A Novel Fiber Delay Line Buffering Architecture for Optical Packet Switching

Due to the lack of optical random access memory, optical fiber delay line (FDL) is currently the only way to implement optical buffering. Feed-forward and feedback are two kinds of FDL structures in optical buffering. Both have advantages and disadvantages. In this paper, we propose a more effective hybrid FDL architecture that combines the merits of both schemes. The core of this switch is the arrayed waveguide grating (AWG) and the tunable wavelength converter (TWC). It requires smaller optical device sizes and fewer wavelengths and has less noise than feedback architecture. At the same time, it can facilitate preemptive priority routing which feed-forward architecture cannot support. Our numerical results show that the new switch architecture significantly reduces packet loss probability.

Selection of Switching Sites in All-Optical Nework Topology Design

In this paper, we consider the problem of topology design for optical networks. We investigate the problem of selecting switching sites to minimize total cost of the optical network. The cost of an optical network can be expressed as a sum of three main factors: the site cost, the link cost, and the switch cost. To the best of our knowledge, this problem has not been studied in its general form as investigated in this paper. We present a mixed integer quadratic programming (MIQP) formulation of the problem to find the optimal value of the total network cost. We also present an efficient heuristic to approximate the solution in polynomial time. The experimental results show good performance of the heuristic. The value of the total network cost computed by the heuristic varies within 2% to 21% of its optimal value in the experiments with 10 nodes. The total network cost computed by the heuristic for 51% of the experiments with 10 node network topologies varies within 8% of its optimal value. We also discuss the insight gained from our experiments.

Partially Observed Markov Random Fields Are Variable Neighborhood Random Fields

The present paper has two goals. First to present a natural example of a new class of random fields which are the variable neighborhood random fields. The example we consider is a partially observed nearest neighbor binary Markov random field. The second goal is to establish sufficient conditions ensuring that the variable neighborhoods are almost surely finite. We discuss the relationship between the almost sure finiteness of the interaction neighborhoods and the presence/absence of phase transition of the underlying Markov random field. In the case where the underlying random field has no phase transition we show that the finiteness of neighborhoods depends on a specific relation between the noise level and the minimum values of the one-point specification of the Markov random field. The case in which there is phase transition is addressed in the frame of the ferromagnetic Ising model. We prove that the existence of infinite interaction neighborhoods depends on the phase.

Inward-Inclined Implant Platform for the Amplified Platform-Switching Concept: 18-Month Follow-up Report of a Prospective Randomized Matched-Pair Controlled Trial

Purpose: This prospective randomized matched-pair controlled trial aimed to evaluate marginal bone levels and soft tissue alterations at implants restored according to the platform-switching concept with a new inward-inclined platform and compare them with external-hexagon implants. Materials and Methods: Traditional external-hexagon (control group) implants and inward-inclined platform implants (test group), all with the same implant body geometry and 13 mm in length, were inserted in a standardized manner in the posterior maxillae of 40 patients. Radiographic bone levels were measured by two independent examiners after 6, 12, and 18 months of prosthetic loading. Buccal soft tissue height was measured at the time of abutment connection and 18 months later. Results: After 18 months of loading, all 80 implants were clinically osseointegrated in the 40 participating patients. Radiographic evaluation showed mean bone losses of 0.5 +/- 0.1 mm (range, 0.3 to 0.7 mm) and 1.6 +/- 0.3 mm (range, 1.1 to 2.2 mm) for test and control implants, respectively. Soft tissue height showed a significant mean decrease of 2.4 mm in the control group, compared to 0.6 mm around the test implants. Conclusions: After 18 months, significantly greater bone loss was observed at implants restored according to the conventional external-hexagon protocol compared to the platform-switching concept. In addition, decreased soft tissue height was associated with the external-hexagon implants versus the platform-switched implants. INT J ORAL MAXILLOFAC IMPLANTS 2012;27:927-934.

A New Method of Current Switching for Linear Wide-Range Measurement Systems

This new and general method here called overflow current switching allows a fast, continuous, and smooth transition between scales in wide-range current measurement systems, like electrometers. This is achieved, using a hydraulic analogy, by diverting only the overflow current, such that no slow element is forced to change its state during the switching. As a result, this approach practically eliminates the long dead time in low-current (picoamperes) switching. Similar to a logarithmic scale, a composition of n adjacent linear scales, like a segmented ruler, measures the current. The use of a linear wide-range system based on this technique assures fast and continuous measurement in the entire range, without blind regions during transitions and still holding suitable accuracy for many applications. A full mathematical development of the method is given. Several computer realistic simulations demonstrated the viability of the technique.

Average control of Markov decision processes with Feller transition probabilities and general action spaces

This paper studies the average control problem of discrete-time Markov Decision Processes (MDPs for short) with general state space, Feller transition probabilities, and possibly non-compact control constraint sets A(x). Two hypotheses are considered: either the cost function c is strictly unbounded or the multifunctions A(r)(x) = {a is an element of A(x) : c(x, a) <= r} are upper-semicontinuous and compact-valued for each real r. For these two cases we provide new results for the existence of a solution to the average-cost optimality equality and inequality using the vanishing discount approach. We also study the convergence of the policy iteration approach under these conditions. It should be pointed out that we do not make any assumptions regarding the convergence and the continuity of the limit function generated by the sequence of relative difference of the alpha-discounted value functions and the Poisson equations as often encountered in the literature. (C) 2012 Elsevier Inc. All rights reserved.

