The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Kazuhiko Terashima - One of the best experts on this subject based on the ideXlab platform.
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wifi rss fingerprint database construction for mobile robot indoor positioning system
Systems Man and Cybernetics, 2016Co-Authors: A H Ismail, Hideo Kitagawa, Ryosuke Tasaki, Kazuhiko TerashimaAbstract:Mobile robot positioning in an indoor environment via Fingerprinting Technique is made by matching the unknown WiFi data to a spatial WiFi power map database. In past years, this Technique has gained reasonable accuracies in the cost of high labor yielded for the database. Thus, automatic database construction by means of interpolating missing data is desired. This paper described a variation of Inverse Distance Weight (IDW) interpolation which is the Modified Shepard's Method (MSM) for our medical-oriented mobile robot - Terapio. By properly selecting the reference locations, we found that this method is better than the conventional IDW method and comparable to the recently popular Kriging interpolation algorithm in an indoor environment by employing the state-of-the-art WKNN positioning algorithm.
Prashant Krishnamurthy - One of the best experts on this subject based on the ideXlab platform.
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analysis of wlan s received signal strength indication for indoor location Fingerprinting
Pervasive and Mobile Computing, 2012Co-Authors: Kamol Kaemarungsi, Prashant KrishnamurthyAbstract:An indoor positioning system that uses a location Fingerprinting Technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems.
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design of indoor positioning systems based on location Fingerprinting Technique
2005Co-Authors: Prashant Krishnamurthy, Kamol KaemarungsiAbstract:Positioning systems enable location-awareness for mobile computers in ubiquitous and pervasive wireless computing. By utilizing location information, location-aware computers can render location-based services possible for mobile users. Indoor positioning systems based on location fingerprints of wireless local area networks have been suggested as a viable solution where the global positioning system does not work well. Instead of depending on accurate estimations of angle or distance in order to derive the location with geometry, the Fingerprinting Technique associates location-dependent characteristics such as received signal strength to a location and uses these characteristics to infer the location. The advantage of this Technique is that it is simple to deploy with no specialized hardware required at the mobile station except the wireless network interface card. Any existing wireless local area network infrastructure can be reused for this kind of positioning system. While empirical results and performance studies of such positioning systems are presented in the literature, analytical models that can be used as a framework for efficiently designing the positioning systems are not available. This dissertation develops an analytical model as a design tool and recommends a design guideline for such positioning systems in order to expedite the deployment process. A system designer can use this framework to strike a balance between the accuracy, the precision, the location granularity, the number of access points, and the location spacing. A systematic study is used to analyze the location fingerprint and discover its unique properties. The location fingerprint based on the received signal strength is investigated. Both deterministic and probabilistic approaches of location fingerprint representations are considered. The main objectives of this work are to predict the performance of such systems using a suitable model and perform sensitivity analyses that are useful for selecting proper system parameters such as number of access points and minimum spacing between any two different locations.
Chahé Nerguizian - One of the best experts on this subject based on the ideXlab platform.
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geolocation in mines with an impulse response Fingerprinting Technique and neural networks
IEEE Transactions on Wireless Communications, 2006Co-Authors: Chahé Nerguizian, Charles Despins, Sofiene AffesAbstract:The location of people, mobile terminals and equipment is highly desirable for operational enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflection, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest direct path between transmitter and receiver are the main sources of range measurement errors. Unreliable measurements of location metrics such as received signal strengths (RSS), angles of arrival (AOA) and times of arrival (TOA) or time differences of arrival (TDOA), result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation Techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the wide band neural network-locate (WBNN-locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).
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3d indoor geolocation with received signal strength Fingerprinting Technique and neural networks
International Conference on Telecommunications, 2005Co-Authors: Chahé Nerguizian, Vahe Nerguizian, Lamia Hamza, M. SaadAbstract:Three dimensional location of a user is highly desirable in indoor environments since it represents an extension to the GPS system where signals can not penetrate the in-building environments. In this paper, we present a method for 3D user location using WLAN's received power (RSS) data applied to an artificial neural network (ANN). The proposed Fingerprinting system learns off-line the location RSS 'signatures' for line of sight (LOS), obstructed line of sight (OLOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a user against the learned set of 'signatures' to accurately locate its three-dimensional position. The x-y location precision of the proposed system, applied to the three floors of a building simultaneously, has been found to be 3 meters for 78% and 63% of trained and untrained data, respectively. As for the z-axis precision, the proposed system has succeeded to identify the exact floor for 92.5% and 85.5% of trained and untrained patterns, respectively.
