DTW - DYNAMIC TIME WARPING

regulations - National Engineering College

AFNI's 3dTcat is used to concatenate datasets. For example, after performing first- and second-level analyses, you may want to join several ...

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice—Once supine and once prone—to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated ...

The U.S. Department of Energy's Office of Scientific and Technical Information

FCC ID OWS-NIC514 Application Details

Today was a walkthrough of the AFNI interface, and a tutorial for how to view timeseries data and model fits. Daniel started off with a sh...

Dirk Eddelbuettel, R, C++, Rcpp

Scripts used for fMRI data analysis in AFNI.

Analysis of exhaled breath samples reveals the presence of many volatile organic compounds (VOCs). The VOC composition of the breath, the so-called breath profile, contains a variety of information including the health status and condition of the organism that...

For a list of relevant conferences to our research axes, click here. Entries: 359 Expand all Collapse all 2023 (14) J. Baumgartner, C. Lillo, and S. Rumley, “Performance Losses with Virtualization: Comparing Bare Metal to VMs and Containers,” in High Performance Computing, Cham, 2023, p. 107–120. [Bibtex] [Abstract] @inproceedings{baumgartner2023performance, author={Baumgartner, Jonatan and Lillo, Christophe and Rumley, […]

Publications, World Academy of Science, Engineering and Technology

This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.

ENSCO will showcase its Assured Positioning, Navigation and Timing (A-PNT) sensing capabilities for edge devices at the Institute of Navigation International Technical Meeting (ITM) virtual meeting, January 25-28, 2021.

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InfoMat is an open access materials science and technology journal covering novel electrical, optical & magnetic materials with applications in information technology.

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