Current Dipole Localization Errors as a Function of the System Noise and the Number of Sensors

We consider the use of superheated superconducting colloids as detectors of weakly interacting galactic halo candidate particles (e.g. photinos, massive neutrinos, and scalar neutrinos). These low temperature detectors are sensitive to the deposition of a few hundreds of eV's. The recoil of a dark matter particle off of a superheated superconducting grain in the detector causes the grain to make a transition to the normal state. Their low energy threshold makes this class of detectors ideal for detecting massive weakly interacting halo particles.We discuss realistic models for the detector and for the galactic halo. We show that the expected count rate (≈103 count/day for scalar and massive neutrinos) exceeds the expected background by several orders of magnitude. For photinos, we expect ≈1 count/day, more than 100 times the predicted background rate. We find that if the detector temperature is maintained at 50 mK and the system noise is reduced below 5 × 10−4 flux quanta, particles with mass as low as 2 GeV can be detected. We show that the earth's motion around the Sun can produce a significant annual modulation in the signal.

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

Brain Topography ◽

10.1007/s10548-021-00850-4 ◽

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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Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas

Sensors ◽

10.3390/s21155196 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5196

Author(s):

Yuki Endo ◽

Ehsan Javanmardi ◽

Shunsuke Kamijo

Keyword(s):

Urban Areas ◽

Structural Information ◽

Average Error ◽

High Definition ◽

Geometrical Structures ◽

Real Environment ◽

Surrounding Environment ◽

Localization Errors ◽

Stable Estimation ◽

High Level

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions.

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