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geolocation in mines with an impulse response Fingerprinting Technique and neural networks
Vehicular Technology Conference, 2004Co-Authors: Chahé Nerguizian, Charles Despins, Sofiene AffesAbstract:The location of people, mobile terminals and equipment is highly desirable for operational and safety enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflections, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh mining environment, unreliable measurements of location metrics such as RSS, AOA and TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation Techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the Wide Band Neural Network-Locate (WBNN-Locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses data for LOS and NLOS situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).
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Mobile robot geolocation with received signal strength (RSS) Fingerprinting Technique and neural networks
Industrial Technology 2004. IEEE ICIT '04. 2004 IEEE International Conference on, 2004Co-Authors: Chahé Nerguizian, Vahe Nerguizian, S. Belkhous, Abdelkrim Azzouz, M. SaadAbstract:The location of a mobile robot is highly desirable for operational enhancements in indoor environments. In an in-building environment, the multipath caused by reflection and diffraction, and the obstruction and/or the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh indoor environment, unreliable measurements of location metrics such as received signal strength (RSS), angle of arrival (AOA) and time or time difference of arrival TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation Techniques have to be considered. In this paper, we present a method for mobile robot location using WLAN's received power (RSS) data applied to an artificial neural network (ANN). The proposed system learns off-line the location RSS 'signatures' for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile robot against the learned set of 'signatures' to accurately locate its position. The location precision of the proposed system, applied in an in-building environment, has been found to be 0.5 meter for 90% of trained data and about 5 meters for 58% of untrained data.
Eiichiro Fukusaki - One of the best experts on this subject based on the ideXlab platform.
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high throughput Technique for comprehensive analysis of japanese green tea quality assessment using ultra performance liquid chromatography with time of flight mass spectrometry uplc tof ms
Journal of Agricultural and Food Chemistry, 2008Co-Authors: Wipawee Pongsuwan, Takeshi Bamba, Kazuo Harada, Tsutomu Yonetani, Akio Kobayashi, Eiichiro FukusakiAbstract:Applications of metabolomics Techniques along with chemometrics provide an understanding in the relationship between metabolome of green tea and its quality. A coupled of ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS) allowed a high-throughput and comprehensive analysis with minimal sample preparation. Using this Technique, a wide range of metabolites were investigated. Data analysis was rapid, considering that the Fingerprinting Technique was performed. A set of green tea samples from 2006 tea contest of the Kansai area was analyzed to prove usefulness of the developed Technique. Green tea with different qualities were discriminated through principal component analysis (PCA). Consequently, projection to latent structure by means of partial least-squares (PLS) was performed to create a constructive quality-predictive model by means of metabolic Fingerprinting. Beside epigallocatechin, other predominant catechins, including epigallocatechin gallate and epicatech...
Kamol Kaemarungsi - One of the best experts on this subject based on the ideXlab platform.
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analysis of wlan s received signal strength indication for indoor location Fingerprinting
Pervasive and Mobile Computing, 2012Co-Authors: Kamol Kaemarungsi, Prashant KrishnamurthyAbstract:An indoor positioning system that uses a location Fingerprinting Technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems.
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design of indoor positioning systems based on location Fingerprinting Technique
2005Co-Authors: Prashant Krishnamurthy, Kamol KaemarungsiAbstract:Positioning systems enable location-awareness for mobile computers in ubiquitous and pervasive wireless computing. By utilizing location information, location-aware computers can render location-based services possible for mobile users. Indoor positioning systems based on location fingerprints of wireless local area networks have been suggested as a viable solution where the global positioning system does not work well. Instead of depending on accurate estimations of angle or distance in order to derive the location with geometry, the Fingerprinting Technique associates location-dependent characteristics such as received signal strength to a location and uses these characteristics to infer the location. The advantage of this Technique is that it is simple to deploy with no specialized hardware required at the mobile station except the wireless network interface card. Any existing wireless local area network infrastructure can be reused for this kind of positioning system. While empirical results and performance studies of such positioning systems are presented in the literature, analytical models that can be used as a framework for efficiently designing the positioning systems are not available. This dissertation develops an analytical model as a design tool and recommends a design guideline for such positioning systems in order to expedite the deployment process. A system designer can use this framework to strike a balance between the accuracy, the precision, the location granularity, the number of access points, and the location spacing. A systematic study is used to analyze the location fingerprint and discover its unique properties. The location fingerprint based on the received signal strength is investigated. Both deterministic and probabilistic approaches of location fingerprint representations are considered. The main objectives of this work are to predict the performance of such systems using a suitable model and perform sensitivity analyses that are useful for selecting proper system parameters such as number of access points and minimum spacing between any two different locations.