A homogenization approach to flashing ratchets

The paper continues the program of the authors to develop a mathematical framework to understand and characterize the notion of “asymmetric” potentials, which has been introduced to explain how molecular motors work, considering flashing ratchets, i.e., molecules diffusing in a potential with periodic switches. The mathematical model is a Fokker–Planck equation with a space–time periodic potential and diffusion of order of magnitude compatible with the period of the potential. After performing a homogenization analysis of the problem the “asymmetric” potentials are characterized by the property that the solution, which models the molecule density, concentrates on one end of the domain. Finally explicit examples are presented exhibiting that the concentration phenomena (motor effect) takes place are presented. The proof uses techniques from the theory of viscosity solutions for the Hamilton–Jacobi equation which, in the homogenization limit, defines the effective hamiltonian.     

Bayesian statistical process control for Phase I count type data

Count data, most often modeled by a Poisson distribution, are common in statistical process control. They are traditionally monitored by frequentist c or u charts, by cumulative sum and by exponentially weighted moving average charts. These charts all assume that the in‐control true mean is known, a common fiction that is addressed by gathering a large Phase I sample and using it to estimate the mean. “Self‐starting” proposals that ameliorate the need for a large Phase I sample have also appeared. All these methods are frequentist, ie, they allow only retrospective inference during Phase I, and they have no coherent way to incorporate less‐than‐perfect prior information about the in‐control mean. In this paper, we introduce a Bayesian procedure that can incorporate prior information, allow online inference, and should be particularly attractive for short‐run settings where large Phase I calibration exercises are impossible or unreasonable.     

Cellular Network Performance Analysis: Handoff Algorithms Based on Mobile Location and Area Information

Mobile world's rapid growth has spurred development of new protocols and new algorithms to meet changing operation requirements such as mobile networking, and quality-of-service support. A key requirement in the bearer capabilities is the handover. The freedom to be able to make and receive calls anywhere, at any time, creating a totally new dimension in human communications has frequently been advertised as the main advantage of new wireless systems. Handovers are a key concept in providing this mobility. It makes it possible for a user to travel from one cell to another while having a seamless connection. Network operators give emphasis to optimize handover, since it is strongly related to dropped calls, network overload and subsequently users' criticism. The ability of a cellular network to perform efficient handovers is crucial to offer attractive services as real-time applications or streaming media as planned in third generation networks. Since signal propagation and pathloss are complex in nature, we can expect unnecessary and wrong handoff executions. Both UMTS and those of the second generation (GSM) systems will require redefined handoff algorithms of active connections as the smooth mobility support and continuous connection are essential issues for obtaining high performance and increasing user satisfaction. In this paper we present a set of intelligent algorithms using the mobile terminal (MT) location information and area awareness to assist safe handoff decisions. The implemented algorithms are validated by means of cellular network simulators that clearly show the impact of these techniques to major system performance metrics.     

Macrodiversity analysis of anM-ary noncoherent orthogonal DS/CDMA system on shadowed Rayleigh channels

DS/CDMA noncoherentM-ary orthogonal signaling is presented in a hexagonal cell with three and six corner-based antennas. Multipath Rayleigh channel with lognormal shadowing and perfect power control are assumed. The asynchronous case (uplink) is studied. Bit error rate analysis is carried out to investigate the benefits of combining theM-ary orthogonal signaling and macrodiversity techniques either with uncorrelated or correlated base stations. Comparative analysis between the one central antenna arrangement and the proposed one is presented. The results show that the combination ofM-ary signaling with multiple corner-based antennas improves system performance especially in terms of accommodating more users and supporting new services. The results also show that shadowing does not degrade system performance significantly.     

The renormalization constants of QED in unitary approximation

A unitary approximation for the time-evolution operator given through the exponential representation is used to calculate the renormalization constants of QED. The results obtained by this method are the same as the renormalization group improvement of the usual perturbation series taking into account terms up to the second order ine.     

Simulated annealing and tabu search approaches to unidirectional flowpath design for Automated Guided Vehicle Systems

In this paper, we address the flowpath design issue of Automated Guided Vehicle Systems (AGVSs). In particular, we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed intelligent heuristics — simulated annealing and tabu search algorithms for the design of unidirectional AGVSs. Different versions of simulated annealing and tabu search algorithms are implemented. Our extensive computational results indicate that both simulated annealing and tabu search yield solutions of adequate quality for all practical purposes. A tabu search implementation with the use of a frequency-based memory structure dominates all tested heuristics in terms of solution quality (i.e. percent deviation from optimality), with an impressive average performance over 45 test problems of less than 0.85% deviation from optimality.     

Policies for the reconfiguration of cognitive wireless infrastructures to 3G Radio Access Technologies

The ceaseless evolution of wireless communications is reflected nowadays on the introduction of Beyond-3G (B3G) systems, characterized by the coexistence and cooperation of various Radio Access Technologies (RATs), over a common infrastructure. Major facilitator of this convergence is the advent of cognitive networks, which deploy elements (base stations and mobile terminals) that are able to proactively adapt to environmental stimuli, so that to optimize their performance. Part of the adaptation action takes place in cognitive base stations that own several reconfigurable transceivers, which are controlled by appropriate management functionality and may dynamically change their operating parameters. Each reconfiguration set includes a specific RAT, carrier frequency, as well as demand volume to be allocated per transceiver. Accordingly, proper evaluation of the various candidate reconfiguration sets appears to be of high significance. To this effect, in this paper we consider a cognitive network segment with transceivers operating at 3G RAT/carrier and we solve the DAMC problem (Demand Allocation into Multiple Carriers problem), which aims at evaluating and selecting the optimum policy to allocate the demand into the available 3G carrier frequencies. Optimality is expressed in terms of minimizing the total transmitted/received power per base station, thus deciding for the reconfigurations with the least impact on network interference. Indicative simulation scenarios and results are also presented for the validation and verification of the proposed functionality.

A descent hybrid conjugate gradient method based on the memoryless BFGS update

In this work, we present a new hybrid conjugate gradient method based on the approach of the convex hybridization of the conjugate gradient update parameters of DY and HS+, adapting a quasi-Newton philosophy. The computation of the hybrization parameter is obtained by minimizing the distance between the hybrid conjugate gradient direction and the self-scaling memoryless BFGS direction. Furthermore, a significant property of our proposed method is that it ensures sufficient descent independent of the accuracy of the line search. The global convergence of the proposed method is established provided that the line search satisfies the Wolfe conditions. Our numerical experiments on a set of unconstrained optimization test problems from the CUTEr collection indicate that our proposed method is preferable and in general superior to classic conjugate gradient methods in terms of efficiency and robustness.