 2 Diabetes: Infrastructure for Comparative Effectiveness Research and Population Health Management

Use of electronic health record (EHR) content for comparative effectiveness research (CER) and population health management requires significant data configuration. A retrospective cohort study was conducted using patients with diabetes followed longitudinally (N=36,353) in the EHR deployed at outpatient practice networks of 2 health care systems. A data extraction and classification algorithm targeting identification of patients with a new diagnosis of type 2 diabetes mellitus (T2DM) was applied, with the main criterion being a minimum 30-day window between the first visit documented in the EHR and the entry of T2DM on the EHR problem list. Chart reviews (N=144) validated the performance of refining this EHR classification algorithm with external administrative data. Extraction using EHR data alone designated 3205 patients as newly diagnosed with T2DM with classification accuracy of 70.1%. Use of external administrative data on that preselected population improved classification accuracy of cases identified as new T2DM diagnosis (positive predictive value was 91.9% with that step). Laboratory and medication data did not help case classification. The final cohort using this 2-stage classification process comprised 1972 patients with a new diagnosis of T2DM. Data use from current EHR systems for CER and disease management mandates substantial tailoring. Quality between EHR clinical data generated in daily care and that required for population health research varies. As evidenced by this process for classification of newly diagnosed T2DM cases, validation of EHR data with external sources can be a valuable step.

LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome.

An Algorithmic Solution for Management of Related Text Objects with Application in Phytopharmacy

This paper presents an algorithmic solution for management of related text objects, in which are integrated algorithms for their extraction from paper or electronic format, for their storage and processing in a relational database. The developed algorithms for data extraction and data analysis enable one to find specific features and relations between the text objects from the database. The algorithmic solution is applied to data from the field of phytopharmacy in Bulgaria. It can be used as a tool and methodology for other subject areas where there are complex relationships between text objects.