Unified Taxonomy for Reference Ontology of Shape Features in Product Model

This paper presents a unified taxonomy of shape features. Such taxonomy is required to construct ontologies to address heterogeneity in product/shape models. Literature provides separate classifications for volumetric, deformation and free-form surface features. The unified taxonomy proposed allows classification, representation and extraction of shape features in a product model. The novelty of the taxonomy is that the classification is based purely on shape entities and therefore it is possible to automatically extract the features from any shape model. This enables the use of this taxonomy to build reference ontology.

Volumetric MRI changes, cognition and personality traits in old age depression.

BACKGROUND: The presence of cognitive and structural deficits in euthymic elderly depressed patients remains a matter of debate. Integrative aetiological models assessing concomitantly these parameters as well as markers of psychological vulnerability such as persistent personality traits, are still lacking for this age group. METHODS: Cross-sectional comparisons of 38 elderly remitted patients with early-onset depression (EOD) and 62 healthy controls included detailed neuropsychological assessment, estimates of brain volumes in limbic areas and white matter hyperintensities, as well as evaluation of the Five-Factor personality dimensions. RESULTS: Both cognitive performances and brain volumes were preserved in euthymic EOD patients. No significant group differences were observed in white matter hyperintensity scores between the two groups. In contrast, EOD was associated with significant increase of Neuroticism and decrease of Extraversion facet scores. LIMITATIONS: Results concern the restricted portion of EOD patients without psychiatric and physical comorbidities. Future longitudinal studies are necessary to determine the temporal relationship between the occurrence of depression and personality dimensions. CONCLUSIONS: After remission from acute depressive symptoms, cognitive performances remain intact in elderly patients with EOD. In contrast to previous observations, these patients display neither significant brain volume loss in limbic areas nor increased vascular burden compared to healthy controls. Further clinical investigations on EOD patterns of vulnerability in old age will gain from focusing on psychological features such as personality traits rather than neurocognitive clues.

Case Study: Interacting with Volumetric Medical Datasets in Networked CAVE Environments

Virtual Reality (VR) is widely used in visualizing medical datasets. This interest has emerged due to the usefulness of its techniques and features. Such features include immersion, collaboration, and interactivity. In a medical visualization context, immersion is important, because it allows users to interact directly and closelywith detailed structures in medical datasets. Collaboration on the other hand is beneficial, because it gives medical practitioners the chance to share their expertise and offer feedback and advice in a more effective and intuitive approach. Interactivity is crucial in medical visualization and simulation systems, because responsiveand instantaneous actions are key attributes in applications, such as surgical simulations. In this paper we present a case study that investigates the use of VR in a collaborative networked CAVE environment from a medical volumetric visualization perspective. The study will present a networked CAVE application, which has been built to visualize and interact with volumetric datasets. We will summarize the advantages of such an application and the potential benefits of our system. We also will describe the aspects related to this application area and the relevant issues of such implementations.

Volumetric morphometrics

Geometric morphometrics (GMM) methods are very popular in physical anthropology. One disadvantage common to the existingGMM methods is that despite significant advancements in computed tomography (CT) and magnetic resonance imaging (MRI)technology, these methods still depend on landmarks or features that are either digitized directly from subject surface or extractedfrom surface models or outlines derived from a laser surface scan or from a CTor MRI scan. All the rest image contents contained ina CTor MRI scan are ignored by these methods. In this paper, we present a complementary solution called Volumetric Morphometrics(VMM). With VMM, we are aiming for a paradigm shift from landmarks and surfaces used in existing GMM approaches todisplacements and volumes in the new VMM approaches, taking the full advantage of modern CTand MRI technology. Preliminaryvalidation results on ancient human skulls are presented.

Volumetric calibration of process vessels using digital computer techniques /

The paper describes a procedure for accurately and speedily calibrating tanks used for the chemical processing of nuclear materials. The procedure features the use of (1) precalibrated vessels certified to deliver known volumes of liquid, (2) calibrated linear measuring devices, and (3) a digital computer for manipulating data and producing printed calibration information. Calibration records of the standards are traceable to primary standards. Logic is incorporated in the computer program to accomplish curve fitting and perform the tests to accept or to reject the calibration, based on statistical, empirical, and report requirements. This logic is believed to be unique.

Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.