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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張璞曾(Fok-Ching Chong) | |
| dc.contributor.author | Sih-Jie Liu | en |
| dc.contributor.author | 劉思杰 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:31:45Z | - |
| dc.date.available | 2017-02-21 | |
| dc.date.copyright | 2012-02-21 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-02-10 | |
| dc.identifier.citation | [1] Yang, Guang-Zhong, ”Body Sensor Networks,” Imperial College London: Springer-Verlag, 2006, ch1.
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Proceedings of the IEEE, vol. 100, pp. 438-452, Feb. 2007. [10] “Getting Started with the Xilinx Virtex-6 FPGA ML605 Evaluation Kit,” Xilinx Inc., Nov.15, 2010. [11] “ML605 Hardware User Guide,” Xilinx Inc., Jul. 18,2011. [12] “ISE In-Depth Tutorial” Xilinx Inc., Sep. 21, 2010. [13] “PlanAhead User Guide” Xilinx Inc., Ju. 6, 2011. [14] “EDK Concepts, Tools and Techniques” Xilinx Inc., Sep. 21, 2010. [15] “MicroBlaze Processor Reference Guide”, Xilinx Inc., Mar. 1, 2011. [16] “Xcell Journal,” http://www.xilinx.com/publications/xcellonline/, Xilinx magazine. [17] “LogiCORE IP XPS HWICAP,” Xilinx Inc., Jun. 22, 2011. [18] “Partial Reconfiguration User Guide,” Xilinx Inc., May 3, 2010. [19] Pil Woo Chun, “Improving cost-effectiveness using a micro-level static architecture for stream applications,” in Proc. 4th IEEE International Symposium, Electronic Design, Test and Applications, Hong Kong, 2008, pp. 368-373. [20] Oscar Gama, “Towards a Reconfigurable Wireless Sensor Network for Biomedical Applications,” in Proc. IEEE Int. Conf., Sensor Technologies and Applications, 2007, pp. 490-495. [21] “ChipScope Pro 13.1 Software and Core,” Xilinx Inc., Mar. 1, 2011. [22] Michael Caffrey, et al, “SEU Mitigation Techniques for Virtex FPGAs in Space Applications,” in Proc. Military Aerosp. Appl. Program. Logic Devices (MAPLD), 1999, pp. B2.1-B2.11. [23] “Overview of Xilinx JTAG Programming Cables and Reference Schematics for Legacy Parallel Cable III (PC3),” Xilinx Inc., Mar. 28, 2008. [24] Gary M. Friesen, Thomas C. Jannett, Manal Afify Jadallah, Stanford L. Yates, Stephen R. Quint, and H. Troy Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. Biomedical Engineering, vol. 37, pp. 85 – 98, 1990. [25] John F. Murray and Jay A. Nadel, “Textbook of Respiratory Medicine,” Philadelphia, Pa, WB Saunders Co, 1988. [26] Hans Pasterkamp et al, “Respiratory Sound-Advance Beyond the Stethoscope,”Am. J. Respir. Crit. Care Med., vol. 156. pp. 974-987, 1997. [27] John G., Webster, “ Medical Instrumentation Application and Design,” John Wiley & Sons INC., 1998, ch4. [28] Hurst JW., “Current Perspective: Naming of the Waves in the ECG, With a Brief Account of Their Genesis,” Circulation, pp.1937-1942, 1998. [29] N. Meslier, G. Charbonneau, J-L. Racineux, “Wheezes,” European Respiratory Journal, vol. 8, pp. 1942-1948, 1995. [30] A. R. A. Sovijarvi, J. Vanderschoot, and J. E. Earis, “Standardization of Computerized Repiratory Sound Analysis” European Respiratory Review, vol. 10, no. 77, pp. 