Records Management System: Indexing Standard, Document Standard, Technology Standard, Laboratory Configuration, 1997

Purpose of Records Management Sysytem indexing standards document is to develop indexing standards that will best support Iowa DOT's effort to build an agency-wide Records Management System

View-sensitive ERP repetition effects indicate automatic holistic processing of spatially unattended objects

This study examined the properties of ERP effects elicited by unattended (spatially uncued) objects using a short-lag repetition-priming paradigm. Same or different common objects were presented in a yoked prime-probe trial either as intact images or slightly scrambled (half-split) versions. Behaviourally, only objects in a familiar (intact) view showed priming. An enhanced negativity was observed at parietal and occipito-parietal electrode sites within the time window of the posterior N250 after the repetition of intact, but not split, images. An additional post-hoc N2pc analysis of the prime display supported that this result could not be attributed to differences in salience between familiar intact and split views. These results demonstrate that spatially unattended objects undergo visual processing but only if shown in familiar views, indicating a role of holistic processing of objects that is independent of attention.

Automatic generation of portable multi-dimensionally decomposed parallel structured mesh software

Abstract not available

Semi-automatic parallelisation of unstructured mesh codes

The availability of CFD software that can easily be used and produce high efficiency on a wide range of parallel computers is extremely limited. The investment and expertise required to parallelise a code can be enormous. In addition, the cost of supercomputers forces high utilisation to justify their purchase, requiring a wide range of software. To break this impasse, tools are urgently required to assist in the parallelisation process that dramatically reduce the parallelisation time but do not degrade the performance of the resulting parallel software. In this paper we discuss enhancements to the Computer Aided Parallelisation Tools (CAPTools) to assist in the parallelisation of complex unstructured mesh-based computational mechanics codes.

Myocardial blood flow quantification by Rb-82 cardiac PET/CT: A detailed reproducibility study between two semi-automatic analysis programs.

BACKGROUND: Several analysis software packages for myocardial blood flow (MBF) quantification from cardiac PET studies exist, but they have not been compared using concordance analysis, which can characterize precision and bias separately. Reproducible measurements are needed for quantification to fully develop its clinical potential. METHODS: Fifty-one patients underwent dynamic Rb-82 PET at rest and during adenosine stress. Data were processed with PMOD and FlowQuant (Lortie model). MBF and myocardial flow reserve (MFR) polar maps were quantified and analyzed using a 17-segment model. Comparisons used Pearson's correlation ρ (measuring precision), Bland and Altman limit-of-agreement and Lin's concordance correlation ρc = ρ·C b (C b measuring systematic bias). RESULTS: Lin's concordance and Pearson's correlation values were very similar, suggesting no systematic bias between software packages with an excellent precision ρ for MBF (ρ = 0.97, ρc = 0.96, C b = 0.99) and good precision for MFR (ρ = 0.83, ρc = 0.76, C b = 0.92). On a per-segment basis, no mean bias was observed on Bland-Altman plots, although PMOD provided slightly higher values than FlowQuant at higher MBF and MFR values (P < .0001). CONCLUSIONS: Concordance between software packages was excellent for MBF and MFR, despite higher values by PMOD at higher MBF values. Both software packages can be used interchangeably for quantification in daily practice of Rb-82 cardiac PET.

Automatic implementation of dynamic load balancing strategies for structured computational mechanics codes

This paper presents a new dynamic load balancing technique for structured mesh computational mechanics codes in which the processor partition range limits of just one of the partitioned dimensions uses non-coincidental limits, as opposed to using coincidental limits in all of the partitioned dimensions. The partition range limits are 'staggered', allowing greater flexibility in obtaining a balanced load distribution in comparison to when the limits are changed 'globally'. as the load increase/decrease on one processor no longer restricts the load decrease/increase on a neighbouring processor. The automatic implementation of this 'staggered' load balancing strategy within an existing parallel code is presented in this paper, along with some preliminary results.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Automatic Incident Detection and Driver Warning in PORTICO.

This paper focuses on the low-volume incident detection and subsequent driver warning objectives of the PORTICO project. It proposes Automatic Incident Detection (AID) which uses a multimodel approach comprised of a number of different algorithms. A set of thresholds and conditions are defined which determine which algorithm(s) need to indicate an incident under different traffic conditions in order to trigger an alarm

Data-driven Automatic Edge Sharpening

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths. We present a method that enhances sharpness of images and thereby their perceptual quality. Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs. When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters. We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

Automatic dynamic mask extraction for PIV images containing an unsteady interface, bubbles, and a moving structure

When performing Particle Image Velocimetry (PIV) measurements in complex fluid flows with moving interfaces and a two-phase flow, it is necessary to develop a mask to remove non-physical measurements. This is the case when studying, for example, the complex bubble sweep-down phenomenon observed in oceanographic research vessels. Indeed, in such a configuration, the presence of an unsteady free surface, of a solid–liquid interface and of bubbles in the PIV frame, leads to generate numerous laser reflections and therefore spurious velocity vectors. In this note, an image masking process is developed to successively identify the boundaries of the ship and the free surface interface. As the presence of the solid hull surface induces laser reflections, the hull edge contours are simply detected in the first PIV frame and dynamically estimated for consecutive ones. As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body. An application of this new mask is finally detailed, allowing a preliminary analysis of the hydrodynamic flow.

Latent semantic indexing eta ikasketa automatikoa hizkuntzaren prozesamenduaren arloan: testu-sailkatzea, hitzen adiera-desanbiguatzea eta korreferentzia-ebaztea SVD bidezko dimentsio murrizketa eta multi-sailkatzailea konbinatuz

220 p.

An Information Theoretic Approach to Automatic Query Expansion

International audience