scholarly journals UWB Localization System for Indoor Applications: Concept, Realization and Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Lukasz Zwirello ◽  
Tom Schipper ◽  
Marlene Harter ◽  
Thomas Zwick
Keyword(s):  
Detection Threshold ◽  
Threshold Level ◽  
Time Measurement ◽  
Error Sources ◽  
Localization Accuracy ◽  
Localization System ◽  
Average Accuracy ◽  
Measurement Results ◽  
Precise Analysis ◽  
Positioning Method

A complete impulse-based ultrawideband localization demonstrator for indoor applications is presented. The positioning method, along with the method of positioning error predicting, based on scenario geometry, is described. The hardware setup, including UWB transceiver and time measurement module, as well as the working principles is explained. The system simulation, used as a benchmark for the quality assessment of the performed measurements, is presented. Finally, the measurement results are discussed. The precise analysis of potential error sources in the system is conducted, based on both simulations and measurement. Furthermore, the methods, how to improve the average accuracy of 9 cm by including the influences of antennas and signal-detection threshold level, are made. The localization accuracy, resulting from those corrections, is 2.5 cm.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

The Kinetics of Methyl Methacrylate Miniemulsion Polymerization Characterized through a Fluorescence Method

2012 ◽  
Vol 178-181 ◽  
pp. 609-612
Author(s):  
Hai Ke Feng ◽  
Hua Yu Qiu ◽  
Li Yuan Ding ◽  
Cun Jin Xu
Keyword(s):  
Methyl Methacrylate ◽  
Anionic Surfactant ◽  
Sodium Dodecyl Sulphate ◽  
Sodium Dodecyl ◽  
Miniemulsion Polymerization ◽  
Time Measurement ◽  
Fluorescence Method ◽  
Real Time Measurement ◽  
Measurement Results ◽  
Kinetics Of

In this paper, we followed the kinetics of methyl methacrylate (MMA) through a novel fluorescence method. The real-time measurement results show that in the regime of very low monomer contents, such as a solution containing 0.1 wt% of MMA with respect to water and with the anionic surfactant of sodium dodecyl sulphate (SDS), the kinetic of the miniemulsion could be followed by this embed fluorescence method. The processes of changing from emulsion to miniemulsion with different amount of surfactant and cosurfactant also have been monitored.

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

A Multi-Sensor Positioning Method-Based Train Localization System for Low Density Line

2018 ◽  
Vol 67 (11) ◽  
pp. 10425-10437 ◽  
Author(s):  
Wei Jiang ◽  
Sirui Chen ◽  
Baigen Cai ◽  
Jian Wang ◽  
Wei ShangGuan ◽  
...  
Keyword(s):  
Low Density ◽  
Localization System ◽  
Positioning Method ◽  
Sensor Positioning

Error sources and models of the laser tracker without a beam steering mirror

2021 ◽  
Author(s):  
Radoslav Choleva ◽  
Alojz Kopáčik
Keyword(s):  
Measurement Accuracy ◽  
Beam Steering ◽  
Systematic Errors ◽  
Error Modeling ◽  
Laser Tracker ◽  
High Demand ◽  
Error Sources ◽  
Error Models ◽  
Measurement Results

AbstractThe laser tracker is a widely used instrument in many industrial and metrological applications with high demand measurement accuracy. Imperfections in construction and misalignment of individual parts deliver systematic errors in the measurement results. All error sources need to be identified and reduced to the minimum to achieve the best possible accuracy. The paper summarizes error sources of the laser tracker without beam steering mirror with emphasis on error modeling. Descriptions of error models are provided for the static and kinematic type of measurement.

Hearing Reaction Time Measurement Concentration Level Tool Integrated Android Smartphone

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhamad Arif Indiarto ◽  
Syamsudduha Syahrorini
Keyword(s):  
Reaction Time ◽  
Power Supply ◽  
High Performance ◽  
Concentration Level ◽  
Time Measurement ◽  
Push Button ◽  
High Productivity ◽  
Measurement Results ◽  
Reaction Time Measurement ◽  
Execution Speed

Performance in a job is very important because it will have an impact on the assessment and productivity of an employee, one of the indicators for evaluating high performance is related to concentration, execution speed and high productivity of the employee. Concentration is needed in working to prevent fatal accidents. In this study, it is possible to monitor measurement results via a smartphone, namely by using the Bluetooth HC-05 sensor as an integration to a smartphone. With 8 pushbutton, Arduino UNO microncontroller, Bluetooth HC-05, 16x2 LCD, and Buzzer. This tool works alternately when the push button Start is pressed, the power from the power supply will provide an electric current to the microncontroller, and continue to be connected to the Bluetooth HC-05, then by providing pushbuttons pressing input. Each pressing instruction on the pushbutton provides a different sound output, consisting of sound output, High, Mid, and Low. And continue on the LCD, and can display the results of the input that has been processed by the microcontroller. The output results are in the form of the amount of time displayed on the LCD, the sound from the buzzer, and from a series of work tools and the output results can be monitored via android smartphone. The results of this study are the accuracy of the tool in each variable low 99%, mid 90%, high 92%. The average tool ranges from 2.44. The error is low 7,4%, mid 7,4%, high 7,6%.

