Applying a volume dipole distribution model to next-generation sensor data for multi-object data inversion and discrimination

2010 ◽  
Author(s):  
Fridon Shubitidze ◽  
David Karkashadze ◽  
Juan Pablo Fernández ◽  
Benjamin E. Barrowes ◽  
Kevin O'Neill ◽  
...  
Keyword(s):  
Sensor Data ◽  
Distribution Model ◽  
Next Generation ◽  
Data Inversion

Guided-Mode Resonance Sensor Data Inversion for Bulk and Surface Variables

2020 ◽  
Author(s):  
Joseph A. Buchanan-Vega ◽  
Mohammad G. Abdallah ◽  
Brett R Wenner ◽  
Jeffrey W. Allen ◽  
Monica S. Allen ◽  
...  
Keyword(s):  
Sensor Data ◽  
Data Inversion ◽  
Guided Mode ◽  
Resonance Sensor ◽  
Guided Mode Resonance

Implementation of VRML/Java Web-Based Animation and Communications for the Next Generation Inspection System (NGIS) Real-Time Controller

2000 ◽  
Author(s):  
William Shackleford ◽  
Keith Stouffer
Keyword(s):  
Real Time ◽  
Web Site ◽  
Remote Access ◽  
Sensor Data ◽  
Open Architecture ◽  
Inspection System ◽  
Next Generation ◽  
Real Time Processing ◽  
Open Architecture Controller ◽  
Measuring Machine

Abstract The Next Generation Inspection System (NGIS) project is a testbed that consists of a Cordax Coordinate Measuring Machine (CMM), advanced sensors, and the National Institute of Standards and Technology (NIST) Real-Time Control System (RCS) open architecture controller. The RCS controller permits real-time processing of sensor data for feedback control of the inspection probe. The open architecture controller permits external access to internal data, such as the current position of the probe. A remote access web site was developed to access this data to drive a Virtual Reality Modeling Language (VRML) * model of the Cordax CMM. The remote access web site contains a client-controlled pan/tilt/zoom camera which sends video to the client as well as the VRML 3D model of the CMM that is controlled by the NGIS controller located at NIST. This remote access web site allows a client to monitor a remote inspection with a PC and an internet connection.

scholarly journals Fieldwork in landscape ecology

2020 ◽  
Author(s):  
Jesse E. D. Miller ◽  
Carly D. Ziter ◽  
Michael J Koontz
Keyword(s):  
Global Change ◽  
Landscape Ecology ◽  
Critical Role ◽  
Sensor Data ◽  
Next Generation ◽  
Field Methods ◽  
Computational Approaches ◽  
A Priority

Fieldwork has played a critical role in the development of landscape ecology, and it remains essential for addressing contemporary challenges such as understanding the landscape ecology of global change. Advances in technology have expanded the scope of fieldwork to include the deployment of drones and other sensors, and in recent years, researchers have expressed concerns that traditional fieldwork (e.g., organismal observation) may be declining. Continuing to train the next generation of researchers in field methods should be a priority for landscape ecologists. Indeed, there is great potential for combining fieldwork with modern sensor data and computational approaches to advance the field of landscape ecology.

Camp Beale live-site handheld-sensor data inversion and classification using advanced EMI models

2012 ◽  
Author(s):  
I. Shamatava ◽  
J. P. Fernández ◽  
B. E. Barrowes ◽  
K. O'Neill ◽  
A. Bijamov ◽  
...  
Keyword(s):  
Sensor Data ◽  
Data Inversion

scholarly journals Fieldwork in landscape ecology

2020 ◽  
Author(s):  
Jesse E. D. Miller ◽  
Carly D. Ziter ◽  
Michael J Koontz
Keyword(s):  
Global Change ◽  
Landscape Ecology ◽  
Critical Role ◽  
Sensor Data ◽  
Next Generation ◽  
Field Methods ◽  
Computational Approaches ◽  
A Priority

Fieldwork has played a critical role in the development of landscape ecology, and it remains essential for addressing contemporary challenges such as understanding the landscape ecology of global change. Advances in technology have expanded the scope of fieldwork to include the deployment of drones and other sensors, and in recent years, researchers have expressed concerns that traditional fieldwork (e.g., organismal observation) may be declining. Continuing to train the next generation of researchers in field methods should be a priority for landscape ecologists. Indeed, there is great potential for combining fieldwork with modern sensor data and computational approaches to advance the field of landscape ecology.

Adapting physically complete models to vehicle-based EMI array sensor data: data inversion and discrimination studies

2010 ◽  
Author(s):  
Fridon Shubitidze ◽  
Jonathan S. Miller ◽  
Gregory M. Schultz ◽  
Jay A. Marble
Keyword(s):  
Sensor Data ◽  
Array Sensor ◽  
Data Inversion
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