The Experts below are selected from a list of 8709 Experts worldwide ranked by ideXlab platform
M. Chiappini - One of the best experts on this subject based on the ideXlab platform.
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The design of the MEG II experiment
The European Physical Journal C, 2018Co-Authors: A. M. Baldini, F. Berg, E. Baracchini, C. Bemporad, G. Boca, P. W. Cattaneo, G. Cavoto, F. Cei, M. Biasotti, M. ChiappiniAbstract:The MEG experiment, designed to search for the $${\mu ^+ \rightarrow \hbox {e}^+ \gamma }$$μ+→e+γ decay, completed data-taking in 2013 reaching a Sensitivity Level of $${5.3\times 10^{-13}}$$5.3×10-13 for the branching ratio. In order to increase the Sensitivity reach of the experiment by an order of magnitude to the Level of $$6\times 10^{-14}$$6×10-14, a total upgrade, involving substantial changes to the experiment, has been undertaken, known as MEG II. We present both the motivation for the upgrade and a detailed overview of the design of the experiment and of the expected detector performance.
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the design of the meg ii experiment
European Physical Journal C, 2018Co-Authors: A. M. Baldini, F. Berg, E. Baracchini, C. Bemporad, G. Boca, P. W. Cattaneo, G. Cavoto, F. Cei, M. Biasotti, M. ChiappiniAbstract:The MEG experiment, designed to search for the \({\mu ^+ \rightarrow \hbox {e}^+ \gamma }\) decay, completed data-taking in 2013 reaching a Sensitivity Level of \({5.3\times 10^{-13}}\) for the branching ratio. In order to increase the Sensitivity reach of the experiment by an order of magnitude to the Level of \(6\times 10^{-14}\), a total upgrade, involving substantial changes to the experiment, has been undertaken, known as MEG II. We present both the motivation for the upgrade and a detailed overview of the design of the experiment and of the expected detector performance.
A. Arsad - One of the best experts on this subject based on the ideXlab platform.
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Performance study of virtual fence unit using Wireless Sensor Network in IoT environment
2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2014Co-Authors: Hui Ting Chan, T.a Rahman, A. ArsadAbstract:Wireless Sensor Networks (WSN) have been widely used in various monitoring applications including virtual fencing. With the integration of Internet of Things (IoT) in which millions of devices, systems and services will be interconnected online, it brings high capabilities for sensing, intercommunicate and actuation. This paper presents the experiments and performance analysis of virtual fence unit consists of microwave motion detector and IEEE 802.15.4 WSN for maximum sensing range integrate with IoT. In particular, the analysis is focusing on the maximum sensing range in terms of azimuth angle, height, Sensitivity Level for indoor and outdoor implementation. With the help of IoT, sensor which detected motion on weak detection angle or signal could communicate through internet and get signal from nearby sensor for better detection signal. The WSN platform is developed using Octopus II sensor nodes while the microwave motion detector is HB100 which detect movement using Doppler effects. Octopus II will uplink all signal through internet and provide corresponding action of virtual fencing when intrusion detected. Results show maximum sensing range of virtual fence unit is decreasing as azimuth angle increasing. With high Sensitivity Level of virtual fence unit, the maximum sensing range of virtual fence unit is larger than the maximum sensing range of virtual fence unit at normal Sensitivity Level. Results also show the virtual fence unit has higher maximum sensing range in indoor environment than outdoor environment.
A. M. Baldini - One of the best experts on this subject based on the ideXlab platform.
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The design of the MEG II experiment
The European Physical Journal C, 2018Co-Authors: A. M. Baldini, F. Berg, E. Baracchini, C. Bemporad, G. Boca, P. W. Cattaneo, G. Cavoto, F. Cei, M. Biasotti, M. ChiappiniAbstract:The MEG experiment, designed to search for the $${\mu ^+ \rightarrow \hbox {e}^+ \gamma }$$μ+→e+γ decay, completed data-taking in 2013 reaching a Sensitivity Level of $${5.3\times 10^{-13}}$$5.3×10-13 for the branching ratio. In order to increase the Sensitivity reach of the experiment by an order of magnitude to the Level of $$6\times 10^{-14}$$6×10-14, a total upgrade, involving substantial changes to the experiment, has been undertaken, known as MEG II. We present both the motivation for the upgrade and a detailed overview of the design of the experiment and of the expected detector performance.
