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Wavelet packet based algorithm for identification of quasi-periodic signals

机译:基于小波包的准周期信号识别算法

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We present a generic approach that identifies and differentiates among signals for wide range of problems. Originally our algorithm was developed to detect the presence of a specific vehicle belonging to a certain class via the analysis of the acoustic signals emitted while it is moving. A crucial factor in having a successful detection (no false alarm) is to construct signatures built from characteristic features that enable to discriminate between the class of interest and the residual information such as background. We construct the signatures of certain classes by the distribution of the energies among blocks which consist of wavelet packet coefficients. We developed an efficient procedure for adaptive selection of the characteristic blocks. We modified the CART algorithm in order to utilize it to be a decision unit in our scheme. However, this technology, which has many algorithmic variations, can be used to solve a wide range of classification and detection problems which are based on acoustic processing and, more generally, for classification and detection of signals which have near-periodic structure. We present results of successful application of the properly modified algorithm to detection of early symptoms of arterial hypertension in children via real-time analysis of pulse signals.
机译:我们提出了一种通用方法,可以针对各种问题识别并区分信号。最初,我们的算法经过开发,可以通过分析行驶中发出的声信号来检测特定类别的特定车辆的存在。成功检测(无误报)的一个关键因素是构造从特征特征构建的签名,这些特征能够区分感兴趣的类别和残留信息(例如背景)。我们通过由小波包系数组成的块之间的能量分布来构造某些类的签名。我们为特征块的自适应选择开发了一种有效的程序。我们修改了CART算法,以便将其用作我们方案中的决策单元。然而,具有许多算法变化的该技术可以用于解决基于声学处理的更广泛的分类和检测问题,并且更普遍地,该技术用于具有近周期结构的信号的分类和检测。我们介绍了通过对脉冲信号进行实时分析,将经过适当修改的算法成功应用于儿童动脉高压早期症状检测的结果。

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