scholarly journals Real-Time Recursive Fingerprint Radio Map Creation Algorithm Combining Wi-Fi and Geomagnetism

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3390 ◽  
Author(s):  
Ju-Hyeon Seong ◽  
Dong-Hoan Seo
Keyword(s):  
Real Time ◽  
Minimum Description Length ◽  
Signal Strength ◽  
Dual Processing ◽  
Minimum Description Length Principle ◽  
Signal Density ◽  
Wireless Signal ◽  
Time Required ◽  
Positioning Algorithm ◽  
Radio Map

Fingerprint is a typical indoor-positioning algorithm, which measures the strength of wireless signals and creates a radio map. Using this radio map, the position is estimated through comparisons with the received signal strength measured in real-time. The radio map has a direct effect on the positioning performance; therefore, it should be designed accurately and managed efficiently, according to the type of wireless signal, amount of space, and wireless-signal density. This paper proposes a real-time recursive radio map creation algorithm that combines Wi-Fi and geomagnetism. The proposed method automatically recreates the radio map using geomagnetic radio-map dual processing (GRDP), which reduces the time required to create it. It also reduces the size of the radio map by actively optimizing its dimensions using an entropy-based minimum description length principle (MDLP) method. Experimental results in an actual building show that the proposed system exhibits similar map creation time as a system using a Wi-Fi–based radio map. Geomagnetic radio maps exhibiting over 80% positioning accuracy were created, and the dimensions of the radio map that combined the two signals were found to be reduced by 23.81%, compared to the initially prepared radio map. The dimensions vary according to the wireless signal state, and are automatically reduced in different environments.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Wital: WiFi-based Real-time Vital Signs Monitoring During Sleep

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Huan Yan ◽  
Zhi Liu ◽  
Fuji Ren
Keyword(s):  
Real Time ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Implementation Process ◽  
Sleep Monitoring ◽  
Daily Lives ◽  
Vital Signs Monitoring ◽  
Quality Sleep ◽  
Enhancement Method ◽  
Positioning Algorithm

High-quality sleep is essential to our daily lives, and real-time monitoring of vital signs during sleep is beneficial. Current sleep monitoring solutions are mostly based on wearable sensors or cameras, the former is worse for sleep quality, the latter is worse for privacy, dissimilar to such methods, we implement our sleep monitoring system based on COTS WiFi devices. There are two challenges need to be overcome in the system implementation process: First, the torso deformation caused by breathing/heartbeat is weak, how to effectively capture this deformation? Second, movements such as turning over will affect the accuracy of vital signs monitoring, how to quickly distinguish such movements? For the former, we propose a motion detection capability enhancement method based on Rice-K theory and Fresnel theory. For the latter, we propose a sleep motion positioning algorithm based on regularity detection. The experimental results indicated the performance of our method.

scholarly journals Oxygen Monitoring Equipment for Sewage-Sludge Composting and Its Application to Aeration Optimization

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4017
Author(s):  
Guodi Zheng ◽  
Yuewei Wang ◽  
Xiankai Wang ◽  
Junxing Yang ◽  
Tongbin Chen
Keyword(s):  
Real Time ◽  
Oxygen Concentration ◽  
Consumption Rate ◽  
Continuous Control ◽  
Test Results ◽  
Monitoring Equipment ◽  
Decomposition Of Organic Matter ◽  
Good Repeatability ◽  
Oxygen Monitoring ◽  
Time Required

Oxygen is an important parameter for organic-waste composting, and continuous control of the oxygen in a composting pile may be beneficial. The oxygen consumption rate can be used to measure the degree of biological oxidation and decomposition of organic matter. However, without having a real-time online device to monitor oxygen levels in the composting pile, the adjustment and optimization of the composting process cannot be directly implemented. In the present study, we researched and developed such a system, and then tested its stability, reliability, and characteristics. The test results showed that the equipment was accurate and stable, and produced good responses with good repeatability. The equilibrium time required to detect oxygen concentration in the composting pile was 50 s, and the response time for oxygen detection was less than 2 s. The equipment could monitor oxygen concentration online and in real time to optimize the aeration strategy for the compost depending on the concentration indicated by the oxygen-measuring equipment.

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012070
Author(s):  
Qianrong Zhang ◽  
Yi Li
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Ultra Wideband ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Mobile Vehicle ◽  
Positioning Method ◽  
Positioning Algorithm ◽  
Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

scholarly journals Substructure Discovery Using Minimum Description Length and Background Knowledge

1994 ◽  
Vol 1 ◽  
pp. 231-255 ◽  
Author(s):  
D. J. Cook ◽  
L. B. Holder
Keyword(s):  
Minimum Description Length ◽  
Background Knowledge ◽  
Structural Data ◽  
Original Data ◽  
Minimum Description Length Principle ◽  
Online Appendix ◽  
Discovery System ◽  
Graph Match ◽  
Domain Description ◽  
Graph Form

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.

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