585, 2000. [31] Y. Shabtai-Musih, J. B. Grotberg, and N. Gavriely, 'Spectral content of forced expiratory wheezes during air, He, and SF6 breathing in normal humans', J Appl. Physiol., vol. 72, pp.629 - 635, 1992. [32] A. Homs-Corbera, R. Jane, J. A. Fiz, and J. Morera, 'Algorithm for time-frequency detection and analysis of wheezes', 22th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society World Congress, 2000. [33] R. J. Riella, P. Nohama, R. F. Borges, A. L. Stelle, “Automatic wheezing recognition in recorded lung sounds,” Proceedings of the Annual International Conference of the IEEE EMBS, vol. 3, pp. 2535-2538, 2003. [34] SA Taplidou, LJ Hadjileontiadis, IK Kitsas, KI Panoulas, T. Penzel, V. Gross, and SMPanas, 'On Applying Continuous Wavelet Transform in Wheeze Analysis,' 26th Annu. Int. Conf. IEEE, Engineering in Medicine and Biology , vol. 2 pp.3832-3835, 2004. [35] Feng Jin, “Adventitious Sounds Identification and Extraction Using Temporal–Spectral Dominance-Based Features,” IEEE Trans. Biomedical Engineering, vol.58, pp. 3078 – 3087, 2011. [36] Chun Yu, “Rapid wheezing detection algorithm for real-time asthma diagnosis and personal health care,” 4th EUROPEAN conference of the INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING, vol.22 pp.264-267, 2009. [37] “System ACE CompactFlash Solution,” Xilinx Inc., Oct. 1, 2008. [38] “Energy Reduction with Run-Time Partial Reconfiguration,” Microsoft Inc., Sep., 2009. [39] “Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [jCirculation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13). [40] Jurgen Becker and R. Hartenstein, “Configware and morphware going mainstream,” J. Syst. Architecture (Special Issue on Reconfigurable Systems), vol. 49, pp. 127-142, Oct. 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66356 | - |
| dc.description.abstract | 無線個人感測器網路(Wireless Body Sensor Networks)應用於生理監測上通常需要在身上裝置多組感測模組，此類系統通常講求可攜性(Portable)、可穿戴性 (Wearable)、低功耗(Low Power Consumption)以及能夠同時即時監測多種不同生理訊號。
以此需求，本研究提出以現場可規劃邏輯閘陣列(Field Programmable Gate Array, FPGA)為基礎的生醫感測平台，此平台可以自行與多種無線生醫裝置連線，感應接收不同種類的生理訊號，如數位資料、醫電訊號、體音訊號等，並進行訊號處理或資料壓縮。此平台在運作的過程中，如果感應到新的裝置，可使用運行中部分重置技術(Run-Time Partial Reconfiguration, RTPR) 在不影響系統正常運作的前提之下對邏輯區塊進行重置，以符合處理新進生理訊號的需求，而在處理生理訊號的過程中如有遇到緊急狀態，亦可更新FPGA中的邏輯區塊為緊急模式。與傳統相同性質的微處理器嵌入式系統相較而言，使用數位積體電路的優勢在於其高速運算能力，能夠解決更為複雜、高資料量的運算需求，且不受微處理器運算能力之限制；尤其在同時處理多個任務的情形之下，使用積體電路能夠達到平行處理，擁有傳統嵌入式系統所無法比擬的優勢，而兼具可重置與積體電路兩大特性的FPGA無疑是最好的選擇。 在此論文中，我們挑選常見的醫電訊號─心電圖及運算量大的體音訊號─肺音來實作，以證明此系統的可靠度和即時訊號處理。我們將心律偵測演算法以及哮鳴音偵測演算法以硬體實現，達到即時的數位訊號處理，同時利用RTPR技術，以最小的硬體成本支出來模擬多位受測者、多種生理訊號同時運行於有限資源下之情境；與傳統相同設計的數位電路相較之下，本系統可節省超過10% 的硬體資源消耗，同時省電模式的設計能夠讓系統在閒置狀態下的消耗功率顯著降低，此外可程式邏輯電路的特性亦使得設計者在設計以及維護上具有極大的彈性及可塑性，證明RTPR技術適用於硬體成本、體積及消耗功率受限的各種生醫感測系統領域。 | zh_TW |
| dc.description.abstract | Wireless Body Sensor Networks (WBSNs) applied on physiological signal monitoring usually consist of several sensor modules that are pasted on human body. These kind of systems are usually requested to have portable, wearable, and low power consumption features, and can real-time monitor several bio-signals simultaneously.
