114 resultados para Object orientation


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OBJECTIVE: In transcranial magnetic stimulation (TMS) of the motor cortex, the optimal orientation of the coil on the scalp is dependent on the muscle under investigation, but not yet known for facial muscles. METHODS: Using a figure-of-eight coil, we compared TMS induced motor evoked potentials (MEPs) from eight different coil orientations when recording from ipsi- and contralateral nasalis muscle. RESULTS: The MEPs from nasalis muscle revealed three components: The major ipsi- and contra-lateral middle latency responses of approximately 10 ms onset latency proved entirely dependent on voluntary pre-innervation. They were most easily obtained from a coil orientation with posterior inducing current direction, and in this respect resembled the intrinsic hand rather than the masseter muscles. Early short duration responses of around 6 ms onset latency were best elicited with an antero-lateral current direction and not pre-innervation dependent, and therefore most probably due to stimulation of the nerve roots. Late responses (>18 ms) could inconsistently be elicited with posterior coil orientations in pre-innervated condition. CONCLUSIONS: By using the appropriate coil orientation and both conditions relaxed and pre-innervated, cortically evoked MEP responses from nasalis muscle can reliably be separated from peripheral and reflex components and also from cross talk of masseter muscle activation.

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Our goal was to validate accuracy, consistency, and reproducibility/reliability of a new method for determining cup orientation in total hip arthroplasty (THA). This method allows matching the 3D-model from CT images or slices with the projected pelvis on an anteroposterior pelvic radiograph using a fully automated registration procedure. Cup orientation (inclination and anteversion) is calculated relative to the anterior pelvic plane, corrected for individual malposition of the pelvis during radiograph acquisition. Measurements on blinded and randomized radiographs of 80 cadaver and 327 patient hips were investigated. The method showed a mean accuracy of 0.7 +/- 1.7 degrees (-3.7 degrees to 4.0 degrees) for inclination and 1.2 +/- 2.4 degrees (-5.3 degrees to 5.6 degrees) for anteversion in the cadaver trials and 1.7 +/- 1.7 degrees (-4.6 degrees to 5.5 degrees) for inclination and 0.9 +/- 2.8 degrees (-5.2 degrees to 5.7 degrees) for anteversion in the clinical data when compared to CT-based measurements. No systematic errors in accuracy were detected with the Bland-Altman analysis. The software consistency and the reproducibility/reliability were very good. This software is an accurate, consistent, reliable, and reproducible method to measure cup orientation in THA using a sophisticated 2D/3D-matching technique. Its robust and accurate matching algorithm can be expanded to statistical models.

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In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

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Behavioral reflection is crucial to support for example functional upgrades, on-the-fly debugging, or monitoring critical applications. However the use of reflective features can lead to severe problems due to infinite metacall recursion even in simple cases. This is especially a problem when reflecting on core language features since there is a high chance that such features are used to implement the reflective behavior itself. In this paper we analyze the problem of infinite meta-object call recursion and solve it by providing a first class representation of meta-level execution: at any point in the execution of a system it can be determined if we are operating on a meta-level or base level so that we can prevent infinite recursion. We present how meta-level execution can be represented by a meta-context and how reflection becomes context-aware. Our solution makes it possible to freely apply behavioral reflection even on system classes: the meta-context brings stability to behavioral reflection. We validate the concept with a robust implementation and we present benchmarks.

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Back-in-time debuggers are extremely useful tools for identifying the causes of bugs, as they allow us to inspect the past states of objects no longer present in the current execution stack. Unfortunately the "omniscient" approaches that try to remember all previous states are impractical because they either consume too much space or they are far too slow. Several approaches rely on heuristics to limit these penalties, but they ultimately end up throwing out too much relevant information. In this paper we propose a practical approach to back-in-time debugging that attempts to keep track of only the relevant past data. In contrast to other approaches, we keep object history information together with the regular objects in the application memory. Although seemingly counter-intuitive, this approach has the effect that past data that is not reachable from current application objects (and hence, no longer relevant) is automatically garbage collected. In this paper we describe the technical details of our approach, and we present benchmarks that demonstrate that memory consumption stays within practical bounds. Furthermore since our approach works at the virtual machine level, the performance penalty is significantly better than with other approaches.