Self-Optimizing IoT Wireless Video Sensor Node With In-Situ Data Analytics and Context-Driven Energy-Aware Real-Time Adaptation

2017 ◽  
Vol 64 (9) ◽  
pp. 2470-2480 ◽  
Author(s):  
Ningyuan Cao ◽  
Saad Bin Nasir ◽  
Shreyas Sen ◽  
Arijit Raychowdhury
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Sensor Node ◽  
Wireless Video ◽  
Energy Aware ◽  
In Situ Data ◽  
Video Sensor

Real-Time Analysis Of In-Situ Spectroscopic Ellipsometric Data During Mbe Growth Of III-V Semiconductors

1991 ◽  
Vol 222 ◽  
Author(s):  
B. Johs ◽  
J. L. Edwards ◽  
K. T. Shiralagi ◽  
R. Droopad ◽  
K. Y. Choi ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Constants ◽  
Growth Parameters ◽  
Ex Situ ◽  
Time Analysis ◽  
Mbe Growth ◽  
In Situ Data ◽  
Real Time Analysis

ABSTRACTA modular spectroscopic ellipsometer, capable of both in-situ and ex-situ operation, has been used to measure important growth parameters of GaAs/AIGaAs structures. The ex-situ measurements provided layer thicknesses and compositions of the grown structures. In-situ ellipsometric measurements allowed the determination of growth rates, layer thicknesses, and high temperature optical constants. By performing a regression analysis of the in-situ data in real-time, the thickness and composition of an AIGaAs layer were extracted during the MBE growth of the structure.

scholarly journals On the Quality of Real-Time Altimeter Gridded Fields: Comparison with In Situ Data

2009 ◽  
Vol 26 (3) ◽  
pp. 556-569 ◽  
Author(s):  
Ananda Pascual ◽  
Christine Boone ◽  
Gilles Larnicol ◽  
Pierre-Yves Le Traon
Keyword(s):  
Real Time ◽  
Sea Level ◽  
Tide Gauge ◽  
Deep Ocean ◽  
Velocity Estimation ◽  
Time Data ◽  
In Situ Data ◽  
Delayed Time

Abstract The timeliness of satellite altimeter measurements has a significant effect on their value for operational oceanography. In this paper, an Observing System Experiment (OSE) approach is used to assess the quality of real-time altimeter products, a key issue for robust monitoring and forecasting of the ocean state. In addition, the effect of two improved geophysical corrections and the number of missions that are combined in the altimeter products are also analyzed. The improved tidal and atmospheric corrections have a significant effect in coastal areas (0–100 km from the shore), and a comparison with tide gauge observations shows a slightly better agreement with the gridded delayed-time sea level anomalies (SLAs) with two altimeters [Jason-1 and European Remote Sensing Satellite-2 (ERS-2)/Envisat] using the new geophysical corrections (mean square differences in percent of tide gauge variance of 35.3%) than those with four missions [Jason-1, ERS/Envisat, Ocean Topography Experiment (TOPEX)/Poseidoninterlaced, and Geosat Follow-On] but using the old corrections (36.7%). In the deep ocean, however, the correction improvements have little influence. The performance of fast delivery products versus delayed-time data is compared using independent in situ data (tide gauge and drifter data). It clearly highlights the degradation of real-time SLA maps versus the delayed-time SLA maps: four altimeters are needed in real time to get the similar quality performance as two altimeters in delayed time (sea level error misfit around 36%, and zonal and meridional velocity estimation errors of 27% and 33%, respectively). This study proves that the continuous improvement of geophysical corrections is very important, and that it is essential to stay above a minimum threshold of four available altimetric missions to capture the main space and time oceanic scales in fast delivery products.

RTMCH: real-time multichannel MAC for wireless video sensor networks

2018 ◽  
Vol 78 (6) ◽  
pp. 7803-7818 ◽  
Author(s):  
Mehdi Hadadian Nejad Yousefi ◽  
Yousef S. Kavian ◽  
Alimorad Mahmoudi
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Wireless Video ◽  
Wireless Video Sensor Networks ◽  
Multichannel Mac ◽  
Video Sensor Networks ◽  
Video Sensor

Effect of the Wind Drag Estimation Methods on Numerical Storm Surge Modeling

2019 ◽  
Author(s):  
C. Gowri Shankar ◽  
Manasa Ranjan Behera
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wind Stress ◽  
Evaluation Method ◽  
Tide Gauge ◽  
Wave Model ◽  
Estimation Methods ◽  
Computational Accuracy ◽  
In Situ Data

Abstract Tropical cyclones have always proved the extent of its catastrophe on several occurrences over the years. In particular, the Bay of Bengal (BoB) basin in the Northern Indian Ocean has produced such historic devastating events, thereby mandating accurate real-time predictions. Numerical modeling of storm surge has always been an arduous task, as it is integrated with various uncertain factors. Among those, the major governing component being the wind forcing or the wind stress — that signifies, the computational accuracy of simulated surge and wave parameters. The present study is aimed at analysing the most suited wind drag evaluation method for real-time predictions of storm surge along the BoB. Cyclone Phailin (2013) was considered for the numerical simulations. To evaluate the wind drag coefficient, three most extensively used linear empirical relations along with the enhanced Wave Boundary Layer Model (e_WBLM) were used. The surge was subsequently simulated (using the coupled hydrodynamic circulation and wave model: ADCIRC and SWAN, respectively), individually for each of the above wind stress methods to obtain the corresponding storm surge (residual) and the storm wave features. The modeled values were further validated with the in-situ data obtained from tide gauge station and buoys respectively. It was quite intuitively observed that, e_WBLM based results correlated well with the in-situ values than its linear counterparts since, the former pragmatically includes the effects of air-sea interaction at high wind speeds in the model. The e_WBLM-based computation of significant wave heights (Hs) in deep as well as shallow water, nevertheless enabled efficient and reasonably-reliable estimations of the peak incidents.

scholarly journals Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

2021 ◽  
Author(s):  
Magfira Syarifuddin ◽  
Susanna F. Jenkins ◽  
Ratih Indri Hapsari ◽  
Qingyuan Yang ◽  
Benoit Taisne ◽  
...  
Keyword(s):  
Grain Size ◽  
Real Time ◽  
Ensemble Prediction ◽  
Merapi Volcano ◽  
Ensemble Prediction System ◽  
Multiple Parameters ◽  
In Situ Data ◽  
Detection And Tracking ◽  
X Band

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

scholarly journals In situ data analytics and indexing of protein trajectories

2017 ◽  
Vol 38 (16) ◽  
pp. 1419-1430 ◽  
Author(s):  
Travis Johnston ◽  
Boyu Zhang ◽  
Adam Liwo ◽  
Silvia Crivelli ◽  
Michela Taufer
Keyword(s):  
Data Analytics ◽  
In Situ Data

ASIS: Autonomous Seismic Imaging System With In Situ Data Analytics and Renewable Energy

2020 ◽  
Vol 14 (1) ◽  
pp. 1277-1284 ◽  
Author(s):  
Jose Clemente ◽  
Fangyu Li ◽  
Maria Valero ◽  
An Chen ◽  
WenZhan Song
Keyword(s):  
Renewable Energy ◽  
Data Analytics ◽  
Seismic Imaging ◽  
Imaging System ◽  
In Situ Data
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