融合脑血氧饱和度及面部表情的多诱因非合适驾驶状态识别
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摘要
摘要：随着人类社会的老龄化及文化发展的多元化,交通事故的主观诱因日趋复杂,这些诱因不仅包含传统意义下的疲劳及分神行为,同时包括饮酒、吸毒、疾病、药物等新型诱因。这些诱因致使驾驶主体进入多诱因非合适驾驶状态(Various Inducement Unfitted Driving States,VIUDS),降低了对驾驶环境的认知能力,并最终导致交通事故。目前,传统的方法只能实现单一诱因的检测,而无法在统一的技术构架下实现多种诱因的同时检测。针对这一问题,本文以生物医学及认知科学的最新研究成果为基础,在分析了多种主观诱因对驾驶主体中枢神经系统认知能力负面共性影响的基础上,选取了面部表情及脑前额叶血氧饱和度作为基本特征量,通过贝叶斯网络实现了认知能力的融合评估,并最终在同一技术构架下实现了VIUDS多诱因的统一检测。本文的主要创新如下：
1、在分析交通事故多种主观成因的基础上,得出了在多诱因作用下VIUDS本质上是中枢神经系统认知能力下降的状态；在分析基于单特征量及现有检测方法局限性的基础上,选取了面部表情及脑血氧饱和度作为反映认知能力的基础特征量,为认知能力的融合评估奠定了基础；
2、提出了一种认知能力的评估方法,该方法首先采用去趋势波动分析的方法计算脑额叶血氧饱和度时间序列的标度指数(Cerebral Oxygenation Saturation Scaling Exponents,COSSE),并通过该指数实现了主体中枢神经系统认知能力的分阶评估；
3、提出了一种基于石英音叉及椭球面声聚焦光声传感结构的脑额叶血氧饱和度检测方法。在检测过程中,额叶组织及石英音叉分别位于椭球面的两个焦点,在光激励脉冲的刺激下,焦点处额叶组织产生的弱光声信号经椭球面聚焦后被另一焦点处的石英音叉以共振的方式拾取,最后根据激励光强、光声信号强度及血氧饱和度之间的定量关系,可实现前额叶脑血氧饱和度的远场无创测量；
4、提出了一种正交流形保持投影的表情方法。针对流形保局投影方法的局限性,即不能在低维投影空间保持流形全局结构的不变性及投影矩阵非正交的问题,重新定义了目标约束函数,并采用格拉姆-施密特正交化过程重新求解投影矩阵,不仅在投影空间有效地保持了流形的全局及局部结构,同时有效地降低了特征维数,提高了表情识别的准确率；
5、提出了一种基于贝叶斯网络的认知能力多特征融合评估方法,该方法采用脑额叶血氧饱和度时间序列的标度指数、疲劳表情、眨眼的频率等作为融合的基本特征,通过贝叶斯网络计算了这些独立随机量对应的后验概率,并采用后验概率作为认知能力的评估依据,从而有效地克服了采用单特征进行检测的局限性,提高了评估的精度。
论文在最后一章对全文工作进行了总结,并指出了后继的工作及研究方向。本文包括图33幅,表10个,参考文献159篇。
Abstract:With the aging of human society and the diversity of cultural development, the subjective reasons of traffic accident become more and more complex, which include not only the traditional fatigue and distraction behavior, but also alcohol, drugs, disease and medicine etc. All these incentives take the drivers into Various Inducement Unfitted Driving States(VIUDS), which reduces the driver's cognitive abilities of the driving environment, and eventually leads to the traffic accidents. At present, the traditional methods can only detect a single inducement but not all the inducements under the unified technical architecture at the same time. In this dissertation, in order to solve the problem, the commonality negative effects of cognitive ability of the central nervous system was analyzed based on the latest research of biomedical and cognition. By selecting the facial expressions and the prefrontal cerebral oxygen saturation as the basic characteristic to implement the fusion assessment of cognitive ability with Bayesian networks, the uniform detection of the inducements of VIUDS was ultimately achieved. The main innovations are summarized as follows.
1. The nature of VIUDS under various subjective inducements is found to be a status of cognitive decline of central nervous system based on the analysis of the traffic accidents. The facial expressions and cerebral oxygen saturation are selected as the basic characteristic of the reflection of the cognitive ability after analyzing the limitations of the detection with a single characteristic and the existing methods, which establishes the foundation for the fusion evaluation of the cognitive ability.
2. The assessment method of the cognitive ability was proposed. In this method, the prefrontal cerebral oxygenation saturation scaling exponents (COSSE) of the prefrontal blood oxygen saturation time series is calculated by the detrended fluctuation analysis method. As a result, the evaluation of cognitive abilities of driver's central nervous system is ultimately realized.
3. A detection method of the prefrontal cerebral oxygenation saturation was proposed based on the photoacoustic sensing architecture with quartz tuning fork and ellipsoid acoustic focusing. In the process of detection, the prefrontal organization and the quartz tuning fork are located at the ellipsoid's two different focuses. Under the stimulus of the excitation laser pulse, the prefrontal organization at one of the focuses generates the weak photoacoustic signal, which is focused by the ellipsoid and collected by the quartz tuning fork located at the other focus. At last, the far-field non-invasive measurement of the prefrontal cerebral oxygenation saturation is fulfilled according to the quantitative relationship between the excitation light intensity, ultrasonic intensity and the oxygenation saturation.
4. An orthogonal manifold preserving projections method was proposed. In order to solve the limitations of the locality preserving projections method that can't preserve the invariance of the global manifold structure in the low dimensional space, and the projection matrix is non-orthogonal, an orthogonal manifold preserving projections method was proposed by redefining the objective constraint function and recalculating the projection matrix by Gramm-Schmidt orthogonalization process. This improvement does not only effectively maintain the global and local manifold structure in the projection space, but also reduces the characteristic dimension and improves the accuracy of the expression recognition.
5. A multi-feature fusion evaluation method of the cognitive ability was proposed based on the Bayesian network. In this method, COSSE, fatigue expressions and blink frequency are used as the basic characteristics for the fusion detection. The posterior probability corresponding to these random variables is calculated by the Bayesian network. Using the posterior probability as the basis for the cognitive ability assessment, the detection limitations with the single feature is overcome and the precision of the evaluation is improved.
In the last chapter, the contents of this dissertation were summarized and the future research direction and work were also discussed. This dissertation contains33figures,10tables and159references.
1、在分析交通事故多种主观成因的基础上,得出了在多诱因作用下VIUDS本质上是中枢神经系统认知能力下降的状态；在分析基于单特征量及现有检测方法局限性的基础上,选取了面部表情及脑血氧饱和度作为反映认知能力的基础特征量,为认知能力的融合评估奠定了基础；
2、提出了一种认知能力的评估方法,该方法首先采用去趋势波动分析的方法计算脑额叶血氧饱和度时间序列的标度指数(Cerebral Oxygenation Saturation Scaling Exponents,COSSE),并通过该指数实现了主体中枢神经系统认知能力的分阶评估；
3、提出了一种基于石英音叉及椭球面声聚焦光声传感结构的脑额叶血氧饱和度检测方法。在检测过程中,额叶组织及石英音叉分别位于椭球面的两个焦点,在光激励脉冲的刺激下,焦点处额叶组织产生的弱光声信号经椭球面聚焦后被另一焦点处的石英音叉以共振的方式拾取,最后根据激励光强、光声信号强度及血氧饱和度之间的定量关系,可实现前额叶脑血氧饱和度的远场无创测量；
4、提出了一种正交流形保持投影的表情方法。针对流形保局投影方法的局限性,即不能在低维投影空间保持流形全局结构的不变性及投影矩阵非正交的问题,重新定义了目标约束函数,并采用格拉姆-施密特正交化过程重新求解投影矩阵,不仅在投影空间有效地保持了流形的全局及局部结构,同时有效地降低了特征维数,提高了表情识别的准确率；
5、提出了一种基于贝叶斯网络的认知能力多特征融合评估方法,该方法采用脑额叶血氧饱和度时间序列的标度指数、疲劳表情、眨眼的频率等作为融合的基本特征,通过贝叶斯网络计算了这些独立随机量对应的后验概率,并采用后验概率作为认知能力的评估依据,从而有效地克服了采用单特征进行检测的局限性,提高了评估的精度。
论文在最后一章对全文工作进行了总结,并指出了后继的工作及研究方向。本文包括图33幅,表10个,参考文献159篇。
Abstract:With the aging of human society and the diversity of cultural development, the subjective reasons of traffic accident become more and more complex, which include not only the traditional fatigue and distraction behavior, but also alcohol, drugs, disease and medicine etc. All these incentives take the drivers into Various Inducement Unfitted Driving States(VIUDS), which reduces the driver's cognitive abilities of the driving environment, and eventually leads to the traffic accidents. At present, the traditional methods can only detect a single inducement but not all the inducements under the unified technical architecture at the same time. In this dissertation, in order to solve the problem, the commonality negative effects of cognitive ability of the central nervous system was analyzed based on the latest research of biomedical and cognition. By selecting the facial expressions and the prefrontal cerebral oxygen saturation as the basic characteristic to implement the fusion assessment of cognitive ability with Bayesian networks, the uniform detection of the inducements of VIUDS was ultimately achieved. The main innovations are summarized as follows.
1. The nature of VIUDS under various subjective inducements is found to be a status of cognitive decline of central nervous system based on the analysis of the traffic accidents. The facial expressions and cerebral oxygen saturation are selected as the basic characteristic of the reflection of the cognitive ability after analyzing the limitations of the detection with a single characteristic and the existing methods, which establishes the foundation for the fusion evaluation of the cognitive ability.