In this thesis, we proposed a bio-signal sensing platform based on Field Programmable Gate Array (FPGA) to fit above requests. The platform can automatically connect to several wireless bio-medical devices to receive various physiological signals, such as digital data, medical electronic signal, sound within body, and etc., and then act signal processing or data compression. If the platform during runtime senses a new bio-medical device, it can utilize the Run-Time Partial Reconfiguration (RTPR) technology of FPGA to dynamically replace the logic blocks during runtim, thus it can change a new algorithm of signal processing for a new coming physiological signal. Furthermore, if the physiological signal during processing is recognized as in emergency situation, the platform will immediately update logic blocks as emergency mode. Compared with traditional microprocessor based embedded system which has similar funtion, the advantage of digital integrated circuits is their high speed computing ability. It let the digital integrated circuit able to solve the needs that more complex and larger amount of data, and not limitation of the computing ability of microprocessor. However, use of the integrated circuit has incomparable advantage with traditional microprocessor based embedded system – the exploitation of parallel working tasks, espically while the system needs to handle multiple tasks simultaneously. And the FPGA has both characters: reconfigurable design and integrated circuit properly be the best choice. In this research, we chose popular medical electronic signal - ECG and high operational sound within body - loung sound to implement. We realized the heart rate and wheeze detection algorithms on FPGA to achieve real-time digital signal processing. And utilizing RTPR to realize running multi-users’ multiple bio-signal processing programs simultaneously in the same resource-limited device that uses least of the hardware resource. Compared with traditional system which has the same digitial circuit design, this system can save more than 10% hardware resource, and the desgin of power saving mode can significant reduce the power consumption. Besides, the feature of programmable logic circuit also provides great flexibility and plasticity in desgin and maintenance aspects. These prove the RTPR technology suit for many bio-signal processing systems whose hardware space, cost and power consumption are limited. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:31:45Z (GMT). No. of bitstreams: 1 ntu-101-R98945026-1.pdf: 5239324 bytes, checksum: a6ad3e78f92d8d0c85dd544e207f67e3 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 摘要 i
Abstract iii List of Figures x List of Tables xiv Chapter 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Objectives 4 1.3 Contributions 5 1.4 Thesis Organization 6 Chapter 2 BACKGROUND AND PLAFORM DESCRIPTION 7 2.1 Software Defined Radio Technologies 7 2.2 FPGA Technologies 9 2.2.1 FPGA Features 9 2.2.2 Hardware Description Language (HDL) 11 2.2.3 Xilinx ISE 12 2.2.4 Xilinx PlanAhead 13 2.2.5 Xilinx EDK tools 14 2.2.6 MicroBlaze Soft-core Processor 16 2.3 FPGA Configuration Interface 18 2.3.1 SelectMAP and JTAG 18 2.3.2 ICAP 19 2.4 Run-time Reconfiguration Technologies 20 2.4.1 Partial Reconfiguration 21 2.4.2 Software Programmable Reconfiguration 23 2.4.3 Design Consideration and Design Flow 24 2.5 Related Works 27 CHAPTER 3 HARDWARE AND SOFTWARE ARCHITECTURE 31 3.1 System Overview 31 3.2 Reconfigurable Hardware 32 3.3 Partial Reconfiguration System Design 34 3.3.1 Static and PR Module Design 35 3.3.2 PR Mechanism 36 3.4 ECG Signal Processing Algorithm in FPGA 39 3.4.1 Concept of ECG Signal 39 3.4.2 First Derivative 41 3.4.3 Software Simulation and Hardware Implementation of Heart Rate Detection 43 3.5 Wheezing Detection Algorithm in FPGA 51 3.5.1 Pulmonary Sound Signal Concept 51 3.5.2 Wheeze Features 52 3.5.3 Correlation-coefficients 55 3.5.4 Software Simulation and Hardware Implementation of Wheezing Detection 59 3.6 Emergency Mode 67 CHAPTER 4 RESULTS AND VERIFICATIONS 68 4.1 Partial Reconfiguration System Implementation 68 4.2 System Testing Results 70 4.2.1 Testing Results of Heart Rate Detection 70 4.2.2 Testing Results of Wheezes Detection 74 4.3 Partial Reconfiguration Verification 77 4.4 System Costs 83 4.4.1 FPGA Resource Cost 83 4.4.2 Power Consumption 86 4.4.3 Physical Floorplan for FPGA Implementation 88 CHAPTER 5 DISCUSSIONS AND FUTURE WORKS 90 CHAPTER 6 CONCLUSION 94 REFERENCES 95 | |
| dc.language.iso | en | |
| dc.subject | 哮鳴音 | zh_TW |
| dc.subject | 感測器網路 | zh_TW |
| dc.subject | 現場可程式邏輯閘陣列 | zh_TW |
| dc.subject | 部分可自動重置 | zh_TW |
| dc.subject | 心律 | zh_TW |
| dc.subject | 部分重置 | zh_TW |
| dc.subject | heart rate | en |
| dc.subject | FPGA | en |
| dc.subject | wheeze | en |
| dc.subject | partial reconfiguration | en |
| dc.subject | ECG | en |
| dc.subject | BSNs | en |
| dc.subject | WSNs | en |
| dc.subject | body sensor network | en |
| dc.subject | wireless sensor network | en |
| dc.subject | partial self-reconfiguration | en |
| dc.title | 基於FPGA的部分可自我重組生醫訊號處理系統 | zh_TW |
| dc.title | FPGA-based Partial Self-Reconfigurable Bio-Signal Processing System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 林伯星(Bor-Shing Lin) | |
| dc.contributor.oralexamcommittee | 盧並裕(Bing-Yuh Lu),林伯?(Bor-Shyh lin) | |
| dc.subject.keyword | 部分重置,部分可自動重置,感測器網路,心律,哮鳴音,現場可程式邏輯閘陣列, | zh_TW |
| dc.subject.keyword | partial reconfiguration,partial self-reconfiguration,wireless sensor network,body sensor network,WSNs,BSNs,ECG,heart rate,wheeze,FPGA, | en |
| dc.relation.page | 99 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-02-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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| ntu-101-1.pdf 未授權公開取用 | 5.12 MB | Adobe PDF |
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