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the design of the meg ii experiment
European Physical Journal C, 2018Co-Authors: A. M. Baldini, F. Berg, E. Baracchini, C. Bemporad, G. Boca, P. W. Cattaneo, G. Cavoto, F. Cei, M. Biasotti, M. ChiappiniAbstract:The MEG experiment, designed to search for the \({\mu ^+ \rightarrow \hbox {e}^+ \gamma }\) decay, completed data-taking in 2013 reaching a Sensitivity Level of \({5.3\times 10^{-13}}\) for the branching ratio. In order to increase the Sensitivity reach of the experiment by an order of magnitude to the Level of \(6\times 10^{-14}\), a total upgrade, involving substantial changes to the experiment, has been undertaken, known as MEG II. We present both the motivation for the upgrade and a detailed overview of the design of the experiment and of the expected detector performance.
Javid Shabbir - One of the best experts on this subject based on the ideXlab platform.
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Estimation of population proportion and Sensitivity Level using optional unrelated question randomized response techniques
Communications in Statistics - Simulation and Computation, 2018Co-Authors: Ghulam Narjis, Javid ShabbirAbstract:AbstractIn this study, we propose optional randomized response technique (RRT) models in binary response situation. Gupta, Gupta, and Singh (2002) introduced the basic premise of optional RRT model...
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Sensitivity ESTIMATION FOR PERSONAL INTERVIEW SURVEY QUESTIONS
Statistica, 2004Co-Authors: Sat Gupta, Javid ShabbirAbstract:This paper proposes an optional RRT method to estimate the Sensitivity Level of personal interview survey questions with quantitative response. The method is similar to the one used by Gupta et al. (2002) but estimates both the average response Level and the Sensitivity Level of the question. A numerical example explains the estimation process and a simulation study assesses the effectiveness of the proposed method. We also compare the performance of the proposed estimator with the “Full” and “Partial” randomized response models.
Xiaosong Ma - One of the best experts on this subject based on the ideXlab platform.
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A new fast moisture Sensitivity analysis method using equal moisture distribution simulation
2014 15th International Conference on Electronic Packaging Technology, 2014Co-Authors: Xiaosong MaAbstract:MSLA, the abbreviation of Moisture Sensitivity Level Analysis, is the meaning of moisture Sensitivity Level analysis. MSLA is to give the proposed package moisture Sensitivity of surface mount components provide a standard of classification, so that different types of components to get the correct packaging, storage and handling to avoid moisture resulted failures in the assembly or repair process. But traditional moisture Sensitivity Level characterizations take long time even the fast Level 1, 85oC/85%RH conditioning needs 168 hours. The purpose of the paper is introducing a fast moisture diffusion method using high temperature and high relative humidity to increase moisture diffusion speed by numerical simulation, which will assist the moisture diffusion time.
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Equivalent moisture distribution calculation for fast moisture Sensitivity Level analysis (MSLA)
2012 13th International Conference on Electronic Packaging Technology & High Density Packaging, 2012Co-Authors: Xiaosong Ma, D. G. Yang, G. Q. ZhangAbstract:Equivalent moisture distibution calaculation for fast moisture Sensitivity Level analysis (MSLA) is disccused in this paper. Moisture diffusion time can be speed up to 1.7 to 3.2 times according to the simulation. Moisture diffusion properties are also disccused in this paper and this method can be used for fast moisture Sensitivity Level analysis.
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A fast moisture Sensitivity Level qualification method
Microelectronics Reliability, 2010Co-Authors: Xiaosong Ma, K.m.b. Jansen, Guoqi Zhang, W.d. Van Driel, O. Van Der Sluis, L.j. Ernst, C. Regards, Christian Gautier, Hélène FremontAbstract:In this paper, a fast moisture Sensitivity Level (MSL) qualification method and a fast moisture characterization method are discussed. The fast moisture characterization uses a stepwise method to obtain more reliable and more material moisture properties. The established relationships for moisture diffusion coefficients and moisture saturation Levels with respect to the temperature and relative humidity can be used to predict moisture properties in the MSL range. Fast moisture Sensitivity Level qualification is accomplished with the aid of simulation combined with the characterized moisture diffusion properties. Moisture absorption processes at different conditions are simulated using a 3D model at conditions according to the moisture Sensitivity test Levels. Simulation of weight change at different condition and simulation of local moisture concentration are performed and compared between different conditions. Simulations show that at 696 h preconditioning time at 30 °C/60%RH for MSL Level 2a can be decreased to 42 h at 85 °C/85%RH. Time required for package reliability and moisture Sensitivity analysis is largely shortened.
