應用於無線身體感測節點之物件偵測與微控制器設計

段閔鈞

doi:10.6840/cycu201700529

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应用於无线身体感测节点之物件侦测与微控制器设计

VLSI Design of an Object-Detector and Micro Control Unit for Wireless Body Sensing Nodes

段闵钧 , 博士　　导师：陈世纶　　

英文 DOI： 10.6840/cycu201700529

无线身体感测网路；自适应性区块分割演算法；物件侦测器；非对称性加密演算法；微控制器； Wireless Body Sensor Networks (WBSNs) ； Adaptive Block Partition Decision Algorithm ； Object-Detector ； Asymmetric Encryption Algorithm ； Micro Control Unit

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摘要 │ 参考文献 (88) │ 国际计量

摘要〈TOP〉

本论文提出一个应用於无线身体感测节点之物件侦测与微控制器设计，加密功能的微控制器电路以及即时的物件侦测提供了无线身体感测网路节点更多更有效的解决方案，尤其是长期的远距医疗的监控服务和个人隐私资料的保护上拥有更多的保障，此设计主要包含两部份：自适应性区块分割物件侦测器和多功能、非对称性的微处理器设计，无线身体感测节点原理是从数位镜头取得视讯影像或是藉由特定感测器侦测人类生理讯号後，透过一个无线通讯协定传送至远端中央控制系统，无线身体感测网路就是由许多的感测节点所组成，为了提供相关专业人员高画质影像和无失真的生理讯号进行即时的判断，并同时满足数位影像与生医讯号处理的需求，一个创新架构的微控制器的设计被使用来实现应用於无线感测网路节点。
    无线身体感测节点的应用包含视讯镜头感测节点，透过此感测节点可侦测远端环境是否有物件进入，达到安全监控的应用，相较於之前的论文，本论文提出了一个较高表现、与较低复杂度的物件侦测器的电路，其架构是基於自适应性区块分割演算法，由於传统的区域差异演算法易造成物件锯齿状失真，所提出的创新区域分割决定演算法，将区域分为数个小区域进行细部的影像分析，最後将结果即时输出至显示萤幕上，透过适当的区块分割，影像中的物件边缘能够有效的被抓取，藉以增强影像局部的细节和提高物件侦测精准度，并将超大型积体电路的设计应用导入物件侦测的领域，利用电路特有的管线设计与硬体共用技术，发展出一个能够即时处理的物件检测器，且此电路为多级管线与90奈米制程的设计，为了能够达到即时进行物件侦测的需求，自适应性的区块分割电演算法成功於硬体实现。
    由於无线身体感测感测节点中的资料利用无线的方式传输至远端中央控制系统，为了能够有效节省系统频宽及延长无线硬体装置讯号纪录使用时间，且同时能进行资料的即时处理和适当保护使用者的个人隐私资料，相比先前的微控制器设计，本论文所提出的创新架构的微控制器设计具有成本考量、多功能以及增强安全性的特点，此微控制器是由非同步介面、多通道感测电路控制器、暂存器缓冲区、硬体共享式滤波器、无失真压缩器、加密编码器、错误更正电路、通用非同步收发介面、电源管理状态机和QRS复合波侦测器，此微控制器主要功能为:处理数位资料、生医讯号与系统间的沟通平台，其中在硬体共享式滤波器被设计用於减少晶片面积，并提供三种类型的滤波器以获得数位信号中的更多资讯;无失真压缩器是为了减少误诊的可能性并降低传输功率，本论文开发了包括自适应趋势预测器和可扩展混合熵编码器;加密编码器的非对称架构，可以在无线传输期间充分保护个人资讯，此外，将QRS复合波检测器的架构并入MCU设计中，能够获得更多生医讯号的资讯，像是心跳等常见的生理讯号。
    物件侦测器与微控制器的架构进行前瞻的90nm电路合成，在此制程中分别包含了6,990逻辑闸与7,910逻辑闸，相较於之前的物件侦测技术，本论文所提出的物件侦测器减少14.3%以上的面积，且操作频率最高能达374.5 MHz，功率消耗仅为1.63 mW，由於在电路架构上使用了流水线设计，这样的设计可以达到3840 × 2160的吞吐量，这是比先前设计所使用的各种不同的模拟平台还要更高速处理，这样的高速率的积体电路设计非常适合应用在4K2K高画质的即时影像处理中，本论文所提出的侦测器是有较高的操作频率、较高的解析度以及较高速的侦测速率;本论文所提出之微控制器操作频率最高为200 MHz其功率消耗为1.33 mW，相较於之前的微控制器技术，本论文所提出的微控制器至少增加了两种功能，同时也具有更低的成本、更高的压缩率、更多的功能、更高的安全性; 最後，高效能、低成本的影像处理器与创新架构的低功耗微控制器设计不仅非常适合无线身体感测节点之应用，也非常适合尺寸小、重量轻的穿戴式装置科技。

