scholarly journals Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Junpeng Zhang ◽  
Yuan Cui ◽  
Lihua Deng ◽  
Ling He ◽  
Junran Zhang ◽  
...  
Keyword(s):  
Source Localization ◽  
Neural Plasticity ◽  
Source Analysis ◽  
Correlated Noise ◽  
Localization Accuracy ◽  
Cortical Sources ◽  
Noise Simulation ◽  
Time Courses ◽  
Adverse Influence ◽  
Highly Correlated

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.

scholarly journals Forgery Detection in Digital Images by Multi-Scale Noise Estimation

2021 ◽  
Vol 7 (7) ◽  
pp. 119
Author(s):  
Marina Gardella ◽  
Pablo Musé ◽  
Jean-Michel Morel ◽  
Miguel Colom
Keyword(s):  
Noise Model ◽  
Correlated Noise ◽  
Forgery Detection ◽  
Color Channel ◽  
Image Block ◽  
Multi Scale ◽  
Compressed Images ◽  
Inconsistency Analysis ◽  
Highly Correlated ◽  
The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

scholarly journals Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiaqi Song ◽  
Haihong Tao
Keyword(s):  
Source Localization ◽  
Near Field ◽  
Nuisance Parameter ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Dual Polarization ◽  
Rank Reduction ◽  
Localization Accuracy ◽  
Noncircular Signals ◽  
Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

scholarly journals Improvement of the Source Localization Accuracy by Microphone Array Adjacent to Main Flow of Wind Tunnel

2008 ◽  
Vol 74 (743) ◽  
pp. 1548-1556
Author(s):  
Nobuhiro YAMAZAKI ◽  
Kiyoshi NAGAKURA ◽  
Toki UDA
Keyword(s):  
Wind Tunnel ◽  
Source Localization ◽  
Microphone Array ◽  
Main Flow ◽  
Localization Accuracy

scholarly journals Electric Source Localization Adds Evidence for Task-Specific CNVs

1998 ◽  
Vol 11 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Ina M. Tarkka ◽  
Luis F. H. Basile
Keyword(s):  
Pattern Recognition ◽  
Source Localization ◽  
Clinical Neurophysiology ◽  
Recognition Task ◽  
Source Analysis ◽  
Visual Spatial ◽  
Spatio Temporal ◽  
Electric Source ◽  
Pattern Recognition Task ◽  
Spatial Recognition

This study was an attempt to replicate recent magnetoencephalographic (MEG) findings on human task-specific CNV sources (Basile et al., Electroencephalography and Clinical Neurophysiology 90, 1994, 157–165) by means of a spatio-temporal electric source localization method (Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 62, 1985, 32–44; Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 65, 1986, 344-360; Scherg and Berg, Brain Electric Source Analysis Handbook, Version 2). The previous MEG results showed CNV sources in the prefrontal cortex of the two hemispheres for two tasks used, namely visual pattern recognition and visual spatial recognition tasks. In the right hemisphere, the sources were more anterior and inferior for the spatial recognition task than for the pattern recognition task. In the present study we obtained CNVs in five subjects during two tasks identical to the MEG study. The elicited electric potentials were modeled with four spatio-temporal dipoles for each task, three of which accounted for the visual evoked response and one that accounted for the CNV. For all subjects the dipole explaining the CNV was always localized in the frontal region of the head, however, the dipole obtained during the visual spatial recognition task was more anterior than the one obtained during the pattern recognition task. Thus, task-specific CNV sources were again observed, although the stable model consisted of only one dipole located close to the midline instead of one dipole in each hemisphere. This was a major difference in the CNV sources between the previous MEG and the present electric source analysis results. We discuss the possible basis for the difference between the two methods used to study slow brain activity that is believed to originate from extended cortical patches.

scholarly journals Locating the Epidemic Source in Complex Networks with Sparse Observers

2019 ◽  
Vol 9 (18) ◽  
pp. 3644 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Disease Spread ◽  
Initial Time ◽  
Epidemic Spread ◽  
Localization Accuracy ◽  
General Network ◽  
Maximum Spanning Tree ◽  
Comparison Algorithms ◽  
Empirical Networks

Epidemic source localization is one of the most meaningful areas of research in complex networks, which helps solve the problem of infectious disease spread. Limited by incomplete information of nodes and inevitable randomness of the spread process, locating the epidemic source becomes a little difficult. In this paper, we propose an efficient algorithm via Bayesian Estimation to locate the epidemic source and find the initial time in complex networks with sparse observers. By modeling the infected time of observers, we put forward a valid epidemic source localization method for tree network and further extend it to the general network via maximum spanning tree. The numerical analyses in synthetic networks and empirical networks show that our algorithm has a higher source localization accuracy than other comparison algorithms. In particular, when the randomness of the spread path enhances, our algorithm has a better performance. We believe that our method can provide an effective reference for epidemic spread and source localization in complex networks.

