Overview of the JET preparation for deuterium-tritium operation with the ITER like-wall

For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des.82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D–T mixtures since 1997 and the first ever D–T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D–T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D–T preparation. This intense preparation includes the review of the physics basis for the D–T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D–T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the three-ions scheme), new diagnostics (neutron camera and spectrometer, active Alfvèn eigenmode antennas, neutral gauges, radiation hard imaging systems...) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D–T campaign provides an incomparable source of information and a basis for the future D–T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant agreement No. 633053Postprint (published version

Utilizing Near-Field Measurements to Characterize Far-Field Radar Signatures

The increased need for stealth aircraft requires an on-site Far-Field (FF) Radar Cross-Section (RCS) measurement process. Conducting these measurements in on-site Near-Field (NF) monostatic facilities results in significant savings for manufacturers and acquisition programs. However, NF measurements are not directly extended to a FF RCS. Therefore, a large target Near-Field to Far-Field Transformation (NFFFT) is needed for RCS measurements. One approach requires an Inverse Synthetic Aperture Radar (ISAR) process to create accurate scattering maps. The focus of this work is the development of accurate NF scattering maps generated by a monostatic ISAR process. As a first look, the process is isolated to a simulated environment to avoid the uncontrollable effects of real measurement environments. The simulation begins with a NF Synthetic Target Generator (STG) which approximates a target using scattering centers illuminated by spherical electromagnetic waves to approximating NF scattering. The resulting NF In-phase and Quadrature (IQ) data is used in a Trapezoidal ISAR process to create spatially distorted images that are accurately corrected within the ISAR process resolution using a newly developed NF correction. The resulting spatially accurate ISAR images do not complete the NFFFT. However, accurate scattering maps are essential for process development

Inter-spike-intervals analysis of AER Poisson-like generator hardware

Address–Event–Representation (AER) is a communication protocol for transferring images between chips, originally developed for bio-inspired image-processing systems. Such systems may consist of a complicated hierarchical structure with many chips that transmit images among them in real time, while performing some processing (for example, convolutions). In developing AER-based systems it is very convenient to have available some means of generating AER streams from on-computer stored images. Rank order coding (ROC) and Poisson rate coding are the extremes of spikes coding. In this paper, we present a pseudo-random hardware method for generating AER streams in real time from a sequence of images stored in a computer’s memory. The Kolmogorov–Smirnov test has been applied to quantify that this method follows a Poisson distribution of the spikes. A USB–AER board, developed by our RTCAR group, have been used for the measurements. An example scenario of use under the EU CAVIAR project is presented.European Commission IST-2001-34124Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-0

Data Driven Sample Generator Model with Application to Classification

Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by machine learning approaches, but they still may require significant amounts of data. In this thesis, we seek to mitigate the latter challenge by developing a data driven sample generator model for the generation of synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained exclusively on continuously generated synthetic data produces the best area under the curve compared to classifiers trained on real data alone