WLAN location determination without active client collaboration

2010 ◽  
Author(s):  
Stefan Kraxberger ◽  
Günther Lackner ◽  
Udo Payer
Keyword(s):  
Location Determination ◽  
Active Client ◽  
Client Collaboration

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

scholarly journals UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2650
Author(s):  
Daegyun Choi ◽  
William Bell ◽  
Donghoon Kim ◽  
Jichul Kim
Keyword(s):  
Visual Information ◽  
Crack Detection ◽  
Image Data ◽  
Image Processing Techniques ◽  
Location Determination ◽  
Aerial Vehicle ◽  
Using Data ◽  
Processing Techniques ◽  
Deep Learning Model ◽  
Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

Fluid Ingress Location Determination Using Distributed Temperature and Acoustic Sensing

2015 ◽  
Author(s):  
Thomas Williams ◽  
Erik Lee ◽  
Jeff Chen ◽  
Xiaowei Wang ◽  
David Lerohl ◽  
...  
Keyword(s):  
Acoustic Sensing ◽  
Location Determination

Complete Mitochondrial Genome Sequence Analysis and Phylogenetic Location Determination of Hydropsyche Fryeri (Insecta: Trichoptera)

2021 ◽  
Author(s):  
Yimin Li ◽  
Honglin Qin ◽  
Xifa Zhong ◽  
Jingcai Huang ◽  
Yujun Wang ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
Genome Sequence ◽  
Complete Mitochondrial Genome ◽  
Phylogenetic Tree Analysis ◽  
Protein Coding ◽  
Mitochondrial Genome Sequence ◽  
Complete Mitochondrial Genome Sequence ◽  
Location Determination ◽  
The Family

Abstract Hydropsyche fryeri belongs to the Trichopteridae family and builds nests in clean and unpolluted streams using stones. It also can be used as an indicator of water quality. Here, we describe the complete mitochondrial genome sequence of Hydropsyche fryeri. The mitochondrial genome is 15,676 bp long and contains 13 protein-coding genes, 22 tRNAs, 2 rRNAs and an AT-rich control region. Phylogenetic tree analysis shows that Hydropsyche fryeri is more closely related to the family Hydroptera than other Trichoptera.

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