SINGULARLY PERTURBED DISCOUNTED MARKOV CONTROL PROCESSES IN A GENERAL STATE SPACE

This paper studies the asymptotic optimality of discrete-time Markov decision processes (MDPs) with general state space and action space and having weak and strong interactions. By using a similar approach as developed by Liu, Zhang, and Yin [Appl. Math. Optim., 44 (2001), pp. 105-129], the idea in this paper is to consider an MDP with general state and action spaces and to reduce the dimension of the state space by considering an averaged model. This formulation is often described by introducing a small parameter epsilon > 0 in the definition of the transition kernel, leading to a singularly perturbed Markov model with two time scales. Our objective is twofold. First it is shown that the value function of the control problem for the perturbed system converges to the value function of a limit averaged control problem as epsilon goes to zero. In the second part of the paper, it is proved that a feedback control policy for the original control problem defined by using an optimal feedback policy for the limit problem is asymptotically optimal. Our work extends existing results of the literature in the following two directions: the underlying MDP is defined on general state and action spaces and we do not impose strong conditions on the recurrence structure of the MDP such as Doeblin's condition.

Optimal mean-variance control for discrete-time linear systems with Markovian jumps and multiplicative noises

In this paper, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises under two criteria. The first one is an unconstrained mean-variance trade-off performance criterion along the time, and the second one is a minimum variance criterion along the time with constraints on the expected output. We present explicit conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. We conclude the paper by presenting a numerical example of a multi-period portfolio selection problem with regime switching in which it is desired to minimize the sum of the variances of the portfolio along the time under the restriction of keeping the expected value of the portfolio greater than some minimum values specified by the investor. (C) 2011 Elsevier Ltd. All rights reserved.

Switching the mode of sucrose utilization by Saccharomyces cerevisiae

Abstract Background Overflow metabolism is an undesirable characteristic of aerobic cultures of Saccharomyces cerevisiae during biomass-directed processes. It results from elevated sugar consumption rates that cause a high substrate conversion to ethanol and other bi-products, severely affecting cell physiology, bioprocess performance, and biomass yields. Fed-batch culture, where sucrose consumption rates are controlled by the external addition of sugar aiming at its low concentrations in the fermentor, is the classical bioprocessing alternative to prevent sugar fermentation by yeasts. However, fed-batch fermentations present drawbacks that could be overcome by simpler batch cultures at relatively high (e.g. 20 g/L) initial sugar concentrations. In this study, a S. cerevisiae strain lacking invertase activity was engineered to transport sucrose into the cells through a low-affinity and low-capacity sucrose-H+ symport activity, and the growth kinetics and biomass yields on sucrose analyzed using simple batch cultures. Results We have deleted from the genome of a S. cerevisiae strain lacking invertase the high-affinity sucrose-H+ symporter encoded by the AGT1 gene. This strain could still grow efficiently on sucrose due to a low-affinity and low-capacity sucrose-H+ symport activity mediated by the MALx1 maltose permeases, and its further intracellular hydrolysis by cytoplasmic maltases. Although sucrose consumption by this engineered yeast strain was slower than with the parental yeast strain, the cells grew efficiently on sucrose due to an increased respiration of the carbon source. Consequently, this engineered yeast strain produced less ethanol and 1.5 to 2 times more biomass when cultivated in simple batch mode using 20 g/L sucrose as the carbon source. Conclusion Higher cell densities during batch cultures on 20 g/L sucrose were achieved by using a S. cerevisiae strain engineered in the sucrose uptake system. Such result was accomplished by effectively reducing sucrose uptake by the yeast cells, avoiding overflow metabolism, with the concomitant reduction in ethanol production. The use of this modified yeast strain in simpler batch culture mode can be a viable option to more complicated traditional sucrose-limited fed-batch cultures for biomass-directed processes of S. cerevisiae.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Reconocimiento de formas manuscritas con modelos ocultos de Markov

Programa de doctorado Tecnologías de las Telecomunicaciones

Modelización econométrica a través de cadenas de Markov de Monte Carlo (MCMC) en valoración contingente

Doctorado en Análisis Económico. Programa en Análisis Económico Aplicado

Amplificatori Switching (ad alto rendimento)

Reperibile direttamente alla pagina http://www.artechhouse.com/default.asp?frame=book.asp&book=978-0-89006-989-9&Country=&Continent=EU&State=#sample

High-performance switching architectures for optical networks

The need for high bandwidth, due to the explosion of new multi\-media-oriented IP-based services, as well as increasing broadband access requirements is leading to the need of flexible and highly reconfigurable optical networks. While transmission bandwidth does not represent a limit due to the huge bandwidth provided by optical fibers and Dense Wavelength Division Multiplexing (DWDM) technology, the electronic switching nodes in the core of the network represent the bottleneck in terms of speed and capacity for the overall network. For this reason DWDM technology must be exploited not only for data transport but also for switching operations. In this Ph.D. thesis solutions for photonic packet switches, a flexible alternative with respect to circuit-switched optical networks are proposed. In particular solutions based on devices and components that are expected to mature in the near future are proposed, with the aim to limit the employment of complex components. The work presented here is the result of part of the research activities performed by the Networks Research Group at the Department of Electronics, Computer Science and Systems (DEIS) of the University of Bologna, Italy. In particular, the work on optical packet switching has been carried on within three relevant research projects: the e-Photon/ONe and e-Photon/ONe+ projects, funded by the European Union in the Sixth Framework Programme, and the national project OSATE funded by the Italian Ministry of Education, University and Scientific Research. The rest of the work is organized as follows. Chapter 1 gives a brief introduction to network context and contention resolution in photonic packet switches. Chapter 2 presents different strategies for contention resolution in wavelength domain. Chapter 3 illustrates a possible implementation of one of the schemes proposed in chapter 2. Then, chapter 4 presents multi-fiber switches, which employ jointly wavelength and space domains to solve contention. Chapter 5 shows buffered switches, to solve contention in time domain besides wavelength domain. Finally chapter 6 presents a cost model to compare different switch architectures in terms of cost.