基于IEEE 1451.5的RFID及无线视频传感系统建模与实现
|
摘要
论文研究基于IEEE1451.5的RFID及无线视频传感系统建模与实现，对加快无线智能传感技术发展、促进现代制造业智能化，具有重要学术价值与实际意义。研究工作得到中国博士后科学基金(2012M511806)、广东省高等学校高层次人才项目(粤教师函[2010]79号文)资助。
论文从基于IEEE1451.5的传感系统建模技术、RFID信息快速获取方法、无线视频传感系统编码技术等方面，论述国内外研究进展，确定研究内容。论文主要工作包括：
⑴深入开展基于IEEE1451.5的传感系统UML与Petri网联合建模方法研究，实现模型可操作性验证与动态性能评估，提高系统开发速度与质量。基于层次化信息流动态建模方法建立了基于IEEE1451.5的RFID与无线视频传感系统通信机制模型，从宏观角度描述RFID与无线视频传感系统静态用例模型、信息交互模型与系统整体物理实现；通过制定UML与Petri网转换规则，实现UML图到Petri网模型转换，分析验证模型结构可操作性与性能；研究基于IEEE1451.5WiFi协议WTIM接入机制与数据传输过程有色Petri网模型，采用CPN Tools工具，分析模型活性、有界性、可达性。最后，还研究WTIM数据流CPN模型结构参数配置对系统性能指标影响规律，提高系统设计合理性与性能。
⑵探讨基于IEEE1451的RFID系统标签数估算与多标签防碰撞方法，提高信息获取速度。针对区间搜索标签数估算法运算量较大问题，提出基于粗精二次搜索(CFDDE)的标签数估算方法，采用由粗至精搜索思想，无需或仅需少量乘法运算估算标签数。第一次搜索通过另外一种评价指标，用加减运算消除切比雪夫不等式估算法平方、开方运算，同时减少第二次搜索范围，一般可使第二次搜索范围减少约90%，估算时间比切比雪夫不等式法减少约54%。进一步研究基于CFDDE的QCFDDE多标签防碰撞方法，推导出帧长度与标签数关系式，针对所求得帧长度通常不为2整数次方、无法直接应用且不能采用最近整数法问题，利用Q值边界条件，以最大信道利用率为原则，研究基于迭代法的Q值选取方法，提高系统读写效率。仿真结果表明，基于QCFDDE多标签防碰撞方法标签功耗比基于碰撞最小值、泊松分布、空闲时隙法标签数估算的防碰撞方法分别减少26.7%、15.4%、15.4%，信道利用率分别提高6.9%、4.2%、4.3%。
⑶研究一种适用于无线视频传感系统的PDP-AAN JPEG图像压缩方法，采用多边形DCT裁剪方法(PDP)增加裁剪模式，结合AAN快速DCT算法优化JPEG压缩过程。PDP裁剪方法针对JPEG图像压缩DCT变换运算量较大、裁剪模式少、应用灵活性不强等问题，通过扩展DCT系数矩阵选取区域，增加裁剪模式，有利于图像压缩质量宽范围细致调节。应用BIC信息准则选取PDP裁剪系数，避免主观因素影响，权衡图像压缩质量与复杂度关系，应用灵活性得到增强。将AAN缩放因子与JPEG量化过程结合，推导得AAN量化表，优化JPEG压缩过程。仿真结果表明，PDP-AAN JPEG图像压缩方法，在图像压缩质量减少0.2%下，二维DCT乘法、加法运算个数分别减少3.8%、6.0%，量化、“Z”字形排列计算量分别减少32.8%、32.8%。
⑷针对资源受限的IEEE1451.5无线视频传感系统，提出一种基于隔块角线编选分离(IDSCS)的无线视频编码方法。其特点在于，采用PDP-AAN JPEG图像压缩方法降低图像压缩运算量，增强应用灵活性；利用隔块角线像素采样差异值自适应更新视频帧，增强编码适应性；将复杂度较大的编码参数选取与动态更新算法从资源受限编码节点转移至运算能力较强的视频集成中心，实现编选分离，降低编码节点复杂度。研究基于IEEE1451.5的无线视频系统性能提升方法，将JPEG文件头与TEDS用户自定义信息结合减少图像数据传输量，并通过优化配置无线视频节点结构参数提高进程并行处理能力。仿真结果表明，基于IDSCS无线视频编码方法与M-JPEG方法相比，解码图像PSNR降低3%下，数据传输量减少约55%。
论文还开展基于IEEE1451.5的RFID系统、无线视频系统实现与性能验证实验，探讨系统应用与效果。实验表明，将RFID及无线视频传感系统建模方法、CFDDE标签数估算方法、QCFDDE多标签防碰撞方法、PDP-AAN JPEG图像压缩方法、隔块角线编选分离视频编码等方法，综合应用到基于IEEE1451.5的RFID及无线视频传感系统建模与实现，能有效提高系统整体性能与开发速度。通过准确度较高的CFDDE标签数估算法，能提高QCFDDE防碰撞方法性能，RFID系统读写性能比基于泊松分布标签数估算法的防碰撞方法提高约17%；基于PDP-AAN JPEG图像压缩方法，能通过裁剪系数调整图像压缩质量，减少无线数据传输量，图像压缩率Crate≈16；基于IDSCS的无线视频编码方法，能动态更新视频传输帧类型与视频编码参数，视频流畅(传输帧率Frate≈18fps)。在智能机房无线视频监控与资产管理初步应用、基于IEEE1451物联网区域环境空气质量监测平台应用，均表明RFID及无线视频编码技术应用已经达到项目预期效果，证明本文所研究方法有效性、适用性，并且还可以在其它领域推广应用。
This paper mainly researches on modeling and realization of IEEE1451.5RFID andwireless video sensing system. The research work is valuable for academic and practicalapplication for the development of wireless smart sensing technology, and promot theintelligentization of modern manufacturing. The research work is supported by ChinaPostdoctoral Science Foundation (2012M511806), Guangdong High-level Talent Project(Guangdong teacher letter [2010]79).
The dissertation summarizes the domestic and foreign research progresses of IEEE1451.5sensing system modeling technology, fast acquisition mehod of RFID information,and coding technology of wireless video sensing system, and determines research contents.The primary work includes:
⑴Make a deep study on the combination modeling method of UML and Petri net forIEEE1451.5wireless smart sensing system, and analysize the operability and dynamicevaluating model performance to improve the system development rate. With hierarchicalinformation flow and dynamic modeling method, the IEEE1451.5RFID and wireless videosensing system communication mechanism is modeled. It macro describes the staticorientied-object model, information interactive flow and system physical implementation ofRFID and wireless video sensing system. Throughing establishing conversion rule, the UMLmodel is converted to Petri net model to analysize, verify model operability and performance.Base on the IEEE1451.5communication mechanism, the WTIM access mechanism and datatransmission based on WiFi protocol are modeled. With the aid of CPN Tools, the liveness,boundedness, and rechability are analyzed. In order to enhance system design, the WTIMdata flow CPN model is builded. The influence characterics of change of structure parameterto the system performace is also studied.
⑵Study on tag estimate and anti-collision method of IEEE1451.5RFID system toaccelerate information acquisition rate. Aiming at the problem of large computation ofsearch-based tag estimate method, a coarse-fine double searching-based tag estimate method(CFDDE) is proposed. It uses the idea of coarse and fine adjustment, no need or only a littermultiplication to estimate tag number. Utilize another evaluating indicator in the first searchto replace multiplication with addition, subtraction, and decrease search area of the secondseach. Generally, the area can be decreased by90%, and the estimation time can be reducedby54%compared to the chebysheve inequality method. The QCFDDEtag anti-collision method is studied based on CFDDE, and the relationship of frame lengthe and tag number is derived.As the frame length is normally not integer power of2, which can not be directed applied, andthe neareset interger is not suitable for Q slection. The Q selection method is proposed basedon interative method with bounded condition to improve channel efficiency. The simulationresults indicate that, the power dissipation of tag based on QCFDDEis less than those based onminimized collision, poison distribution, and idle slot tag estimation by26.7%,15.4%, and15.4%, respectively. The channel efficiency is increased by6.9%,4.2%,4.3%, respectively.
