Relevant bibliographies by topics / Interarrival time

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Interarrival time'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Interarrival time"

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Korolev, A., and P. R. Field. "Assessment of performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probes measurements." Atmospheric Measurement Techniques Discussions 7, no. 10 (October 8, 2014): 10249–92. http://dx.doi.org/10.5194/amtd-7-10249-2014.

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Abstract. Shattering presents a serious obstacle to current airborne in-situ methods of characterizing the microphysical properties of ice clouds. Small shattered fragments result from the impact of natural ice crystals with the forward parts of aircraft-mounted measurement probes. The presence of these shattered fragments may result in a significant overestimation of the measured concentration of small ice crystals, contaminating the measurement of the ice particle size distribution (PSD). One method of identifying shattered particles is to use an interarrival time algorithm. This method is based on the assumption that shattered fragments form spatial clusters that have short interarrival times between particles, relative to natural particles, when they pass through the sample volume of the probe. The interarrival time algorithm is a successful technique for the classification of shattering artifacts and natural particles. This study assesses the limitations and efficiency of the interarrival time algorithm. The analysis has been performed using simultaneous measurements of 2-D optical array probes with the standard and antishattering "K-tips" collected during the Airborne Icing Instrumentation Experiment (AIIE). It is shown that the efficiency of the algorithm depends on ice particle size, concentration and habit. Additional numerical simulations indicate that the effectiveness of the interarrival time algorithm to eliminate shattering artifacts can be significantly restricted in some cases. Improvements to the interarrival time algorithm are discussed.
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Liu, Yi, Xiaoxia Luo, Jingxian Liu, Zongzhi Li, and Ryan Wen Liu. "Mixed Models of Single-Berth Interarrival Time Distributions." Journal of Waterway, Port, Coastal, and Ocean Engineering 144, no. 1 (January 2018): 04017034. http://dx.doi.org/10.1061/(asce)ww.1943-5460.0000421.

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Righter, Rhonda. "A note on losses in M/GI/1/n queues." Journal of Applied Probability 36, no. 04 (December 1999): 1240–43. http://dx.doi.org/10.1017/s0021900200018015.

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Let L n be the number of losses during a busy period of an M/GI/1/n queueing system. We develop a coupling between L n and L n+1 and use the resulting relationship to provide a simple proof that when the mean service time equals the mean interarrival time, EL n = 1 for all n. We also show that L n is increasing in the convex sense when the mean service time equals the mean interarrival time, and it is increasing in the increasing convex sense when the mean service time is less than the mean interarrival time.
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Righter, Rhonda. "A note on losses in M/GI/1/n queues." Journal of Applied Probability 36, no. 4 (December 1999): 1240–43. http://dx.doi.org/10.1239/jap/1032374770.

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Let Ln be the number of losses during a busy period of an M/GI/1/n queueing system. We develop a coupling between Ln and Ln+1 and use the resulting relationship to provide a simple proof that when the mean service time equals the mean interarrival time, ELn = 1 for all n. We also show that Ln is increasing in the convex sense when the mean service time equals the mean interarrival time, and it is increasing in the increasing convex sense when the mean service time is less than the mean interarrival time.
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ZHAO, RUIQING, and BAODING LIU. "RENEWAL PROCESS WITH FUZZY INTERARRIVAL TIMES AND REWARDS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 05 (October 2003): 573–86. http://dx.doi.org/10.1142/s0218488503002338.

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This paper considers a renewal process in which the interarrival times and rewards are characterized as fuzzy variables. A fuzzy elementary renewal theorem shows that the expected number of renewals per unit time is just the expected reciprocal of the interarrival time. Furthermore, the expected reward per unit time is provided by a fuzzy renewal reward theorem. Finally, a numerical example is presented for illustrating the theorems introduced in the paper.
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Xie, Qiang, Luping Xu, and Hua Zhang. "X-ray pulsar signal detection using photon interarrival time." Journal of Systems Engineering and Electronics 24, no. 6 (December 2013): 899–905. http://dx.doi.org/10.1109/jsee.2013.00104.

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Jain, Sudha. "Estimating The Change Point Of Erlang Interarrival Time Distribution." INFOR: Information Systems and Operational Research 39, no. 2 (May 2001): 200–207. http://dx.doi.org/10.1080/03155986.2001.11732436.

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Field, P. R., A. J. Heymsfield, and A. Bansemer. "Shattering and Particle Interarrival Times Measured by Optical Array Probes in Ice Clouds." Journal of Atmospheric and Oceanic Technology 23, no. 10 (October 1, 2006): 1357–71. http://dx.doi.org/10.1175/jtech1922.1.

