DEVELOPMENT OF VIRTUAL CAVE EXPLORATION ENVIRONMENT USING LIDAR AND SFM PHOTOGRAMMETRY

2019 ◽  
Author(s):  
Angel A. Acosta Colón ◽  
Author(s):  
Varghese George ◽  
Jan M. Rabaey

Author(s):  
Rui Peng ◽  
Kien A. Hua ◽  
Hao Cheng ◽  
Fei Xie

The rapid increase of sensor networks has brought a revolution in pervasive computing. However, data from these fragmented and heterogeneous sensor networks are easily shared. Existing sensor computing environments are based on the traditional database approach, in which sensors are tightly coupled with specific applications. Such static configurations are effective only in situations where all the participating sources are precisely known to the application developers, and users are aware of the applications. A pervasive computing environment raises more challenges, due to ad hoc user requests and the vast number of available sources, making static integration less effective. This paper presents an Internet framework called iSEE (Internet Sensor Exploration Environment) which provides a more complete environment for pervasive sensor computing. iSEE enables advertising and sharing of sensors and applications on the Internet with unsolicited users much like how Web pages are publicly shared today.


2018 ◽  
Vol 56 ◽  
pp. 169-183
Author(s):  
Umer Farooq ◽  
Roselyne Chotin-Avot ◽  
Moazam Azeem ◽  
Maminionja Ravoson ◽  
Habib Mehrez

Geophysics ◽  
1985 ◽  
Vol 50 (5) ◽  
pp. 867-869
Author(s):  
C. Patrick Ervin

In the exploration environment, a primary application of gravity surveying is regional reconnaissance. The first step in such a survey is to establish a base‐station network. Since an error in the network will propagate to many stations in the subsequent survey, careful field work and accurate reduction of these data are particularly critical. Optimally, successive base stations are tied by minimum‐time loops using at least two meters read simultaneously. Using two meters has the obvious advantage of doubling the number of ties with minimal increase in time and cost. Erroneous readings are also much easier to detect and correct with two meters. Furthermore, the simultaneous operation of the meters allows calibrations of the two to be compared by computing a linear regression of the readings of one meter against the corresponding readings of the other. If the meter calibrations are identical, the regression line should have a slope of 1. A significant deviation from 1 indicates a systematic variation in calibration.


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