The Experts below are selected from a list of 15456 Experts worldwide ranked by ideXlab platform
Kaveh Pahlavan - One of the best experts on this subject based on the ideXlab platform.
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Indoor Geolocation science and technology
IEEE Communications Magazine, 2002Co-Authors: Kaveh Pahlavan, Xinrong Li, Juha Pekka MäkeläAbstract:This article presents an overview of the technical aspects of the\nexisting technologies for wireless indoor location systems. The two\nmajor challenges for accurate location finding in indoor areas are the\ncomplexity of radio propagation and the ad hoc nature of the deployed\ninfrastructure in these areas. Because of these difficulties a variety\nof signaling techniques, overall system architectures, and location\nfinding algorithms are emerging for this application. This article\nprovides a fundamental understanding of the issues related to indoor\nGeolocation science that are needed for design and performance\nevaluation of emerging indoor Geolocation systems
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comparison of indoor Geolocation methods in dsss and ofdm wireless lan systems
Vehicular Technology Conference, 2000Co-Authors: Kaveh Pahlavan, Matti Latvaaho, Mika YlianttilaAbstract:Geolocation methods for HIPERLAN/2 OFDM systems were reported previously. This paper continues to study the possibility of overlaying Geolocation functions in 802.11 DSSS wireless LANs. In this paper, a delay measurement-based TDOA measuring method is proposed for 802.11 wireless LANs, which eliminate the requirement of initial synchronization in the conventional methods. The performance of the potential overlaid Geolocation systems for DSSS and OFDM wireless LANs are analyzed and compared in terms of symbol synchronization performance.
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indoor Geolocation using ofdm signals in hiperlan 2 wireless lans
Personal Indoor and Mobile Radio Communications, 2000Co-Authors: Kaveh Pahlavan, Matti Latvaaho, Mika YlianttilaAbstract:With the finalization of new series of IEEE 802.11 and ETSI HIPERLAN standards, it becomes very important and interesting to study the methods to integrate Geolocation functionalities into the next generation wireless LANs. We investigate Geolocation methods and system architectures using OFDM signals in HIPERLAN/2 wireless LANs. We propose a novel method to measure Geolocation metrics by exploiting the HIPERLAN/2 MAC frame structure. Computer simulation results are presented to show the performance of the Geolocation systems using OFDM signals.
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an overview of wireless indoor Geolocation techniques and systems
Mobile and Wireless Communication Networks, 2000Co-Authors: Kaveh Pahlavan, Mika Ylianttila, Ranvir Chana, Matti LatvaahoAbstract:Wireless indoor networks are finding their way into the home and office environments. Also, exploiting location information becomes very popular for both wireless service providers and consumers applications. However, the indoor radio channel causes challenges in extracting accurate location information in indoor environment so that traditional GPS and cellular location systems cannot work properly in indoor areas. This paper provides an overview of the indoor Geolocation techniques. After introducing an overall architecture for indoor Geolocation systems, technical overview of two indoor Geolocation systems are presented. To demonstrate the predicted performance of such systems some simulation results obtained from an indoor Geolocation demonstrator are presented.
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Mobile and Wireless Communication Networks - An Overview of Wireless Indoor Geolocation Techniques and Systems
Mobile and Wireless Communications Networks, 2000Co-Authors: Kaveh Pahlavan, Xinrong Li, Mika Ylianttila, Ranvir Chana, Matti Latva-ahoAbstract:Wireless indoor networks are finding their way into the home and office environments. Also, exploiting location information becomes very popular for both wireless service providers and consumers applications. However, the indoor radio channel causes challenges in extracting accurate location information in indoor environment so that traditional GPS and cellular location systems cannot work properly in indoor areas. This paper provides an overview of the indoor Geolocation techniques. After introducing an overall architecture for indoor Geolocation systems, technical overview of two indoor Geolocation systems are presented. To demonstrate the predicted performance of such systems some simulation results obtained from an indoor Geolocation demonstrator are presented.
Niels Peek - One of the best experts on this subject based on the ideXlab platform.