2. The assessment method of the cognitive ability was proposed. In this method, the prefrontal cerebral oxygenation saturation scaling exponents (COSSE) of the prefrontal blood oxygen saturation time series is calculated by the detrended fluctuation analysis method. As a result, the evaluation of cognitive abilities of driver's central nervous system is ultimately realized.
3. A detection method of the prefrontal cerebral oxygenation saturation was proposed based on the photoacoustic sensing architecture with quartz tuning fork and ellipsoid acoustic focusing. In the process of detection, the prefrontal organization and the quartz tuning fork are located at the ellipsoid's two different focuses. Under the stimulus of the excitation laser pulse, the prefrontal organization at one of the focuses generates the weak photoacoustic signal, which is focused by the ellipsoid and collected by the quartz tuning fork located at the other focus. At last, the far-field non-invasive measurement of the prefrontal cerebral oxygenation saturation is fulfilled according to the quantitative relationship between the excitation light intensity, ultrasonic intensity and the oxygenation saturation.
4. An orthogonal manifold preserving projections method was proposed. In order to solve the limitations of the locality preserving projections method that can't preserve the invariance of the global manifold structure in the low dimensional space, and the projection matrix is non-orthogonal, an orthogonal manifold preserving projections method was proposed by redefining the objective constraint function and recalculating the projection matrix by Gramm-Schmidt orthogonalization process. This improvement does not only effectively maintain the global and local manifold structure in the projection space, but also reduces the characteristic dimension and improves the accuracy of the expression recognition.
5. A multi-feature fusion evaluation method of the cognitive ability was proposed based on the Bayesian network. In this method, COSSE, fatigue expressions and blink frequency are used as the basic characteristics for the fusion detection. The posterior probability corresponding to these random variables is calculated by the Bayesian network. Using the posterior probability as the basis for the cognitive ability assessment, the detection limitations with the single feature is overcome and the precision of the evaluation is improved.
In the last chapter, the contents of this dissertation were summarized and the future research direction and work were also discussed. This dissertation contains33figures,10tables and159references.
引文
[1]王正国.道路交通伤研究和思考[J].中国医学科学院学报.2007,29(4)：456-458.
[2]李昌吉.汽车驾驶员的人为因素与交通安全[J].疾病控制杂志.2004,8(6)：576-578.
[3]European Commission. Annual Statistics Report 2011[EB/OL]. European: European Commission,2012-02-06 [2012-05-26]. http://ec.europa. eu/transport/ roadsafety_library/care/doc/safetynet/2011/.
[4]DL Hendricks.The Relative Frequency of Unsafe Driving ACTS in Serious Traffic Crashes[EB/OL].USA:National Highway Traffic Safety Administration,2001-05-14 [2012-05-26].http://www. nhtsa. gov/Data/.
[5]R Vivoli, M Bergomi. Biological and behavioral factors affecting driving safety[J]. Journal of Preventive Medicine and Hygiene.2006,47:69-73.
[6]Jane C. The Role of Driver Distraction in Traffic Crashes[EB/OL].USA:AAA Foundation for Traffic Safety,2001-12-06[2012-05-26]. HTTP://www. aaafoundation. Org.
[7]NHTSA. Alcohol-Impaired Driving 2009 Data[EB/OL].USA:NHTSA,2010-03-25 [2012-05-26]. http://www.nhtsa.gov/Data/.
[8]European Commission.Drinking and driving[EB/OL]. European:European Commission,2010-10-01 [2012-05-26]. http://ec.europa.eu/transport/road_safety/.
[9]NHTSA. A Highway Safety Countermeasure Guide 2011[EB/OL]. USA:NHTSA, 2012-02-18 [2012-05-26].http://www. nhtsa.gov/.
[10]EC. Fatigue[EB/OL]. European:European Commission,2011-07-25[2012-05-26]. http://ec.europa.eu/transport/roadsafety/specialist/knowledge.
[11]NHTSA. Research on Drowsy Driving[EB/OL]. USA:NHTSA,2009-10-28 [2012-05-26]. http://www.nhtsa.gov/Driving+ Safety/.
[12]Treuls Vaa. Impairments, Diseases, Age and Their Relative Risks of Accident Involvement:Results from Meta-analysis[R]. Norway:Institute of Transport Economics,2003.
[13]Daniel J. Cox. Physiological and Performance Differences Between Drivers With Type 1 Diabetes With and Without a Recent History of Driving Mishaps:An Exploratory Study[J]. Canadian Journal of Diabetes.2003;27(1):23-28.
[14]岳睿.警用呼气式酒精传感器的研究进展[J].化学传感器.2006,26(3)：6-10.
[15]于勇,王俊,周鸣.电子鼻技术的研究进展及其在农产品加工中的应用[J].浙江大学学报(农业与生命科学版).2003,29(5)：579-584.
[16]中华人民共和国司法部.血液中乙醇测定的顶空气相色谱法[S].北京：中华人民共和国司法部,2010.
[17]林涛,于海燕,应义斌.可见/近红外光谱技术在液态食品检测中的应用[J].光谱与光谱分析.2008,28(2)：285-290.
[18]许峰,王海龙,关亚风.离子迁移谱研究进展[J].化学进展.2005,17(3)：514-521.
[19]蔡禾,郭雪娇,和挺等.太赫兹技术及其应用研究进展[J].中国光学与应用光学.2010,3(3)：209-221.
[20]NHTSA, AAMVA. Driver Fitness Medical Guidelines[EB/OL]. USA:NHTSA, 2010-08-22 [2012-05-26]. http://www.nhtsa. gov/.
[21]Claire C, Wang MD. Physician's Guide to Assessing and Counseling Older Drivers[EB/OL]. USA:American Medical Association,2004-08-01 [2012-05-26]. http://www.ama-assn.org/ama/.
[22]Jason J, et al. Noninvasive Blood Glucose Measurements by Near-Infrared Transmission Spectroscopy Across Human Tongues[J]. Diabetes Technology & Therapeutics.2000,2(1):5-16.
[23]Takei Y, Furukawa Y. Estimate of Driver's Fatigue Through Steering Motion[C].// IEEE Int. Conf Syst. USA:Man Cybern,2005(2):1765-1770.
[24]King L M, Nguyen H T, Lal S K L. Early Driver Fatigue Detection From Electroencephalography Signals using Artificial Neural Networks[C].//IEEE. Proceedings of the 28th IEEE EMBS Annual International Conference. USA:IEEE, 2006:2187-2190.
[25]Calcagnini G, Biancalana G, Giubilei F, et al. Spectral Analysis of Heart Rate Variability Signal During Sleep Stages[C],//IEEE. Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. USA:IEEE,1994:1252-1253.
[26]Jeong I C, Lee D H, Park S W, et al.Automobile Driver's Stress Index Provision System that Utilizes Electrocardiogram[C].//IEEE. IEEE Intelligent Vehicles Symposium. Turkey:IEEE,2007:652-656.
[27]Zengyong Li, Ming Zhang, Xiaoyin Zhang,et al. Assessment of Cerebral Oxygenation During Prolonged Simulated Driving sing Near Infrared Spectroscopy [J]. European Journal of Applied Physiology.2009,107(3):281-287.
[28]Philip W, Kithil. Driver Alertness Detection Research Using Capacitive Sensor Array[C].//D V MCGEHEE. Proceedings of the First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. USA:SAE,2001:325-329.
[29]Mandalapu S, Preeti Bj. Driver Fatigue Detection Using Mouth and Yawning Analysis[J]. IJCSNS International Journal of Computer Science and Network Security.2008,8(6):183-188.
[30]Qiang Ji, Zhiwei Zhu, Peilin Lan. Real Time Nonintrusive Monitoring and Prediction of Driver Fatigue[J]. IEEE Transactions on Vehicular Technology,2004, 53(4):1052-1068.
[31]Qiang Ji, Xiaojie Yang. Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance[J]. Real-Time Imaging.2002,8(5):357-377.
[32]P Ekman, W Friesen. Facial Action Coding System:A Technique for the Measurement of Facial Movement[M]. Consulting Psychologists Press, Palo Alto, CA, 1978.
[33]Esra Vural, Mujdat Cetin, Aytul Ercil et al. Drowsy Driver Detection Through Facial Movement Analysis[C].//IEEE.Proceedings of the 2007 IEEE International Conference on Human-computer Interaction. Germany:Springer Berlin Heidelberg, 2007:6-18.
[34]Zhengyou Zhang, Michael Lyons, Michael Schuster, et al. Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron[C].//IEEE. Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition. Japan:IEEE Computer Society,1998:454-459.
[35]Ira Cohen, Nicu Sebe, Ashutosh Garg, et al. Facial Expression Recognition from Video Sequences:Temporal and Static Modeling[J]. Computer Vision and Image Understanding.2003,91(1-2):160-187.
[36]Demb J B, Desmond J E, Wager A D, et al. Semantic Encoding and Retrieval in the Left Inferior Prefrontal Cortex:a Functional MRI Study of Task Difficulty and Process Specificity [J]. Journal of Neuroscience,1995,15(9):5870-5878.
[37]John D E, Gabrieli, Russell A, Poldrack, et al. The Role of Left Prefrontal Cortex in Language and Memory[J]. PNAS.1998,95(3):906-913.
[38]Charan R, Robert T K. Prefrontal Cortex and Episodic Memory:Integrating Findings from Neuropsychology and Functional Brain Imaging[J]. Neuroscientist. 2007,13(3):280-291.
[39]Clayton E, Curtis, Mark D Esposito. Persistent Activity in the Prefrontal Cortex During Working Memory[J]. TRENDS in Cognitive Sciences.2003,7(9):415-423.
[40]West, Robert L. An Application of Prefrontal Cortex Function Theory to Cognitive Aging[J]. Psychological Bulletin,1996,120(2):272-292.
[41]Earl K M, Jonathan D C. An Integrative Theory of Prefrontal Cortex Function [J]. Annual Review of Neuroscience.2001,24:167-202.