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Abstract Optical array probes are one of the most important tools for determining the microphysical structure of clouds. It has been known for some time that the shattering of ice crystals on the housing of these probes can lead to incorrect measurements of particle size distributions and subsequently derived microphysical properties if the resulting spurious particles are not rejected. In this paper it is shown that the interarrival times of particles measured by these probes can be bimodal—the “cloud” probes are more affected than the “precipitation” probes. The long interarrival time mode represents real cloud structure while the short interarrival time mode results from fragments of shattered ice particles. It is demonstrated for the flights considered here that if the fragmented particles are filtered using an interarrival time threshold of 2 × 10−4 s in three of the four cases and 1 × 10−5 s in the other, then the measured total concentration can be affected by up to a factor of 4 in situations where large particles are present as determined by the mass-weighted mean size exceeding 1 mm, or the exponential slope parameter falling below 30 cm−1. When the size distribution is narrow (mass weighted mean size <1 mm), ice water contents can be overestimated by 20%–30% for the cases presented here.
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Morin, Samantha, Myron Hlynka, and Shan Xu. "Comments on a Two Queue Network." International Journal of Statistics and Probability 6, no. 6 (September 20, 2017): 43. http://dx.doi.org/10.5539/ijsp.v6n6p43.

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A special customer must complete service from two servers, each with an $M/M/1$ queueing system. It is assumed that the two queueing systems have initiial numbers of customers $a$ and $b$ at the instant when the special customer arrives, and subsequent interarrival times and service times are independent. We find the expected total time (ETT) for the special customer to complete service. We show that even if the interarrival and service time parameters of two queues are identical, there exist examples (specific values of the parameters and initial lengths a and b) for which the special customer surprisingly has a lower expected total time to completion by joining the longer queue first rather than the shorter one.
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R, Elangovan. "Stochastic Model to Determine the Expected Time to Recruitment with Three Sources of Depletion of Manpower under Correlated Interarrival Times." Bonfring International Journal of Industrial Engineering and Management Science 4, no. 1 (February 28, 2014): 34–42. http://dx.doi.org/10.9756/bijiems.4802.

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Dissertations / Theses on the topic "Interarrival time"

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Alvaro, Maria Magdalena Kcala. "Comparação de modelos estatísticos para estimação do intervalo de tempos entre ultrapasses de um limiar de temperatura na cidade de P. Prudente-SP /." Presidente Prudente, 2019. http://hdl.handle.net/11449/183311.

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Orientador: Mário Hissamitsu Tarumoto
Resumo: A observação de fenômenos naturais, como as mudanças de temperatura é bastante frequente no mundo atual, de forma que vários estudos têm sido realizados com o intuito de prever a ocorrência delas tendo em vista o que ocorreu no passado. Estudos desta natureza, em que a coleta de dados ocorre de forma contínua, seja por medida horária ou diária, não apresenta independência entre as observações. Entre as possíveis formas de análise, há a aplicação de técnicas de séries temporais ou também a teoria dos valores extremos. No entanto, um dos objetivos deste estudo é construir uma matriz de transição, de tal forma que possamos determinar a probabilidade, por exemplo, de alta temperatura amanhã, dado que hoje foi observado este fenômeno. Para a obtenção deste resultado, uma possibilidade é construir um modelo baseado em dados dependentes que seguem um processo de Markov, em que a suposição é de que exista dependência somente com o dia anterior. Neste trabalho, pretendemos construir este modelo e realizar a aplicação em dados de temperatura na cidade de Presidente Prudente-SP no período de janeiro de 2011 a dezembro de 2016. Posteriormente vamos realizar comparações entre o modelo markoviano de nido a partir da distribuição Weibull bivariada de Marshall e Olkin e outros modelos markovianos de nidos a partir das funções cópulas.
Abstract: The observation of natural phenomena, such as temperature changes, is quite frequent in the world today, so that several studies have been carried out with the intention of predicting their occurrence in view of what has happened in the past. Data of this nature, in which the data collection occurs continuously, whether by hourly or daily measurement, does not present independence between observations. Among the possible forms of analysis is the application of time-series techniques, however, the purpose of this study is to construct a transition matrix, so that we can determine the probability, for example, of high temperature tomorrow, since today this phenomenon was observed. To obtain this result, one possibility is to construct a model based on dependent data that follows a Markov process, in which the assumption is that there is dependence only with the previous day. In this work, we intend to build this model and perform the application on temperature data in the city of Presidente Prudente-SP from January 2011 to December 2016. For which comparisons were made between the Markovian model de ned from the distribution Weibull bivariate of Marshall and Olkin and other Markovian models de ned from the copula functions.
Mestre
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Oner, Erdinc. "An Evaluation of Entrance Ramp Metering for Freeway Work Zones using Digital Simulation." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1228753047.