⑶Study on a PDP-AAN JPEG image compression method that suitablue for wirelessvideo sensing system. With polygonal DCT pruning method, the pruning mode is enlargedand combining AAN fast DCT algorithm to optimize the JPEG image compression process.PDP pruning method aims at decresing large computation of traditional JPEG, enlarging thepruning mode, and enhancing the application flexibility. Throughing explanding the selectionarea of DCT coefficient, the pruning mode is increased which is meaningful for image qualityfine adjustment. BIC information criterion is applied to determine the PDP pruningcoefficient, which can avoid human influence, and balance the relationship between ofcompression quality and comutation. In order to optimize the JPEG compression process, thescalable factor of AAN fast DCT algorithm and the quantization table is combined. The AANquantization table is derived. The simulation results indicate that, compared to standard JPEGmethod, PDP-AAN JPEG image compression method can reduce computation ofmultiplication, addition of2-dimension DCT coefficient by3.8%, and6.0%, the quantizationand “Z” arrangement process by32.8%,32.8%.
⑷Aiming at the resource limited system of IEEE1451.5wireless video sensing system,the interval-block diagonal-line and seperation of coding and parameter-selection(IDSCS)-based wireless video coding method is proposed. The feature of this method is,PDP-AAN JPEG image compression method is used to decrease computation, utilizinginterval-block diagonal-line pixel sampling difference to adaptive update video frame toenhance the suitability, and tranforing the complexed parameter selection algorithm fromresource limited coding node to decoding system which separate the coding and parameterprocess. The coding adaption is enhanced, and the computation is decreased. Proposeperformance enhance measures, such as comining the user TEDS with the JPEG headinformation to reduce data transmission quantity, optimize the structure parameter of wirelessvideo coding node to enhance the parallel process ability. The simulation results indicate that,compared to M-JPEG method, the IDSCS-based video coding method can reduced ablut 55%data transmission quantity while the decoding image quality is decreased only about3%.
The design and implementation of IEEE1451.5RFID and wireless video sensingsystem is carried out and the application effect is discussed. The experiment results indicatethat, applied the method of joined modeling of RFID and wireless video sensing system,CFDDE tag estimate, QCFDDEanti-collision, PDP-AAN JPEG, IDSCS, and etc, caneffectively improve the system performance and development rate. The RFID system canread both ISO18000-6B and ISO18000-6C tag. The reading speed is improved by17%thanpoison distribution tag estimation method. The PDP-AAN JPEG compression method canprun the image compression quality, reduce wireless data send quantity, the overall imagecompression rate Crate≈16. The IDSCS-based video coding method can dynamic update videoframe type and coding parameter, and the video is smooth (frame rate≈18fps).The initialapplication result to the data center computer room superviosn and management, and appliesto the IEEE1451and Internet of Things based district environment air quality monitorplatform. Both applicaton result show that, RFID and wireless video sensing technologachieve the project expected target which prove the study method of this dissertation iseffective and applicability, and can be expended to other application fields.
论文从基于IEEE1451.5的传感系统建模技术、RFID信息快速获取方法、无线视频传感系统编码技术等方面，论述国内外研究进展，确定研究内容。论文主要工作包括：
⑴深入开展基于IEEE1451.5的传感系统UML与Petri网联合建模方法研究，实现模型可操作性验证与动态性能评估，提高系统开发速度与质量。基于层次化信息流动态建模方法建立了基于IEEE1451.5的RFID与无线视频传感系统通信机制模型，从宏观角度描述RFID与无线视频传感系统静态用例模型、信息交互模型与系统整体物理实现；通过制定UML与Petri网转换规则，实现UML图到Petri网模型转换，分析验证模型结构可操作性与性能；研究基于IEEE1451.5WiFi协议WTIM接入机制与数据传输过程有色Petri网模型，采用CPN Tools工具，分析模型活性、有界性、可达性。最后，还研究WTIM数据流CPN模型结构参数配置对系统性能指标影响规律，提高系统设计合理性与性能。
⑵探讨基于IEEE1451的RFID系统标签数估算与多标签防碰撞方法，提高信息获取速度。针对区间搜索标签数估算法运算量较大问题，提出基于粗精二次搜索(CFDDE)的标签数估算方法，采用由粗至精搜索思想，无需或仅需少量乘法运算估算标签数。第一次搜索通过另外一种评价指标，用加减运算消除切比雪夫不等式估算法平方、开方运算，同时减少第二次搜索范围，一般可使第二次搜索范围减少约90%，估算时间比切比雪夫不等式法减少约54%。进一步研究基于CFDDE的QCFDDE多标签防碰撞方法，推导出帧长度与标签数关系式，针对所求得帧长度通常不为2整数次方、无法直接应用且不能采用最近整数法问题，利用Q值边界条件，以最大信道利用率为原则，研究基于迭代法的Q值选取方法，提高系统读写效率。仿真结果表明，基于QCFDDE多标签防碰撞方法标签功耗比基于碰撞最小值、泊松分布、空闲时隙法标签数估算的防碰撞方法分别减少26.7%、15.4%、15.4%，信道利用率分别提高6.9%、4.2%、4.3%。
⑶研究一种适用于无线视频传感系统的PDP-AAN JPEG图像压缩方法，采用多边形DCT裁剪方法(PDP)增加裁剪模式，结合AAN快速DCT算法优化JPEG压缩过程。PDP裁剪方法针对JPEG图像压缩DCT变换运算量较大、裁剪模式少、应用灵活性不强等问题，通过扩展DCT系数矩阵选取区域，增加裁剪模式，有利于图像压缩质量宽范围细致调节。应用BIC信息准则选取PDP裁剪系数，避免主观因素影响，权衡图像压缩质量与复杂度关系，应用灵活性得到增强。将AAN缩放因子与JPEG量化过程结合，推导得AAN量化表，优化JPEG压缩过程。仿真结果表明，PDP-AAN JPEG图像压缩方法，在图像压缩质量减少0.2%下，二维DCT乘法、加法运算个数分别减少3.8%、6.0%，量化、“Z”字形排列计算量分别减少32.8%、32.8%。
⑷针对资源受限的IEEE1451.5无线视频传感系统，提出一种基于隔块角线编选分离(IDSCS)的无线视频编码方法。其特点在于，采用PDP-AAN JPEG图像压缩方法降低图像压缩运算量，增强应用灵活性；利用隔块角线像素采样差异值自适应更新视频帧，增强编码适应性；将复杂度较大的编码参数选取与动态更新算法从资源受限编码节点转移至运算能力较强的视频集成中心，实现编选分离，降低编码节点复杂度。研究基于IEEE1451.5的无线视频系统性能提升方法，将JPEG文件头与TEDS用户自定义信息结合减少图像数据传输量，并通过优化配置无线视频节点结构参数提高进程并行处理能力。仿真结果表明，基于IDSCS无线视频编码方法与M-JPEG方法相比，解码图像PSNR降低3%下，数据传输量减少约55%。
论文还开展基于IEEE1451.5的RFID系统、无线视频系统实现与性能验证实验，探讨系统应用与效果。实验表明，将RFID及无线视频传感系统建模方法、CFDDE标签数估算方法、QCFDDE多标签防碰撞方法、PDP-AAN JPEG图像压缩方法、隔块角线编选分离视频编码等方法，综合应用到基于IEEE1451.5的RFID及无线视频传感系统建模与实现，能有效提高系统整体性能与开发速度。通过准确度较高的CFDDE标签数估算法，能提高QCFDDE防碰撞方法性能，RFID系统读写性能比基于泊松分布标签数估算法的防碰撞方法提高约17%；基于PDP-AAN JPEG图像压缩方法，能通过裁剪系数调整图像压缩质量，减少无线数据传输量，图像压缩率Crate≈16；基于IDSCS的无线视频编码方法，能动态更新视频传输帧类型与视频编码参数，视频流畅(传输帧率Frate≈18fps)。在智能机房无线视频监控与资产管理初步应用、基于IEEE1451物联网区域环境空气质量监测平台应用，均表明RFID及无线视频编码技术应用已经达到项目预期效果，证明本文所研究方法有效性、适用性，并且还可以在其它领域推广应用。
This paper mainly researches on modeling and realization of IEEE1451.5RFID andwireless video sensing system. The research work is valuable for academic and practicalapplication for the development of wireless smart sensing technology, and promot theintelligentization of modern manufacturing. The research work is supported by ChinaPostdoctoral Science Foundation (2012M511806), Guangdong High-level Talent Project(Guangdong teacher letter [2010]79).
