Reach of future colliders in probing the structure of the photon

A comparison of the potentials of ep and e^+e^-$machines to probe the structure of the photon is performed. In particular, the kinematic reach of a proposed future ep facility, THERA, is compared with those of current colliders, LEP and HERA, and with the proposed linear collider, TESLA. THERA like HERA will use a proton beam of 920 GeV but with an increased electron beam energy of 250 GeV allowing higher scales, Q^2, and lower values of parton momentum fraction in the photon, x_\gamma, to be probed.Comment: 5 pages, 2 figures. To appear in "The THERA Book", DESY-LC-REV-2001-062. IFT 2001/1

Equi-energy sampler with applications in statistical inference and statistical mechanics

We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature--energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical estimation, for example, the calculation of the density of states and microcanonical averages in statistical mechanics. The equi-energy sampler is applied to a variety of problems, including exponential regression in statistics, motif sampling in computational biology and protein folding in biophysics.Comment: This paper discussed in: [math.ST/0611217], [math.ST/0611219], [math.ST/0611221], [math.ST/0611222]. Rejoinder in [math.ST/0611224]. Published at http://dx.doi.org/10.1214/009053606000000515 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Energy-Efficient Power Allocation in OFDM Systems with Wireless Information and Power Transfer

This paper considers an orthogonal frequency division multiplexing (OFDM) downlink point-to-point system with simultaneous wireless information and power transfer. It is assumed that the receiver is able to harvest energy from noise, interference, and the desired signals. We study the design of power allocation algorithms maximizing the energy efficiency of data transmission (bit/Joule delivered to the receiver). In particular, the algorithm design is formulated as a high-dimensional non-convex optimization problem which takes into account the circuit power consumption, the minimum required data rate, and a constraint on the minimum power delivered to the receiver. Subsequently, by exploiting the properties of nonlinear fractional programming, the considered non-convex optimization problem, whose objective function is in fractional form, is transformed into an equivalent optimization problem having an objective function in subtractive form, which enables the derivation of an efficient iterative power allocation algorithm. In each iteration, the optimal power allocation solution is derived based on dual decomposition and a one-dimensional search. Simulation results illustrate that the proposed iterative power allocation algorithm converges to the optimal solution, and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer: (1) In the low transmit power regime, maximizing the system capacity may maximize the energy efficiency. (2) Wireless power transfer can enhance the energy efficiency, especially in the interference limited regime.Comment: 6 pages, Accepted for presentation at the IEEE International Conference on Communications (ICC) 201

Energy-Efficient Resource Allocation in Multiuser OFDM Systems with Wireless Information and Power Transfer

In this paper, we study the resource allocation algorithm design for multiuser orthogonal frequency division multiplexing (OFDM) downlink systems with simultaneous wireless information and power transfer. The algorithm design is formulated as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit/Joule delivered to the users). In particular, the problem formulation takes into account the minimum required system data rate, heterogeneous minimum required power transfers to the users, and the circuit power consumption. Subsequently, by exploiting the method of time-sharing and the properties of nonlinear fractional programming, the considered non-convex optimization problem is solved using an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation and user selection solution are derived based on Lagrange dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm achieves the maximum energy efficiency of the system and reveal how energy efficiency, system capacity, and wireless power transfer benefit from the presence of multiple users in the system.Comment: 6 pages. The paper has been accepted for publication at the IEEE Wireless Communications and Networking Conference (WCNC) 2013, Shanghai, China, Apr. 201

Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions

To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans. So the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to re-charge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being re-charged. Based on these new perspectives, we formulate the Electric Vehicle Charging Station Placement Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.Comment: Submitted to IEEE Transactions on Smart Grid, revise