Frequency Domain System Identification
In: Intelligent Systems, Control and Automation: Science and Engineering; Linear and Nonlinear Control of Small-Scale Unmanned Helicopters, S. 47-72
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In: Intelligent Systems, Control and Automation: Science and Engineering; Linear and Nonlinear Control of Small-Scale Unmanned Helicopters, S. 47-72
In: Paolo Baffi Centre Research Paper No. 2012-130
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Working paper
In: Bank of Finland Research Discussion Paper No. 1/2023
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In: Economic Research Initiatives at Duke (ERID) Working Paper No. 298
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Working paper
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In: Bank of Finland Research Discussion Paper No. 2/2020
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In: Hong Kong Institute for Monetary and Financial Research (HKIMR) Research Paper WP No. 17/2013
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International audience ; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
BASE
International audience ; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
BASE
International audience ; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
BASE
International audience ; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
BASE
International audience ; The recent development of Internet of Things (IoT) has democratized activity monitoring. Even if the data collected can be useful for healthcare, sharing this sensitive information exposes users to privacy threats and re-identification. This paper presents two approaches to anonymize the motion sensor data. The first is an extension of an earlier work based on filtering in the time-frequency plane and convolutional neural network; and the second is based on handcrafted features extracted from the zeros distribution of the time-frequency representation. The two approaches are evaluated on a public dataset to assess the accuracy of activity recognition and user re-identification. With the first approach we obtained an accuracy rate in activity recognition of 73% while limiting the identity recognition to an accuracy rate of 30% which corresponds to an activity identity ratio of 2.4. With the second approach we succeeded in improving the activity and identity ratio to 2.67 by attaining an accuracy rate in activity recognition of 80% while maintaining the re-identification rate at 30%.
BASE
In: IEEE antennas & propagation magazine, Band 37, Heft 5, S. 78
ISSN: 1558-4143
In: NBER Working Paper No. w19416
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