The dissertation summarizes the domestic and foreign research progresses of IEEE1451.5sensing system modeling technology, fast acquisition mehod of RFID information,and coding technology of wireless video sensing system, and determines research contents.The primary work includes:
⑴Make a deep study on the combination modeling method of UML and Petri net forIEEE1451.5wireless smart sensing system, and analysize the operability and dynamicevaluating model performance to improve the system development rate. With hierarchicalinformation flow and dynamic modeling method, the IEEE1451.5RFID and wireless videosensing system communication mechanism is modeled. It macro describes the staticorientied-object model, information interactive flow and system physical implementation ofRFID and wireless video sensing system. Throughing establishing conversion rule, the UMLmodel is converted to Petri net model to analysize, verify model operability and performance.Base on the IEEE1451.5communication mechanism, the WTIM access mechanism and datatransmission based on WiFi protocol are modeled. With the aid of CPN Tools, the liveness,boundedness, and rechability are analyzed. In order to enhance system design, the WTIMdata flow CPN model is builded. The influence characterics of change of structure parameterto the system performace is also studied.
⑵Study on tag estimate and anti-collision method of IEEE1451.5RFID system toaccelerate information acquisition rate. Aiming at the problem of large computation ofsearch-based tag estimate method, a coarse-fine double searching-based tag estimate method(CFDDE) is proposed. It uses the idea of coarse and fine adjustment, no need or only a littermultiplication to estimate tag number. Utilize another evaluating indicator in the first searchto replace multiplication with addition, subtraction, and decrease search area of the secondseach. Generally, the area can be decreased by90%, and the estimation time can be reducedby54%compared to the chebysheve inequality method. The QCFDDEtag anti-collision method is studied based on CFDDE, and the relationship of frame lengthe and tag number is derived.As the frame length is normally not integer power of2, which can not be directed applied, andthe neareset interger is not suitable for Q slection. The Q selection method is proposed basedon interative method with bounded condition to improve channel efficiency. The simulationresults indicate that, the power dissipation of tag based on QCFDDEis less than those based onminimized collision, poison distribution, and idle slot tag estimation by26.7%,15.4%, and15.4%, respectively. The channel efficiency is increased by6.9%,4.2%,4.3%, respectively.
⑶Study on a PDP-AAN JPEG image compression method that suitablue for wirelessvideo sensing system. With polygonal DCT pruning method, the pruning mode is enlargedand combining AAN fast DCT algorithm to optimize the JPEG image compression process.PDP pruning method aims at decresing large computation of traditional JPEG, enlarging thepruning mode, and enhancing the application flexibility. Throughing explanding the selectionarea of DCT coefficient, the pruning mode is increased which is meaningful for image qualityfine adjustment. BIC information criterion is applied to determine the PDP pruningcoefficient, which can avoid human influence, and balance the relationship between ofcompression quality and comutation. In order to optimize the JPEG compression process, thescalable factor of AAN fast DCT algorithm and the quantization table is combined. The AANquantization table is derived. The simulation results indicate that, compared to standard JPEGmethod, PDP-AAN JPEG image compression method can reduce computation ofmultiplication, addition of2-dimension DCT coefficient by3.8%, and6.0%, the quantizationand “Z” arrangement process by32.8%,32.8%.
⑷Aiming at the resource limited system of IEEE1451.5wireless video sensing system,the interval-block diagonal-line and seperation of coding and parameter-selection(IDSCS)-based wireless video coding method is proposed. The feature of this method is,PDP-AAN JPEG image compression method is used to decrease computation, utilizinginterval-block diagonal-line pixel sampling difference to adaptive update video frame toenhance the suitability, and tranforing the complexed parameter selection algorithm fromresource limited coding node to decoding system which separate the coding and parameterprocess. The coding adaption is enhanced, and the computation is decreased. Proposeperformance enhance measures, such as comining the user TEDS with the JPEG headinformation to reduce data transmission quantity, optimize the structure parameter of wirelessvideo coding node to enhance the parallel process ability. The simulation results indicate that,compared to M-JPEG method, the IDSCS-based video coding method can reduced ablut 55%data transmission quantity while the decoding image quality is decreased only about3%.
The design and implementation of IEEE1451.5RFID and wireless video sensingsystem is carried out and the application effect is discussed. The experiment results indicatethat, applied the method of joined modeling of RFID and wireless video sensing system,CFDDE tag estimate, QCFDDEanti-collision, PDP-AAN JPEG, IDSCS, and etc, caneffectively improve the system performance and development rate. The RFID system canread both ISO18000-6B and ISO18000-6C tag. The reading speed is improved by17%thanpoison distribution tag estimation method. The PDP-AAN JPEG compression method canprun the image compression quality, reduce wireless data send quantity, the overall imagecompression rate Crate≈16. The IDSCS-based video coding method can dynamic update videoframe type and coding parameter, and the video is smooth (frame rate≈18fps).The initialapplication result to the data center computer room superviosn and management, and appliesto the IEEE1451and Internet of Things based district environment air quality monitorplatform. Both applicaton result show that, RFID and wireless video sensing technologachieve the project expected target which prove the study method of this dissertation iseffective and applicability, and can be expended to other application fields.
引文
[1]中华人民共和国国务院.国家重大科技基础设施建设中长期规划(2012—2030年)[J/OL].http://www.gov.cn/zwgk/2013-03/04/content_2344891.htm,2013.2.
[2]中华人民共和国工业和信息化部.物联网“十二五”发展规划[J/OL].http://www.gov.cn/zwgk/2012-02/14/content_2065999.htm,2011.11.
[3] E. Y. Song, K. Lee. Understanding IEEE1451-Networked smart transducer interface standard-Whatis a smart transducer?[J]. IEEE Transactions on Instrumentation&Measurement Magazine,2008,11(2):11-17.
[4] ISO/IEC/IEEE21450-1010, Information technology-Smart transducer interface for sensors andactuators-Common functions, communication protocols, and Transducer Electronic Data Sheet(TEDS) formats[S]. New York,2010.
[5] ISO/IEC/IEEE21451-1-2010, Information technology-Smart transducer interface for sensors andactuators-Part1: Network Capable Application Processor (NCAP) information model[S]. NewYork,2010.
[6] ISO/IEC/IEEE21451-2-2010, Information technology-Smart transducer interface for sensors andactuators-Part2: Transducer to microprocessor communication protocols and Transducer ElectronicData Sheet (TEDS) formats[S]. New York,2010.
[7] IEEE1451.3-2003, IEEE Standard for a Smart Transducer Interface for Sensors andActuators-Digital Communication and Transducer Electronic Data Sheet (TEDS) Formats forDistributed Multidrop Systems[S]. New York,2003.
[8] ISO/IEC/IEEE21451-4-2010, Information technology-Smart transducer interface for sensors andactuators-Part4: Mixed-mode communication protocols and Transducer Electronic Data Sheet(TEDS) formats[S]. New York,2010.