scholarly journals Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Information Diffusion ◽  
Large Scale ◽  
Computational Cost ◽  
Likelihood Estimation ◽  
Power Grids ◽  
Optimization Approach ◽  
Localization Accuracy ◽  
Diffusion Source

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

scholarly journals Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaosheng Yu ◽  
Jianning Chi ◽  
Ying Wang ◽  
Hao Chu
Keyword(s):  
Sensor Network ◽  
Source Localization ◽  
Mean Shift ◽  
High Energy ◽  
False Alarms ◽  
Localization Accuracy ◽  
Pearson Criterion ◽  
Binary Sensor ◽  
Sensing Model ◽  
Novel Method

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

scholarly journals Effect of Brain-to-Skull Conductivity Ratio on EEG Source Localization Accuracy

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Wang ◽  
Doutian Ren
Keyword(s):  
Source Localization ◽  
Conductivity Ratio ◽  
Source Reconstruction ◽  
Localization Accuracy ◽  
Scalp Eeg ◽  
Skull Conductivity ◽  
Localization Errors ◽  
The Mean ◽  
Eeg Source Localization ◽  
Dipole Sources

The goal of this study was to investigate the influence of the brain-to-skull conductivity ratio (BSCR) on EEG source localization accuracy. In this study, we evaluated four BSCRs: 15, 20, 25, and 80, which were mainly discussed according to the literature. The scalp EEG signals were generated by BSCR-related forward computation for each cortical dipole source. Then, for each scalp EEG measurement, the source reconstruction was performed to identify the estimated dipole sources by the actual BSCR and the misspecified BSCRs. The estimated dipole sources were compared with the simulated dipole sources to evaluate EEG source localization accuracy. In the case of considering noise-free EEG measurements, the mean localization errors were approximately equal to zero when using actual BSCR. The misspecified BSCRs resulted in substantial localization errors which ranged from 2 to 16 mm. When considering noise-contaminated EEG measurements, the mean localization errors ranged from 8 to 18 mm despite the BSCRs used in the inverse calculation. The present results suggest that the localization accuracy is sensitive to the BSCR in EEG source reconstruction, and the source activity can be accurately localized when the actual BSCR and the EEG scalp signals with high signal-to-noise ratio (SNR) are used.

Noise-Robust MUSIC-Based Sound Source Localization Using Steering Vector Transformation for Small Humanoids

2017 ◽  
Vol 29 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Ryu Takeda ◽  
◽  
Kazunori Komatani
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Computational Cost ◽  
Internal Noise ◽  
Humanoid Robots ◽  
Eigenvalue Decomposition ◽  
Sound Source Localization ◽  
Noise Robustness ◽  
Localization Accuracy ◽  
Steering Vector

[abstFig src='/00290001/03.jpg' width='300' text='Sound source localization and problem' ] We focus on the problem of localizing soft/weak voices recorded by small humanoid robots, such as NAO. Sound source localization (SSL) for such robots requires fast processing and noise robustness owing to the restricted resources and the internal noise close to the microphones. Multiple signal classification using generalized eigenvalue decomposition (GEVD-MUSIC) is a promising method for SSL. It achieves noise robustness by whitening robot internal noise using prior noise information. However, whitening increases the computational cost and creates a direction-dependent bias in the localization score, which degrades the localization accuracy. We have thus developed a new implementation of GEVD-MUSIC based on steering vector transformation (TSV-MUSIC). The application of a transformation equivalent to whitening to steering vectors in advance reduces the real-time computational cost of TSV-MUSIC. Moreover, normalization of the transformed vectors cancels the direction-dependent bias and improves the localization accuracy. Experiments using simulated data showed that TSV-MUSIC had the highest accuracy of the methods tested. An experiment using real recoded data showed that TSV-MUSIC outperformed GEVD-MUSIC and other MUSIC methods in terms of localization by about 4 points under low signal-to-noise-ratio conditions.

201 Experimental Studies on Sound Source Localization Accuracy by MUSIC and AH Methods

2006 ◽  
Vol 2006.5 (0) ◽  
pp. 82-86
Author(s):  
Michio Uneda ◽  
Yuusuke Muraoka ◽  
Keisuke Miyake ◽  
Ken-ichi Ishikawa
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Experimental Studies ◽  
Sound Source Localization ◽  
Localization Accuracy
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