Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array

In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip patch elements by considering only a part of array elements for a chosen sector. This reduces neural network sizes and also improves the spatial filtering performance. The proposed algorithm improves MN-MUST algorithm in the sense of accuracy and speed while covering the full azimuth range

A 3D diamond detector for particle tracking

International audienceA novel device using single-crystal chemical vapour deposited diamond and resistive electrodes in the bulk forming a 3D diamond detector is presented. The electrodes of the device were fabricated with laser assisted phase change of diamond into a combination of diamond-like carbon, amorphous carbon and graphite. The connections to the electrodes of the device were made using a photo-lithographic process. The electrical and particle detection properties of the device were investigated. A prototype detector system consisting of the 3D device connected to a multi-channel readout was successfully tested with 120 GeV protons proving the feasibility of the 3D diamond detector concept for particle tracking applications for the first time

Erratum to ‘‘A 3D diamond detector for particle tracking’’ [Nuclear Instruments Methods A 786 (2015) 97–104]

International audienceThe publisher regretsp 98,“The electrode yield, i.e. the fraction of electrodes produced with a continuous conducting path over the thickness of the diamondsample, was estimated by optical and electrical inspection and found to be9273%.”(Was“9270.3%”)p101, Figure 8 of the paper misses the legend, corrected figure:p103, Figure 13b) of the paper misses 3 blue bin entries (corner bins) of the four, only one entry is visible