[42]Roberts, Angela C. The Prefrontal Cortex:Executive and Cognitive Functions[M]. New York, NY, US:Oxford University Press.1998.
[43]Akira M, Naomi P F, Michael J, et al. The Unity and Diversity of Executive Functions and Their Contributions to Complex "Frontal Lobe" Tasks:A Latent Variable Analysis[J]. Cognitive Psychology.2000,41(1):49-100.
[44]Jeremy R G, Todd S B, Marcus E R. Integration of Emotion and Cognition in the Lateral Prefrontal Cortex[J]. PNAS.2002,99(6):4115-4120.
[45]Rudolf N C, John A P, Jeremy H, et al. Emotion and Motivation:the Role of the Amygdala, Ventral Striatum, and Prefrontal Cortex[J]. Neuroscience & Biobehavioral Reviews.2002,26(3):321-352.
[46]Critchley H, Daly E, Phillips M, et al. Explicit and Implicit Neural Mechanisms for Processing of Social Information From Facial Expressions:a Functional Magnetic Resonance Imaging Study [J]. Hum Brain Mapp.2000,9(2):93-105.
[47]Haxby JV, Hoffman EA, Gobbini MI. The Distributed Human Neural System for Face Perception[J]. Trends Cogn Sci.2000,4(6):223-233.
[48]Phillips ML, Drevets WC, Ranch SL, et al. Neurobiology of Emotion Perception I: The Neural Basis of Normal Emotion Perception [J]. Biological Psychiatry.2003,54(5): 504-514.
[49]Ide K, Horn A, Secher N H. Cerebral Metabolic Response to Submaximal Exercise[J]. Journal of Applied Physiology 1999,87(5):1604-1608.
[50]Van Duinen H, Renken R, Maurits N, Zijdewind I. Effects of Motor Fatigue on Human Brain Activity, an fMRI Study[J]. Neuroimage.2007,35(4):1438-1449.
[51]Rupp T, Perrey S, Eur J. Prefrontal Cortex Oxygenation and Neuromuscular Responses to Exhaustive Exercise[J]. European Journal of Applied Physiology.2007, 102(2):153-163.
[52]Shibuya K, Tachi M. Oxygenation in the Motor Cortex During Exhaustive Pinching Exercise[J]. Respiratory Physiology & Neurobiology.2006,153(3):261-266.
[53]Baddeley A D. Working Memory and Language:An Overview[J].Communication Disorders,2003,6(3):189-208.
[54]Smith E E, Jonides J. Storage and Executive Processes in the Frontal Lobes[J]. Science,1999,283(5408):1657-1661.
[55]Jonathan D, Cohen, Steven D, et al. Activation of the Prefrontal Cortex in a Nonspatial Working Memory Task With Functional MRI[J]. Human Brain Mapping.1993,1(4):293-304.
[56]Prabhakaran V, Narayanan K, Zhao Z, et al. Integration of Diverse Information in Working Memory Within theFrontal Lobe[J]. Nat Neurosci.2000,3(1):85-90.
[57]Heller W, Nitschke JB, Etienne MA, et al. Patterns of Regional Brain Activity Differentiate Types of Anxiety[J]. J Abnorm Psychol.1997;106(3):376-385.
[58]Moratti S, Keil A, Stolarova M. Motivated Attention in Emotional Picture Processing is Reflected by Activity Modulation in Cortical Attention Networks[J]. Neuroimage.2004;21(3):954-964.
[59]Kevin K, Tremper, Steven J. et al. Pulse Oximetry[J]. Anesthesiology,1989, 70(1):98-108.
[60]Alan W Salmoni, Adam P Cann, E. Kent Gillin, et al. Case Studies in Whole-body Vibration Assessment in the Transportation Industry Challenges in the Field[J]. International Journal of Industrial Ergonomics.2008,38(9):783-791.
[61]Mohammad Muzammil, Saad S Siddiqui, Faisal Hasan. Physiological Effect of Vibrations on Tractor Drivers under Variable Ploughing Conditions [J]. Journal of Occupational Health 2004,46:403-409.
[62]Kun Jiao, ZengYong Li, Ming Chen, et al. Effect of Different Vehicle Speeds on Mental Fatigue in Healthy Drivers[C].//Society of Automotive Engineers. SAE 2004 World Congress & Exhibition, USA:Society of Automotive Engineers,2004:13-17.
[63]De Jesus Gomez M, Sierra G,Helie F, et al. Assessment of Autonomic Function Indicators of Heart Rate Variability Aimed to Assess Cumulative Fatigue [C].//IEEE. Proceedings of the 19th Annual International Conference of the IEEE. USA:IEEE, 1997:306-307.
[64]Helmut SEIDEL. On the Relationship between Whole-body Vibration Exposure and Spinal Health Risk[J]. Industrial Health.2005,43(3):361-377.
[65]Pierre Thiffault, Jacques Bergeron. Monotony of Road Environment and Driver Fatigue:a Simulator Study [J]. Accident Analysis and Prevention.2003,35(3):381-391
[66]Zhang Xing-Hai. Alcohol Metabolism Mechanism and Toxicity Mechanism[J]. Journal of Hubei Medical Staff College,1998, (2):50-52.
[67]M Soyka, C Aichmuller, U Preuss, et al. Effects of Acamprosate on Psychomotor Performance and Driving Ability in Abstinent Alcoholics[J]. Pharmacopsychiatry, 1998,31(6):232-235.
[68]Weiler JM, Bloomfield JR, Woodworth G G, et al. Effects of Fexofenadine, Diphenhydramine, and Alcohol on Driving Performance. A randomized, Placebo-controlled Trial in the Iowa Driving Simulator [J]. Ann Intern Med,2000, 132(5):354-363.
[69]Burian SE, Liguori A, Robinson JH. Effects of Alcohol on Risk-taking during Simulated Driving[J]. Hum Psychopharmacol.2002,17(3):141-150.
[70]J G Ramaekers, G Berghaus, M van Laar, et al. Dose Related Risk of Motor Vehicle Crashes after Cannabis Use[J]. Drug and Alcohol Dependence.2004,73(2): 109-119.
[71]Scott MacDonald, R Mann, M Chipman, B Pakula, et al. Driving Behavior Under the Influence of Cannabis or Cocaine[J]. Traffic Injury Prevention.2008,9(3): 190-194.
[72]Silber Beata Y, Papafotiou, Katherine, Croft Rodney J, et al. The effects of Dexamphetamine on Simulated Driving Performance [J]. Psychopharmacology.2005, 179(3):536-543.
[73]R Haselhorst, K M Diirsteler-MacFarland, K Scheffler, et al. Frontocortical N-acetylaspartate Reduction Associated With Long-term i. v. Heroin Use[J]. Neurology.2002,58(2):305-307.
[74]Verstraete A G. Which Medicinal Drugs Impair Driving Performance? An Overview of the European Experience[C].//Swedish Road Administration. Preceedings of T2000-15TH Conference on Alcohol, Drugs and Traffic Safty. Sweden: Swedish Road Administration,2000:781-787.
[75]Melamed Samuel, Shirom Arie, Toker, at al. Burnout and Risk of Cardiovascular Disease:Evidence, Possible Causal Paths, and Promising Research Directions[J]. Psychological Bulletin,2006,132(3):327-353.
[76]Kristin Falk, Karl Swedberg, Fannie Gaston-Johansson, et al. Fatigue and Anaemia in Patients with Chronic Heart Failure[J]. European Journal of Heart Failure. 2006,8(7):744-749.
[77]Klaus K Witte, Andrew L Clark, Why does Chronic Heart Failure Cause Breathlessness and Fatigue?[J]. Prog Cardiovasc Dis.2007,49(5):366-384. [78]Liew G, Mitchell P, Wong TY. Systemic Management of Diabetic Retinopathy[J]. BMJ.2009;12:338-b441.
[79]Tesfaye S. Advances in the Management of Diabetic Peripheral Neuropathy [J]. Current Opinion in Supportive and Palliative Care.2009,3(2):136-143.
[80]J Ditzel. The Problem of Tissue Oxygenation in Diabetes Mellitus[J]. Acta Medica Scandinavica.1975,197(s578):69-83.
[81]Hiroto Miura, Ruth E Wachtel, Fausto R Loberiza Jr, et al. Diabetes Mellitus Impairs Vasodilation to Hypoxia in Human Coronary Arterioles:Reduced Activity of ATP-sensitive Potassium Channels [J]. Circulation Research.2003,92(2):151-158.
[82]Ollinger J M, Fessler J A. Positron-emission Tomography [J]. Signal Processing Magazine.1997,14(1):43-55.
[83]Ogawa S, Lee T M, Kay A R, et al. Brain Magnetic Resonance Imaging with Contrast Dependent on Blood Oxygenation[J]. PNAS.1990,87(24):9868-9872. [84]Sfareni R, Boffi A, Quaresima V, et al. Near Infrared Absorption Spectra of Human Deoxy-and Oxyhaemoglobin in the Temperature Range 20-40 Degrees C[J]. Biochimica et Biophysica Acta.1997,1340(2):165-169.
[85]Ito H, Kanno I, Kato C, et al. Database of Normal Human Cerebral Blood Flow, Cerebral Blood Volume, Cerebral Oxygen Extraction Fraction and Cerebral Metabolic Rate of Oxygen Measured by Positron Emission Tomography with 15O-labelled Carbon Dioxide or Water, Carbon Monoxide and Oxygen:a Multicentre Study in Japan[J]. Eur J Nucl Med Mol Imaging 2004,31(5):635-643.
[86]Yamaguchi T, Kanno I, Uemura K, Ogawa T, et al. Reduction in Regional Cerebral Metabolic Rate of Oxygen During Human Aging[J]. Stroke 1986,17(6): 1220-1228.
