NEXT-100 Technical Design Report (TDR). Executive Summary

In this Technical Design Report (TDR) we describe the NEXT-100 detector that will search for neutrinoless double beta decay (bbonu) in Xe-136 at the Laboratorio Subterraneo de Canfranc (LSC), in Spain. The document formalizes the design presented in our Conceptual Design Report (CDR): an electroluminescence time projection chamber, with separate readout planes for calorimetry and tracking, located, respectively, behind cathode and anode. The detector is designed to hold a maximum of about 150 kg of xenon at 15 bar, or 100 kg at 10 bar. This option builds in the capability to increase the total isotope mass by 50% while keeping the operating pressure at a manageable level. The readout plane performing the energy measurement is composed of Hamamatsu R11410-10 photomultipliers, specially designed for operation in low-background, xenon-based detectors. Each individual PMT will be isolated from the gas by an individual, pressure resistant enclosure and will be coupled to the sensitive volume through a sapphire window. The tracking plane consists in an array of Hamamatsu S10362-11-050P MPPCs used as tracking pixels. They will be arranged in square boards holding 64 sensors (8 times8) with a 1-cm pitch. The inner walls of the TPC, the sapphire windows and the boards holding the MPPCs will be coated with tetraphenyl butadiene (TPB), a wavelength shifter, to improve the light collection.Comment: 32 pages, 22 figures, 5 table

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized