Improved Speech Synthesis Using Fuzzy Methods

The paper presents theoretical support for and describes the use of a fuzzy paradigm in implementing a TTS system for the Romanian language, employing a rule-based formant synthesizer. In the framework of classic TTS systems, we propose a new approach in order to improve formant trace computation, aiming at increasing synthetic speech perceptual quality. A fuzzy system is proposed for solving the problem of the phonemes that are prone to multi-definitions in rule-based speech synthesis. In the introductory section, we briefly present the background of the problem and our previous results in speech synthesis. In the second section, we deal with the problem of the context-dependent phonemes at the letter-to-sound module level of our TTS system. Then, we discuss the case of the phoneme /l/ and the solution adopted to define it for different contexts. A fuzzy system is associated with each parameter (denoted F1 and F2) to implement the results of the complete analysis of the phoneme /l/ behavior. The knowledge used in implementing the fuzzy module is acquired by natural speech analysis. In the third section, we exemplify the computation of the synthesis parameters F1 and F2 of the phoneme /l/ in the context of the two syllable sequences. The parameter values are contrasted with those obtained from the spectrogram analysis of the natural speech sequences. The last section presents the main conclusions and further research objectives.     

Anderson accelerated fixed-stress splitting schemes for consolidation of unsaturated porous media

In this paper, we study the robust linearization of nonlinear poromechanics of unsaturated materials. The model of interest couples the Richards equation with linear elasticity equations, generalizing the classical Biot equations. In practice a monolithic solver is not always available, defining the requirement for a linearization scheme to allow the use of separate simulators. It is not met by the classical Newton method. We propose three different linearization schemes incorporating the fixed-stress splitting scheme, coupled with an L-scheme, Modified Picard and Newton linearization of the flow equations. All schemes allow the efficient and robust decoupling of mechanics and flow equations. In particular, the simplest scheme, the Fixed-Stress-L-scheme, employs solely constant diagonal stabilization, has low cost per iteration, and is very robust. Under mild, physical assumptions, it is theoretically shown to be a contraction. Due to possible break-down or slow convergence of all considered splitting schemes, Anderson acceleration is applied as post-processing. Based on a special case, we justify theoretically the general ability of the Anderson acceleration to effectively accelerate convergence and stabilize the underlying scheme, allowing even non-contractive fixed-point iterations to converge. To our knowledge, this is the first theoretical indication of this kind. Theoretical findings are confirmed by numerical results. In particular, Anderson acceleration has been demonstrated to be very effective for the considered Picard-type methods. Finally, the Fixed-Stress-Newton scheme combined with Anderson acceleration shows the best performance among the splitting schemes.     

Increased sensibility of mixed ferrite humidity sensors by subsequent heat treatment

The study presents the effects of different calcination temperatures on the size and morphology of mixed Co‐Ni ferrites, and the respective changes in the magnetic and electrical properties, with emphasis on their applicability as humidity sensors. The data suggests that the heat treatment does not significantly change the magnetic properties, but significant variations on the morphology of the nanoparticles, correlated with large changes in the electrical properties of the nanoparticles in terms of electrical permittivity and resistivity with electrical field frequency and humidity, with a clear influence of the calcination temperature over the sensibility of the materials to humidity.     

Alternatives for multiobjective optimization of a polymerization process

The optimal control policies for a polymerization process, particularly for batch free‐radical polymerization of methyl methacrylate, were determined using a multiobjective optimization technique. The process objectives considered in the optimization include monomer conversion, polydispersity index, polymerization degree, and total reaction time, weighted and combined in a scalar objective function. The decision variables were the initial concentration of the initiator and the temperature represented by isothermal steps. For solving the optimization problem, several methods based on sequential quadratic programming and a genetic algorithm were used and compared. Combining them into a hybrid method (the genetic algorithm provided the initial values for the traditional iterative method) led to the best results. The aims of this study were to develop an approach for optimizing the polymerization process and to describe alternatives for formulating and solving this problem, emphasizing the importance of user decision in choosing solutions based on technological criteria. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3680–3695, 2006     

High-resolution Kelvin probe microscopy in corrosion science: Scanning Kelvin probe force microscopy (SKPFM) versus classical scanning Kelvin probe (SKP)

With the introduction of a Kelvin probe mode to atomic force microscopy, the so called scanning Kelvin probe force microscopy (SKPFM), the Kelvin probe technique finds application in a steadily increasing number of different fields, from corrosion science to microelectronics and biosciences. For many of these applications, high resolution is required as the relevant information lies in the sub-microscopic distribution of work functions or potentials, which explains the increasing interest in SKPFM. However, compared to the standard scanning Kelvin probe (SKP) technique SKPFM is prone to much more artefacts, which are often not taken into account in the interpretation of the results, as is also the case with the real physical nature of the measured data. A critical discussion of possible artefacts and on the interpretation of the data is presented in this paper, with the main focus on application in corrosion science.     