[9] IEEE1451.5-2007, IEEE Standard for a Smart Transducer Interface for Sensors andActuators-Wireless Communication Protocols and Transducer Electronic Data Sheet (TEDS)Formats[S]. New York,2007.
[10] ISO/IEC/IEEE21451-7-2010, Information technology--Smart transducer interface for sensors andactuators--Part7: Transducer to radio frequency identification (RFID) systems communicationprotocols and Transducer Electronic Data Sheet (TEDS) formats[S]. New York,2011.
[11] J. E. Higuera, J. Polo. IEEE1451standard in6LoWPAN sensor networks using a compactphysical-layer transducer electronic datasheet[J]. IEEE Transactions on Instrumentation andMeasurement,2011,60(8):2751-2758.
[12] F. Figueroa, J. Schmalzel, J. Morris, et al. A framework for intelligent rocket test facilities with smartsensor elements[C]. ISA/IEEE Sensors for Industry Conference,2004:91-95.
[13] D. Gurkan, X. Yuan, D. Benhaddou, et al. UH-ToSS: A Sensor Networking Testbed with IEEE1451Compatibility for Space Exploration[C].20073rd International Conference on Testbeds andResearch Infrastructure for the Development of Networks and Communities,2007:1-6.
[14] L. H. Eccles. The need for smart transducers: an aerospace test and evaluation perspective[J]. IEEETransactions on Instrumentation&Measurement Magazine,2008,11(2):23-28.
[15] J.-D. Kim, J.-H. Lee, Y.-K. Ham, et al. Sensor-Ball system based on IEEE1451for monitoring thecondition of power transmission lines[J]. Sensors and Actuators A: Physical,2009,154(1):157-168.
[16] J. Higuera, J. Polo. Autonomous and Interoperable Smart Sensors for Environmental MonitoringApplications[M]. Springer,2012:323-359.
[17] M. Lee, T. M. Gatton. Wireless health data exchange for home healthcare monitoring systems[J].Sensors,2010,10(4):3243-3260.
[18] E. Dorronzoro, A. V. Medina, I. Gómez, et al. A Standard-Based Body Sensor Network SystemProposal[M]. Springer,2012:106-115.
[19] F. Barrero, S. Toral, M. Vargas, et al. Networked Electronic Equipments Using the IEEE1451Standard—VisioWay: A Case Study in the ITS Area[J]. International Journal of Distributed SensorNetworks,2012,2012:1-12.
[20]基于UML的面向对象建模方法研究[J].软件导刊,2009,8(1):47-49.
[21]马超,林红昌,丁佐华.基于UML和Petri网的建模及其验证[J].浙江理工大学学报,2010,27(6):889-894.
[22] E. Y. Song, K. Lee. An implementation of the proposed IEEE1451.0and1451.5standards[C].Proceedings of the2006IEEE Sensors Applications Symposium,2006:72-77.
[23] E. Y. Song, K. B. Lee. STWS: A unified web service for IEEE1451smart transducers[J]. IEEETransactions on Instrumentation and Measurement,2008,57(8):1749-1756.
[24]黄国健,刘桂雄,洪晓斌,等. IEEE1451网络化智能传感器的通用建模方法及应用[J].光学精密工程,2010,18(8):1914-1921.
[25]孙建召,曾巧明.基于面向对象Petri网的工作流建模及性能分析[J].计算机技术与发展,2007,17(10):73-79.
[26] K. Jensen, L. M. Kristensen, L. Wells. Coloured Petri Nets and CPN Tools for modelling andvalidation of concurrent systems[J]. International Journal on Software Tools for Technology Transfer,2007,9(3-4):213-254.
[27]刘铭,张国印,姚爱红,等.基于层次实时有色Petri网的实时系统建模与分析方法研究[J].电子与信息学报,2011,33(3):580-586.
[28] M. A. Azgomi, A. Khalili. Performance Evaluation of Sensor Medium Access Control ProtocolUsing Coloured Petri Nets[J]. Electronic Notes in Theoretical Computer Science,2009,242(2):31-42.
[29] A. Shareef, Y. Zhu. Energy modeling of wireless sensor nodes based on Petri nets[C].201039thInternational Conference on Parallel Processing,2010:101-110.
[30] D. Martínez, A. González, F. Blanes, et al. Formal specification and design techniques for wirelesssensor and actuator networks[J]. Sensors,2011,11(1):1059-1077.
[31]夏侯士戟,马敏,王厚军.基于跚皿和Petri网的雷达测试系统建模方法研究[J].仪器仪表学报,2009,30(1):7-13.
[32] S. Jabri, T. Bourdeaud’huy, E. Lemaire. European railway traffic management system validationusing UML/Petri nets modelling strategy[J]. European Transport Research Review,2010,2(2):113-128.
[33]周岳斌. IEEE1451混合接入模式下网络化智能传感系统建模与实现[D].华南理工大学,2012.
[34]李慧,张治国.不定长RFID标签反碰撞识别算法[J]. Computer Engineering,2010,36(20):241-243.
[35] H. Vogt. Efficient Object Identification with Passive RFID Tags[M]. Springer Berlin Heidelberg,2002:98-113.
[36] M. Kodialam, T. Nandagopal. Fast and reliable estimation schemes in RFID systems[C].Proceedings of the12th annual international conference on Mobile computing and networking,2006:322-333.
[37]颜元,武岳山,熊立志.一种新型多标签估算方法[J].计算机应用研究,2012,29(3):930-932.
[38] J.-R. Cha, J.-H. Kim. Novel anti-collision algorithms for fast object identification in RFIDsystem[C].11th International Conference on Parallel and Distributed Systems,2005:63-67.
[39] G. Khandelwal, K. Lee, A. Yener, et al. ASAP: A MAC Protocol for Dense and Time-ConstrainedRFID Systems[J]. EURASIP Journal on Wireless Communications and Networking,2007,2007(1):1-13.
[40]郭宏博,赵玉萍.一种新的导数可靠度的RFID标签数目估计[J].北京大学学报(自然科学版),2008,44(5):6.
[41] W.-T. Chen. An accurate tag estimate method for improving the performance of an RFIDanticollision algorithm based on dynamic frame length ALOHA[J]. IEEE Transactions onAutomation Science and Engineering,2009,6(1):9-15.
[42] E. Vahedi, V. W. Wong, I. F. Blake, et al. Probabilistic Analysis and Correction of Chen's TagEstimate Method[J]. IEEE Transactions on Automation Science and Engineering,2011,8(3):659-663.
[43]吴海锋,曾玉. RFID动态帧时隙ALOHA防冲突中的标签估计和帧长确定[J].自动化学报,2010,36(4):620-624.
[44] C. Floerkemeier. Bayesian transmission strategy for framed ALOHA based RFID protocols[C].2007IEEE International Conference on RFID,2007:228-235.
[45] Q. Tong, X. Zou, H. Tong. Dynamic framed slotted ALOHA algorithm based on Bayesian estimationin RFID system[C].2009World Congress on Computer Science and Information Engineering,2009:384-388.
[46] H. Vogt. Multiple object identification with passive RFID tags[C].2002IEEE InternationalConference on Systems, Man and Cybernetics,2002:6-9.
[47] W. Haifeng, Z. Yu. Efficient Framed Slotted Aloha Protocol for RFID Tag Anticollision[J]. IEEETransactions on Automation Science and Engineering,2011,8(3):581-588.
[48] S. Ullah, W. Alsalih, A. Alsehaim, et al. A Review of Tags Anti-collision and Localization Protocolsin RFID Networks[J]. Journal of Medical Systems,2012,36(6):4037-4050.
[49] H. Wu, Y. Zeng. Bayesian tag estimate and optimal frame length for anti-collision aloha RFIDsystem[J]. IEEE Transactions on Automation Science and Engineering,2010,7(4):963-969.
[50] D. K. Klair, K.-W. Chin, R. Raad. A survey and tutorial of RFID anti-collision protocols[J]. IEEECommunications Surveys&Tutorials,2010,12(3):400-421.
[51] ISO18000-6C-2004, Information technology-Radio frequency identification for item management-Part6: Parameters for air interface communications at860MHz to960MHz[S]. Switzerland,2004.
[52] C. Ying, Z. Fu-hong. Study on Anti-collision Q Algorithm for UHF RFID[C].2010InternationalConference on Communications and Mobile Computing,2010:168-170.
[53] C. Floerkemeier, M. Wille. Comparison of transmission schemes for framed ALOHA based RFIDprotocols[C]. Proceedings of the International Symposium on Applications and the InternetWorkshops,2006:97-100.
[54] W.-T. Chen, W.-B. Kao. A Novel Q-algorithm for EPCglobal Class-1Generation-2Anti-collisionProtocol[C]. World Academy of Science, Engineering and Technology,2011:801-804.
[55] J. Teng, X. Xuan, Y. Bai. A Fast Q Algorithm Based on EPC Generation-2RFID Protocol[C].20106th International Conference on Wireless Communications Networking and Mobile Computing,2010:1-4.
[56]徐圆圆,曾隽芳,陈琳,等. EPC Gen2标准防碰撞方案的研究与改进[J].计算机应用,2008,28(12):3271-3273.
[57]韩振伟,宋克非.射频识别防碰撞Q算法的分析及改进[J].计算机工程与设计,2011,32(7):2314-2318.
[58] X. Fan, I. Song, K. Chang, et al. Gen2-based tag anti-collision algorithms using Chebyshev'sinequality and adjustable frame size[J]. ETRI journal,2008,30(5):653-662.
[59] L.-C. Wang, H.-C. Liu. A novel anti-collision algorithm for EPC Gen2RFID systems[C].20063rdInternational Symposium on Wireless Communication Systems,2006:761-765.
[60] C.-F. Lin, F.-S. Lin. Efficient estimation and collision-group-based anticollision algorithms fordynamic frame-slotted ALOHA in RFID networks[J]. IEEE Transactions on Automation Science andEngineering,2010,7(4):840-848.
[61] D. Liu, Z. Wang, J. Tan, et al. ALOHA algorithm considering the slot duration difference in RFIDsystem[C].2009IEEE International Conference on RFID,2009:56-63.
[62] J. Vales-Alonso, V. Bueno-Delgado, E. Egea-Lopez, et al. Multiframe maximum-likelihood tagestimation for RFID anticollision protocols[J]. IEEE Transactions on Industrial Informatics,2011,7(3):487-496.
[63] R. Puri, A. Majumdar, P. Ishwar, et al. Distributed video coding in wireless sensor networks[J]. IEEESignal Processing Magazine,2006,23(4):94-106.
[64]樊晓平,熊哲源,陈志杰,等.无线多媒体传感器网络视频编码研究[J].通信学报,2011,32(9):137-146.
[65] S. Misra, M. Reisslein, G. Xue. A survey of multimedia streaming in wireless sensor networks[J].IEEE Communications Surveys&Tutorials,2008,10(4):18-39.
[66] A. Aaron, R. Zhang, B. Girod. Wyner-Ziv coding of motion video[C].2002Conference Record ofthe Thirty-Sixth Asilomar Conference on Signals, Systems and Computers,2002:240-244.
[67] A. Aaron, E. Setton, B. Girod. Towards practical Wyner-Ziv coding of video[C].2003InternationalConference on Image Processing,2003:869-872.
[68] A. Aaron, S. D. Rane, E. Setton, et al. Transform-domain Wyner-Ziv codec for video[C].2004Visual Communications and Image Processing,2004:520-528.
[69] R. Puri, A. Majumdar, K. Ramchandran. PRISM: a video coding paradigm with motion estimation atthe decoder[J]. IEEE Transactions on Image Processing,2007,16(10):2436-2448.
[70] P. Wang, R. Dai, I. F. Akyildiz. Collaborative data compression using clustered source coding forwireless multimedia sensor networks[C].2010Proceedings IEEE INFOCOM,2010:1-9.
[71]苏颜军,张瑞华. WMSNs的自适应分布式图像压缩的自适应分布式图像压缩算法[J]. ComputerEngineering,2011,37(16):215-217.
[72]张洋,张楠,尹宝才.多描述编码研究现状[J].计算机学报,2007,30(9):1612-1624.
[73] H. Wu, A. A. Abouzeid. Power aware image transmission in energy constrained wirelessnetworks[C].2004Ninth International Symposium on Computers and Communications,2004:202-207.
[74] G. Pekhteryev, Z. Sahinoglu, P. Orlik, et al. Image transmission over IEEE802.15.4and zigbeenetworks[C].2005IEEE International Symposium on Circuits and Systems,2005:3539-3542.
[75] C. Li Wern, C. Wai Chong, A. Li-minn, et al. Very low-memory wavelet compression architectureusing strip-based processing for implementation in wireless sensor networks[J]. EURASIP journalon embedded systems,2009,2009:1-16.
[76] W. C. Chia, L.-M. Ang, K. P. Seng. Multiview image compression for wireless multimedia sensornetwork using image stitching and SPIHT coding with EZW tree structure[C].2009InternationalConference on Intelligent Human-Machine Systems and Cybernetics,2009,2:298-301.
[77] P. Kumar, A. Pande, A. Mittal. Efficient compression and network adaptive video coding fordistributed video surveillance[J]. Multimedia Tools and Applications,2012,56(2):365-384.
[78] S. Li, R. K. Neelisetti, C. Liu, et al. Efficient multi-path protocol for wireless sensor networks[J].International Journal of Wireless and Mobile Networks,2010,2(1):110-130.
[79]蔡文郁,唐军,张昱.无线传感器网络MDC视频传输的跨层多径路由协议[J].浙江大学学报:工学版,2010,44(1):61-67.
[80] Y. Liao, J. D. Gibson. Routing-aware multiple description video coding over mobile ad-hocnetworks[J]. IEEE Transactions on Multimedia,2011,13(1):132-142.
[81] C. Loeffler, A. Ligtenberg, G. S. Moschytz. Practical fast1-D DCT algorithms with11multiplications[C].1989International Conference on Acoustics, Speech, and Signal,1989:988-991.
[82] Y. Arai, T. Agui, M. Nakajima. A fast dct-sq scheme for images[J]. IEICE Transactions (1976-1990),1988, E71(11):1095-1097.
[83] B. Heyne, C.-C. Sun, J. Goetze, et al. A computationally efficient high-quality cordic based DCT[C].2006European Signal Processing Conference,2006:1-6.
[84] J. Bracamonte, M. Ansorge, F. Pellandini. VLSI systems for image compression: apower-consumption/image-resolution trade-off approach[C]. Digital Compression Technologies andSystems for Video Communications,1996:591-596.
[85] A. Mammeri, A. Khoumsi, D. Ziou, et al. Energy-aware JPEG for visual sensor networks[C].2008IEEE Maghrebian Conference on Software Engineering and Artificial Intelligence,2008:1-7.
[86] A. Mammeri, A. Khoumsi, D. Ziou, et al. Modeling and adapting JPEG to the energy requirementsof VSN[C].200817th International Conference on Computer Communications and Networks,2008:1-6.
[87] V. Lecuire, L. Makkaoui, J.-M. Moureaux. Fast zonal DCT for energy conservation in wirelessimage sensor networks[J]. Electronics letters,2012,48(2):125-127.
[88] W.-C. Feng, E. Kaiser, W. C. Feng, et al. Panoptes: scalable low-power video sensor networkingtechnologies[J]. ACM Trans. Multimedia Comput. Commun. Appl.,2005,1(2):151-167.
[89] H. S. Aghdasi, M. Abbaspour, M. E. Moghadam, et al. An energy-efficient and high-quality videotransmission architecture in wireless video-based sensor networks[J]. Sensors,2008,8(8):4529-4559.