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the minimum sampling rate and sampling duration when applying Geolocation data technology to human activity monitoring
Artificial Intelligence in Medicine in Europe, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
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AIME - The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
Artificial Intelligence in Medicine, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
Mika Ylianttila - One of the best experts on this subject based on the ideXlab platform.
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comparison of indoor Geolocation methods in dsss and ofdm wireless lan systems
Vehicular Technology Conference, 2000Co-Authors: Kaveh Pahlavan, Matti Latvaaho, Mika YlianttilaAbstract:Geolocation methods for HIPERLAN/2 OFDM systems were reported previously. This paper continues to study the possibility of overlaying Geolocation functions in 802.11 DSSS wireless LANs. In this paper, a delay measurement-based TDOA measuring method is proposed for 802.11 wireless LANs, which eliminate the requirement of initial synchronization in the conventional methods. The performance of the potential overlaid Geolocation systems for DSSS and OFDM wireless LANs are analyzed and compared in terms of symbol synchronization performance.
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indoor Geolocation using ofdm signals in hiperlan 2 wireless lans
Personal Indoor and Mobile Radio Communications, 2000Co-Authors: Kaveh Pahlavan, Matti Latvaaho, Mika YlianttilaAbstract:With the finalization of new series of IEEE 802.11 and ETSI HIPERLAN standards, it becomes very important and interesting to study the methods to integrate Geolocation functionalities into the next generation wireless LANs. We investigate Geolocation methods and system architectures using OFDM signals in HIPERLAN/2 wireless LANs. We propose a novel method to measure Geolocation metrics by exploiting the HIPERLAN/2 MAC frame structure. Computer simulation results are presented to show the performance of the Geolocation systems using OFDM signals.
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an overview of wireless indoor Geolocation techniques and systems
Mobile and Wireless Communication Networks, 2000Co-Authors: Kaveh Pahlavan, Mika Ylianttila, Ranvir Chana, Matti LatvaahoAbstract:Wireless indoor networks are finding their way into the home and office environments. Also, exploiting location information becomes very popular for both wireless service providers and consumers applications. However, the indoor radio channel causes challenges in extracting accurate location information in indoor environment so that traditional GPS and cellular location systems cannot work properly in indoor areas. This paper provides an overview of the indoor Geolocation techniques. After introducing an overall architecture for indoor Geolocation systems, technical overview of two indoor Geolocation systems are presented. To demonstrate the predicted performance of such systems some simulation results obtained from an indoor Geolocation demonstrator are presented.
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Mobile and Wireless Communication Networks - An Overview of Wireless Indoor Geolocation Techniques and Systems
Mobile and Wireless Communications Networks, 2000Co-Authors: Kaveh Pahlavan, Xinrong Li, Mika Ylianttila, Ranvir Chana, Matti Latva-ahoAbstract:Wireless indoor networks are finding their way into the home and office environments. Also, exploiting location information becomes very popular for both wireless service providers and consumers applications. However, the indoor radio channel causes challenges in extracting accurate location information in indoor environment so that traditional GPS and cellular location systems cannot work properly in indoor areas. This paper provides an overview of the indoor Geolocation techniques. After introducing an overall architecture for indoor Geolocation systems, technical overview of two indoor Geolocation systems are presented. To demonstrate the predicted performance of such systems some simulation results obtained from an indoor Geolocation demonstrator are presented.
Paolo Fraccaro - One of the best experts on this subject based on the ideXlab platform.
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the minimum sampling rate and sampling duration when applying Geolocation data technology to human activity monitoring
Artificial Intelligence in Medicine in Europe, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
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AIME - The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
Artificial Intelligence in Medicine, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
Yan Zheng - One of the best experts on this subject based on the ideXlab platform.
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the minimum sampling rate and sampling duration when applying Geolocation data technology to human activity monitoring
Artificial Intelligence in Medicine in Europe, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
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AIME - The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
Artificial Intelligence in Medicine, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation data required to accurately recognise out-of-home activities. We collected Geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.