[87]Leenders KL, Perani D, Lammertsma AA, et al. Cerebral Blood Flow, Blood Volume and Oxygen Utilization. Normal Values and Effect of Age[J]. Brain 1990, 113(1):27-47.
[88]Hatazawa J, Fujita H, Kanno I, et al. Regional Cerebral Blood Flow, Blood Volume, Oxygen Extraction Fraction, and Oxygen Utilization Rate in Normal Volunteers Measured by the Autoradiographic Technique and the Single Breath Inhalation Method[J]. Ann Nucl Med 1995; 9(1):15-21.
[89]Koyama M, Kawashima R, Ito H, et al. SPECT imaging of normal subjects with technetium-99m-HMPAO and technetium-99m-ECD[J]. J Nucl Med 1997;38: 587-592.
[90]Inoue K, Nakagawa M, Goto R, et al. Regional Differences between 99mTc-ECD and 99mTc-HMPAO SPET in Perfusion Changes with Age and Gender in Healthy Adults[J]. Eur J Nucl Med Mol Imaging.2003,30:1489-1497.
[91]Martin AJ, Friston KJ, Colebatch JG, Frackowiak RS. Decreases in Regional Cerebral Blood Flow with Normal Aging[J]. J Cereb Blood Flow Metab.1991,11: 684-689.
[92]Goto R, Kawashima R, Ito H, et al. A Comparison of Tc-99m HMPAO Brain SPECT Images of Young and Aged Normal Individuals[J]. Ann Nucl Med 1998,12: 333-339.
[95]Paulson OB, Strandgaard S, Edvinsson L. Cerebral Autoregulation[J]. Cerebrovasc Brain Metab Rev 1990,2(2):161-192.
[96]Ekta Khandelwal, Ashok Kumar Jaryal, K K Deepak. Cardiovascular Autonomic Functions & Cerebral Autoregulation in Patients with Orthostatic Hypotension[J]. Indian J MED RES.2011,134(4):463-469.
[97]Powers W J, Grubb R L, Raichle M E. Physiological Responses to Focal Cerebral ischemia in Humans[J]. Ann Neurol.1984,16(5):546-552.
[98]Ito H, Yokoyama I, Iida H, et al. Regional Differences in Cerebral Vascular Response to PaCO2 Changes in Humans Measured by Positron Emission Tomography[J]. Journal of Cerebral Blood Flow & Metabolism 2000,20(8): 1264-1270.
[99]Ian Driver, Nicholas Blockley, Joseph Fisher, et al. The Change in Cerebro vascular Reactivity between 3T and 7T Measured using Graded Hypercapnia[J]. NeuroImage 2010,51(1),274-279.
[100]Shimosegawa E, Kanno I, Hatazawa J, et al. Photic Stimulation Study of Changing the Arterial Partial Pressure Level of Carbon Dioxide[J]. J Cereb Blood Flow Metab 1995,15(1):111-114.
[101]Ito H, Kanno I, Ibaraki M, et al. Changes in Human Cerebral Blood Flow and Cerebral Blood Volume during Hypercapnia and Hypocapnia Measured by Positron Emission Tomography [J]. J Cereb Blood Flow Metab.2003,23(6):665-670.
[102]Vafaee MS, Gjedde A. Model of Blood-brain Transfer of Oxygen Explains Nonlinear Flow-metabolism Coupling during Stimulation of Visual Cortex[J]. J Cereb Blood Flow Metab.2000,20:747-754.
[103]Ito H, Ibaraki M, Kanno I, et al. Changes in Cerebral Blood Flow and Cerebral Oxygen Metabolism during Neural Activation Measured by Positron Emission Tomography:Comparison with Blood Oxygenation Level Dependent Contrast Measured by Functional Magnetic Resonance Imaging[J]. J Cereb Blood Flow Metab 2005,25:371-377.
[104]Brian N P, Ben A I, Ralph D F. Analysis of Oxygen Metabolism Implies a Neural Origin for the Negative BOLD Response in Human Visual Cortex[J]. NeuroImage.2007,36(2):269-276.
[105]Ai-Ling Lin, Peter T. Fox, Jean Hardie, et al. Nonlinear Coupling between Cerebral Blood Flow, Oxygen Consumption, and ATP Production in Human Visual Cortex[J]. Proc Natl Acad Sci USA 2010,107(18):8446-8451.
[106]Kenneth K. Kwong, John W. Belliveav, David A. Chesler, et al. Dynamic Magnetic Resonance Imaging of Human Brain Activity during Primary Sensory Stimulation. Proc Natl Acad Sci USA,1992,89(12):5675-5679.
[107]Raichle ME. Behind the Scenes of Functional Brain Imaging:a Historical and Physiological Perspective[J]. Proc Natl Acad Sci USA 1998;95(3):765-772.
[108]Ulrich Schridde, Manjula Khubchandani, Joshua E. Motelow, et al. Negative BOLD with Large Increases in Neuronal Activity. Cerebral Cortex.2008,18(8): 1814-1827.
[109]Kaoru Sakatani, Yoichi Katayama, Takamitsu Yamamoto, et al. Changes in Cerebral Blood Oxygenation of the Frontal Lobe Induced by Direct Electrical Stimulation of Thalamus and Globus Pallidus:a Near Infrared Spectroscopy Study[J]. J Neurol Neurosurg Psychiatry.1999;67(6):769-773.
[110]Hiroshi Ito, Kazuhiro Takahashi, Jun Hatazawa, et al. Changes in Human Regional Cerebral Blood Flow and Cerebral Blood Volume During Visual Stimulation Measured by Positron Emission Tomography [J]. Journal of Cerebral Blood Flow and Metabolism [J]. Journal of Cerebral Blood Flow and Metabolism.2001,21(5):608-612.
[111]Ito H, Ibaraki M, Kanno I, et al. Changes in the Arterial Fraction of Human Cerebral Blood Volume During Hypercapnia and Hypocapnia Measured by Positron Emission Tomography [J]. J Cereb Blood Flow Metab.2005,25(7):852-857.
[112]Sergio Fantini. A Haemodynamic Model for the Physiological Interpretation of in vivo Measurements of the Concentration and Oxygen Saturation of Haemoglobin[J]. Physics in Medicine and Biology.2002,47(18):249-257.
[113]Matthews F, Pearlmutter B A, Ward T E, et al. Hemodynamics for Brain-Computer Interfaces [J]. Signal Processing Magazine.2008,25(1):87-94.
[114]Shirley M Coyle, Tomas E Ward, Charles M Markham. Brain-computer Interface Using a Simplified Functional Near-infrared Spectroscopy System [J]. Journal of Neural Engineering.2007,4(3):219-226.
[115]Ranganatha Sitarama, Andrea Caria, Niels Birbaumer. Hemodynamic Brain-computer Interfaces for Communication and Rehabilitation[J]. Neural Networks.2009,22(9):1320-1328.
[116]Takafumi Nagaoka, Kaoru Sakatani, Takayuki Awano, Development of a New Rehabilitation System Based on a Brain-Computer Interface Using Near-Infrared Spectroscopy [J]. Biomedical and Life Sciences.2010,662(5):497-503.
[117]Vishwajeet Singh, K P Miyapuram, Raju S Bapi. Detection of Cognitive States from fMRI Data using Machine Learning Techniques[C].//IEEE. IJCAI'07 Proceedings of the 20th International Joint Conference on Artifical Intelligence. USA: Morgan Kaufmann,2007:587-592.
[118]Sergio Fantin, Payal Aggarwa, Kathleen Che, et al. Near-infrared Spectroscopy and Polysomnography During All-night Sleep in Human Subjects[C].//SPIE. SARATOV FALL Meeting 2002:Optical Technologies in Biophysics and Medicine IV 5068. USA:SPIE,2002:155-162.
[119]Christopher O Olopade, Edward Mensah, Rajarsi Gupta, et al. Noninvasive Determination of Brain Tissue Oxygenation during Sleep in Obstructive Sleep Apnea: A Near-Infrared Spectroscopic Approach[J]. Sleep.2007,30(12):1747-1755.
[120]Mazoyer B, Zago L, Mellet E, et al. Cortical Networks for Working Memory and Executive Functions Sustain the Conscious Resting State in Man[J].Brain Res Bull, 2001,54(3):287-298.
[121]Raichle M E, MacLeod A M, Snyder A Z, et al. A Default Mode of Brain Function[J]. Proc Natl Acad Sci USA,2001,98(2):676-682.
[122]Peng C K, Buldyrev S V, Goldberger A L, et al. Long-range Correlations in Nucleotide Sequences[J]. Nature,1992,356(6365):168-170.
[123]Kantelhardt Jan W, Zschiegner Stephan A, Koscielny-Bunde Eva, et al. Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series[J]. Physica A,2002,316(1-4):87-114.
[124]Pena MA, Echeverria JC, Garcia MT, et al. Applying Fractal Analysis to Short Sets of Heart Rate Variability Data[J]. Medical and Biological Engineering and Computing.2009,47(7):709-717.
[125]Thomas Penzel, Jan W Kantelhardt, Ludger Grote, et al. Comparison of Detrended Fluctuation Analysis and Spectral Analysis for Heart Rate Variability in Sleep and Sleep Apnea[J]. IEEE Trans Biomed Eng.2003;50(10):1143-1151.
[126]Jong-Min Lee, Dae-Jin Kim, In-Young Kim, et al. Nonlinear Analysis of Sleep Stages Using Detrended Fluctuation Analysis:Normal vs Sleep Apnea[J]. Medical Engineering & Physics.2004,26(9):773-776.
[127]J Laufer, E Zhang, P Beard. Quantitative in vivo Measurements of Blood Oxygen Saturation Using Multiwavelength Photoacoustic Imaging[C].//SPIE. The Eighth Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics. USA:SPIE,2007:64371Z-64371Z-9.
