Search results for "sensor phenomena"
showing 3 items of 3 documents
Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions
2021
The estimation of biophysical variables from remote sensing data raises important challenges in terms of the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains the spatial resolution while significantly increasing the subpixel land-cover heterogeneity. Precisely, this spatial variability often makes that rather different canopy structures are aggregated together, which eventually generates important deviations in the corresponding parameter quantification. In the context of the Copernicus program (and other related Earth Explorer missions), this article propose…
Application of Room Temperature Photoluminescence From ZnO Nanorods for Salmonella Detection
2014
ZnO nanorods grown by gaseous-disperse synthesis are confirmed by XRD analysis to have the wurtzite crystal structure. The obtained crystallites, as found from SEM studies, are 57 +/- 9 nm in diameter and 470 +/- 30 nm long on the average. Two emission bands of photoluminescence from ZnO nanorods observed at room temperature are centered at 376 and 520 nm. A biosensitive layer is prepared by immobilization of anti-Salmonella antibodies from liquid solutions on the ZnO surface. Immobilization of the biosensitive layer onto ZnO nanorods is found to increase the intensity of PL. After further reaction with Salmonella antigens (Ags), the PL intensity is found to decrease proportional to Ag conc…
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments
2017
The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the infe…