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系統識別號 U0026-0909201615212500
論文名稱(中文) 腦波訊號壓縮系統基於適應性壓縮感知演算法
論文名稱(英文) EEG Compression System based on Compressive Sensing with Adaptive Algorithm
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 105
學期 1
出版年 105
研究生(中文) 謝宇唐
研究生(英文) Yu-Tang Hsieh
學號 N26031380
學位類別 碩士
語文別 中文
論文頁數 72頁
口試委員 指導教授-雷曉方
口試委員-郭致宏
口試委員-邱瀝毅
口試委員-張名先
中文關鍵字 壓縮感知  腦波訊號壓縮  霍夫曼編碼 
英文關鍵字 Compressive Sensing  EEG Compression  Huffman Coding 
學科別分類
中文摘要 壓縮感知(Compressed sensing)是近年來一種新穎的資料壓縮技術,有別於傳統壓縮方式較複雜的編碼端,期望能夠達到降低在編碼端的運算量,致力於將此方法發展在可攜式裝置上;另外,生理訊號是目前普遍重視的一項議題,為了避免在訊號處理的過程中產生龐大的資料量,生理訊號壓縮便成為重要的一部分,本篇論文提出用於壓縮腦波訊號之壓縮感知基於適應性能量封裝效率演算法,透過將腦波訊號先進行前處理步驟,以達到降低還原誤差的效果,壓縮感知理論提出若要使原始訊號能夠完美還原,則原始訊號必須先有一定的稀疏性。由於腦波訊號本身不具稀疏性,在過去文獻中會將其利用某些稀疏基底做轉換如離散餘弦轉換、離散小波轉換,本篇論文是基於此條件下在更進一步提高訊號的稀疏性。我們的做法是利用能量封裝效率演算法來計算出一個閥值,將已經轉換到稀疏基底上的稀疏係數做閥值設定,也就是稀疏訊號上只要低於此閥值就將其值設為零,藉此可以進一步提高訊號稀疏性,根據本論文模擬結果顯示還原誤差與其他文獻比較可減少約15%。本論文中除了壓縮感知以外還會結合霍夫曼編碼來做訊號壓縮,因為霍夫曼編碼屬於無失真性編碼(Lossless Coding),故能在不影響還原誤差的情況下將壓縮率提高。
英文摘要 Compressive sensing is a novel technique for developing data compression in recent years. Unlike conventional compression method is more complex encoding-end, we expect to be able to reduce the amount of computation in the encoding-end. We are committed to the development of this method on a portable device. In addition, the physiological signal is a current subject of widespread attention. In order to avoid the huge amount of data in the course of signal processing, physiological signal compression has become important. This thesis proposes an EEG compression algorithm using compressive sensing based on adative energy packing efficiency algorithm. We will preprocess EEG signals before data compression to achieve low reconstruction error. Compressive sensing theory indicate that original signal must be sparse enough to reconstruct perfectly. Since EEG is non-sparse in the time domain, it will be transformed by the other sparse basis in the previous work. We can make EEG signals more sparse on the previous foundation. The sparse coefficients which were transformed by sparse basis are thresholded using a threshold base on a desired energy packing efficiency. That is to say, once the point value is smaller than the threshold value, the point will be set to zero. Furthermore, it can improve the sparsity of the coefficients. The proposed architecture for EEG signals can reduce the percentage mean square error by 15% compared to other papers. This thesis proposes an EEG data compression by using compressive sensing. Besides, the architecture includes Huffman Coing. Huffman Coing is a lossless compression algorithm. So, we can enhance compression ratio and make reconstruction error do not decrease.
論文目次 中文摘要 I
EXTENDED ABSTRACT III
誌謝 IX
目錄 XI
表目錄 XIII
圖目錄 XIV
第一章 緒論 1
1.1. 研究背景 1
1.2. 腦電圖介紹 3
1.3. 論文章節組織 8
第二章 相關文獻回顧與探討 9
2.1. 資料壓縮介紹 9
2.2. 壓縮感知理論 11
2.2.1. 稀疏表示 12
2.2.1.1 離散小波轉換 15
2.2.2. 測量矩陣 17
2.2.3. 重建訊號 19
2.2.3.1正交匹配追蹤演算法 21
2.3. 相關文獻探討 25
2.3.1. Rajoub et al. 針對ECG訊號編碼演算法[26] 25
第三章 腦波訊號壓縮演算法 29
3.1. 演算法架構之流程簡介 29
3.2. 壓縮端流程 30
3.2.1. 離散餘弦轉換基底 31
3.2.2. 前處理 32
3.2.2.1. 基於能量封裝效率演算法之稀疏化處理 32
3.2.2.2. 最佳誤差之稀疏度統計 33
3.2.2.3. 適應性演算法 36
3.2.3. 測量矩陣 39
3.2.4. 量化 40
3.2.5. 霍夫曼編碼 41
3.3. 解壓縮端流程 46
3.3.1. 霍夫曼解碼 46
3.3.2. 還原演算法 47
3.3.3. 反離散餘弦轉換 49
第四章 演算法分析與結果比較 51
4.1. 稀疏度比較 51
4.2. 效能分析與比較 56
4.2.1. 測量指標 56
4.2.2. 模擬結果與比較 57
4.3. 功率頻譜分析 62
第五章 結論與未來展望 69
參考文獻 71
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