Results 51 to 60 of about 97,482 (247)

Towards Terrain-Based Navigation Using Side-Scan Sonar

2023 26th International Conference on Information Fusion (FUSION), 2023
This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using side-scan sonar (SSS) data. The presented approach relies on slant range measurements extracted from the received ping of a SSS. In particular, incorporating slant range measurements to landmarks for navigation constrains
Davenport, Ellen, Jang, Junsu, Meyer, Florian   +2 more
openaire   +2 more sources

Evaluation of laser range-finder mapping for agricultural spraying vehicles [PDF]

, 2013
In this paper, we present a new application of laser range-finder sensing to agricultural spraying vehicles. The current generation of spraying vehicles use automatic controllers to maintain the height of the sprayer booms above the crop. However, these
A Nüchter, AM Endalew, F Rovira-Más, F-A Moreno, H Jeon, J Anthonis, J Wei, M Magnusson, P Walklate, S Pedersen, S Thrun   +10 more
core   +1 more source

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

Remote Sensing, 2023
This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images.
Xing Du, Yongfu Sun, Yupeng Song, Lifeng Dong, Xiaolong Zhao   +4 more
semanticscholar   +1 more source

Olympic Coast National Marine Sanctuary Habitat Mapping: Survey report and classification of side scan sonar data from surveys HMPR-114-2004-02 and HMPR-116-2005-01. [PDF]

, 2006
The Olympic Coast National Marine Sanctuary (OCNMS) continues to invest significant resources into seafloor mapping activities along Washington’s outer coast (Intelmann and Cochrane 2006; Intelmann et al. 2006; Intelmann 2006).
Cochrane, Guy R., Intelmann, Steven S.
core  

Bilinear Pooling With Poisoning Detection Module for Automatic Side Scan Sonar Data Analysis

IEEE Access, 2023
Side-scan sonar (SSS) images are difficult for automatic analysis due to the acoustic measurement parameters as well as the number of different objects that can be distant. In addition, there is a risk that the seabed analysis application may be attacked.
Dawid Połap, Antoni Jaszcz, Natalia Wawrzyniak, Grzegorz Zaniewicz   +3 more
semanticscholar   +1 more source

Navigating the Ethereal Tightrope: The Nanogenerator Manipulates Neurons for Immune Equilibrium

Advanced Science, EarlyView.
This review explores how nanogenerators modulate neuroimmune responses, offering innovative strategies for treating neurological disorders. By interfacing with neural pathways, they enable precise control of immune activity, especially via vagus nerve stimulation.
Jia Du, Rui Deng, Ya Wu, Hong‐Xia Ren, Zong‐Hong Lin, Yang‐Bao Miao   +5 more
wiley   +1 more source

Side-Scan Sonar Image Matching Method Based on Topology Representation

Journal of Marine Science and Engineering
In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping.
Dianyu Yang, Jingfeng Yu, Can Wang, Chensheng Cheng, Guang Pan, Xin Wen, Feihu Zhang   +6 more
doaj   +1 more source

Using multibeam echosounder backscatter to characterize seafloor features [PDF]

, 2010
Geocoder Processing Gives Multibeam Echosounder Backscatter an Advantage Over Side Scan Sonar in Producing Reliable Seafloor ...
Hewitt, Antony, Salisbury, Richard, Wilson, Jerry   +2 more
core  

On-Line Sea Beam Acoustic Imaging [PDF]

, 1987
This paper describes a system designed and built at the Marine Physical Laboratory of the Scripps Institution of Oceanography to produce acoustic images of the seafloor on-line with a Sea Beammultibeam echo-sounder.
de Moustier, Christian, Pavlicek, F V
core   +2 more sources

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

Advanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera, Gaurav Duggal, Rolf Müller   +2 more
wiley   +1 more source