In wireless sensor networks, accurate location information is important for precise tracking of targets. In order to satisfy hardware installation cost and localization accuracy requirements, a weighted centroid localization (WCL) algorithm, which is considered a promising localization algorithm, was introduced. In our previous research, we proposed a test node-based WCL algorithm using a distance boundary to improve the localization accuracy in the corner and side areas. The proposed algorithm estimates the target location by averaging the test node locations that exactly match with the number of anchor nodes in the distribution map. However, since the received signal strength has large variability in real channel conditions, the number of anchor nodes is not exactly matched and the localization accuracy may deteriorate. Thus, we propose an intersection threshold to compensate for the localization accuracy in this paper. The simulation results show that the proposed test node-based WCL algorithm provides higher-precision location information than the conventional WCL algorithm in entire areas, with a reduced number of physical anchor nodes. Moreover, we show that the localization accuracy is improved by using the intersection threshold when considering small-scale fading channel conditions.
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http://dx.doi.org/10.3390/s18041054 | DOI Listing |
PLoS Comput Biol
January 2025
Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Sensory neurons continually adapt their response characteristics according to recent stimulus history. However, it is unclear how such a reactive process can benefit the organism. Here, we test the hypothesis that adaptation actually acts proactively in the sense that it optimally adjusts sensory encoding for future stimuli.
View Article and Find Full Text PDFPLoS One
January 2025
School of Civil and Architectural Engineering, Harbin University, Harbin, China.
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency.
View Article and Find Full Text PDFPLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
J Phys Chem A
January 2025
University of Göttingen, Institute for Physical Chemistry, Tammannstraße 6, 37077,Göttingen Germany.
Rotational spectroscopy is an excellent tool for structure determination, which can provide additional insights into local electronic structure by investigating the hyperfine pattern due to nuclear quadrupole coupling. Jet-cooled molecules are good experimental benchmark targets for electronic structure calculations, as they are free of environmental effects. We report the rotational spectra of 2-chlorobenzaldehyde, 3-chlorobenzaldehyde, and 4-chlorobenzaldehyde, including a complete experimental description of the nuclear quadrupole coupling constants, which were previously not experimentally determined.
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