[128]Karabutov A A, Podymova N, Letokhov V S. Time-resolved Laser Optoacoustic Tomography of Inhomogeneous Media[J]. Applied Physics B.1996,63(6):545-563.
[129]Esenaliev R O, Larina I V, Larin K V, et al. Optoacoustic Technique for Noninvasive Monitoring of Blood Oxygenation:a Feasibility Study[J]. Appl Opt. 2002,41(22):4722-4731.
[130]Xueding Wang, Yongjiang Pang, Geng Ku, et al. Noninvasive Laser-induced Photoacoustic Tomography for Structural and Functional in vivo Imaging of the Brain[J]. Nature Biotechnology.2003,21(7):803-806.
[131]Jiao S, Jiang M, Hu J, et al. Photoacoustic Ophthalmoscopy for in vivo Retinal Imaging[J]. Optics Express.2010,18(4):3967-3972.
[132]Zhang E Z, Povazay B, Laufer J, et al. Multimodal Photoacoustic and Optical Coherence Tomography Scanner Using an All Optical Detection Scheme for 3D Morphological Skin Imaging[J]. Biomed Opt Express.2011,2(8):2202-2215.
[133]Wang Shiou-han, Wei Chen-wei. Jee Shiou-hwa, et al. Quantitative Thermal Imaging for Plasmonic Photothermal Therapy[J]. J Med Biol Eng.2011,31(6): 387-393.
[134]Rousseau G, Blouin A, Monchalin JP. Non-contact Photoacoustic Tomography and Ultrasonography for Tissue Imaging[J]. Biomed Opt Express.2012,3(1):16-25
[135]Esenaliev R O, Irina I V, Larina K V. et al. Optoacoustic Technique for Noninvasive Monitoring of Blood Oxygenation:A Feasibility Study [J]. Applied Optics.2002,41(22):4722-4731.
[136]Kosterev A A, Bakhirkin Yu A, Curl R F, et al. Quartz-enhanced Photoacoustic Spectroscopy[J]. Optics Letters.2002,27(21):1902-1904.
[137]Kosterev A A, Buerki P R, Dong L, et al. QEPAS Detector for Rapid Spectral Measurements[J]. Appl Phys B.2010,100(1):173-180.
[138]Chen X, Cheng L, Guo D, et al. Quantum Cascade Laser Based Standoff Photoacoustic Chemical Detection[J]. Opt Express.2011,19(21):20251-20257.
[139]Wang K, Chien H T, Raptis A, et al. Remote Detection of O3 in an Open Environment using Photoacoustic Spectroscopy[R]. USA:Argonne National Laboratory,2011.
[140]J M Friedt, E Carry. Introduction to the Quartz Tuning Fork[J]. American Journal of Physics.2007,75(5):415-435.
[141]Kosterev A A, Tittel K K. Applications of Quartz Tuning Forks in Spectroscopic Gas Sensing[J]. Review of Scientific Instruments.2005,76(4):0431050-0431059.
[142]Jorge Otero, Laura Gonzalez, Manel Puig-Vidal. Nanocharacterization of Soft Biological Samples in Shear Mode with Quartz Tuning Fork Probes[J]. sensors.2012, 12(4):4803-4819.
[143]Dong L, Kosterev A A, Thomazy D, et al. QEPAS Spectrophones:Design, Optimization, and Performance[J]. Applied Physics B.2010,100(3):627-635.
[144]Bohren C F, Huffman D R. Absorption and Scattering of Light by Small Particles[M]. New York:A Wiley-interscience Publication.1983:28-30.
[145]Paul Beard. Biomedical Photoacoustic Imaging[J]. Interface Focus.2011,1(4): 602-631.
[146]M Xu, L V Wang. Photoacoustic Imaging in Biomedicine. Review of Scientific Instruments.2006,77(4):041101-041122.
[147]Jing Wang, Tan Liu, Shuliang Jiao, et al. Saturation Effect in Functional Photoacoustic Imaging[J]. Journal of Biomedical Optics.2010,15(2): 021317-021317-5.
[148]丁辉.计算超声学—声场分析及应用[M].北京：科学出版社.2010：10-15.
[149]J Lanfer,C Elwell. D Delpy et al. Pulsed Near-infrared Photoacoustic Spectroscopy of Blood[C].//SPIE. Proc of SPIE. USA:SPIE,2004:57-68.
[150]Meng-Lin Li, Jung-Taek Oh, Xueyi Xie, et al. Simultaneous Molecular and Hypoxia Imaging of Brain Tumors In Vivo Using Spectroscopic Photoacoustic Tomography[C].//IEEE. Proceedings of the IEEE. USA:IEEE Institute of Electrical and Electronics,2008,96(3):481-489.
[151]Horn B K P, Schunck B G. Determining Optical Flow[J]. Artificial Intelligence, 1981,17(1/2/3):185-204.
[152]He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3): 328-340.
[153]夏利民,张良春.基于自适应粒子滤波器的物体跟踪[J].中国图象图形学报.2009,14(1)：112-117.
[154]Turk M, Pentland A P. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience,1991,3(1):71-86.
[155]林士敏,田凤占,陆玉昌.贝叶斯网络的建造及其在数据采掘中的应用[J1.清华大学学报(自然科学版),2001,41(1)：49-52.
[156]杜友田,陈峰,徐文立.基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法[J].自动化学报.2009,35(3)：225-232.
[157]王永强,律方成,李和明.基于粗糙集理论和贝叶斯网络的电力变压器故障诊断方法[J].中国电机工程学报.2006,26(8)：137-141.
[158]王成刚,周晓东,王学伟.基于贝叶斯网络的复杂装备测试性评估[J].电子测量与仪器学报.2009,23(5)：17-21.
[159]杨旗,薛定宇.基于双尺度动态贝叶斯网络及多信息融合的步态识别.电子与信息学报.2012,34(5)：1148-1153.
[2]李昌吉.汽车驾驶员的人为因素与交通安全[J].疾病控制杂志.2004,8(6)：576-578.
[3]European Commission. Annual Statistics Report 2011[EB/OL]. European: European Commission,2012-02-06 [2012-05-26]. http://ec.europa. eu/transport/ roadsafety_library/care/doc/safetynet/2011/.
[4]DL Hendricks.The Relative Frequency of Unsafe Driving ACTS in Serious Traffic Crashes[EB/OL].USA:National Highway Traffic Safety Administration,2001-05-14 [2012-05-26].http://www. nhtsa. gov/Data/.
[5]R Vivoli, M Bergomi. Biological and behavioral factors affecting driving safety[J]. Journal of Preventive Medicine and Hygiene.2006,47:69-73.
[6]Jane C. The Role of Driver Distraction in Traffic Crashes[EB/OL].USA:AAA Foundation for Traffic Safety,2001-12-06[2012-05-26]. HTTP://www. aaafoundation. Org.
[7]NHTSA. Alcohol-Impaired Driving 2009 Data[EB/OL].USA:NHTSA,2010-03-25 [2012-05-26]. http://www.nhtsa.gov/Data/.
[8]European Commission.Drinking and driving[EB/OL]. European:European Commission,2010-10-01 [2012-05-26]. http://ec.europa.eu/transport/road_safety/.
[9]NHTSA. A Highway Safety Countermeasure Guide 2011[EB/OL]. USA:NHTSA, 2012-02-18 [2012-05-26].http://www. nhtsa.gov/.
[10]EC. Fatigue[EB/OL]. European:European Commission,2011-07-25[2012-05-26]. http://ec.europa.eu/transport/roadsafety/specialist/knowledge.
[11]NHTSA. Research on Drowsy Driving[EB/OL]. USA:NHTSA,2009-10-28 [2012-05-26]. http://www.nhtsa.gov/Driving+ Safety/.
[12]Treuls Vaa. Impairments, Diseases, Age and Their Relative Risks of Accident Involvement:Results from Meta-analysis[R]. Norway:Institute of Transport Economics,2003.
[13]Daniel J. Cox. Physiological and Performance Differences Between Drivers With Type 1 Diabetes With and Without a Recent History of Driving Mishaps:An Exploratory Study[J]. Canadian Journal of Diabetes.2003;27(1):23-28.
[14]岳睿.警用呼气式酒精传感器的研究进展[J].化学传感器.2006,26(3)：6-10.
[15]于勇,王俊,周鸣.电子鼻技术的研究进展及其在农产品加工中的应用[J].浙江大学学报(农业与生命科学版).2003,29(5)：579-584.
[16]中华人民共和国司法部.血液中乙醇测定的顶空气相色谱法[S].北京：中华人民共和国司法部,2010.
[17]林涛,于海燕,应义斌.可见/近红外光谱技术在液态食品检测中的应用[J].光谱与光谱分析.2008,28(2)：285-290.
[18]许峰,王海龙,关亚风.离子迁移谱研究进展[J].化学进展.2005,17(3)：514-521.
[19]蔡禾,郭雪娇,和挺等.太赫兹技术及其应用研究进展[J].中国光学与应用光学.2010,3(3)：209-221.
[20]NHTSA, AAMVA. Driver Fitness Medical Guidelines[EB/OL]. USA:NHTSA, 2010-08-22 [2012-05-26]. http://www.nhtsa. gov/.
[21]Claire C, Wang MD. Physician's Guide to Assessing and Counseling Older Drivers[EB/OL]. USA:American Medical Association,2004-08-01 [2012-05-26]. http://www.ama-assn.org/ama/.
[22]Jason J, et al. Noninvasive Blood Glucose Measurements by Near-Infrared Transmission Spectroscopy Across Human Tongues[J]. Diabetes Technology & Therapeutics.2000,2(1):5-16.
[23]Takei Y, Furukawa Y. Estimate of Driver's Fatigue Through Steering Motion[C].// IEEE Int. Conf Syst. USA:Man Cybern,2005(2):1765-1770.
