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39
Optimal Broadcast Scheduling in Packet Radio Networks Using Mean Field Annealing
 IEEE Journal on Selected Areas in Communications
, 1997
"... Packet radio (PR) is a technology that applies the packet switching technique to the broadcast radio environment. In a PR network, a single highspeed wideband channel is shared by all PR stations. When a timedivision multiaccess protocol is used, the access to the channel by stations' transm ..."
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Packet radio (PR) is a technology that applies the packet switching technique to the broadcast radio environment. In a PR network, a single highspeed wideband channel is shared by all PR stations. When a timedivision multiaccess protocol is used, the access to the channel by stations' transmissions must be properly scheduled in both time and space domains in order to avoid collisions or interferences. It is proven in this paper that such a scheduling problem is NPcomplete. Therefore, an efficient polynomial algorithm rarely exists, and a mean field annealingbased algorithm is proposed to schedule the stations' transmissions in a frame consisting of certain number of time slots. Numerical examples and comparisons with some existing scheduling algorithms have shown that the proposed scheme can find nearoptimal solutions with reasonable computational complexity. Both time delay and channel utilization are calculated based on the found schedules.
Theory of molecular machines. I. Channel capacity of molecular machines
 J. Theor. Biol
, 1991
"... Schneider, T. D. (1991). Theory of molecular machines. I. Channel capacity ..."
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Cited by 30 (13 self)
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Schneider, T. D. (1991). Theory of molecular machines. I. Channel capacity
Thermodynamics and Garbage Collection
 In ACM Sigplan Notices
, 1994
"... INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over ..."
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Cited by 16 (0 self)
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INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over time (i.e., constants of motion). Second, statistical thermodynamics successfully reduces macroscopic properties of a system to the statistical behavior of large numbers of microscopic processes. As computer systems become large assemblages of small components, an explanation of their macroscopic behavior may also be obtained as the aggregate statistical behavior of its component parts. If not, the elegance of the statistical thermodynamical approach can at least provide inspiration for new classes of models. 1 Third, the components of computer systems are approaching the same size as the microscopic pr
Correlation of entropy with similarity and symmetry
 Journal of Chemical Information and Computer Sciences
, 1996
"... Informational entropy is quantitatively related to similarity and symmetry. Some tacit assumptions regarding their correlation have been shown to be wrong. The Gibbs paradox statement (indistinguishability corresponds to minimum entropy, which is zero) has been rejected. All their correlations are b ..."
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Cited by 10 (6 self)
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Informational entropy is quantitatively related to similarity and symmetry. Some tacit assumptions regarding their correlation have been shown to be wrong. The Gibbs paradox statement (indistinguishability corresponds to minimum entropy, which is zero) has been rejected. All their correlations are based on the relation that less information content corresponds to more entropy. Higher value of entropy is correlated to higher molecular similarity. The maximum entropy of any system (e.g., a mixture or an assemblage) corresponds to indistinguishability (total loss of information), to perfect symmetry or highest symmetry, and to the highest simplicity. This conforms without exception to all the experimental facts of both dynamic systems and static structures and the related information loss processes. 1.
Thermodynamics of Linear Systems
"... We rigorously derive the main results of thermodynamics, including Carnot’s theorem, in the framework of timevarying linear systems. ..."
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Cited by 7 (6 self)
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We rigorously derive the main results of thermodynamics, including Carnot’s theorem, in the framework of timevarying linear systems.
On Lossless Approximations, the FluctuationDissipation Theorem, and Limitations of Measurements
, 2010
"... In this paper, we take a controltheoretic approach to answering some standard questions in statistical mechanics, and use the results to derive limitations of classical measurements. A central problem is the relation between systems which appear macroscopically dissipative but are microscopically l ..."
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Cited by 4 (0 self)
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In this paper, we take a controltheoretic approach to answering some standard questions in statistical mechanics, and use the results to derive limitations of classical measurements. A central problem is the relation between systems which appear macroscopically dissipative but are microscopically lossless. We show that a linear system is dissipative if, and only if, it can be approximated by a linear lossless system over arbitrarily long time intervals. Hence lossless systems are in this sense dense in dissipative systems. A linear active system can be approximated by a nonlinear lossless system that is charged with initial energy. As a byproduct, we obtain mechanisms explaining the Onsager relations from timereversible lossless approximations, and the fluctuationdissipation theorem from uncertainty in the initial state of the lossless system. The results are applied to measurement devices and are used to quantify limits on the socalled observer effect, also called back action, which is the impact the measurement device has on the observed system. In particular, it is shown that deterministic back action can be compensated by using active elements, whereas stochastic back action is unavoidable and depends on the temperature of the measurement device.
1 Monte Carlo model of ion mobility and diffusion for low and high electric fields
"... A Monte Carlo method is described to model the mobility and diffusion of ions drifting in response to an electric field in a neutral gas. The model uses a collision frequency that is dependent upon the ion velocity and neutral gas thermal velocity. When implemented with a constant collision cross se ..."
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Cited by 2 (1 self)
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A Monte Carlo method is described to model the mobility and diffusion of ions drifting in response to an electric field in a neutral gas. The model uses a collision frequency that is dependent upon the ion velocity and neutral gas thermal velocity. When implemented with a constant collision cross section for momentum transfer, the model gives a mobility that is constant for small electric fields (those giving a subsonic drift velocity) and that for larger fields falls inversely with the square root of the electric field. For argon ions drifting in argon, the model gives close agreement with experimental data for the mobility for a wide range of electric fields when implemented with an energy dependent cross section. For modeling of transverse diffusion, agreement with data is improved if the collisions are a combination of idealized chargeexchange collisions and hardsphere collisions.
Dynamics of barrier crossing in classical nucleation theory
 J. Phys. Chem
, 2001
"... The dynamics of nucleation barrier crossing is examined using BeckerDoring kinetics, matrix methods, and stochastic model simulation. Fundamental connections between resistance to crossing and fluctuations in cluster size are derived using the KuboNyquist relations. For analysis of nucleation kine ..."
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Cited by 2 (2 self)
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The dynamics of nucleation barrier crossing is examined using BeckerDoring kinetics, matrix methods, and stochastic model simulation. Fundamental connections between resistance to crossing and fluctuations in cluster size are derived using the KuboNyquist relations. For analysis of nucleation kinetics, the matrix approach of Shugard and Reiss is supplemented with a novel extension based on recursion/projection operator methods. The combined approach yields nested sequences of upper and lower bounds to the relaxation rates of clusters coupled to a thermal bath. Fluctuations are studied using simulations based on a stochastic model of cluster evaporation/growth. Under typical conditions, it is found that relaxation from the top of the barrier is slow, due to multiple recrossings, and the transmission coefficient for nucleation is small and extremely difficult to estimate from singlecluster simulations using standard BennettChandler and Kramers models. A new approach based on relaxation on a “dual ” potential surface is introduced. It is shown that the dual model provides an optimal weighted cluster sampling and reliable estimation of the transmission coefficient (to within a few percent). Collectively, these methods address the efficient determination of nucleation rates from computer simulations of individual cluster evaporation/growth events. 1.