Informatica

Wireless Mesh Networks (WMNs) have become an important networking infrastructure due to their low cost for providing broadband connectivity. Issues for achieving the network connectivity and user coverage are related to the node placement problem. Several optimization problems are showing their usefulness to the efficient design of WMNs. These problems are related to optimizing network connectivity, user coverage and stability. In this paper, we formulate the optimization problems using a multi-objective optimization model. For the mesh router nodes placement, the bi-objective optimization problem is obtained consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage. We evaluate the performance of WMN-GA system for node placement problem in WMNs. For evaluation, we consider Normal, Exponential and Weibull Distribution of mesh clients and different selection and mutation operators. The population size is considered 64 and the number of generation 200. The simulation results show that WMN-GA system performs better for Single Mutation, Linear Ranking selection and Normal distribution of mesh clients.

This paper investigates in a formal context some fundamental controllability properties “from” and “to” the origin of probabilistic discrete-time dynamic systems as well as their uniform versions and complete controllability in a class of probabilistic metric spaces or probabilistic normed spaces, in particular, in probabilistic Menger spaces. Some related approximate probabilistic controllability properties are also investigated for the case when a nominal controllable system is subject to either parametrical perturbations or unmodelled dynamics. In this context, the approximate controllability of a perturbed system is a robustness-type approximate controllability provided that the nominal system is controllable. Some illustrative examples are also given.

This paper presents a model for signal compression, which consists of a piecewise uniform quantizer and a new lossless coder. The model is designed in a general manner, i.e. for any symmetrical signal distribution; this general theory is applied to design models for Gaussian and Laplacian distributions. Rigorous mathematical derivation of the expression for the bit-rate is presented. Forward adaptation of the model is done for non-stationary signals. Theory is proved by simulations in MATLAB and by an experiment with a real speech signal. The most important advantages of the model are low complexity and good performances – it satisfies G.712 standard for the speech transmission quality with 6.18 bps (bits per sample), which is significantly smaller than 8 bps required for quantizers used in PSTN (public switched telephone network) defined with G.711 standard.

Certificateless short signature (CLSS) possesses the advantages of both certificateless signature and short signature. CLSS eliminates the certificate management in conventional signatures and solves the key escrow problem in ID-based signatures. In the meantime, due to its short signature length, CLSS reduces the bandwidth for communication so that it is suitable for some specific authentication applications requiring bandwidth-constrained communication environments. However, up to now, there is no work on studying the revocation problem in existing CLSS schemes. In this article, we address the revocation problem and propose the first revocable certificateless short signature (RCLSS) scheme. Based on the computational Diffie–Hellman (CDH) assumption, we demonstrate that our RCLSS scheme possesses strong unforgeability against adaptive chosen-message attacks under an accredited security model. It turns out that our scheme has the shortest signature length while retaining computational efficiency. Thus, the proposed RCLSS scheme is well suited for low-bandwidth communication environments. Finally, we combine the proposed RCLSS scheme with cloud revocation authority (CRA) to present a CRA-aided authentication scheme with period-limited privileges for mobile multi-server environment.

Phoneme duration modelling is one of the stages in prosody modelling for text-to-speech systems. The rule-based phoneme duration model proposed by Klatt (1979) is still quite a popular method. One of the main shortcomings of this method is that the values of the parameters are selected in an experimental way. This work proposes a new iterative algorithm for the automatic estimation of the factors for the Klatt model using the corpus of an annotated audio record of the speaker. The phoneme duration models were built for three different Lithuanian speakers. The quality of the estimation of phonemes durations was evaluated by the root mean square error, the mean absolute error and the correlation coefficient.

An optimization problem is formulated in the tropical mathematics setting to maximize a nonlinear objective function defined by conjugate transposition on vectors in a semimodule over a general idempotent semifield. The study is motivated by problems from project scheduling, where the deviation between completion times of activities is to be maximized subject to precedence constraints. To solve the unconstrained problem, we establish an upper bound for the function, and then obtain a complete solution to a system of vector equations to find all vectors that yield the bound. An extension of the solution to handle constrained problems is discussed. The results are applied to give direct solutions to the motivational problems, and illustrated with numerical examples.

The paper presents analytic and stochastic methods of structure parameters estimation for a model selection problem. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize model parameters and structure parameters we maximize a model evidence, a convolution of a data likelihood with a prior distribution of model parameters. The analytic methods are based on the derivatives computation of the approximated model evidence. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on the synthetic and real data.

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions.

The aim of this paper is twofold. Firstly, to discuss a clustering of a given set of the European banks into groups based on their performance during 1999–2013. Secondly, to compare different dissimilarity measures and to determine which of them suits best for clustering banking ratios. Six ratios that reveal profitability, efficiency, stability and loan portfolio quality of the banks were used. The similarity/dissimilarity between banks was estimated using measures that are based on time series or functional data properties. Two dissimilarity measures that are not commonly used in the literature are proposed and two measures are extended from univariate into multivariate case. The results of our study show that there is no dissimilarity measure which would provide the best clustering results for all ratios. However, dissimilarity measures based on functional data properties in many cases outperfomed measures based on time series properties. The choice of the number of clusters is not that clear. According to different banking ratios, it is found that banks could be grouped into 6–12 clusters.

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or ${\mathit{F}}_0$, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models were evaluated by synthesizing ${\mathit{F}}_0$ contours and by comparing them to the original ${\mathit{F}}_0$ contours using criteria of root mean square error (RMSE) and voicing classification error. The HMM-based models showed an improvement of the RMSE over the mean-based model that predicted ${\mathit{F}}_0$ of the vowel on the basis of its average normalized pitch.