The WiMap

2013 ◽  
pp. 31-41
Author(s):  
Junjun Xu ◽  
Haiyong Luo ◽  
Fang Zhao ◽  
Rui Tao ◽  
Yiming Lin ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Signal Strength ◽  
Gaussian Mixture ◽  
Threshold Control ◽  
Localization Accuracy ◽  
Signal Distribution ◽  
Localization System ◽  
Design And Implementation ◽  
The Internet Of Things ◽  
Grid Based

As positioning technology is an important foundation of the Internet of Things, a dynamic indoor WLAN localization system is proposed in this paper. This paper mainly concentrates on the design and implementation of the WiMap-a dynamic indoor WLAN localization system, which employs grid-based localization method using RSS (received signal strength). To achieve high localization accuracy and low computational complexity, Gaussian mixture model is applied to approximate the signal distribution and a ROI (region of interest) is defined to limit the search region. The authors also discuss other techniques like AP selection and threshold control, which affects the localization accuracy. The experimental results indicate that an accuracy of 3m with 73.8% probability can be obtained in WiMap. Moreover, the running time is reduced greatly with limited ROI method.

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

scholarly journals On the Nature of Pressure Wave Propagation through Ducts for Structural Health Monitoring Application

2019 ◽  
Vol 9 (5) ◽  
pp. 837
Author(s):  
Zoé Jardon ◽  
Michaël Hinderdael ◽  
Tamas Regert ◽  
Jeroen Van Beeck ◽  
Patrick Guillaume
Keyword(s):  
Structural Health Monitoring ◽  
Shock Waves ◽  
Health Monitoring ◽  
Constant Velocity ◽  
Propagation Speed ◽  
Localization Accuracy ◽  
Localization System ◽  
Structural Health ◽  
Effective Velocity ◽  
Dynamics Simulations

An effective structural health monitoring system fully exploits the flexibility offered by the 3D printing process by integrating a smart structural health monitoring technology inside the 3D-printed components. The system relies on the propagation of pressure waves with constant propagation speed through circular capillaries embedded in the structure. The nature of these waves seems to be determinant for the accuracy of the crack localization system. To achieve a better physical understanding of the nature of the propagating waves through the capillaries, computational fluid dynamics simulations are performed and compared to experimental results obtained with a self-built test setup. The presence of propagating shock waves is observed in the simulations and experiments, as well as a complex reflection mechanism around the leak location. Shock waves show the characteristic of not propagating at a constant velocity. This property complicates the actual localization system. To solve this, the constant velocity assumption should be replaced with the effective velocity evolution to increase the localization accuracy. The amplitude of the shock wave is attenuated with propagating distance, which proves that the effect of friction plays an important role and can, in turn, influence the localization system.

scholarly journals Deep Learning for Fingerprint Localization in Indoor and Outdoor Environments

2020 ◽  
Vol 9 (4) ◽  
pp. 267 ◽  
Author(s):  
Da Li ◽  
Yingke Lei ◽  
Xin Li ◽  
Haichuan Zhang
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Fingerprint Image ◽  
Density Peak ◽  
Localization Accuracy ◽  
Localization System ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Wi-Fi and magnetic field fingerprinting-based localization have gained increased attention owing to their satisfactory accuracy and global availability. The common signal-based fingerprint localization deteriorates due to well-known signal fluctuations. In this paper, we proposed a Wi-Fi and magnetic field-based localization system based on deep learning. Owing to the low discernibility of magnetic field strength (MFS) in large areas, the unsupervised learning density peak clustering algorithm based on the comparison distance (CDPC) algorithm is first used to pick up several center points of MFS as the geotagged features to assist localization. Considering the state-of-the-art application of deep learning in image classification, we design a location fingerprint image using Wi-Fi and magnetic field fingerprints for localization. Localization is casted in a proposed deep residual network (Resnet) that is capable of learning key features from a massive fingerprint image database. To further enhance localization accuracy, by leveraging the prior information of the pre-trained Resnet coarse localizer, an MLP-based transfer learning fine localizer is introduced to fine-tune the coarse localizer. Additionally, we dynamically adjusted the learning rate (LR) and adopted several data enhancement methods to increase the robustness of our localization system. Experimental results show that the proposed system leads to satisfactory localization performance both in indoor and outdoor environments.

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