The Experts below are selected from a list of 154788 Experts worldwide ranked by ideXlab platform
Dingyi Fang - One of the best experts on this subject based on the ideXlab platform.
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ICDCS - WiMi: Target Material Identification with Commodity Wi-Fi Devices
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong TangAbstract:Target Material Identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in Material Identification. This paper introduces WiMi, a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. We also design a new Material feature which is only related to the Material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained Material Identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
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SenSys - Material Identification with Commodity Wi-Fi Devices
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Xiaojiang Chen, Dingyi Fang, Li Xinyi, Baoying Liu, Feng Chen, Zhang TaoAbstract:Target Material Identification is playing an important role in our everyday life. This paper introduces a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.
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S3@MobiCom - Target Material Identification with Commodity RFID Devices
Proceedings of the 9th ACM Workshop on Wireless of the Students by the Students and for the Students - S3 '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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WiNTECH@MobiCom - Demo: Material Identification with Commodity RFID Devices
Proceedings of the 11th Workshop on Wireless Network Testbeds Experimental evaluation & CHaracterization - WiNTECH '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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tagscan simultaneous target imaging and Material Identification with commodity rfid devices
ACM IEEE International Conference on Mobile Computing and Networking, 2017Co-Authors: Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi FangAbstract:Target imaging and Material Identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the Material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different Materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either Material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
Jie Xiong - One of the best experts on this subject based on the ideXlab platform.
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ICDCS - WiMi: Target Material Identification with Commodity Wi-Fi Devices
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong TangAbstract:Target Material Identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in Material Identification. This paper introduces WiMi, a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. We also design a new Material feature which is only related to the Material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained Material Identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
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SenSys - Material Identification with Commodity Wi-Fi Devices
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Xiaojiang Chen, Dingyi Fang, Li Xinyi, Baoying Liu, Feng Chen, Zhang TaoAbstract:Target Material Identification is playing an important role in our everyday life. This paper introduces a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.
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S3@MobiCom - Target Material Identification with Commodity RFID Devices
Proceedings of the 9th ACM Workshop on Wireless of the Students by the Students and for the Students - S3 '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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WiNTECH@MobiCom - Demo: Material Identification with Commodity RFID Devices
Proceedings of the 11th Workshop on Wireless Network Testbeds Experimental evaluation & CHaracterization - WiNTECH '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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tagscan simultaneous target imaging and Material Identification with commodity rfid devices
ACM IEEE International Conference on Mobile Computing and Networking, 2017Co-Authors: Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi FangAbstract:Target imaging and Material Identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the Material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different Materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either Material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
Xiaojiang Chen - One of the best experts on this subject based on the ideXlab platform.
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ICDCS - WiMi: Target Material Identification with Commodity Wi-Fi Devices
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong TangAbstract:Target Material Identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in Material Identification. This paper introduces WiMi, a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. We also design a new Material feature which is only related to the Material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained Material Identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
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SenSys - Material Identification with Commodity Wi-Fi Devices
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Xiaojiang Chen, Dingyi Fang, Li Xinyi, Baoying Liu, Feng Chen, Zhang TaoAbstract:Target Material Identification is playing an important role in our everyday life. This paper introduces a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.
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S3@MobiCom - Target Material Identification with Commodity RFID Devices
Proceedings of the 9th ACM Workshop on Wireless of the Students by the Students and for the Students - S3 '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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WiNTECH@MobiCom - Demo: Material Identification with Commodity RFID Devices
Proceedings of the 11th Workshop on Wireless Network Testbeds Experimental evaluation & CHaracterization - WiNTECH '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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tagscan simultaneous target imaging and Material Identification with commodity rfid devices
ACM IEEE International Conference on Mobile Computing and Networking, 2017Co-Authors: Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi FangAbstract:Target imaging and Material Identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the Material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different Materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either Material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
Ju Wang - One of the best experts on this subject based on the ideXlab platform.
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ICDCS - WiMi: Target Material Identification with Commodity Wi-Fi Devices
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong TangAbstract:Target Material Identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in Material Identification. This paper introduces WiMi, a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. We also design a new Material feature which is only related to the Material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained Material Identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
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S3@MobiCom - Target Material Identification with Commodity RFID Devices
Proceedings of the 9th ACM Workshop on Wireless of the Students by the Students and for the Students - S3 '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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WiNTECH@MobiCom - Demo: Material Identification with Commodity RFID Devices
Proceedings of the 11th Workshop on Wireless Network Testbeds Experimental evaluation & CHaracterization - WiNTECH '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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tagscan simultaneous target imaging and Material Identification with commodity rfid devices
ACM IEEE International Conference on Mobile Computing and Networking, 2017Co-Authors: Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi FangAbstract:Target imaging and Material Identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the Material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different Materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either Material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
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MobiCom - TagScan: Simultaneous Target Imaging and Material Identification with Commodity RFID Devices
Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, 2017Co-Authors: Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi FangAbstract:Target imaging and Material Identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the Material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different Materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either Material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
Chao Feng - One of the best experts on this subject based on the ideXlab platform.
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ICDCS - WiMi: Target Material Identification with Commodity Wi-Fi Devices
2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong TangAbstract:Target Material Identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in Material Identification. This paper introduces WiMi, a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. We also design a new Material feature which is only related to the Material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained Material Identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.
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SenSys - Material Identification with Commodity Wi-Fi Devices
Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018Co-Authors: Chao Feng, Jie Xiong, Liqiong Chang, Xiaojiang Chen, Dingyi Fang, Li Xinyi, Baoying Liu, Feng Chen, Zhang TaoAbstract:Target Material Identification is playing an important role in our everyday life. This paper introduces a device-free target Material Identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different Materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate Material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.
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S3@MobiCom - Target Material Identification with Commodity RFID Devices
Proceedings of the 9th ACM Workshop on Wireless of the Students by the Students and for the Students - S3 '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.
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WiNTECH@MobiCom - Demo: Material Identification with Commodity RFID Devices
Proceedings of the 11th Workshop on Wireless Network Testbeds Experimental evaluation & CHaracterization - WiNTECH '17, 2017Co-Authors: Li Xinyi, Chao Feng, Jie Xiong, Ju Wang, Xiaojiang Chen, Nana Ding, Ren Yuhui, Dingyi FangAbstract:Target Material Identification plays an important role in many real-life applications. This paper introduces a system that can identify the Material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different Materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either Material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% Material Identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.