Adaptive scheduling algorithm for media-optimized traffic management in software defined networks

Multi-policy resource management have been considered as an efficient methodology for delivering ready-to-use media-optimized applications in Software-Defined Networks (SDNs). Prioritized flow scheduling ensures high-speed communication in SDNs under large-scale distribution, heterogeneity of network resources, and exponential distribution of the flows granularity. The effectiveness of priority-based approaches depends usually on the control mechanism of the resource management. In this paper we improve the resource utilization by developing a novel adaptive scheduling strategy. We came with an effecting scheduling strategy to determine what resource to be allocated to a set of flows keeping their priority, increasing the average utilization of resources and, most importantly, establishing a virtual circuit for a specific flow over a network. Our theoretical remarks and extensive simulation results show that the proposed scheduling strategies can achieve the described goals.     

A framework for efficient performance prediction of distributed applications in heterogeneous systems

Predicting distributed application performance is a constant challenge to researchers, with an increased difficulty when heterogeneous systems are involved. Research conducted so far is limited by application type, programming language, or targeted system. The employed models become too complex and prediction cost increases significantly. We propose dPerf, a new performance prediction tool. In dPerf, we extended existing methods from the frameworks Rose and SimGrid. New methods have also been proposed and implemented such that dPerf would perform (i) static code analysis and (ii) trace-based simulation. Based on these two phases, dPerf predicts the performance of C, C++ and Fortran applications communicating using MPI or P2PSAP. Neither one of the used frameworks was developed explicitly for performance prediction, making dPerf a novel tool. dPerf accuracy is validated by a sequential Laplace code and a parallel NAS benchmark. For a low prediction cost and a high gain, dPerf yields accurate results.     

Individualized Follow-up of Pregnant Women with Asymptomatic Autoimmune Thyroid Disease

Maternal hormones are essential for the normal fetal development during pregnancy. Autoimmune thyroid disease is a frequent pathology in our iodine replete region. The aim of this study is to evaluate the occurrence of subclinical hypothyroidism (SCH) in cases with known autoimmune thyroid disease, which were in a euthyroid state prior to pregnancy, and to assess the association between supplemental treatments administered and the outcome of the pregnancy. The study is a prospective interventional controlled study. The two cohorts comprise the interventional group, consisting of 109 pregnant women with known autoimmune asymptomatic thyroid disease, without any levothyroxine (LT4) treatment and an aged-matched control group, with an unknown thyroid disease. After the pregnancy, a monthly evaluation of TSH, FT3, and FT4 was performed. Offspring evaluation was made at birth time. 88.8% of the women developed SCH in the first four weeks of pregnancy. Average LT4 doses increased as the pregnancy progressed. The monthly adjustment was 12.5 or 25 μg. All SCH cases developed in the first trimester of pregnancy. There was no significant difference regarding the gestational week, weight, or length at birth between the interventional group and controls, when TSH values were in the optimal range, during the whole pregnancy. Premature birth was described in one case in the interventional group.     

A rewriting logic approach to operational semantics

This paper shows how rewriting logic semantics (RLS) can be used as a computational logic framework for operational semantic definitions of programming languages. Several operational semantics styles are addressed: big-step and small-step structural operational semantics (SOS), modular SOS, reduction semantics with evaluation contexts, continuation-based semantics, and the chemical abstract machine. Each of these language definitional styles can be faithfully captured as an RLS theory, in the sense that there is a one-to-one correspondence between computational steps in the original language definition and computational steps in the corresponding RLS theory. A major goal of this paper is to show that RLS does not force or pre-impose any given language definitional style, and that its flexibility and ease of use makes RLS an appealing framework for exploring new definitional styles.