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研究生： 賴秉暄
Lai, Ping-Hsuan
論文名稱： 局部均值分解硬體實現
Hardware Implementation of Local Mean Decomposition
指導教授： 陳培殷
Chen, Pei-Yin
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 60
中文關鍵詞： 局部均值分解 、超大型積體電路
外文關鍵詞： Local Mean Decomposition, VLSI
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    • 在現代許多科學領域中，訊號的分析相當重要，合適的訊號分析方式是必須的。Jonathan S. Smith 於2005年提出了局部均值分解 (Local Mean Decomposition, LMD) 。這是一種自適應性 (adaptive) 訊號處理的演算法，可以有效的處理非穩態及非線性的訊號。經由分解出來的訊號，我們可以從其特性得到如瞬時頻率的資訊。近年來，LMD逐漸被應用於各領域中，在某些方面，需要即時性 (real-time) 且精準的LMD運算。由於LMD需要相當耗時的運算，改進運算效能成了一個重要的議題。在過去LMD的設計尚未在超大型積體電路 (Very-Large-Scale Integration, VLSI) 或是可程式邏輯 (Field Programmable Gate Array, FPGA) 中提出，為了增進運算效能與節省成本，我們對LMD演算法詳細研究並找出其特性來做最佳化。
    在本論文中，我們對LMD提出一個基於超大型積體電路的架構，並配合各模組的最佳化，實現一個低成本、低複雜度且高效能的電路架構，並可根據使用者不同的需求選擇精確度，或是使用情境的不同選擇部分模組的代換，使得LMD的訊號拆解可以實現對使用者最相符的要求。

    In many domains of modern science, signal analysis is very important. An appropriate signal analysis method is essential. Local Mean Decomposition (LMD) was proposed by Jonathan S. Smith in 2005. It is an adaptive signal decomposition algorithm. It is effective in processing not only non-stationary signal but also non-linear signal. According to these decomposed signals, we can get the information such as instantaneous frequency from its features. In recent years, LMD has been gradually applied in many domains. In some aspects, it needs real-time precise calculation in LMD processing. However, it takes time to fulfill the LMD operation. Improving the calculation becomes an important issue. In the past years, the design of LMD had not been proposed in Very-Large-Scale Integration (VLSI) or Field Programmable Gate Array (FPGA). To promote the performance and save the resource, we do research on LMD algorithm in detail to optimize it according to its character.
    We propose the architecture based on VLSI in our design in this thesis. With the optimization of every module, we realize a low-cost, low complexity and high performance architecture. We choose different precisions based on the user choice. Also, the different phenomenon is that the different modules can be replaced so that the user can choose the most coincident requirement of the LMD signal decomposition.

    摘要............................................I Abstract.......................................II Acknowledgements...............................III Table Options..................................VI Figure Options.................................VII Chapter1 Introduction..........................1 1.1 Overview...................................1 1.2 Motivation.................................3 1.3 Organization...............................4 Chapter2 LMD Algorithm Techniques..............5 2.1 Introduction...............................5 2.2 Comparisons between LMD and EMD............6 2.2.1 Computation of Local Mean................6 2.2.2 Component of Decomposed Signals..........7 2.2.3 Instantaneous Frequency Calculation......7 2.2.4 Signal Characteristic....................8 2.3 LMD Algorithm..............................9 Chapter3 Proposed Hardware Design..............20 3.1 System Architecture........................20 3.2 Feature Extractor..........................23 3.3 Interpolation Module.......................27 3.4 Smoothing Module...........................29 3.5 Approximation Module.......................32 3.6 Calculation Sub-Module.....................35 Chapter4 Experiment Result.....................39 4.1 Precision Issue............................39 4.2 Simulation Result..........................39 4.2.1 Test data 1..............................40 4.2.2 Test data 2..............................44 4.2.3 Test data 3..............................48 4.2.4 Test data 4..............................52 4.3 Comparison.................................56 Chapter5 Conclusion and Future Work............58 5.1 Conclusion.................................58 5.2 Future Work................................58 Reference......................................59

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