Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

Author manuscript, published in "33rd Annual Symposium of the German Association for Pattern Recognition (DAGM) (2011)" Efficient and Robust Alignment of Unsynchronized Video Sequences

Abstract. This paper addresses the problem of aligning two unsynchronized video sequences. We present a novel approach that allows for temporal and spatial alignment of similar videos captured from independently moving cameras. The goal is to synchronize two videos of a scene such that changes between the videos can be detected automatically. This aims at applications in driver assistance or surveillance systems but we also envision applications in map building. Our approach is novel in that it adapts an efficient information retrieval framework to a computer vision problem. In addition, we extend the recent ECC imagealignment algorithm to the temporal dimension in order to improve spatial registration and enable synchro refinement. Experiments with traffic videos recorded by in-vehicle cameras demonstrate the efficiency of the proposed method and verify its effectiveness with respect to spatio-temporal alignment accuracy.

Generic active appearance models revisited

The proposed Active Orientation Models (AOMs) are gen- erative models of facial shape and appearance. Their main dierences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a dierent statistical model of appearance, (ii) they are accompanied by a robust algorithm for model tting and parameter es- timation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complex- ity. The project-out version of AOMs is as computationally ecient as the standard project-out inverse compositional algorithm which is ad- mittedly the fastest algorithm for tting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outper- forms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments

A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

International audienceHeterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a supervised learning schema based on random forest for automatic initialization and propagation of statistical shape and appearance model. Parametric representation of the statistical model of shape and appearance is derived from principal component analysis (PCA) of the probability distribution inside the prostate and PCA of the contour landmarks obtained from the training images. Unlike traditional statistical models of shape and intensity priors, the appearance model in this paper is derived from the posterior probabilities obtained from random forest classification. This probabilistic information is then used for the initialization and propagation of the statistical model. The proposed method achieves mean Dice Similarity Coefficient (DSC) value of 0.96±0.01, with a mean segmentation time of 0.67±0.02 seconds when validated with 24 images from 6 datasets with considerable shape, size, and intensity variations, in a leave-one-patient-out validation framework. The model achieves sta- tistically significant t-test p-value<0.0001 in mean DSC and mean mean absolute distance (MAD) values compared to traditional statistical models of shape and intensity priors

Impact of nitrogen seeding on confinement and power load control of a high-triangularity JET ELMy H-mode plasma with a metal wall

This paper reports the impact on confinement and power load of the high-shape 2.5MA ELMy H-mode scenario at JET of a change from an all carbon plasma facing components to an all metal wall. In preparation to this change, systematic studies of power load reduction and impact on confinement as a result of fuelling in combination with nitrogen seeding were carried out in JET-C and are compared to their counterpart in JET with a metallic wall. An unexpected and significant change is reported on the decrease of the pedestal confinement but is partially recovered with the injection of nitrogen.Comment: 30 pages, 16 figure

Impact of nitrogen seeding on carbon erosion in the JET divertor

Nitrogen has been introduced in H-mode plasmas in JET in order to study its radiation cooling capability and impact on the erosion of divertor plasma-facing components made of carbon-fiber composites (CFC). Experiments in the ionizing plasma regime with low nitrogen injection show a reduction of the total carbon erosion in the divertor measured with the aid of optical spectroscopy on C(+). Though chemical sputtering by nitrogen takes place, identified by the appearance of CN B-X band emission, the additional carbon source is overcompensated by a reduction of regular sputtering by deuterium bombardment. Moderate plasma cooling associated with reduction of the sputtering yield and dilution of the CFC surface by nitrogen can be attributed to the favorable reduction of the carbon source