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Sathyanarayana, Supreeth. "Characterizing the effects of device components on network traffic." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47640.

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When a network packet is formed by a computer's protocol stack, there are many components (e.g., Memory, CPU, etc.) of the computer that are involved in the process. The objective of this research is to identify, characterize and analyze the effects of the various components of a device (e.g., Memory, CPU, etc.) on the device's network traffic by measuring the changes in its network traffic with changes in its components. We also show how this characterization can be used to effectively perform counterfeit detection of devices which have counterfeit components (e.g., Memory, CPU, etc.). To obtain this characterization, we measure and apply statistical analyses like probability distribution fucntions (PDFs) on the interarrival times (IATs) of the device's network packets (e.g., ICMP, UDP, TCP, etc.). The device is then modified by changing just one component (e.g., Memory, CPU, etc.) at a time while holding the rest constant and acquiring the IATs again. This, over many such iterations provides an understanding of the effect of each component on the overall device IAT statistics. Such statistics are captured for devices (e.g., field-programmable gate arrays (FPGAs) and personal computers (PCs)) of different types. Some of these statistics remain stable across different IAT captures for the same device and differ for different devices (completely different devices or even the same device with its components changed). Hence, these statistical variations can be used to detect changes in a device's composition, which lends itself well to counterfeit detection. Counterfeit devices are abundant in today's world and cause billions of dollars of loss in revenue. Device components are substituted with inferior quality components or are replaced by lower capacity components. Armed with our understanding of the effects of various device components on the device's network traffic, we show how such substitutions or alterations of legitimate device components can be detected and hence perform effective counterfeit detection by statistically analyzing the deviation of the device's IATs from that of the original legitimate device. We perform such counterfeit detection experiments on various types of device configurations (e.g., PC with changed CPU, RAM, etc.) to prove the technique's efficacy. Since this technique is a fully network-based solution, it is also a non-destructive technique which can quickly, inexpensively and easily verify the device's legitimacy. This research also discusses the limitations of network-based counterfeit detection.
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Suravaram, Kiran R. "Modeling the Interarrival Times for Non-Signalized Freeway Entrance Ramps." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1183662416.

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Shao, Zhi-Guo, and 邵治國. "Interarrival Time Improvement for Timer Interrupts." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/55222557991181627256.

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碩士
國立中正大學
資訊工程研究所
91
In a real-time system, timing constrains of real-time tasks must be satisfied otherwise it might not only be valueless but also sometimes harmful. Thus, fine timing control is an important feature in a real-time system. A simple approach to do the fine control is to improve interarrival time of timer interrupts i.e. reduce interarrival time and increase its timing accuracy. There are basically two modes to generate timer interrupts, periodic and aperiodic. Some previous works use the aperiodic mode to improve the interarrival time, however it is limited by the setup time of hardware timers. To solve the problem, we study and analyze system architectures of uniprocessor and multiprocessor with multiple hardware timers where timer interrupts are generated aperiodically. Furthermore, to reduce the overhead caused by aperiodic timer interrupts, we propose an adjustable periodic method (APM) using periodic mode. Experiments show that APM can reduce about five percentages of the overhead. In order to improve timing granularity and accuracy, related system issues on kernel, software timer and system time are also presented.
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"Stochastic and dynamic vehicle routing with general demand and interarrival time distributions." Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/2364.

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Dimitris J. Bertsimas, Garrett van Ryzin.
Includes bibliographical references (p. 38-40).
Supported by the National Science Foundation. DDM-9014751 Supported by Draper Laboratories and the UPS Foundation.
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Yang, Wei-Jing, and 楊薇靜. "Gamma interarrival times model-with applications to earthquakes in Taiwan." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/t9zt6h.

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Book chapters on the topic "Interarrival time"

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Martin, Niels, Benoît Depaire, and An Caris. "Using Event Logs to Model Interarrival Times in Business Process Simulation." In Business Process Management Workshops, 255–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42887-1_21.

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Nielsen, S. R. K., R. Iwankiewicz, and P. S. Skjærbæk. "Moment Equations for Non-Linear Systems Under Renewal-Driven Random Impulses with Gamma-Distributed Interarrival Times." In IUTAM Symposium on Advances in Nonlinear Stochastic Mechanics, 331–40. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0321-0_31.

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Kambo, N. S., Dervis Z. Deniz, and Taswar Iqbal. "Measurement–Based MMPP Modeling of Voice Traffic in Computer Networks Using Moments of Packet Interarrival Times." In Networking — ICN 2001, 570–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47734-9_56.