[90] I. F. Akyildiz, T. Melodia, K. R. Chowdhury. A survey on wireless multimedia sensor networks[J].Computer Networks,2007,51(4):921-960.
[91]秦旺君,陈燕,杨明.基于信息流结构分析的系统建模[J].大连海事大学学报,2011,37(2):97-100.
[92]沈如松,张育林.基于UML和Petri网的武器装备体系需求分析方法[J].系统工程理论与实践,2006,26(1):136-140.
[93]熊杰,刘湘伟,陈根忠,等.基于UML和OCPN的军事电子信息系统建模[J].火力与指挥控制,2011,36(6):116-119.
[94]吴海,孙永雄,付庆兴,等. UML图转有色Petri网图文法[J].吉林大学学报(信息科学版),2011,29(4):357-365.
[95]韩江洪,方华,刘小平. Petri网的公平性及分析[J].系统仿真学报,2012,24(3):521-535.
[96] V. Kirubanand, S. Palaniammal. A Hybrid Model in Comparing the Performance of Wired andWireless Technologies by Using the Markov Algorithm and Queuing Petri Nets[J]. InternationalJournal of Computer Applications,2010,7(4):1-4.
[97] Y. Dong, Y. Xia, T. Sun, et al. Modeling and performance evaluation of service choreography basedon stochastic Petri net[J]. Journal of Computers,2010,5(4):516-523.
[98] A. A. Akinsete. BLOCKED NETWORK OF TANDEM QUEQUEs WITH WITHDRAWAL[J].Kragujevac Journal of Mathematics,2001,(23):63-73.
[99]彭康,徐闯,余洪江,等.远程识别中多标签防冲突算法的改进[J].电子测量与仪器学报,2011,25(6):558-563.
[100] FAQS.ORG.Compression picture[J/OL]. http://www.faqs.org/faqs/compression-faq/part1/section-30.html,2013.1.
[101] ARIZONA STATE UNIVERSITY.YUV Video Sequences[J/OL]. http://trace.eas.asu.edu/yuv/,2013.1.
[102] D. T. Vo, T. Q. Nguyen. Quality Enhancement for Motion JPEG Using Temporal Redundancies[J].IEEE Transactions on Circuits and Systems for Video Technology,2008,18(5):609-619.
[103]刘桂雄,朱明武.动力环境定制化监控软件[Z].计算机软件版权登记号:2012SR075994,2012.
[104]中国赛宝实验室.基于物联网的环境空气质量监测系统测试报告[R]. No:002031612113510,2012.
[105]朱红祥,柴欣生,王双飞.纸浆卡伯值测定方法的研究进展[J].中国造纸,2006,25(7):49-49.
[106]卫威,梁辰,柴欣生,等.适用于工业生产的纸浆卡伯值快速测定法[J].中国造纸,2007,26(3):1-3.
[107]刘桂雄,吴国光,柴欣生.基于光纤补偿网络的纸浆卡伯值测定装置[J].光通信技术,2011,8:34-37.
[108] G. Wu, G. Liu, X. Chai. The Compensation Mechanism&Signal Identifying Method of Four-NodeMulti-Wavelength Fiber-Sensing Network[J]. Procedia Engineering,2012,29:2491-2495.
[2]中华人民共和国工业和信息化部.物联网“十二五”发展规划[J/OL].http://www.gov.cn/zwgk/2012-02/14/content_2065999.htm,2011.11.
[3] E. Y. Song, K. Lee. Understanding IEEE1451-Networked smart transducer interface standard-Whatis a smart transducer?[J]. IEEE Transactions on Instrumentation&Measurement Magazine,2008,11(2):11-17.
[4] ISO/IEC/IEEE21450-1010, Information technology-Smart transducer interface for sensors andactuators-Common functions, communication protocols, and Transducer Electronic Data Sheet(TEDS) formats[S]. New York,2010.
[5] ISO/IEC/IEEE21451-1-2010, Information technology-Smart transducer interface for sensors andactuators-Part1: Network Capable Application Processor (NCAP) information model[S]. NewYork,2010.
[6] ISO/IEC/IEEE21451-2-2010, Information technology-Smart transducer interface for sensors andactuators-Part2: Transducer to microprocessor communication protocols and Transducer ElectronicData Sheet (TEDS) formats[S]. New York,2010.
[7] IEEE1451.3-2003, IEEE Standard for a Smart Transducer Interface for Sensors andActuators-Digital Communication and Transducer Electronic Data Sheet (TEDS) Formats forDistributed Multidrop Systems[S]. New York,2003.
[8] ISO/IEC/IEEE21451-4-2010, Information technology-Smart transducer interface for sensors andactuators-Part4: Mixed-mode communication protocols and Transducer Electronic Data Sheet(TEDS) formats[S]. New York,2010.
[9] IEEE1451.5-2007, IEEE Standard for a Smart Transducer Interface for Sensors andActuators-Wireless Communication Protocols and Transducer Electronic Data Sheet (TEDS)Formats[S]. New York,2007.
[10] ISO/IEC/IEEE21451-7-2010, Information technology--Smart transducer interface for sensors andactuators--Part7: Transducer to radio frequency identification (RFID) systems communicationprotocols and Transducer Electronic Data Sheet (TEDS) formats[S]. New York,2011.
[11] J. E. Higuera, J. Polo. IEEE1451standard in6LoWPAN sensor networks using a compactphysical-layer transducer electronic datasheet[J]. IEEE Transactions on Instrumentation andMeasurement,2011,60(8):2751-2758.
[12] F. Figueroa, J. Schmalzel, J. Morris, et al. A framework for intelligent rocket test facilities with smartsensor elements[C]. ISA/IEEE Sensors for Industry Conference,2004:91-95.
[13] D. Gurkan, X. Yuan, D. Benhaddou, et al. UH-ToSS: A Sensor Networking Testbed with IEEE1451Compatibility for Space Exploration[C].20073rd International Conference on Testbeds andResearch Infrastructure for the Development of Networks and Communities,2007:1-6.
[14] L. H. Eccles. The need for smart transducers: an aerospace test and evaluation perspective[J]. IEEETransactions on Instrumentation&Measurement Magazine,2008,11(2):23-28.
[15] J.-D. Kim, J.-H. Lee, Y.-K. Ham, et al. Sensor-Ball system based on IEEE1451for monitoring thecondition of power transmission lines[J]. Sensors and Actuators A: Physical,2009,154(1):157-168.
[16] J. Higuera, J. Polo. Autonomous and Interoperable Smart Sensors for Environmental MonitoringApplications[M]. Springer,2012:323-359.
[17] M. Lee, T. M. Gatton. Wireless health data exchange for home healthcare monitoring systems[J].Sensors,2010,10(4):3243-3260.
[18] E. Dorronzoro, A. V. Medina, I. Gómez, et al. A Standard-Based Body Sensor Network SystemProposal[M]. Springer,2012:106-115.
[19] F. Barrero, S. Toral, M. Vargas, et al. Networked Electronic Equipments Using the IEEE1451Standard—VisioWay: A Case Study in the ITS Area[J]. International Journal of Distributed SensorNetworks,2012,2012:1-12.
[20]基于UML的面向对象建模方法研究[J].软件导刊,2009,8(1):47-49.
[21]马超,林红昌,丁佐华.基于UML和Petri网的建模及其验证[J].浙江理工大学学报,2010,27(6):889-894.
[22] E. Y. Song, K. Lee. An implementation of the proposed IEEE1451.0and1451.5standards[C].Proceedings of the2006IEEE Sensors Applications Symposium,2006:72-77.
[23] E. Y. Song, K. B. Lee. STWS: A unified web service for IEEE1451smart transducers[J]. IEEETransactions on Instrumentation and Measurement,2008,57(8):1749-1756.
[24]黄国健,刘桂雄,洪晓斌,等. IEEE1451网络化智能传感器的通用建模方法及应用[J].光学精密工程,2010,18(8):1914-1921.
