生醫訊號與影像無失真壓縮器積體電路設計

VLSI Implementation of a Lossless Biomedical Signal and Image Compressor Design

祁存廣(Tsun-Kuang Chi)

指導教授 ： 陳世綸

中原大學/電機資訊學院/電子工程研究所/碩士(2015年)

https://doi.org/10.6840/CYCU.2015.00489

摘要

本論文提出將粒子群最佳化演算法應用在生醫訊號及影像的壓縮演算法中，並將此演算法實現於積體電路上。鑒於現今可攜式裝置的使用量變大，而可攜式裝置中的健康偵測功能也越來越普及，便有了透過可攜式裝置來提供健康照顧的服務。這項服務將建立在服務端及客戶端中不斷的資訊互通，在生醫訊號非常龐大的資料量下，無線網路的頻寬可能無法負荷；因此，在可攜式裝置中加入壓縮器不但大幅減少傳輸數據和傳輸時間，也同時降低了可攜式裝置傳輸過程所需的耗能，使可攜式裝置使用時間增加。在生醫訊號及影像趨勢的預測中，使用基於粒子群最佳化演算法的優化步驟尋找最佳的參數來校正一般預測所造成過大的誤差，使預測過後的值能夠近似於零，使壓縮率上升；熵編碼的部分參考了霍夫曼編碼，將編碼中的單元分成兩個階級，在第一階以區域性劃分預測值，待預測值被第一階段的分類後再以不同的方式進行編碼，此編碼方式能夠涵蓋所有訊號的範圍，並改善在低機率誤測值造成壓縮率下降的編碼方式。在壓縮率的部分，對比於先前的電路設計，生醫訊號透過壓縮器可以提高約6%的壓縮率，影像的部分，則是驗證此壓縮器在影像方向的可行性，在成果的部分會列出所有實驗過程中的壓縮數據；此演算法以生醫訊號及影像兩個部分，分別被實現在積體電路中，以0.18微米及90奈米的CMOS製程，能操作於200MHz，生醫訊號壓縮電路的gate count為1.9k，影像壓縮電路為5.16k，對比於先前的電路設計，分別降低了8.2%及6%。

關鍵字

心電圖 ； 模糊決策 ； 健康照護 ； 優化器 ； 霍夫曼 ； 無失真 ； 積體電路

並列摘要

This thesis presents a hardware-oriented lossless biomedical signal and image compression algorithm for very large-scale integration (VLSI) circuit design. In order to achieve the target of high performance and low complexity, a novel prediction method based on fuzzy decision and concept of particle swarm optimizer (PSO) was developed. The accuracy of prediction was advanced efficiently by using the optimization algorithm based on PSO to find the optimal parameters which provided 64 situations for the fuzzy decision. In addition, a novel low-complexity and high-performance entropy coding algorithm based on Huffman coding was developed, which used one limited Huffman coding to encode the main region and five-region codes to encode the extending regions. The average compression rate of whole MIT-BIH arrhythmia database was up to 2.84 by combing the proposed fuzzy PSO based prediction and modified Huffman entropy coding techniques. An lossless image compressor was also realized in this thesis. A hardware sharing technique was used to reduce hardware cost in this design. The VLSI architecture of this study contained only 1.9-K gate counts for biomedical signal compressor and 5.1-K for image compressor. These two compressors are synthesised by using TSMC 0.18μm and 90-nm CMOS process, respectively. Compared with previous low-complexity designs, this work not only improved the average compression rate by over 6.4% but also reduced at least 8.2% and 6 % gate counts for lossless biomedical and image compression designs, respectively.

並列關鍵字

ECG ； Fuzzy Decision ； Healthcare ； Optimazation ； Huffman ； Lossless ； VLSI

參考文獻

[1] S.L. Chen, H. Y. Lee, C. A. Chen H. Y. Huang, and C. H. Luo,“Wireless body sensor network with adaptive low power design for biometrics and healthcare Application”. IEEE Systems Journal, vol.3, no.4, pp. 398-409, Dec. 2009.

[2] C. A. Chen, S. L. Chen, H. Y. Huang, and C. H. Luo, “An efficient micro control unit with a reconfigurable filter design for wireless body sensor networks (WBSNs)”. Sensors, 2012, 12, (12), pp.16211-16227.

[3] E. Chua, and W. C. Fang, “Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems”. IEEE Trans. Consum. Electron. 2011, 57, (1), pp. 267-273.

[4] N. Memon, X. Kong, and J. Cinkler, “Context-based lossless and near-lossless compression of EEG signals”. IEEE Trans. Inf. Technol. Biomed., vol. 3, no. 3, pp. 231-238, Sep. 1999.

[5] Z. Arnavut, “ECG signal compression based on Burrows-Wheeler transformation and inversion ranks of linear prediction,” IEEE Transactions on Biomedical Engineering, vol.54, no.3, pp.410-418, Mar. 2007.