scholarly journals A Study on Water Pollution Source Localization in Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Yang ◽  
Xu Luo
Keyword(s):  
Water Pollution ◽  
Diffusion Model ◽  
Source Localization ◽  
Pollution Source ◽  
Good Choice ◽  
Diffusion Models ◽  
Hypotheses Testing ◽  
Localization Algorithm ◽  
Source Detection ◽  
Localization Method

The water pollution source localization is of great significance to water environment protection. In this paper, a study on water pollution source localization is presented. Firstly, the source detection is discussed. Then, the coarse localization methods and the localization methods based on diffusion models are introduced and analyzed, respectively. In addition, the localization method based on the contour is proposed. The detection and localization methods are compared in experiments finally. The results show that the detection method using hypotheses testing is more stable. The performance of the coarse localization algorithm depends on the nodes density. The localization based on the diffusion model can yield precise localization results; however, the results are not stable. The localization method based on the contour is better than the other two localization methods when the concentration contours are axisymmetric. Thus, in the water pollution source localization, the detection using hypotheses testing is more preferable in the source detection step. If concentration contours are axisymmetric, the localization method based on the contour is the first option. And, in case the nodes are dense and there is no explicit diffusion model, the coarse localization algorithm can be used, or else the localization based on diffusion models is a good choice.

scholarly journals Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 606 ◽  
Author(s):  
Junjie Ma ◽  
Fansheng Meng ◽  
Yuexi Zhou ◽  
Yeyao Wang ◽  
Ping Shi
Keyword(s):  
Water Pollution ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Source Localization ◽  
Pollution Source ◽  
Wireless Sensor ◽  
Uv Visible

scholarly journals Schemes of propagation models and source estimators for rumor source detection in online social networks: A short survey of a decade of research

2021 ◽  
pp. 2130002
Author(s):  
Rong Jin ◽  
Weili Wu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Model ◽  
Online Social Networks ◽  
Online Social Network ◽  
Diffusion Models ◽  
Source Detection ◽  
Propagation Models ◽  
Rumor Propagation ◽  
Independent Cascade

Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challenging factor for source detection in networks, but it is less studied. This paper provides an overview of three representative schemes of modeling the pattern of rumor propagation as well as three major schemes of rumor source estimator in the Independent Cascade-based model, the Epidemic-based model, and the Learning-based model, respectively, since their inception a decade ago.

scholarly journals Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography

2021 ◽  
Author(s):  
Abhishek S. Bhutada ◽  
Chang Cai ◽  
Danielle Mizuiri ◽  
Anne Findlay ◽  
Jessie Chen ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Source Localization ◽  
Clinical Population ◽  
Evoked Responses ◽  
Localization Algorithm ◽  
Equivalent Current Dipole ◽  
Current Dipole ◽  
Tumor Patients ◽  
Equivalent Current ◽  
Sensory Evoked

AbstractMagnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

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