Design, development and performance study of six-gap glass MRPC detectors

The Multigap Resistive Plate Chambers (MRPCs) are gas ionization detectors with multiple gas sub-gaps made of resistive electrodes. The high voltage (HV) is applied on the outer surfaces of outermost resistive plates only, while the interior plates are left electrically floating. The presence of multiple narrow sub--gaps with high electric field results in faster signals on the outer electrodes, thus improving the detector's time resolution. Due to their excellent performance and relatively low cost, the MRPC detector has found potential application in Time-of-Flight (TOF) systems. Here we present the design, fabrication, optimization of the operating parameters such as the HV, the gas mixture composition, and, performance of six--gap glass MRPC detectors of area 27cm $\times$ 27 cm, which are developed in order to find application as trigger detectors, in TOF measurement etc. The design has been optimized with unique spacers and blockers to ensure a proper gas flow through the narrow sub-gaps, which are 250 $\mu$m wide. The gas mixture consisting of R134A, Isobutane and SF$_{6}$, and the fraction of each constituting gases has been optimized after studying the MRPC performance for a set of different concentrations. The counting efficiency of the MRPC is about 95% at $17.9$ kV. At the same operating voltage, the time resolution, after correcting for the walk effect, is found to be about $219$ ps.Comment: Revised version with 15 pages, 14 figures, 2 tables. Accepted for publication in the European Physical Journal

The MRPC-based ALICE Time-Of-Flight detector: status and performance

The large Time-Of-Flight (TOF) array is one of the main detectors devoted to charged hadron identification in the mid-rapidity region of the ALICE experiment at the LHC. It allows separation among pions, kaons and protons up to a few GeV/c, covering the full azimuthal angle and -0.9 < eta < 0.9. The TOF exploits the innovative MRPC technology capable of an intrinsic time resolution better than 50 ps with an efficiency close to 100% and a large operational plateau; the full array consists of 1593 MRPCs covering a cylindrical surface of 141 m2. The TOF detector has been efficiently taking data since the first pp collisions recorded in ALICE in December 2009. In this report, the status of the TOF detector and the performance achieved for both pp and Pb--Pb collisions are described.Comment: 4 pages, 6 figure

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201

A Review of Bankruptcy Prediction Studies: 1930-Present

One of the most well-known bankruptcy prediction models was developed by Altman [1968] using multivariate discriminant analysis. Since Altman\u27s model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to facilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 1930\u27s, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studies have evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. For example, discriminant analysis was the primary method used to develop models in the 1960\u27s and 1970\u27s. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 1980\u27s and 1990\u27s. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are just as capable of accurate prediction as models with 21 factors

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI tract and to diagnose various diseases and pathologies. Since the development of this technology, medical device companies and many groups have made significant progress to turn such passive capsule endoscopes into robotic active capsule endoscopes to achieve almost all functions of current active flexible endoscopes. However, the use of robotic capsule endoscopy still has some challenges. One such challenge is the precise localization of such active devices in 3D world, which is essential for a precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of the explored inner organ could assist the doctors to make more intuitive and correct diagnosis. In this paper, we propose to our knowledge for the first time in literature a visual simultaneous localization and mapping (SLAM) method specifically developed for endoscopic capsule robots. The proposed RGB-Depth SLAM method is capable of capturing comprehensive dense globally consistent surfel-based maps of the inner organs explored by an endoscopic capsule robot in real time. This is achieved by using dense frame-to-model camera tracking and windowed surfelbased fusion coupled with frequent model refinement through non-rigid surface deformations