Search results for "Detection"
showing 10 items of 2543 documents
Mass calibration of the energy axis in ToF- E elastic recoil detection analysis
2016
We report on procedures that we have developed to mass-calibrate the energy axis of ToF-E histograms in elastic recoil detection analysis. The obtained calibration parameters allow one to transform the ToF-E histogram into a calibrated ToF-M histogram.
Oxy-nitrides characterization with a new ERD-TOF system
2017
Abstract A new time-of-flight (TOF) camera was installed on Elastic Recoil Detection (ERD) measurement setup on the Tandem Accelerator at Universite de Montreal. The camera consists of two timing detectors, developed and built by the Jyvaskyla group, that use a thin carbon foil and microchannel plates (MCP) to produce the start and stop signals. The position of the first detector is fixed at 18 cm from the target, while the position of the second detector can be varied between 50 and 90 cm from the first detector. This allows to increase time resolution by increasing the distance between the time-of-flight detectors or to increase solid angle by decreasing the distance. Moving the detector …
Determination of the chemical warfare agents Sarin, Soman and Tabun in natural waters employing fluorescent hybrid silica materials
2017
[EN] A novel mesoporous silica material containing boron-dipyrromethene (BODIPY) moieties (I) is employed for the detection of nerve agent simulants (NASs) and the organophosphate nerve or chemical warfare agents (CWAs) Sarin (GB), Soman (GD), and Tabun (GA) in aqueous environments. The reactive BODIPY dye with an optimum positioned hydroxyl group undergoes acylation reactions with phosph(on)ate substrates, yielding a bicyclic ring. Due to aggregation of the dyes in water, the sensitivity of the free dye in solution is very low. Only after immobilization of the BODIPY moieties into the silica substrates is aggregation inhibited and a sensitive determination of the NASs diethyl cyanophosphon…
2,4,5-Triaryl imidazole probes for the selective chromo-fluorogenic detection of Cu(II). Prospective use of the Cu(II) complexes for the optical reco…
2019
The sensing behaviour toward metal cations and biothiols of two 2,4,5-triarylimidazole probes (3a and 3b) is tested in acetonitrile and in acetonitrile-water. In acetonitrile the two probes present charge-transfer absorption bands in the 320-350 nm interval. Among all cations tested only Cu(11) is able to induce bathochromic shifts of the absorption band in the two probes, which is reflected in marked colour changes. Colour modulations are ascribed to the formation of 1:1 Cu(II)-probe complexes in which the cation interacts with the imidazole acceptor heterocycle. Besides, the two probes present intense emission bands (at 404 and 437 nm for 3a and 3b respectively) in acetonitrile that are q…
Gravitational-wave Detection and Parameter Estimation for Accreting Black-hole Binaries and Their Electromagnetic Counterpart
2020
We study the impact of gas accretion on the orbital evolution of black-hole binaries initially at large separation in the band of the planned Laser Interferometer Space Antenna (LISA). We focus on two sources: (i)~stellar-origin black-hole binaries~(SOBHBs) that can migrate from the LISA band to the band of ground-based gravitational-wave observatories within weeks/months; and (ii) intermediate-mass black-hole binaries~(IMBHBs) in the LISA band only. Because of the large number of observable gravitational-wave cycles, the phase evolution of these systems needs to be modeled to great accuracy to avoid biasing the estimation of the source parameters. Accretion affects the gravitational-wave p…
Cloud detection on the Google Earth engine platform
2017
The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.
Understanding deep learning in land use classification based on Sentinel-2 time series
2020
AbstractThe use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims …
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress
2019
Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…