[25]孙建召,曾巧明.基于面向对象Petri网的工作流建模及性能分析[J].计算机技术与发展,2007,17(10):73-79.
[26] K. Jensen, L. M. Kristensen, L. Wells. Coloured Petri Nets and CPN Tools for modelling andvalidation of concurrent systems[J]. International Journal on Software Tools for Technology Transfer,2007,9(3-4):213-254.
[27]刘铭,张国印,姚爱红,等.基于层次实时有色Petri网的实时系统建模与分析方法研究[J].电子与信息学报,2011,33(3):580-586.
[28] M. A. Azgomi, A. Khalili. Performance Evaluation of Sensor Medium Access Control ProtocolUsing Coloured Petri Nets[J]. Electronic Notes in Theoretical Computer Science,2009,242(2):31-42.
[29] A. Shareef, Y. Zhu. Energy modeling of wireless sensor nodes based on Petri nets[C].201039thInternational Conference on Parallel Processing,2010:101-110.
[30] D. Martínez, A. González, F. Blanes, et al. Formal specification and design techniques for wirelesssensor and actuator networks[J]. Sensors,2011,11(1):1059-1077.
[31]夏侯士戟,马敏,王厚军.基于跚皿和Petri网的雷达测试系统建模方法研究[J].仪器仪表学报,2009,30(1):7-13.
[32] S. Jabri, T. Bourdeaud’huy, E. Lemaire. European railway traffic management system validationusing UML/Petri nets modelling strategy[J]. European Transport Research Review,2010,2(2):113-128.
[33]周岳斌. IEEE1451混合接入模式下网络化智能传感系统建模与实现[D].华南理工大学,2012.
[34]李慧,张治国.不定长RFID标签反碰撞识别算法[J]. Computer Engineering,2010,36(20):241-243.
[35] H. Vogt. Efficient Object Identification with Passive RFID Tags[M]. Springer Berlin Heidelberg,2002:98-113.
[36] M. Kodialam, T. Nandagopal. Fast and reliable estimation schemes in RFID systems[C].Proceedings of the12th annual international conference on Mobile computing and networking,2006:322-333.
[37]颜元,武岳山,熊立志.一种新型多标签估算方法[J].计算机应用研究,2012,29(3):930-932.
[38] J.-R. Cha, J.-H. Kim. Novel anti-collision algorithms for fast object identification in RFIDsystem[C].11th International Conference on Parallel and Distributed Systems,2005:63-67.
[39] G. Khandelwal, K. Lee, A. Yener, et al. ASAP: A MAC Protocol for Dense and Time-ConstrainedRFID Systems[J]. EURASIP Journal on Wireless Communications and Networking,2007,2007(1):1-13.
[40]郭宏博,赵玉萍.一种新的导数可靠度的RFID标签数目估计[J].北京大学学报(自然科学版),2008,44(5):6.
[41] W.-T. Chen. An accurate tag estimate method for improving the performance of an RFIDanticollision algorithm based on dynamic frame length ALOHA[J]. IEEE Transactions onAutomation Science and Engineering,2009,6(1):9-15.
[42] E. Vahedi, V. W. Wong, I. F. Blake, et al. Probabilistic Analysis and Correction of Chen's TagEstimate Method[J]. IEEE Transactions on Automation Science and Engineering,2011,8(3):659-663.
[43]吴海锋,曾玉. RFID动态帧时隙ALOHA防冲突中的标签估计和帧长确定[J].自动化学报,2010,36(4):620-624.
[44] C. Floerkemeier. Bayesian transmission strategy for framed ALOHA based RFID protocols[C].2007IEEE International Conference on RFID,2007:228-235.
[45] Q. Tong, X. Zou, H. Tong. Dynamic framed slotted ALOHA algorithm based on Bayesian estimationin RFID system[C].2009World Congress on Computer Science and Information Engineering,2009:384-388.
[46] H. Vogt. Multiple object identification with passive RFID tags[C].2002IEEE InternationalConference on Systems, Man and Cybernetics,2002:6-9.
[47] W. Haifeng, Z. Yu. Efficient Framed Slotted Aloha Protocol for RFID Tag Anticollision[J]. IEEETransactions on Automation Science and Engineering,2011,8(3):581-588.
[48] S. Ullah, W. Alsalih, A. Alsehaim, et al. A Review of Tags Anti-collision and Localization Protocolsin RFID Networks[J]. Journal of Medical Systems,2012,36(6):4037-4050.
[49] H. Wu, Y. Zeng. Bayesian tag estimate and optimal frame length for anti-collision aloha RFIDsystem[J]. IEEE Transactions on Automation Science and Engineering,2010,7(4):963-969.
[50] D. K. Klair, K.-W. Chin, R. Raad. A survey and tutorial of RFID anti-collision protocols[J]. IEEECommunications Surveys&Tutorials,2010,12(3):400-421.
[51] ISO18000-6C-2004, Information technology-Radio frequency identification for item management-Part6: Parameters for air interface communications at860MHz to960MHz[S]. Switzerland,2004.
[52] C. Ying, Z. Fu-hong. Study on Anti-collision Q Algorithm for UHF RFID[C].2010InternationalConference on Communications and Mobile Computing,2010:168-170.
[53] C. Floerkemeier, M. Wille. Comparison of transmission schemes for framed ALOHA based RFIDprotocols[C]. Proceedings of the International Symposium on Applications and the InternetWorkshops,2006:97-100.
[54] W.-T. Chen, W.-B. Kao. A Novel Q-algorithm for EPCglobal Class-1Generation-2Anti-collisionProtocol[C]. World Academy of Science, Engineering and Technology,2011:801-804.
[55] J. Teng, X. Xuan, Y. Bai. A Fast Q Algorithm Based on EPC Generation-2RFID Protocol[C].20106th International Conference on Wireless Communications Networking and Mobile Computing,2010:1-4.
[56]徐圆圆,曾隽芳,陈琳,等. EPC Gen2标准防碰撞方案的研究与改进[J].计算机应用,2008,28(12):3271-3273.
[57]韩振伟,宋克非.射频识别防碰撞Q算法的分析及改进[J].计算机工程与设计,2011,32(7):2314-2318.
[58] X. Fan, I. Song, K. Chang, et al. Gen2-based tag anti-collision algorithms using Chebyshev'sinequality and adjustable frame size[J]. ETRI journal,2008,30(5):653-662.
[59] L.-C. Wang, H.-C. Liu. A novel anti-collision algorithm for EPC Gen2RFID systems[C].20063rdInternational Symposium on Wireless Communication Systems,2006:761-765.
[60] C.-F. Lin, F.-S. Lin. Efficient estimation and collision-group-based anticollision algorithms fordynamic frame-slotted ALOHA in RFID networks[J]. IEEE Transactions on Automation Science andEngineering,2010,7(4):840-848.
[61] D. Liu, Z. Wang, J. Tan, et al. ALOHA algorithm considering the slot duration difference in RFIDsystem[C].2009IEEE International Conference on RFID,2009:56-63.
[62] J. Vales-Alonso, V. Bueno-Delgado, E. Egea-Lopez, et al. Multiframe maximum-likelihood tagestimation for RFID anticollision protocols[J]. IEEE Transactions on Industrial Informatics,2011,7(3):487-496.
[63] R. Puri, A. Majumdar, P. Ishwar, et al. Distributed video coding in wireless sensor networks[J]. IEEESignal Processing Magazine,2006,23(4):94-106.
[64]樊晓平,熊哲源,陈志杰,等.无线多媒体传感器网络视频编码研究[J].通信学报,2011,32(9):137-146.
[65] S. Misra, M. Reisslein, G. Xue. A survey of multimedia streaming in wireless sensor networks[J].IEEE Communications Surveys&Tutorials,2008,10(4):18-39.
[66] A. Aaron, R. Zhang, B. Girod. Wyner-Ziv coding of motion video[C].2002Conference Record ofthe Thirty-Sixth Asilomar Conference on Signals, Systems and Computers,2002:240-244.
