Exploration of underground buried objects by noncontact acoustic inspection using normalized SSE analysis

Author(s):  
Kazuko Sugimoto ◽  
Tsuneyoshi Sugimoto
2002 ◽  
Author(s):  
Arnaud Jacotin ◽  
Elodie Bachelier ◽  
Francois Liousse ◽  
Pierre Millan

Author(s):  
W. Swiderski ◽  
P. Hłosta ◽  
L. Szugajew ◽  
J. Usowicz

2009 ◽  
Vol 2009 ◽  
pp. 1-4
Author(s):  
Dong Han ◽  
Caroline Fossati ◽  
Salah Bourennane ◽  
Zineb Saidi

A new algorithm which associates (Multiple Signal Classification) MUSIC with acoustic scattering model for bearing and range estimation is proposed. This algorithm takes into account the reflection and the refraction of wave in the interface of water-sediment in underwater acoustics. A new directional vector, which contains the Direction-Of-Arrival (DOA) of objects and objects-sensors distances, is used in MUSIC algorithm instead of classical model. The influence of the depth of buried objects is discussed. Finally, the numerical results are given in the case of buried cylindrical shells.


Geophysics ◽  
1999 ◽  
Vol 64 (4) ◽  
pp. 1335-1335
Author(s):  
Michael Zhdanov

The authors present a method of localizing underground objects with low‐frequency electromagnetic field based on ideas of electromagnetic holography. Though I do strongly support this method and practical results presented in the paper, I should note that the authors neglected in their paper to reference the previous publications on this subject, where the basic principles, ideas, methods, and term “electromagnetic holography” have been already introduced and developed as applied to low‐frequency electromagnetic field underground imaging.


2021 ◽  
Vol 10 (4) ◽  
pp. 1-27
Author(s):  
Shengxin Jia ◽  
Veronica J. Santos

The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signal processing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.


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