Search results for "Signal reconstruction"
showing 9 items of 19 documents
Online Edge Flow Imputation on Networks
2022
Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respe…
Implementation and Performance of the Signal Reconstruction in the ATLAS Hadronic Tile Calorimeter
2012
AbstractThe Tile Calorimeter (TileCal) for the ATLAS experiment at the CERN Large Hadron Collider (LHC) is currently taking data with proton-proton collisions. The Tile Calorimeter is a sampling calorimeter with steel as absorber and scintillators as active medium. The scintillators are read-out by wavelength shifting fibers coupled to photomultiplier tubes (PMT). The analogue signals from the PMTs are amplified, shaped and digitized by sampling the signal every 25ns. The TileCal front-end electronics allows to read-out the signals produced by about 10000 channels measuring energies ranging from ∼30 MeV to ∼2 TeV. The read-out system is designed to reconstruct the data in real-time fulfilli…
The ATLAS TileCal read-out drivers signal reconstruction
2009
TileCal is the hadronic calorimeter of the ATLAS experiment at the LHC collider at CERN. The Read-Out Drivers (ROD) are the core of the off-detector electronics. The main components of the RODs are the Digital Signal Processor (DSP) placed on the Processing Unit (PU) dautherboards. This paper describes the DSP code and its performance with calibration and real data. The code is divided into two different parts: the first part contains the core functionalities and the second one the reconstruction algorithms. The core acts as an operating system and it controls the configuration, the data reception, transmission, online monitoring and the synchronization between front-end data and the Trigge…
Signal Characteristics of a Resistive-Strip Micromegas Detector with an Integrated Two-Dimensional Readout
2014
In recent years, micropattern gaseous detectors, which comprise a two-dimensional readout structure within one PCB layer, received significant attention in the development of precision and cost-effective tracking detectors in medium and high energy physics experiments. In this article, we present for the first time a systematic performance study of the signal characteristics of a resistive strip micromegas detector with a two-dimensional readout, based on test-beam and X-ray measurements. In particular, comparisons of the response of the two independent readout-layers regarding their signal shapes and signal reconstruction efficiencies are discussed.
Iterative Reconstruction of Signals on Graph
2020
We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. Our method is derived from the well known Papoulis-Gerchberg algorithm by considering the optimal value of a constant involved in the iteration step. Compared with existing graph signal reconstruction algorithms, the proposed method achieves similar or better performance both in terms of convergence rate and computational efficiency.
A probabilistic compressive sensing framework with applications to ultrasound signal processing
2019
Abstract The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of random transformations to project high-dimensional measurements onto a much lower-dimensional domain. The second is the use of sparse regression to reconstruct the original signal. This assumes that a sparse representation exists for this signal in some known domain, manifested by a dictionary. The original formulation for CS specifies the use of an l 1 penalised regression method, the Lasso. Whilst this has worked well in l…
A nonstationary model for the analysis of transient speech signals
1987
In this correspondence, a model is presented for the analysis of transient speech signals, which is based on a sum of the impulsive responses corresponding to a number of poles with time-dependent parameters. The aim of this analysis is to obtain discriminative features of the different transient elements of speech.
Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets
2012
Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this paper a novel wavelet-based approach is introduced to reproduce acceleration pulse of a vehicle involved in a crash event. The acceleration of a colliding vehicle is measured in its center of gravity-this crash pulse contains detailed information about vehicle behavior throughout a collision. Three types of signal analysis are elaborated here: time domain analysis (i.e. description of kinematics of…
XENON1T Dark Matter Data Analysis: Signal Reconstruction, Calibration and Event Selection
2019
The XENON1T experiment at the Laboratori Nazionali del Gran Sasso is the most sensitive direct detection experiment for dark matter in the form of weakly interacting particles (WIMPs) with masses above $6\,$GeV/$c^2$ scattering off nuclei. The detector employs a dual-phase time projection chamber with 2.0 metric tons of liquid xenon in the target. A one metric $\mathrm{ton}\times\mathrm{year}$ exposure of science data was collected between October 2016 and February 2018. This article reports on the performance of the detector during this period and describes details of the data analysis that led to the most stringent exclusion limits on various WIMP-nucleon interaction models to date. In pa…