为有效提高体域网的实时性和降低体域网的功耗,提出一种基于块稀疏贝叶斯学习的体域网心电压缩采样方法。该方法在体域网框架下,利用压缩采样理论,在体域网的传感节点利用二进制随机观测矩阵对心电信号进行压缩采样,远程监护中心获得采...为有效提高体域网的实时性和降低体域网的功耗,提出一种基于块稀疏贝叶斯学习的体域网心电压缩采样方法。该方法在体域网框架下,利用压缩采样理论,在体域网的传感节点利用二进制随机观测矩阵对心电信号进行压缩采样,远程监护中心获得采样值之后,利用块稀疏贝叶斯学习重构算法和离散余弦稀疏变换矩阵对心电信号进行重构。实验结果表明,当心电信号压缩率在70%~90%时,基于块稀疏贝叶斯学习的重构算法要比其他重构算法的重构信噪比高出3 d B^21 d B。该方法能有效减少数据采样,减轻后续的数据存储、数据传输压力,提高体域网的实时性。同时该方法具有功耗低,易于硬件实现的优点。展开更多
针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块...针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块稀疏贝叶斯学习边界优化(block sparsEbayesian learning-thEbound optimization,BSBL-BO)算法的DME脉冲干扰抑制方法。首先,利用OFDM接收机空子载波不传输有用信号的特点构造针对DME脉冲干扰信号的压缩感知模型;然后基于BSBL-BO算法重构DME脉冲干扰信号;最后在时域进行干扰消除。仿真结果表明,该方法比已有的脉冲干扰抑制方法具有更高的重构精度和更快的运算速度,进一步降低了OFDM接收机的误比特率,提高了L-DACS1系统前向链路传输性能。展开更多
L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)是未来面向航路阶段的空地数据链路,其工作频段部署在两个测距仪(distance measure equipment,DME)工作频段之间,为了消除测距仪产生的高功率脉冲信号对L-D...L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)是未来面向航路阶段的空地数据链路,其工作频段部署在两个测距仪(distance measure equipment,DME)工作频段之间,为了消除测距仪产生的高功率脉冲信号对L-DACS系统前向链路正交频分复用接收机的干扰,本文提出基于扩展稀疏贝叶斯-边界优化(extended block sparse Bayesian learning-boundary optimization,EBSBL-BO)算法的高功率DME脉冲干扰抑制方法。首先,利用L-DACS系统正交频分复用接收机的空子载波建立DME干扰信号压缩感知模型;然后,基于EBSBL-BO算法对DME信号进行重构;最后将高功率DME脉冲信号在时域消除。仿真结果显示:本文算法与其他稀疏贝叶斯重构算法相比,本文算法DME脉冲信号重构精度更高,正交频分复用接收机误码率更低,可有效改善L-DACS系统正交频分复用接收性能。展开更多
The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve ex...The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient’s mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.展开更多
文摘为有效提高体域网的实时性和降低体域网的功耗,提出一种基于块稀疏贝叶斯学习的体域网心电压缩采样方法。该方法在体域网框架下,利用压缩采样理论,在体域网的传感节点利用二进制随机观测矩阵对心电信号进行压缩采样,远程监护中心获得采样值之后,利用块稀疏贝叶斯学习重构算法和离散余弦稀疏变换矩阵对心电信号进行重构。实验结果表明,当心电信号压缩率在70%~90%时,基于块稀疏贝叶斯学习的重构算法要比其他重构算法的重构信噪比高出3 d B^21 d B。该方法能有效减少数据采样,减轻后续的数据存储、数据传输压力,提高体域网的实时性。同时该方法具有功耗低,易于硬件实现的优点。
文摘针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块稀疏贝叶斯学习边界优化(block sparsEbayesian learning-thEbound optimization,BSBL-BO)算法的DME脉冲干扰抑制方法。首先,利用OFDM接收机空子载波不传输有用信号的特点构造针对DME脉冲干扰信号的压缩感知模型;然后基于BSBL-BO算法重构DME脉冲干扰信号;最后在时域进行干扰消除。仿真结果表明,该方法比已有的脉冲干扰抑制方法具有更高的重构精度和更快的运算速度,进一步降低了OFDM接收机的误比特率,提高了L-DACS1系统前向链路传输性能。
文摘The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient’s mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.