[24]King L M, Nguyen H T, Lal S K L. Early Driver Fatigue Detection From Electroencephalography Signals using Artificial Neural Networks[C].//IEEE. Proceedings of the 28th IEEE EMBS Annual International Conference. USA:IEEE, 2006:2187-2190.
[25]Calcagnini G, Biancalana G, Giubilei F, et al. Spectral Analysis of Heart Rate Variability Signal During Sleep Stages[C],//IEEE. Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. USA:IEEE,1994:1252-1253.
[26]Jeong I C, Lee D H, Park S W, et al.Automobile Driver's Stress Index Provision System that Utilizes Electrocardiogram[C].//IEEE. IEEE Intelligent Vehicles Symposium. Turkey:IEEE,2007:652-656.
[27]Zengyong Li, Ming Zhang, Xiaoyin Zhang,et al. Assessment of Cerebral Oxygenation During Prolonged Simulated Driving sing Near Infrared Spectroscopy [J]. European Journal of Applied Physiology.2009,107(3):281-287.
[28]Philip W, Kithil. Driver Alertness Detection Research Using Capacitive Sensor Array[C].//D V MCGEHEE. Proceedings of the First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. USA:SAE,2001:325-329.
[29]Mandalapu S, Preeti Bj. Driver Fatigue Detection Using Mouth and Yawning Analysis[J]. IJCSNS International Journal of Computer Science and Network Security.2008,8(6):183-188.
[30]Qiang Ji, Zhiwei Zhu, Peilin Lan. Real Time Nonintrusive Monitoring and Prediction of Driver Fatigue[J]. IEEE Transactions on Vehicular Technology,2004, 53(4):1052-1068.
[31]Qiang Ji, Xiaojie Yang. Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance[J]. Real-Time Imaging.2002,8(5):357-377.
[32]P Ekman, W Friesen. Facial Action Coding System:A Technique for the Measurement of Facial Movement[M]. Consulting Psychologists Press, Palo Alto, CA, 1978.
[33]Esra Vural, Mujdat Cetin, Aytul Ercil et al. Drowsy Driver Detection Through Facial Movement Analysis[C].//IEEE.Proceedings of the 2007 IEEE International Conference on Human-computer Interaction. Germany:Springer Berlin Heidelberg, 2007:6-18.
[34]Zhengyou Zhang, Michael Lyons, Michael Schuster, et al. Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron[C].//IEEE. Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition. Japan:IEEE Computer Society,1998:454-459.
[35]Ira Cohen, Nicu Sebe, Ashutosh Garg, et al. Facial Expression Recognition from Video Sequences:Temporal and Static Modeling[J]. Computer Vision and Image Understanding.2003,91(1-2):160-187.
[36]Demb J B, Desmond J E, Wager A D, et al. Semantic Encoding and Retrieval in the Left Inferior Prefrontal Cortex:a Functional MRI Study of Task Difficulty and Process Specificity [J]. Journal of Neuroscience,1995,15(9):5870-5878.
[37]John D E, Gabrieli, Russell A, Poldrack, et al. The Role of Left Prefrontal Cortex in Language and Memory[J]. PNAS.1998,95(3):906-913.
[38]Charan R, Robert T K. Prefrontal Cortex and Episodic Memory:Integrating Findings from Neuropsychology and Functional Brain Imaging[J]. Neuroscientist. 2007,13(3):280-291.
[39]Clayton E, Curtis, Mark D Esposito. Persistent Activity in the Prefrontal Cortex During Working Memory[J]. TRENDS in Cognitive Sciences.2003,7(9):415-423.
[40]West, Robert L. An Application of Prefrontal Cortex Function Theory to Cognitive Aging[J]. Psychological Bulletin,1996,120(2):272-292.
[41]Earl K M, Jonathan D C. An Integrative Theory of Prefrontal Cortex Function [J]. Annual Review of Neuroscience.2001,24:167-202.
[42]Roberts, Angela C. The Prefrontal Cortex:Executive and Cognitive Functions[M]. New York, NY, US:Oxford University Press.1998.
[43]Akira M, Naomi P F, Michael J, et al. The Unity and Diversity of Executive Functions and Their Contributions to Complex "Frontal Lobe" Tasks:A Latent Variable Analysis[J]. Cognitive Psychology.2000,41(1):49-100.
[44]Jeremy R G, Todd S B, Marcus E R. Integration of Emotion and Cognition in the Lateral Prefrontal Cortex[J]. PNAS.2002,99(6):4115-4120.
[45]Rudolf N C, John A P, Jeremy H, et al. Emotion and Motivation:the Role of the Amygdala, Ventral Striatum, and Prefrontal Cortex[J]. Neuroscience & Biobehavioral Reviews.2002,26(3):321-352.
[46]Critchley H, Daly E, Phillips M, et al. Explicit and Implicit Neural Mechanisms for Processing of Social Information From Facial Expressions:a Functional Magnetic Resonance Imaging Study [J]. Hum Brain Mapp.2000,9(2):93-105.
[47]Haxby JV, Hoffman EA, Gobbini MI. The Distributed Human Neural System for Face Perception[J]. Trends Cogn Sci.2000,4(6):223-233.
[48]Phillips ML, Drevets WC, Ranch SL, et al. Neurobiology of Emotion Perception I: The Neural Basis of Normal Emotion Perception [J]. Biological Psychiatry.2003,54(5): 504-514.
[49]Ide K, Horn A, Secher N H. Cerebral Metabolic Response to Submaximal Exercise[J]. Journal of Applied Physiology 1999,87(5):1604-1608.
[50]Van Duinen H, Renken R, Maurits N, Zijdewind I. Effects of Motor Fatigue on Human Brain Activity, an fMRI Study[J]. Neuroimage.2007,35(4):1438-1449.
[51]Rupp T, Perrey S, Eur J. Prefrontal Cortex Oxygenation and Neuromuscular Responses to Exhaustive Exercise[J]. European Journal of Applied Physiology.2007, 102(2):153-163.
[52]Shibuya K, Tachi M. Oxygenation in the Motor Cortex During Exhaustive Pinching Exercise[J]. Respiratory Physiology & Neurobiology.2006,153(3):261-266.
[53]Baddeley A D. Working Memory and Language:An Overview[J].Communication Disorders,2003,6(3):189-208.
[54]Smith E E, Jonides J. Storage and Executive Processes in the Frontal Lobes[J]. Science,1999,283(5408):1657-1661.
[55]Jonathan D, Cohen, Steven D, et al. Activation of the Prefrontal Cortex in a Nonspatial Working Memory Task With Functional MRI[J]. Human Brain Mapping.1993,1(4):293-304.
[56]Prabhakaran V, Narayanan K, Zhao Z, et al. Integration of Diverse Information in Working Memory Within theFrontal Lobe[J]. Nat Neurosci.2000,3(1):85-90.
[57]Heller W, Nitschke JB, Etienne MA, et al. Patterns of Regional Brain Activity Differentiate Types of Anxiety[J]. J Abnorm Psychol.1997;106(3):376-385.
[58]Moratti S, Keil A, Stolarova M. Motivated Attention in Emotional Picture Processing is Reflected by Activity Modulation in Cortical Attention Networks[J]. Neuroimage.2004;21(3):954-964.
[59]Kevin K, Tremper, Steven J. et al. Pulse Oximetry[J]. Anesthesiology,1989, 70(1):98-108.
[60]Alan W Salmoni, Adam P Cann, E. Kent Gillin, et al. Case Studies in Whole-body Vibration Assessment in the Transportation Industry Challenges in the Field[J]. International Journal of Industrial Ergonomics.2008,38(9):783-791.
[61]Mohammad Muzammil, Saad S Siddiqui, Faisal Hasan. Physiological Effect of Vibrations on Tractor Drivers under Variable Ploughing Conditions [J]. Journal of Occupational Health 2004,46:403-409.
[62]Kun Jiao, ZengYong Li, Ming Chen, et al. Effect of Different Vehicle Speeds on Mental Fatigue in Healthy Drivers[C].//Society of Automotive Engineers. SAE 2004 World Congress & Exhibition, USA:Society of Automotive Engineers,2004:13-17.
[63]De Jesus Gomez M, Sierra G,Helie F, et al. Assessment of Autonomic Function Indicators of Heart Rate Variability Aimed to Assess Cumulative Fatigue [C].//IEEE. Proceedings of the 19th Annual International Conference of the IEEE. USA:IEEE, 1997:306-307.
[64]Helmut SEIDEL. On the Relationship between Whole-body Vibration Exposure and Spinal Health Risk[J]. Industrial Health.2005,43(3):361-377.
[65]Pierre Thiffault, Jacques Bergeron. Monotony of Road Environment and Driver Fatigue:a Simulator Study [J]. Accident Analysis and Prevention.2003,35(3):381-391
[66]Zhang Xing-Hai. Alcohol Metabolism Mechanism and Toxicity Mechanism[J]. Journal of Hubei Medical Staff College,1998, (2):50-52.
[67]M Soyka, C Aichmuller, U Preuss, et al. Effects of Acamprosate on Psychomotor Performance and Driving Ability in Abstinent Alcoholics[J]. Pharmacopsychiatry, 1998,31(6):232-235.
[68]Weiler JM, Bloomfield JR, Woodworth G G, et al. Effects of Fexofenadine, Diphenhydramine, and Alcohol on Driving Performance. A randomized, Placebo-controlled Trial in the Iowa Driving Simulator [J]. Ann Intern Med,2000, 132(5):354-363.
[69]Burian SE, Liguori A, Robinson JH. Effects of Alcohol on Risk-taking during Simulated Driving[J]. Hum Psychopharmacol.2002,17(3):141-150.
