scholarly journals Implementation of Simultaneous Multi-Parameter Monitoring Based in LC-Type Passive Wireless Sensing with Partial Overlapping and Decoupling Coils

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5183
Author(s):  
Juan Ignacio Sancho ◽  
Noemí Perez ◽  
Joaquin De Nó ◽  
Jaizki Mendizabal
Keyword(s):  
Remote Sensing ◽  
Wireless Sensors ◽  
Mutual Coupling ◽  
The Other ◽  
Wireless Sensing ◽  
Passive Wireless Sensors ◽  
Parameter Monitoring

Inductor–capacitor (LC) passive wireless sensors are widely used for remote sensing. These devices are limited in applications where multiparameter sensing is required, because of the mutual coupling between neighboring sensors. This article presents two effective decoupling techniques for multiparameter sensing, based on partially overlapped sensors and decoupling coils, which, when combined, reduce the mutual coupling between sensors to near zero. A multiparameter LC sensor prototype with these two decoupling mechanisms has been designed, simulated, and measured. This prototype is capable of simultaneously measuring four parameters. The measurements demonstrate that the changes in capacitance in one individual sensor do not affect the measurements of the other sensors. This principle has been applied to simultaneous wear sensing using four identical wear sensors.

Simultaneous Remote Sensing of Temperature and Humidity by LC-Type Passive Wireless Sensors

2015 ◽  
Vol 24 (4) ◽  
pp. 1117-1123 ◽  
Author(s):  
Qing-Ying Ren ◽  
Li-Feng Wang ◽  
Jian-Qiu Huang ◽  
Cong Zhang ◽  
Qing-An Huang
Keyword(s):  
Remote Sensing ◽  
Wireless Sensors ◽  
Temperature And Humidity ◽  
Passive Wireless Sensors

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

scholarly journals Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations

2019 ◽  
Vol 11 (13) ◽  
pp. 1598 ◽  
Author(s):  
Hua Su ◽  
Xin Yang ◽  
Wenfang Lu ◽  
Xiao-Hai Yan
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Estimation Error ◽  
Satellite Observations ◽  
The Other ◽  
Global Ocean ◽  
Gradient Boosting ◽  
Mean Square ◽  
Thermohaline Structure

Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for retrieving subsurface thermohaline anomalies, including the subsurface temperature anomaly (STA) and the subsurface salinity anomaly (SSA), in the upper 2000 m of the global ocean. The model combines surface satellite observations and in situ Argo data for estimation, and uses root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), and R2 as accuracy evaluations. The results show that the proposed XGBoost model can easily retrieve subsurface thermohaline anomalies and outperforms the gradient boosting decision tree (GBDT) model. The XGBoost model had good performance with average R2 values of 0.69 and 0.54, and average NRMSE values of 0.035 and 0.042, for STA and SSA estimations, respectively. The thermohaline anomaly patterns presented obvious seasonal variation signals in the upper layers (the upper 500 m); however, these signals became weaker as the depth increased. The model performance fluctuated, with the best performance in October (autumn) for both STA and SSA, and the lowest accuracy occurred in January (winter) for STA and April (spring) for SSA. The STA estimation error mainly occurred in the El Niño-Southern Oscillation (ENSO) region in the upper ocean and the boundary of the ocean basins in the deeper ocean; meanwhile, the SSA estimation error presented a relatively even distribution. The wind speed anomalies, including the u and v components, contributed more to the XGBoost model for both STA and SSA estimations than the other surface parameters; however, its importance at deeper layers decreased and the contributions of the other parameters increased. This study provides an effective remote sensing technique for subsurface thermohaline estimations and further promotes long-term remote sensing reconstructions of internal ocean parameters.

scholarly journals On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 384 ◽  
Author(s):  
Park ◽  
Lee
Keyword(s):  
Remote Sensing ◽  
Detection Rate ◽  
Detection Method ◽  
Incidence Angle ◽  
Accuracy Assessment ◽  
Temporal Variations ◽  
The Other ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Simple Change

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

Estimation of global vegetation productivity from 1981 to 2018 With remote sensing data

2020 ◽  
Author(s):  
Rui Sun ◽  
Juanmin Wang ◽  
Zhiqiang Xiao ◽  
Anran Zhu ◽  
Mengjia Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Rate ◽  
Remote Sensing Data ◽  
The Other ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Vegetation Productivity ◽  
Sensing Data ◽  
Global Vegetation

<p><span>ly during 1981 and 2018, which was in great agreement with the other similar products. The global NPP has shown a significant increase trend, with an annual growth rate of 0.10 PgC/yr (R<sup>2</sup>=0.4684) </span></p>

Sign in / Sign up

Export Citation Format

Share Document