78,500 research outputs found
Comment on ``Coherent Control of a V-Type Three-Level System in a Single Quantum Dot''
This is a Comment on Phys. Rev. Lett., {\bf 95}, 187404 (2005)Comment: 1 page
Parallel and Distributed Algorithms for the Housing Allocation Problem
We give parallel and distributed algorithms for the housing allocation
problem. In this problem, there is a set of agents and a set of houses. Each
agent has a strict preference list for a subset of houses. We need to find a
matching such that some criterion is optimized. One such criterion is Pareto
Optimality. A matching is Pareto optimal if no coalition of agents can be
strictly better off by exchanging houses among themselves. We also study the
housing market problem, a variant of the housing allocation problem, where each
agent initially owns a house. In addition to Pareto optimality, we are also
interested in finding the core of a housing market. A matching is in the core
if there is no coalition of agents that can be better off by breaking away from
other agents and switching houses only among themselves.
In the first part of this work, we show that computing a Pareto optimal
matching of a house allocation is in {\bf CC} and computing the core of a
housing market is {\bf CC}-hard. Given a matching, we also show that verifying
whether it is in the core can be done in {\bf NC}. We then give an algorithm to
show that computing a maximum Pareto optimal matching for the housing
allocation problem is in {\bf RNC}^2 and quasi-{\bf NC}^2. In the second part
of this work, we present a distributed version of the top trading cycle
algorithm for finding the core of a housing market. To that end, we first
present two algorithms for finding all the disjoint cycles in a functional
graph: a Las Vegas algorithm which terminates in rounds with high
probability, where is the length of the longest cycle, and a deterministic
algorithm which terminates in rounds, where is the
number of nodes in the graph. Both algorithms work in the synchronous
distributed model and use messages of size
Investigation of the existence of city-scale three-dimensional macroscopic fundamental diagrams for bi-modal traffic
Recent research has demonstrated that the Macroscopic Fundamental Diagram (MFD) is reliable and practical tool for modeling traffic dynamics and network performance in single-mode (cars only) urban road networks. In this paper, we first extend the modeling of the single-mode MFD to a bi-modal (bus and cars) one. Based on simulated data, we develop a three-dimensional MFD (3D-MFD) relating the accumulation of cars and buses, and the total circulating flow in the network. We propose an exponential function to capture the shape of the 3D-MFD, which shows a good fit to the data. We also propose an elegant estimation for passenger car equivalent of buses (PCU), which has a physical meaning and depends on the bi-modal traffic in the network. Moreover, we analyze a 3D-MFD for passenger network flows and derive its analytical function. Finally, we investigate an MFD for networks with dedicated bus lanes and the relationship between the shape of the MFD and the operational characteristics of buses. The output of this paper is an extended 3D-MFD model that can be used to (i) monitor traffic performance and, (ii) develop various traffic management strategies in bi-modal urban road networks, such as redistribution of urban space among different modes, perimeter control, and bus priority strategies
Beamforming Optimization for Full-Duplex Wireless-powered MIMO Systems
We propose techniques for optimizing transmit beamforming in a full-duplex
multiple-input-multiple-output (MIMO) wireless-powered communication system,
which consists of two phases. In the first phase, the wireless-powered mobile
station (MS) harvests energy using signals from the base station (BS), whereas
in the second phase, both MS and BS communicate to each other in a full-duplex
mode. When complete instantaneous channel state information (CSI) is available,
the BS beamformer and the time-splitting (TS) parameter of energy harvesting
are jointly optimized in order to obtain the BS-MS rate region. The joint
optimization problem is non-convex, however, a computationally efficient
optimum technique, based upon semidefinite relaxation and line-search, is
proposed to solve the problem. A sub-optimum zero-forcing approach is also
proposed, in which a closed-form solution of TS parameter is obtained. When
only second-order statistics of transmit CSI is available, we propose to
maximize the ergodic information rate at the MS, while maintaining the outage
probability at the BS below a certain threshold. An upper bound for the outage
probability is also derived and an approximate convex optimization framework is
proposed for efficiently solving the underlying non-convex problem. Simulations
demonstrate the advantages of the proposed methods over the sub-optimum and
half-duplex ones.Comment: 14 pages, accepte
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