[70]J G Ramaekers, G Berghaus, M van Laar, et al. Dose Related Risk of Motor Vehicle Crashes after Cannabis Use[J]. Drug and Alcohol Dependence.2004,73(2): 109-119.
[71]Scott MacDonald, R Mann, M Chipman, B Pakula, et al. Driving Behavior Under the Influence of Cannabis or Cocaine[J]. Traffic Injury Prevention.2008,9(3): 190-194.
[72]Silber Beata Y, Papafotiou, Katherine, Croft Rodney J, et al. The effects of Dexamphetamine on Simulated Driving Performance [J]. Psychopharmacology.2005, 179(3):536-543.
[73]R Haselhorst, K M Diirsteler-MacFarland, K Scheffler, et al. Frontocortical N-acetylaspartate Reduction Associated With Long-term i. v. Heroin Use[J]. Neurology.2002,58(2):305-307.
[74]Verstraete A G. Which Medicinal Drugs Impair Driving Performance? An Overview of the European Experience[C].//Swedish Road Administration. Preceedings of T2000-15TH Conference on Alcohol, Drugs and Traffic Safty. Sweden: Swedish Road Administration,2000:781-787.
[75]Melamed Samuel, Shirom Arie, Toker, at al. Burnout and Risk of Cardiovascular Disease:Evidence, Possible Causal Paths, and Promising Research Directions[J]. Psychological Bulletin,2006,132(3):327-353.
[76]Kristin Falk, Karl Swedberg, Fannie Gaston-Johansson, et al. Fatigue and Anaemia in Patients with Chronic Heart Failure[J]. European Journal of Heart Failure. 2006,8(7):744-749.
[77]Klaus K Witte, Andrew L Clark, Why does Chronic Heart Failure Cause Breathlessness and Fatigue?[J]. Prog Cardiovasc Dis.2007,49(5):366-384. [78]Liew G, Mitchell P, Wong TY. Systemic Management of Diabetic Retinopathy[J]. BMJ.2009;12:338-b441.
[79]Tesfaye S. Advances in the Management of Diabetic Peripheral Neuropathy [J]. Current Opinion in Supportive and Palliative Care.2009,3(2):136-143.
[80]J Ditzel. The Problem of Tissue Oxygenation in Diabetes Mellitus[J]. Acta Medica Scandinavica.1975,197(s578):69-83.
[81]Hiroto Miura, Ruth E Wachtel, Fausto R Loberiza Jr, et al. Diabetes Mellitus Impairs Vasodilation to Hypoxia in Human Coronary Arterioles:Reduced Activity of ATP-sensitive Potassium Channels [J]. Circulation Research.2003,92(2):151-158.
[82]Ollinger J M, Fessler J A. Positron-emission Tomography [J]. Signal Processing Magazine.1997,14(1):43-55.
[83]Ogawa S, Lee T M, Kay A R, et al. Brain Magnetic Resonance Imaging with Contrast Dependent on Blood Oxygenation[J]. PNAS.1990,87(24):9868-9872. [84]Sfareni R, Boffi A, Quaresima V, et al. Near Infrared Absorption Spectra of Human Deoxy-and Oxyhaemoglobin in the Temperature Range 20-40 Degrees C[J]. Biochimica et Biophysica Acta.1997,1340(2):165-169.
[85]Ito H, Kanno I, Kato C, et al. Database of Normal Human Cerebral Blood Flow, Cerebral Blood Volume, Cerebral Oxygen Extraction Fraction and Cerebral Metabolic Rate of Oxygen Measured by Positron Emission Tomography with 15O-labelled Carbon Dioxide or Water, Carbon Monoxide and Oxygen:a Multicentre Study in Japan[J]. Eur J Nucl Med Mol Imaging 2004,31(5):635-643.
[86]Yamaguchi T, Kanno I, Uemura K, Ogawa T, et al. Reduction in Regional Cerebral Metabolic Rate of Oxygen During Human Aging[J]. Stroke 1986,17(6): 1220-1228.
[87]Leenders KL, Perani D, Lammertsma AA, et al. Cerebral Blood Flow, Blood Volume and Oxygen Utilization. Normal Values and Effect of Age[J]. Brain 1990, 113(1):27-47.
[88]Hatazawa J, Fujita H, Kanno I, et al. Regional Cerebral Blood Flow, Blood Volume, Oxygen Extraction Fraction, and Oxygen Utilization Rate in Normal Volunteers Measured by the Autoradiographic Technique and the Single Breath Inhalation Method[J]. Ann Nucl Med 1995; 9(1):15-21.
[89]Koyama M, Kawashima R, Ito H, et al. SPECT imaging of normal subjects with technetium-99m-HMPAO and technetium-99m-ECD[J]. J Nucl Med 1997;38: 587-592.
[90]Inoue K, Nakagawa M, Goto R, et al. Regional Differences between 99mTc-ECD and 99mTc-HMPAO SPET in Perfusion Changes with Age and Gender in Healthy Adults[J]. Eur J Nucl Med Mol Imaging.2003,30:1489-1497.
[91]Martin AJ, Friston KJ, Colebatch JG, Frackowiak RS. Decreases in Regional Cerebral Blood Flow with Normal Aging[J]. J Cereb Blood Flow Metab.1991,11: 684-689.
[92]Goto R, Kawashima R, Ito H, et al. A Comparison of Tc-99m HMPAO Brain SPECT Images of Young and Aged Normal Individuals[J]. Ann Nucl Med 1998,12: 333-339.
[95]Paulson OB, Strandgaard S, Edvinsson L. Cerebral Autoregulation[J]. Cerebrovasc Brain Metab Rev 1990,2(2):161-192.
[96]Ekta Khandelwal, Ashok Kumar Jaryal, K K Deepak. Cardiovascular Autonomic Functions & Cerebral Autoregulation in Patients with Orthostatic Hypotension[J]. Indian J MED RES.2011,134(4):463-469.
[97]Powers W J, Grubb R L, Raichle M E. Physiological Responses to Focal Cerebral ischemia in Humans[J]. Ann Neurol.1984,16(5):546-552.
[98]Ito H, Yokoyama I, Iida H, et al. Regional Differences in Cerebral Vascular Response to PaCO2 Changes in Humans Measured by Positron Emission Tomography[J]. Journal of Cerebral Blood Flow & Metabolism 2000,20(8): 1264-1270.
[99]Ian Driver, Nicholas Blockley, Joseph Fisher, et al. The Change in Cerebro vascular Reactivity between 3T and 7T Measured using Graded Hypercapnia[J]. NeuroImage 2010,51(1),274-279.
[100]Shimosegawa E, Kanno I, Hatazawa J, et al. Photic Stimulation Study of Changing the Arterial Partial Pressure Level of Carbon Dioxide[J]. J Cereb Blood Flow Metab 1995,15(1):111-114.
[101]Ito H, Kanno I, Ibaraki M, et al. Changes in Human Cerebral Blood Flow and Cerebral Blood Volume during Hypercapnia and Hypocapnia Measured by Positron Emission Tomography [J]. J Cereb Blood Flow Metab.2003,23(6):665-670.
[102]Vafaee MS, Gjedde A. Model of Blood-brain Transfer of Oxygen Explains Nonlinear Flow-metabolism Coupling during Stimulation of Visual Cortex[J]. J Cereb Blood Flow Metab.2000,20:747-754.
[103]Ito H, Ibaraki M, Kanno I, et al. Changes in Cerebral Blood Flow and Cerebral Oxygen Metabolism during Neural Activation Measured by Positron Emission Tomography:Comparison with Blood Oxygenation Level Dependent Contrast Measured by Functional Magnetic Resonance Imaging[J]. J Cereb Blood Flow Metab 2005,25:371-377.
[104]Brian N P, Ben A I, Ralph D F. Analysis of Oxygen Metabolism Implies a Neural Origin for the Negative BOLD Response in Human Visual Cortex[J]. NeuroImage.2007,36(2):269-276.
[105]Ai-Ling Lin, Peter T. Fox, Jean Hardie, et al. Nonlinear Coupling between Cerebral Blood Flow, Oxygen Consumption, and ATP Production in Human Visual Cortex[J]. Proc Natl Acad Sci USA 2010,107(18):8446-8451.
[106]Kenneth K. Kwong, John W. Belliveav, David A. Chesler, et al. Dynamic Magnetic Resonance Imaging of Human Brain Activity during Primary Sensory Stimulation. Proc Natl Acad Sci USA,1992,89(12):5675-5679.
[107]Raichle ME. Behind the Scenes of Functional Brain Imaging:a Historical and Physiological Perspective[J]. Proc Natl Acad Sci USA 1998;95(3):765-772.
[108]Ulrich Schridde, Manjula Khubchandani, Joshua E. Motelow, et al. Negative BOLD with Large Increases in Neuronal Activity. Cerebral Cortex.2008,18(8): 1814-1827.
[109]Kaoru Sakatani, Yoichi Katayama, Takamitsu Yamamoto, et al. Changes in Cerebral Blood Oxygenation of the Frontal Lobe Induced by Direct Electrical Stimulation of Thalamus and Globus Pallidus:a Near Infrared Spectroscopy Study[J]. J Neurol Neurosurg Psychiatry.1999;67(6):769-773.
[110]Hiroshi Ito, Kazuhiro Takahashi, Jun Hatazawa, et al. Changes in Human Regional Cerebral Blood Flow and Cerebral Blood Volume During Visual Stimulation Measured by Positron Emission Tomography [J]. Journal of Cerebral Blood Flow and Metabolism [J]. Journal of Cerebral Blood Flow and Metabolism.2001,21(5):608-612.
[111]Ito H, Ibaraki M, Kanno I, et al. Changes in the Arterial Fraction of Human Cerebral Blood Volume During Hypercapnia and Hypocapnia Measured by Positron Emission Tomography [J]. J Cereb Blood Flow Metab.2005,25(7):852-857.
