The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform
Masoumeh Nasiri-kenari - One of the best experts on this subject based on the ideXlab platform.
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On Molecular Flow Velocity Meters
IEEE Transactions on Molecular Biological and Multi-Scale Communications, 2020Co-Authors: Maryam Farahnak-ghazani, Mahtab Mirmohseni, Masoumeh Nasiri-kenariAbstract:The concentration of molecules in the medium can provide us very useful information about the medium. In this paper, we use this information and design a Molecular Flow velocity meter using a molecule releasing node and a receiver that counts these molecules. We first assume $M$ hypotheses according to $M$ possible medium Flow velocity values and an $L$-sample decoder at the receiver and obtain the Flow velocity detector using maximum-a-posteriori (MAP) method. To analyze the performance of the proposed Flow velocity detector, we obtain the error probability, and its Gaussian approximation and Chernoff information (CI) upper bound. We obtain the optimum sampling times which minimize the error probability and the sub-optimum sampling times which minimize the Gaussian approximation and the CI upper bound. When we have binary hypothesis, we show that the sub-optimum sampling times which minimize the CI upper bound are equal. When we have $M$ hypotheses and $L \rightarrow \infty$, we show that the sub-optimum sampling times that minimize the CI upper bound yield to $M \choose 2$ sampling times with $M \choose 2$ weights. Then, we assume a randomly chosen constant Flow velocity and obtain the MAP and minimum mean square error (MMSE) estimators for the $L$-sample receiver. We consider the mean square error (MSE) to investigate the error performance of the Flow velocity estimators and obtain the Bayesian Cramer-Rao (BCR) and expected Cramer-Rao (ECR) lower bounds on the MSE of the estimators. Further, we obtain the sampling times which minimize the MSE. We show that when the Flow velocity is in the direction of the connecting line between the releasing node and the receiver with uniform distribution for the magnitude of the Flow velocity, and $L \rightarrow \infty$, two different sampling times are enough for the MAP estimator.
Maryam Farahnak-ghazani - One of the best experts on this subject based on the ideXlab platform.
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On Molecular Flow Velocity Meters
IEEE Transactions on Molecular Biological and Multi-Scale Communications, 2020Co-Authors: Maryam Farahnak-ghazani, Mahtab Mirmohseni, Masoumeh Nasiri-kenariAbstract:The concentration of molecules in the medium can provide us very useful information about the medium. In this paper, we use this information and design a Molecular Flow velocity meter using a molecule releasing node and a receiver that counts these molecules. We first assume $M$ hypotheses according to $M$ possible medium Flow velocity values and an $L$-sample decoder at the receiver and obtain the Flow velocity detector using maximum-a-posteriori (MAP) method. To analyze the performance of the proposed Flow velocity detector, we obtain the error probability, and its Gaussian approximation and Chernoff information (CI) upper bound. We obtain the optimum sampling times which minimize the error probability and the sub-optimum sampling times which minimize the Gaussian approximation and the CI upper bound. When we have binary hypothesis, we show that the sub-optimum sampling times which minimize the CI upper bound are equal. When we have $M$ hypotheses and $L \rightarrow \infty$, we show that the sub-optimum sampling times that minimize the CI upper bound yield to $M \choose 2$ sampling times with $M \choose 2$ weights. Then, we assume a randomly chosen constant Flow velocity and obtain the MAP and minimum mean square error (MMSE) estimators for the $L$-sample receiver. We consider the mean square error (MSE) to investigate the error performance of the Flow velocity estimators and obtain the Bayesian Cramer-Rao (BCR) and expected Cramer-Rao (ECR) lower bounds on the MSE of the estimators. Further, we obtain the sampling times which minimize the MSE. We show that when the Flow velocity is in the direction of the connecting line between the releasing node and the receiver with uniform distribution for the magnitude of the Flow velocity, and $L \rightarrow \infty$, two different sampling times are enough for the MAP estimator.
D M Murphy - One of the best experts on this subject based on the ideXlab platform.
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the effect of water evaporation on photoacoustic signals in transition and Molecular Flow
Aerosol Science and Technology, 2009Co-Authors: D M MurphyAbstract:Evaporation of water changes the response of photoacoustic instruments to light-absorbing particles. Previous calculations of this effect are valid for particles much larger than the mean free path of air. These calculations are extended here to include transition and Molecular Flow as well as various mass accommodation coefficients for water. For commonly encountered conditions, evaporation can significantly reduce the photoacoustic signal if the mass accommodation coefficient of water on aerosol particles is larger than about 0.01. Unlike the growth of cloud droplets, the photoacoustic signal is very sensitive to changes in the accommodation coefficient between 0.1 and 1. This may provide a way to measure large accommodation coefficients. For a given accommodation coefficient, the change in the photoacoustic signal depends more on absolute than relative humidity. To minimize the effects of evaporation it is better to remove water from the air rather than reduce relative humidity with heating.
Hanno Essén - One of the best experts on this subject based on the ideXlab platform.
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Response to “Comment on ‘Force on a spinning sphere moving in a rarefied gas’ and ‘On the inverse Magnus effect in free Molecular Flow’” [Phys. Fluids 16, 3832 (2004)]
Physics of Fluids, 2004Co-Authors: Karl I. Borg, Lars H. Söderholm, Hanno EssénAbstract:Response to "Comment on 'Force on a spinning sphere moving in a rarefied gas' and 'On the inverse Magnus effect in free Molecular Flow'" [Phys. Fluids 16, 3832 (2004)]
Mahtab Mirmohseni - One of the best experts on this subject based on the ideXlab platform.
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On Molecular Flow Velocity Meters
IEEE Transactions on Molecular Biological and Multi-Scale Communications, 2020Co-Authors: Maryam Farahnak-ghazani, Mahtab Mirmohseni, Masoumeh Nasiri-kenariAbstract:The concentration of molecules in the medium can provide us very useful information about the medium. In this paper, we use this information and design a Molecular Flow velocity meter using a molecule releasing node and a receiver that counts these molecules. We first assume $M$ hypotheses according to $M$ possible medium Flow velocity values and an $L$-sample decoder at the receiver and obtain the Flow velocity detector using maximum-a-posteriori (MAP) method. To analyze the performance of the proposed Flow velocity detector, we obtain the error probability, and its Gaussian approximation and Chernoff information (CI) upper bound. We obtain the optimum sampling times which minimize the error probability and the sub-optimum sampling times which minimize the Gaussian approximation and the CI upper bound. When we have binary hypothesis, we show that the sub-optimum sampling times which minimize the CI upper bound are equal. When we have $M$ hypotheses and $L \rightarrow \infty$, we show that the sub-optimum sampling times that minimize the CI upper bound yield to $M \choose 2$ sampling times with $M \choose 2$ weights. Then, we assume a randomly chosen constant Flow velocity and obtain the MAP and minimum mean square error (MMSE) estimators for the $L$-sample receiver. We consider the mean square error (MSE) to investigate the error performance of the Flow velocity estimators and obtain the Bayesian Cramer-Rao (BCR) and expected Cramer-Rao (ECR) lower bounds on the MSE of the estimators. Further, we obtain the sampling times which minimize the MSE. We show that when the Flow velocity is in the direction of the connecting line between the releasing node and the receiver with uniform distribution for the magnitude of the Flow velocity, and $L \rightarrow \infty$, two different sampling times are enough for the MAP estimator.