并列摘要〈TOP〉

This thesis presents an object-detector and a micro control unit (MCU) for wireless body sensing nodes. It includes an object detector of adaptive block partition decision and a high efficiency, multi-function and asymmetric MCU design. In wireless body sensing nodes solutions, the digital images are taken by image sensors or difference physical signals are captured by various analog sensors. These specified signals are process in digital format and then transmitted to the remote central control system by the wireless communication system. Wireless body sensor networks (WBSNs) is exactly consists by many sensing nodes. In order to provide high resolution images and lossless physical signals for medical expert diagnosing, a high efficiency image processor and a novel architecture of MCU design are proposed for the wireless body sensing nodes.
  The architecture of proposed object detector is based on adaptive block partition decision (ABPD) algorithm. Although block-based algorithm separates a frame into many blocks can promote the accuracy of the object detection, the edge information will be missed due to the blocking effect. Hence, this thesis develop an edge detection technique to enhance the object detection ability by divided the blocks into numerous sub-blocks. In addition, the liquid-crystal-display (LCD) panel can also display the detected objects by the human eyes to check the result visibly. In contrast to related works, it had the benefits of low complexity, local detail enhancement, and fewer block filter effects. Moreover, shapes in each block can be adjusted according to the predefined judgement conditions to increase the accuracy of the proposed ABPD algorithm. To be able to achieve objection detecting in real time, the proposed ABPD algorithm was realized by using a VLSI technique. The proposed object-detector design achieved the processing of 30 frame- per-second (fps) for 4K2K resolution in the video applications of displaying.            
  Furthermore, this thesis proposed MCU design consists of an asynchronous interface, a multi-sensor controller, a register bank, a hardware-shared filter, a lossless compressor, an encryption encoder, an error correct coding (ECC) circuit, a universal asynchronous receiver/transmitter (UART) interface, a power management, and a QRS complex detector.  A hardware-sharing technique was added to reduce the silicon area of a hardware-shared filter and provided functions in terms of high-pass, low-pass and band-pass filters according to the usages of various body signals. The QRS complex detector was designed for calculating QRS information of the ECG signals. In addition, the QRS information is helpful to obtain the heart-beats. The lossless compressor consists of an adaptive trending predictor and an extensible hybrid entropy encoder, which provides various methods to compress the different characteristics of body signals adaptively. Furthermore, an encryption encoder based on an asymmetric cryptography technique was designed to protect the private physical information during wireless transmission. 
  The VLSI architecture of the proposed object-detector design contains 6.99-K gate counts. Its power consumption is 1.63 mW and its operating frequency is to 374.5 MHz by using a 90 nm CMOS technology. Compared with previous object-detector designs, the proposed design not only achieves reduction of more silicon area, but also increase the processing throughput, and accuracy of objet-detection for real-time video display. In addition, the proposed MCU design in this thesis contained 7.61-K gate counts and consumed 1.33 mW when operating at 200 MHz by using a 90 nm CMOS process. Compared with previous MCU designs, this work has benefits of increasing the average compression rate by over 12% in ECG signal, providing body signals analysis, and enhancing security of the WBSNs. Finally, the high performance and low-cost VLSI architecture of an object detector and MCU design can not only favorable used in wireless sensing nodes, but also suitable for wearable technique and other fields to make more contribution for the world.

参考文献 ( 88 ) 〈TOP〉

[1] M. Naeem, U. Pareek, D. C. Lee, A. S. Khwaja, and A. Anpalagan, “Wireless resource allocation in next generation healthcare facilities,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1463-1474, March 2015.
连结：
[2] H.-C. Chuang, C.-Y. Shih, C.-H. Chou, J.-T. Huang, and C.-J. Wu, “The development of a blood leakage monitoring system for the applications in hemodialysis therapy,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1515-1522, March 2015.
连结：
[3] P. J. F. White, B. W. Podaima, and M. R. Friesen, “Algorithms for smartphone and tablet image analysis for healthcare applications,” IEEE Access, vol. 2, pp. 831-840, August 2014.
连结：
[4] D. Kwon, M. R. Hodkiewicz, J. Fan, T. Shibutani, and M. G. Pecht, “IoT-based prognostics and systems health management for industrial applications,” IEEE Access, vol. 4, pp. 3659-3670, 2016.
连结：
[5] J.-F. Cheng, J.-C. Chou, T.-P. Sun, S.-K. Hsiung, and H.-L. Kao, “Study on a multi-ions sensing system for monitoring of blood electrolytes with wireless home-care system,” IEEE Sensors Journal, vol. 12, no. 5, pp. 967-977, May 2012.
连结：

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