-
公开(公告)号：US11257509B2
公开(公告)日：2022-02-22
申请号：US16198255
申请日：2018-11-21
IPC分类号： G10L21/0232 , G06F17/14 , G10L15/20 , G10L21/0216 , G06N7/00 , G10L21/0264
摘要： Techniques for EMD-based signal de-noising are disclosed that use statistical characteristics of IMFs to identify information-carrying IMFs for the purposes of partially reconstructing the identified relevant IMFs into a de-noised signal. The present disclosure has identified that the statistical characteristics of IMFs with noise tend to follow a generalized Gaussian distribution (GGD) versus only a Gaussian or Laplace distribution. Accordingly, a framework for relevant IMF selection is disclosed that includes, in part, performing a null hypothesis test against a distribution of each IMF derived from the use of a generalized probability density function (PDF). IMFs that contribute more noise than signal may thus be identified through the null hypothesis test. Conversely, the aspects and embodiments disclosed herein enable the determination of which IMFs have a contribution of more signal than noise. Thus, a signal may be partially reconstructed based on the predominately information-carrying IMFs to result in de-noised output signal.
-
公开(公告)号：US11169190B2
公开(公告)日：2021-11-09
申请号：US16334345
申请日：2017-09-19
摘要： An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.
-
公开(公告)号：US20190164564A1
公开(公告)日：2019-05-30
申请号：US16198255
申请日：2018-11-21
IPC分类号： G10L21/0232 , G06N7/00 , G06F17/14 , G10L21/0264
摘要： Techniques for EMD-based signal de-noising are disclosed that use statistical characteristics of IMFs to identify information-carrying IMFs for the purposes of partially reconstructing the identified relevant IMFs into a de-noised signal. The present disclosure has identified that the statistical characteristics of IMFs with noise tend to follow a generalized Gaussian distribution (GGD) versus only a Gaussian or Laplace distribution. Accordingly, a framework for relevant IMF selection is disclosed that includes, in part, performing a null hypothesis test against a distribution of each IMF derived from the use of a generalized probability density function (PDF). IMFs that contribute more noise than signal may thus be identified through the null hypothesis test. Conversely, the aspects and embodiments disclosed herein enable the determination of which IMFs have a contribution of more signal than noise. Thus, a signal may be partially reconstructed based on the predominately information-carrying IMFs to result in de-noised output signal.
-
公开(公告)号：US20220120795A1
公开(公告)日：2022-04-21
申请号：US17452815
申请日：2021-10-29
摘要： An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.
-
公开(公告)号：US20190212378A1
公开(公告)日：2019-07-11
申请号：US16334345
申请日：2017-09-19
IPC分类号： G01R23/20
摘要： An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.
-
-
-
-
搜索结果
5 条记录 (耗时 0.012 秒)
