Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano techno...Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.展开更多
Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises suc...Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not been detected successfully in author’s previous studies.展开更多
It is well known that the adaptive line enhancer (ALE) is effective detector of CW signal with unknown frequency in the background of white noise. The system processing gain of ALE, when the LMS algorithm is used, how...It is well known that the adaptive line enhancer (ALE) is effective detector of CW signal with unknown frequency in the background of white noise. The system processing gain of ALE, when the LMS algorithm is used, however, is not satisfactory because of the presence of iterative noise and weight noise. In this paper, the coherent accumulation algorithm of ALE, called as ALECA, is suggested. It is shown that the adaptive filter employing this new algorithm possesses the ARMA structure. The experimental results also show that the processing gain of ALECA is about 14dB higher than that of conventional ALE.展开更多
对舰船辐射噪声中线谱成分的检测,是被动声纳对目标定位识别中的关键技术。应用自适应线谱增强器(Adaptive Line Enhancer,ALE),可以有效从宽带背景噪声中提取出舰船辐射噪声的线谱成分。基于最小均方误差(Least-Mean-Square,LMS)的ALE...对舰船辐射噪声中线谱成分的检测,是被动声纳对目标定位识别中的关键技术。应用自适应线谱增强器(Adaptive Line Enhancer,ALE),可以有效从宽带背景噪声中提取出舰船辐射噪声的线谱成分。基于最小均方误差(Least-Mean-Square,LMS)的ALE收敛速度慢,提出应用基于快速横向滤波(Fast Transversal Filter,FTF)的ALE对舰船辐射噪声的非线谱成分进行抑制。仿真结果显示,FTF算法自适应线谱增强器收敛速度快,输出精度较高,有利于对舰船辐射噪声线谱成分的提取。展开更多
文摘Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.
文摘Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However, this component is very small in the early stage of the fault and masked by various noises such as gear meshing components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not been detected successfully in author’s previous studies.
文摘It is well known that the adaptive line enhancer (ALE) is effective detector of CW signal with unknown frequency in the background of white noise. The system processing gain of ALE, when the LMS algorithm is used, however, is not satisfactory because of the presence of iterative noise and weight noise. In this paper, the coherent accumulation algorithm of ALE, called as ALECA, is suggested. It is shown that the adaptive filter employing this new algorithm possesses the ARMA structure. The experimental results also show that the processing gain of ALECA is about 14dB higher than that of conventional ALE.
文摘对舰船辐射噪声中线谱成分的检测,是被动声纳对目标定位识别中的关键技术。应用自适应线谱增强器(Adaptive Line Enhancer,ALE),可以有效从宽带背景噪声中提取出舰船辐射噪声的线谱成分。基于最小均方误差(Least-Mean-Square,LMS)的ALE收敛速度慢,提出应用基于快速横向滤波(Fast Transversal Filter,FTF)的ALE对舰船辐射噪声的非线谱成分进行抑制。仿真结果显示,FTF算法自适应线谱增强器收敛速度快,输出精度较高,有利于对舰船辐射噪声线谱成分的提取。