[67] A. Aaron, E. Setton, B. Girod. Towards practical Wyner-Ziv coding of video[C].2003InternationalConference on Image Processing,2003:869-872.
[68] A. Aaron, S. D. Rane, E. Setton, et al. Transform-domain Wyner-Ziv codec for video[C].2004Visual Communications and Image Processing,2004:520-528.
[69] R. Puri, A. Majumdar, K. Ramchandran. PRISM: a video coding paradigm with motion estimation atthe decoder[J]. IEEE Transactions on Image Processing,2007,16(10):2436-2448.
[70] P. Wang, R. Dai, I. F. Akyildiz. Collaborative data compression using clustered source coding forwireless multimedia sensor networks[C].2010Proceedings IEEE INFOCOM,2010:1-9.
[71]苏颜军,张瑞华. WMSNs的自适应分布式图像压缩的自适应分布式图像压缩算法[J]. ComputerEngineering,2011,37(16):215-217.
[72]张洋,张楠,尹宝才.多描述编码研究现状[J].计算机学报,2007,30(9):1612-1624.
[73] H. Wu, A. A. Abouzeid. Power aware image transmission in energy constrained wirelessnetworks[C].2004Ninth International Symposium on Computers and Communications,2004:202-207.
[74] G. Pekhteryev, Z. Sahinoglu, P. Orlik, et al. Image transmission over IEEE802.15.4and zigbeenetworks[C].2005IEEE International Symposium on Circuits and Systems,2005:3539-3542.
[75] C. Li Wern, C. Wai Chong, A. Li-minn, et al. Very low-memory wavelet compression architectureusing strip-based processing for implementation in wireless sensor networks[J]. EURASIP journalon embedded systems,2009,2009:1-16.
[76] W. C. Chia, L.-M. Ang, K. P. Seng. Multiview image compression for wireless multimedia sensornetwork using image stitching and SPIHT coding with EZW tree structure[C].2009InternationalConference on Intelligent Human-Machine Systems and Cybernetics,2009,2:298-301.
[77] P. Kumar, A. Pande, A. Mittal. Efficient compression and network adaptive video coding fordistributed video surveillance[J]. Multimedia Tools and Applications,2012,56(2):365-384.
[78] S. Li, R. K. Neelisetti, C. Liu, et al. Efficient multi-path protocol for wireless sensor networks[J].International Journal of Wireless and Mobile Networks,2010,2(1):110-130.
[79]蔡文郁,唐军,张昱.无线传感器网络MDC视频传输的跨层多径路由协议[J].浙江大学学报:工学版,2010,44(1):61-67.
[80] Y. Liao, J. D. Gibson. Routing-aware multiple description video coding over mobile ad-hocnetworks[J]. IEEE Transactions on Multimedia,2011,13(1):132-142.
[81] C. Loeffler, A. Ligtenberg, G. S. Moschytz. Practical fast1-D DCT algorithms with11multiplications[C].1989International Conference on Acoustics, Speech, and Signal,1989:988-991.
[82] Y. Arai, T. Agui, M. Nakajima. A fast dct-sq scheme for images[J]. IEICE Transactions (1976-1990),1988, E71(11):1095-1097.
[83] B. Heyne, C.-C. Sun, J. Goetze, et al. A computationally efficient high-quality cordic based DCT[C].2006European Signal Processing Conference,2006:1-6.
[84] J. Bracamonte, M. Ansorge, F. Pellandini. VLSI systems for image compression: apower-consumption/image-resolution trade-off approach[C]. Digital Compression Technologies andSystems for Video Communications,1996:591-596.
[85] A. Mammeri, A. Khoumsi, D. Ziou, et al. Energy-aware JPEG for visual sensor networks[C].2008IEEE Maghrebian Conference on Software Engineering and Artificial Intelligence,2008:1-7.
[86] A. Mammeri, A. Khoumsi, D. Ziou, et al. Modeling and adapting JPEG to the energy requirementsof VSN[C].200817th International Conference on Computer Communications and Networks,2008:1-6.
[87] V. Lecuire, L. Makkaoui, J.-M. Moureaux. Fast zonal DCT for energy conservation in wirelessimage sensor networks[J]. Electronics letters,2012,48(2):125-127.
[88] W.-C. Feng, E. Kaiser, W. C. Feng, et al. Panoptes: scalable low-power video sensor networkingtechnologies[J]. ACM Trans. Multimedia Comput. Commun. Appl.,2005,1(2):151-167.
[89] H. S. Aghdasi, M. Abbaspour, M. E. Moghadam, et al. An energy-efficient and high-quality videotransmission architecture in wireless video-based sensor networks[J]. Sensors,2008,8(8):4529-4559.
[90] I. F. Akyildiz, T. Melodia, K. R. Chowdhury. A survey on wireless multimedia sensor networks[J].Computer Networks,2007,51(4):921-960.
[91]秦旺君,陈燕,杨明.基于信息流结构分析的系统建模[J].大连海事大学学报,2011,37(2):97-100.
[92]沈如松,张育林.基于UML和Petri网的武器装备体系需求分析方法[J].系统工程理论与实践,2006,26(1):136-140.
[93]熊杰,刘湘伟,陈根忠,等.基于UML和OCPN的军事电子信息系统建模[J].火力与指挥控制,2011,36(6):116-119.
[94]吴海,孙永雄,付庆兴,等. UML图转有色Petri网图文法[J].吉林大学学报(信息科学版),2011,29(4):357-365.
[95]韩江洪,方华,刘小平. Petri网的公平性及分析[J].系统仿真学报,2012,24(3):521-535.
[96] V. Kirubanand, S. Palaniammal. A Hybrid Model in Comparing the Performance of Wired andWireless Technologies by Using the Markov Algorithm and Queuing Petri Nets[J]. InternationalJournal of Computer Applications,2010,7(4):1-4.
[97] Y. Dong, Y. Xia, T. Sun, et al. Modeling and performance evaluation of service choreography basedon stochastic Petri net[J]. Journal of Computers,2010,5(4):516-523.
[98] A. A. Akinsete. BLOCKED NETWORK OF TANDEM QUEQUEs WITH WITHDRAWAL[J].Kragujevac Journal of Mathematics,2001,(23):63-73.
[99]彭康,徐闯,余洪江,等.远程识别中多标签防冲突算法的改进[J].电子测量与仪器学报,2011,25(6):558-563.
[100] FAQS.ORG.Compression picture[J/OL]. http://www.faqs.org/faqs/compression-faq/part1/section-30.html,2013.1.
[101] ARIZONA STATE UNIVERSITY.YUV Video Sequences[J/OL]. http://trace.eas.asu.edu/yuv/,2013.1.
[102] D. T. Vo, T. Q. Nguyen. Quality Enhancement for Motion JPEG Using Temporal Redundancies[J].IEEE Transactions on Circuits and Systems for Video Technology,2008,18(5):609-619.
[103]刘桂雄,朱明武.动力环境定制化监控软件[Z].计算机软件版权登记号:2012SR075994,2012.
[104]中国赛宝实验室.基于物联网的环境空气质量监测系统测试报告[R]. No:002031612113510,2012.
[105]朱红祥,柴欣生,王双飞.纸浆卡伯值测定方法的研究进展[J].中国造纸,2006,25(7):49-49.
[106]卫威,梁辰,柴欣生,等.适用于工业生产的纸浆卡伯值快速测定法[J].中国造纸,2007,26(3):1-3.
[107]刘桂雄,吴国光,柴欣生.基于光纤补偿网络的纸浆卡伯值测定装置[J].光通信技术,2011,8:34-37.
[108] G. Wu, G. Liu, X. Chai. The Compensation Mechanism&Signal Identifying Method of Four-NodeMulti-Wavelength Fiber-Sensing Network[J]. Procedia Engineering,2012,29:2491-2495.