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Rincón, David, and Sebastià Sallent. "Scaling Properties of Network Traffic." In Encyclopedia of Internet Technologies and Applications, 483–89. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59140-993-9.ch068.

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The availability of good stochastic models of network traffic is the key to developing protocols and services. A precise statistical characterization of packet interarrival time, size distribution, and connection arrival rate help network engineers to design network equipment and evaluate its performance.
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"Exponential Interarrival and Service Times: Closed-Form Expressions." In Analysis of Queues, 69–116. CRC Press, 2012. http://dx.doi.org/10.1201/b11858-9.

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"Exponential Interarrival and Service Times: Numerical Techniques and Approximations." In Analysis of Queues, 117–74. CRC Press, 2012. http://dx.doi.org/10.1201/b11858-10.

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"General Interarrival and/or Service Times: Closed-Form Expressions and Approximations." In Analysis of Queues, 175–264. CRC Press, 2012. http://dx.doi.org/10.1201/b11858-11.

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Lef`evre, Claude, and Philippe Picard. "Busy Periods for Some Queues with Deterministic Interarrival or Service Times." In Probability and Statistical Models with Applications. Chapman and Hall/CRC, 2000. http://dx.doi.org/10.1201/9781420036084.ch10.

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Naumov, V. "Spectral properties of finite buffers with phase type service and interarrival times." In Providing Quality of Service in Heterogeneous Environments, Proceedings of the 18th International Teletraffic Congress - ITC-18, 611–19. Elsevier, 2003. http://dx.doi.org/10.1016/s1388-3437(03)80210-x.

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FERREIRA, F., and A. PACHECO. "AN EFFICIENT APPROACH TO ANALYZE FINITE BUFFER QUEUES WITH GENERALIZED PARETO INTERARRIVAL TIMES AND EXPONENTIAL SERVICE." In Recent Advances in Stochastic Operations Research, 205–23. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812706683_0014.

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Conference papers on the topic "Interarrival time"

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Niknafs, Mina, Ivan Ukhov, Petru Eles, and Zebo Peng. "Two-Phase Interarrival Time Prediction for Runtime Resource Management." In 2017 Euromicro Conference on Digital System Design (DSD). IEEE, 2017. http://dx.doi.org/10.1109/dsd.2017.42.

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Mehr, Kamal Adli, Sina Khoshabi Nobar, and Javad Musevi Niya. "Interarrival time distribution of IEEE 802.15.4 under saturated traffic condition." In 2015 23rd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2015. http://dx.doi.org/10.1109/iraniancee.2015.7146211.

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Pacifico, Racyus, Pablo Goulart, Alex B. Vieira, Marcos A. M. Vieira, and Jose Augusto M. Nacif. "Hardware Modules for Packet Interarrival Time Monitoring for Software Defined Measurements." In 2016 IEEE 41st Conference on Local Computer Networks (LCN). IEEE, 2016. http://dx.doi.org/10.1109/lcn.2016.39.

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Meszaros, Andras, and Miklos Telek. "A two-phase MAP fitting method with APH interarrival time distribution." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465300.

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Zhu, Tong, Qiang Ma, Junliang Liu, Shichao Liu, and Yuan He. "P^2IT: Predicting Packet Interarrival Time in Asynchronous Duty-Cycling Sensor Networks." In 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS). IEEE, 2013. http://dx.doi.org/10.1109/mass.2013.81.

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Zaitoun, Toqa' Abu, Manal Bani Issa, Sajeda Banat, and Wail Mardini. "Evaluation and Enhancement of the EdgeCloudSim using Poisson Interarrival time and Load capacity." In 2018 8th International Conference on Computer Science and Information Technology (CSIT). IEEE, 2018. http://dx.doi.org/10.1109/csit.2018.8486288.

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Kleban, Stephen D., and Scott H. Clearwater. "Hierarchical Dynamics, Interarrival Times, and Performance." In the 2003 ACM/IEEE conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/1048935.1050179.

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Hyytia, Esa, Guomundur Magnusson, and Rhonda Righter. "Controlling Queues with Constant Interarrival Times." In 2019 31st International Teletraffic Congress (ITC 31). IEEE, 2019. http://dx.doi.org/10.1109/itc31.2019.00012.

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Rottondi, Cristina, and Giacomo Verticale. "Using packet interarrival times for Internet traffic classification." In 2011 IEEE Third Latin-American Conference on Communications (LATINCOM). IEEE, 2011. http://dx.doi.org/10.1109/latincom.2011.6107404.

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Wang, Shuming, Xiao-Dong Dai, and Yan-kui Liu. "Fuzzy Renewal Process with T-Independent Interarrival Times." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.333.

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