scholarly journals Real-time Smartphone implementation of noise-robust Speech source localization algorithm for hearing aid users

2017 ◽  
Author(s):  
Anshuman Ganguly ◽  
Abdullah Kucuk ◽  
Issa Panahi
Keyword(s):  
Real Time ◽  
Source Localization ◽  
Hearing Aid ◽  
Localization Algorithm ◽  
Speech Source Localization ◽  
Noise Robust

scholarly journals Real-Time Convolutional Neural Network-Based Speech Source Localization on Smartphone

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 169969-169978 ◽  
Author(s):  
Abdullah Kucuk ◽  
Anshuman Ganguly ◽  
Yiya Hao ◽  
Issa M. S. Panahi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Source Localization ◽  
Speech Source Localization

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

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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