The Experts below are selected from a list of 106149 Experts worldwide ranked by ideXlab platform

Yuanhao Huang - One of the best experts on this subject based on the ideXlab platform.

  • a uwb radar signal Processing Platform for real time human respiratory feature extraction based on four segment linear waveform model
    IEEE Transactions on Biomedical Circuits and Systems, 2016
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents an ultra-wideband (UWB) impulse-radio radar signal Processing Platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal Processing Platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal Processing Platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

  • human respiratory feature extraction on an uwb radar signal Processing Platform
    International Symposium on Circuits and Systems, 2013
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio radar signal Processing Platform. The conventional human detection algorithms only extract the respiration rate by the radar system. However, there is more information that is never explored in the radar-detected respiratory signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a radar signal Processing Platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory signal.

Murugan Sankaradass - One of the best experts on this subject based on the ideXlab platform.

  • system design methodologies for a wireless security Processing Platform
    Design Automation Conference, 2002
    Co-Authors: Srivaths Ravi, Anand Raghunathan, Nachiketh Potlapally, Murugan Sankaradass
    Abstract:

    Security protocols are critical to enabling the growth of a wide range of wireless data services and applications. However, they impose a high computational burden that is mismatched with the modest Processing capabilities and battery resources available on wireless clients. Bridging the security Processing gap, while retaining sufficient programmability in order to support a wide range of current and future security protocol standards, requires the use of novel system architectures and design methodologies.We present the system-level design methodology used to design a programmable security processor Platform for next-generation wireless handsets. The Platform architecture is based on (i) a configurable and extensible processor that is customized for efficient domain-specific Processing, and (ii) layered software libraries implementing cryptographic algorithms that are optimized to the hardware Platform. Our system-level design methodology enables the efficient co design of optimal cryptographic algorithms and an optimized system architecture. It includes novel techniques for algorithmic exploration and tuning, performance characterization and macro-modeling of software libraries, and architecture refinement based on selection of instruction extensions to accelerate performance-critical, computation-intensive operations. We have designed a programmable security processor Platform to support both public-key and private key operations using the proposed methodology, and have evaluated its performance through extensive system simulations as well as hardware prototyping. Our experiments demonstrate large performance improvements (e.g., 31.0X for DES, 33.9X for 3DES, 17.4X for AES, and upto 66.4X for RSA) compared to well-optimized software implementations on a state-of-the-art embedded processor.

  • DAC - System design methodologies for a wireless security Processing Platform
    Proceedings of the 39th conference on Design automation - DAC '02, 2002
    Co-Authors: Srivaths Ravi, Anand Raghunathan, Nachiketh Potlapally, Murugan Sankaradass
    Abstract:

    Security protocols are critical to enabling the growth of a wide range of wireless data services and applications. However, they impose a high computational burden that is mismatched with the modest Processing capabilities and battery resources available on wireless clients. Bridging the security Processing gap, while retaining sufficient programmability in order to support a wide range of current and future security protocol standards, requires the use of novel system architectures and design methodologies.We present the system-level design methodology used to design a programmable security processor Platform for next-generation wireless handsets. The Platform architecture is based on (i) a configurable and extensible processor that is customized for efficient domain-specific Processing, and (ii) layered software libraries implementing cryptographic algorithms that are optimized to the hardware Platform. Our system-level design methodology enables the efficient co design of optimal cryptographic algorithms and an optimized system architecture. It includes novel techniques for algorithmic exploration and tuning, performance characterization and macro-modeling of software libraries, and architecture refinement based on selection of instruction extensions to accelerate performance-critical, computation-intensive operations. We have designed a programmable security processor Platform to support both public-key and private key operations using the proposed methodology, and have evaluated its performance through extensive system simulations as well as hardware prototyping. Our experiments demonstrate large performance improvements (e.g., 31.0X for DES, 33.9X for 3DES, 17.4X for AES, and upto 66.4X for RSA) compared to well-optimized software implementations on a state-of-the-art embedded processor.

Chihsuan Hsieh - One of the best experts on this subject based on the ideXlab platform.

  • a uwb radar signal Processing Platform for real time human respiratory feature extraction based on four segment linear waveform model
    IEEE Transactions on Biomedical Circuits and Systems, 2016
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents an ultra-wideband (UWB) impulse-radio radar signal Processing Platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal Processing Platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal Processing Platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

  • human respiratory feature extraction on an uwb radar signal Processing Platform
    International Symposium on Circuits and Systems, 2013
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio radar signal Processing Platform. The conventional human detection algorithms only extract the respiration rate by the radar system. However, there is more information that is never explored in the radar-detected respiratory signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a radar signal Processing Platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory signal.

Yufang Chiu - One of the best experts on this subject based on the ideXlab platform.

  • a uwb radar signal Processing Platform for real time human respiratory feature extraction based on four segment linear waveform model
    IEEE Transactions on Biomedical Circuits and Systems, 2016
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents an ultra-wideband (UWB) impulse-radio radar signal Processing Platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal Processing Platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal Processing Platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

  • human respiratory feature extraction on an uwb radar signal Processing Platform
    International Symposium on Circuits and Systems, 2013
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio radar signal Processing Platform. The conventional human detection algorithms only extract the respiration rate by the radar system. However, there is more information that is never explored in the radar-detected respiratory signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a radar signal Processing Platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory signal.

Yihsiang Shen - One of the best experts on this subject based on the ideXlab platform.

  • a uwb radar signal Processing Platform for real time human respiratory feature extraction based on four segment linear waveform model
    IEEE Transactions on Biomedical Circuits and Systems, 2016
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents an ultra-wideband (UWB) impulse-radio radar signal Processing Platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal Processing Platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal Processing Platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

  • human respiratory feature extraction on an uwb radar signal Processing Platform
    International Symposium on Circuits and Systems, 2013
    Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao Huang
    Abstract:

    This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio radar signal Processing Platform. The conventional human detection algorithms only extract the respiration rate by the radar system. However, there is more information that is never explored in the radar-detected respiratory signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a radar signal Processing Platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory signal.