| Summary: | 博士 === 國立臺灣海洋大學 === 環境生物與漁業科學學系 === 99 === Bioacoustics has drawn a great deal of attentions for monitoring system health situation of organism, population, community, and species diversity with it’s reliable, non-invasive characteristics. But it also has different challenges and complexities organism’s very large and long-term time series data of biology, such as signal of electrocardiogram (ECG), electroencephalography (EEG), interbeat interval (RR), electro-oculography (EOG), long-term temperature changes data, or 1/f noise of ecology such as population dynamics etc. Although a researcher can use approximate entropy (AE), sample entropy (SE) or multi-scale entropy (MSE) to examine the complexity of the huge data, unfortunately, the researcher must first solve those high difficulties, expensive execution time in computation of the MSE or AE. In automatic monitoring species diversity, an ecologist must takes the challenges to reduce the sound complexity, getting the fine structure sound and keep the background sound of habitat from the original sound with noise recorded in wildlife. To solve the limitations of bioacoustics, the author suggests a new algorithm called sliding kd tree (SKD) to fast up the computational time of MSE.
The author in this thesis first try to compute multiscale entropy with orthogonal range search of kd tree (KD), to develop a new algorithm by SKD, and successful to reduce the computation time cost of MSE from O(N2) to O(N3/2) when d=2. Moreover, if used in digital typed data such as biomedical data, the execution time can advance be improved to O(N), which is fast enough for on line diagnosis. Besides, he also successful applied MSE in biomedical signal of sleeping EEG online diagnosis and achieved to the correct rate of 76% or higher before any tune, and other areas such as machine vibration and optimal two dimensional orthogonal range search in VLSI automated design.
To solve the noise problem of ecology monitoring, the author suggests independent component analysis (ICA) to separate the focal animal and back ground habitat sound. He first reviewed the acoustic signal filter processing, experimenting with wildlife frog’s sound, suggests filtering to enhance the loudest or louder sound, and suppress the other sounds, which is based on ICA method and using two microphone to record the focal animal’s sound and then use ICA to separate the focal sample sound and the other noises for high quality sample sound.
With the new algorithm of MSE, SE and AE and new ICA filter in wildlife, bioacoustics can then get more chance to be implemented in more areas of organism to community. This may lead humanity easier to discover more biology and ecology secret, and be helpful to manage the biological resources and the sustainable development of nature resources.
|
|---|