[112]Sergio Fantini. A Haemodynamic Model for the Physiological Interpretation of in vivo Measurements of the Concentration and Oxygen Saturation of Haemoglobin[J]. Physics in Medicine and Biology.2002,47(18):249-257.
[113]Matthews F, Pearlmutter B A, Ward T E, et al. Hemodynamics for Brain-Computer Interfaces [J]. Signal Processing Magazine.2008,25(1):87-94.
[114]Shirley M Coyle, Tomas E Ward, Charles M Markham. Brain-computer Interface Using a Simplified Functional Near-infrared Spectroscopy System [J]. Journal of Neural Engineering.2007,4(3):219-226.
[115]Ranganatha Sitarama, Andrea Caria, Niels Birbaumer. Hemodynamic Brain-computer Interfaces for Communication and Rehabilitation[J]. Neural Networks.2009,22(9):1320-1328.
[116]Takafumi Nagaoka, Kaoru Sakatani, Takayuki Awano, Development of a New Rehabilitation System Based on a Brain-Computer Interface Using Near-Infrared Spectroscopy [J]. Biomedical and Life Sciences.2010,662(5):497-503.
[117]Vishwajeet Singh, K P Miyapuram, Raju S Bapi. Detection of Cognitive States from fMRI Data using Machine Learning Techniques[C].//IEEE. IJCAI'07 Proceedings of the 20th International Joint Conference on Artifical Intelligence. USA: Morgan Kaufmann,2007:587-592.
[118]Sergio Fantin, Payal Aggarwa, Kathleen Che, et al. Near-infrared Spectroscopy and Polysomnography During All-night Sleep in Human Subjects[C].//SPIE. SARATOV FALL Meeting 2002:Optical Technologies in Biophysics and Medicine IV 5068. USA:SPIE,2002:155-162.
[119]Christopher O Olopade, Edward Mensah, Rajarsi Gupta, et al. Noninvasive Determination of Brain Tissue Oxygenation during Sleep in Obstructive Sleep Apnea: A Near-Infrared Spectroscopic Approach[J]. Sleep.2007,30(12):1747-1755.
[120]Mazoyer B, Zago L, Mellet E, et al. Cortical Networks for Working Memory and Executive Functions Sustain the Conscious Resting State in Man[J].Brain Res Bull, 2001,54(3):287-298.
[121]Raichle M E, MacLeod A M, Snyder A Z, et al. A Default Mode of Brain Function[J]. Proc Natl Acad Sci USA,2001,98(2):676-682.
[122]Peng C K, Buldyrev S V, Goldberger A L, et al. Long-range Correlations in Nucleotide Sequences[J]. Nature,1992,356(6365):168-170.
[123]Kantelhardt Jan W, Zschiegner Stephan A, Koscielny-Bunde Eva, et al. Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series[J]. Physica A,2002,316(1-4):87-114.
[124]Pena MA, Echeverria JC, Garcia MT, et al. Applying Fractal Analysis to Short Sets of Heart Rate Variability Data[J]. Medical and Biological Engineering and Computing.2009,47(7):709-717.
[125]Thomas Penzel, Jan W Kantelhardt, Ludger Grote, et al. Comparison of Detrended Fluctuation Analysis and Spectral Analysis for Heart Rate Variability in Sleep and Sleep Apnea[J]. IEEE Trans Biomed Eng.2003;50(10):1143-1151.
[126]Jong-Min Lee, Dae-Jin Kim, In-Young Kim, et al. Nonlinear Analysis of Sleep Stages Using Detrended Fluctuation Analysis:Normal vs Sleep Apnea[J]. Medical Engineering & Physics.2004,26(9):773-776.
[127]J Laufer, E Zhang, P Beard. Quantitative in vivo Measurements of Blood Oxygen Saturation Using Multiwavelength Photoacoustic Imaging[C].//SPIE. The Eighth Conference on Biomedical Thermoacoustics, Optoacoustics, and Acousto-optics. USA:SPIE,2007:64371Z-64371Z-9.
[128]Karabutov A A, Podymova N, Letokhov V S. Time-resolved Laser Optoacoustic Tomography of Inhomogeneous Media[J]. Applied Physics B.1996,63(6):545-563.
[129]Esenaliev R O, Larina I V, Larin K V, et al. Optoacoustic Technique for Noninvasive Monitoring of Blood Oxygenation:a Feasibility Study[J]. Appl Opt. 2002,41(22):4722-4731.
[130]Xueding Wang, Yongjiang Pang, Geng Ku, et al. Noninvasive Laser-induced Photoacoustic Tomography for Structural and Functional in vivo Imaging of the Brain[J]. Nature Biotechnology.2003,21(7):803-806.
[131]Jiao S, Jiang M, Hu J, et al. Photoacoustic Ophthalmoscopy for in vivo Retinal Imaging[J]. Optics Express.2010,18(4):3967-3972.
[132]Zhang E Z, Povazay B, Laufer J, et al. Multimodal Photoacoustic and Optical Coherence Tomography Scanner Using an All Optical Detection Scheme for 3D Morphological Skin Imaging[J]. Biomed Opt Express.2011,2(8):2202-2215.
[133]Wang Shiou-han, Wei Chen-wei. Jee Shiou-hwa, et al. Quantitative Thermal Imaging for Plasmonic Photothermal Therapy[J]. J Med Biol Eng.2011,31(6): 387-393.
[134]Rousseau G, Blouin A, Monchalin JP. Non-contact Photoacoustic Tomography and Ultrasonography for Tissue Imaging[J]. Biomed Opt Express.2012,3(1):16-25
[135]Esenaliev R O, Irina I V, Larina K V. et al. Optoacoustic Technique for Noninvasive Monitoring of Blood Oxygenation:A Feasibility Study [J]. Applied Optics.2002,41(22):4722-4731.
[136]Kosterev A A, Bakhirkin Yu A, Curl R F, et al. Quartz-enhanced Photoacoustic Spectroscopy[J]. Optics Letters.2002,27(21):1902-1904.
[137]Kosterev A A, Buerki P R, Dong L, et al. QEPAS Detector for Rapid Spectral Measurements[J]. Appl Phys B.2010,100(1):173-180.
[138]Chen X, Cheng L, Guo D, et al. Quantum Cascade Laser Based Standoff Photoacoustic Chemical Detection[J]. Opt Express.2011,19(21):20251-20257.
[139]Wang K, Chien H T, Raptis A, et al. Remote Detection of O3 in an Open Environment using Photoacoustic Spectroscopy[R]. USA:Argonne National Laboratory,2011.
[140]J M Friedt, E Carry. Introduction to the Quartz Tuning Fork[J]. American Journal of Physics.2007,75(5):415-435.
[141]Kosterev A A, Tittel K K. Applications of Quartz Tuning Forks in Spectroscopic Gas Sensing[J]. Review of Scientific Instruments.2005,76(4):0431050-0431059.
[142]Jorge Otero, Laura Gonzalez, Manel Puig-Vidal. Nanocharacterization of Soft Biological Samples in Shear Mode with Quartz Tuning Fork Probes[J]. sensors.2012, 12(4):4803-4819.
[143]Dong L, Kosterev A A, Thomazy D, et al. QEPAS Spectrophones:Design, Optimization, and Performance[J]. Applied Physics B.2010,100(3):627-635.
[144]Bohren C F, Huffman D R. Absorption and Scattering of Light by Small Particles[M]. New York:A Wiley-interscience Publication.1983:28-30.
[145]Paul Beard. Biomedical Photoacoustic Imaging[J]. Interface Focus.2011,1(4): 602-631.
[146]M Xu, L V Wang. Photoacoustic Imaging in Biomedicine. Review of Scientific Instruments.2006,77(4):041101-041122.
[147]Jing Wang, Tan Liu, Shuliang Jiao, et al. Saturation Effect in Functional Photoacoustic Imaging[J]. Journal of Biomedical Optics.2010,15(2): 021317-021317-5.
[148]丁辉.计算超声学—声场分析及应用[M].北京：科学出版社.2010：10-15.
[149]J Lanfer,C Elwell. D Delpy et al. Pulsed Near-infrared Photoacoustic Spectroscopy of Blood[C].//SPIE. Proc of SPIE. USA:SPIE,2004:57-68.
[150]Meng-Lin Li, Jung-Taek Oh, Xueyi Xie, et al. Simultaneous Molecular and Hypoxia Imaging of Brain Tumors In Vivo Using Spectroscopic Photoacoustic Tomography[C].//IEEE. Proceedings of the IEEE. USA:IEEE Institute of Electrical and Electronics,2008,96(3):481-489.
[151]Horn B K P, Schunck B G. Determining Optical Flow[J]. Artificial Intelligence, 1981,17(1/2/3):185-204.
[152]He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3): 328-340.
[153]夏利民,张良春.基于自适应粒子滤波器的物体跟踪[J].中国图象图形学报.2009,14(1)：112-117.
[154]Turk M, Pentland A P. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience,1991,3(1):71-86.
[155]林士敏,田凤占,陆玉昌.贝叶斯网络的建造及其在数据采掘中的应用[J1.清华大学学报(自然科学版),2001,41(1)：49-52.
[156]杜友田,陈峰,徐文立.基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法[J].自动化学报.2009,35(3)：225-232.
[157]王永强,律方成,李和明.基于粗糙集理论和贝叶斯网络的电力变压器故障诊断方法[J].中国电机工程学报.2006,26(8)：137-141.
[158]王成刚,周晓东,王学伟.基于贝叶斯网络的复杂装备测试性评估[J].电子测量与仪器学报.2009,23(5)：17-21.
[159]杨旗,薛定宇.基于双尺度动态贝叶斯网络及多信息融合的步态识别.电子与信息学报.2012,34(5)：1148-1153.