The Experts below are selected from a list of 51297 Experts worldwide ranked by ideXlab platform
Yuanhao Huang - One of the best experts on this subject based on the ideXlab platform.
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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, 2016Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract: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.
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human respiratory feature extraction on an uwb Radar Signal processing platform
International Symposium on Circuits and Systems, 2013Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract: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.
Zhian Deng - One of the best experts on this subject based on the ideXlab platform.
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Radar Signal intra pulse modulation recognition based on convolutional neural network
IEEE Access, 2018Co-Authors: Zhiyu Qu, Zhian DengAbstract:In this paper, to solve the problem of the low recognition rate of the existing approaches at low Signal-to-noise ratio (SNR), an intra-pulse modulation recognition approach for Radar Signal is proposed. The approach identifies the modulation of Radar Signals using the techniques of time-frequency analysis, image processing, and convolutional neural network (CNN). Through Cohen class time-frequency distribution (CTFD), the time-frequency images (TFIs) of received Signals are extracted. In order to obtain the high-quality TFIs of received Signals, we introduce a new kernel function for the CTFD, which has stronger anti-noise ability than Choi–Williams time-frequency distribution. A series of image processing techniques, including 2-D Wiener filtering, bilinear interpolation, and Otsu method, are applied to remove the background noise of the TFI and obtain a fixed-size binary image that contains only morphological features of the TFI. We design a CNN classifier to identify the processed TFIs. The proposed approach can identify up to 12 kinds of modulation Signals, including frequency modulation, phase modulation, and composite modulation. Simulation results show that, for 12 kinds of modulation Signals, the proposed approach achieves an overall probability of successful recognition of 96.1% when SNR is −6 dB.
Chihsuan Hsieh - One of the best experts on this subject based on the ideXlab platform.
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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, 2016Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract: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.
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human respiratory feature extraction on an uwb Radar Signal processing platform
International Symposium on Circuits and Systems, 2013Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract: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.
Zhiyu Qu - One of the best experts on this subject based on the ideXlab platform.
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Radar Signal intra pulse modulation recognition based on convolutional neural network
IEEE Access, 2018Co-Authors: Zhiyu Qu, Zhian DengAbstract:In this paper, to solve the problem of the low recognition rate of the existing approaches at low Signal-to-noise ratio (SNR), an intra-pulse modulation recognition approach for Radar Signal is proposed. The approach identifies the modulation of Radar Signals using the techniques of time-frequency analysis, image processing, and convolutional neural network (CNN). Through Cohen class time-frequency distribution (CTFD), the time-frequency images (TFIs) of received Signals are extracted. In order to obtain the high-quality TFIs of received Signals, we introduce a new kernel function for the CTFD, which has stronger anti-noise ability than Choi–Williams time-frequency distribution. A series of image processing techniques, including 2-D Wiener filtering, bilinear interpolation, and Otsu method, are applied to remove the background noise of the TFI and obtain a fixed-size binary image that contains only morphological features of the TFI. We design a CNN classifier to identify the processed TFIs. The proposed approach can identify up to 12 kinds of modulation Signals, including frequency modulation, phase modulation, and composite modulation. Simulation results show that, for 12 kinds of modulation Signals, the proposed approach achieves an overall probability of successful recognition of 96.1% when SNR is −6 dB.
Moeness G Amin - One of the best experts on this subject based on the ideXlab platform.
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Radar Signal processing for elderly fall detection the future for in home monitoring
IEEE Signal Processing Magazine, 2016Co-Authors: Moeness G Amin, Yimin Zhang, Fauzia AhmadAbstract:Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the Signal processing algorithms and techniques involved in elderly fall detection using Radar. A human?s Radar Signal returns differ in their Doppler characteristics, depending on the nature of the human gross motor activities. These Signals are nonstationary in nature, inviting time-frequency analysis in both its linear and bilinear aspects, to play a fundamental role in motion identification, including fall features determination and classification. This article employs real fall data to demonstrate the success of existing detection algorithms as well as to report on some of the challenges facing technology developments for fall detection.
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fall detection based on sequential modeling of Radar Signal time frequency features
IEEE International Conference on Healthcare Informatics, 2013Co-Authors: Xiaoxiao Dai, Moeness G Amin, Yimin Zhang, Bradley S Davidson, Jun ZhangAbstract:Falls are one of the greatest threats to elderly health as they carry out their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be rendered. Radar is an effective non-intrusive sensing modality which is well suited for this purpose. It can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. In this paper, we use micro-Doppler features in Radar Signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler Radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition for feature extraction and fall detection. The extracted features include the principal components of the time-frequency Signal representations. To analyze the sequential characteristics of typical falls, we use the extracted Signal features for training and testing hidden Markov models and support vector machines indifferent falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections.
