Informatica

The FDA's Quality by Design initiative and associated design space construct (ICH, 2009), have stimulated the use of quantitative methods, mathematical and statistical models, and designed experiments in the process of drug development and manufacture. For a given drug product, the design space may be interpreted as the constrained region of the manufacturing operating variable space within which assurance can be provided that drug product quality specifications will be met. It is now understood, at least conceptually, that this assurance is not deterministic, rather it must be stated in probabilistic terms. In this paper, we report on the use of Bayesian methods to develop a suitable risk metric based on both mathematical and statistical models of the manufacturing processes and product properties. The Bayesian estimation is carried out to determine the joint posterior distribution of the probability of the product meeting quality specifications. The computations are executed using a novel Variational Bayes approximation. In this paper the direct computational approach using this approximation is compared to the widely used but computationally very intensive Markov Chain Monte Carlo method. The approach is illustrated using experimental data and models drawn from a recent QbD study on the drug gabapentin in which the authors were participants.

A well-known example of global optimization that provides solutions within fixed error limits is optimization of functions with a known Lipschitz constant. In many real-life problems this constant is unknown.

The most classical visualization methods, including multidimensional scaling and its particular case – Sammon's mapping, encounter difficulties when analyzing large data sets. One of possible ways to solve the problem is the application of artificial neural networks. This paper presents the visualization of large data sets using the feed-forward neural network – SAMANN. This back propagation-like learning rule has been developed to allow a feed-forward artificial neural network to learn Sammon's mapping in an unsupervised way. In its initial form, SAMANN training is computation expensive. In this paper, we discover conditions optimizing the computational expenditure in visualization even of large data sets. It is shown possibility to reduce the original dimensionality of data to a lower one using small number of iterations. The visualization results of real-world data sets are presented.

In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a problem of optimization on the set of either matrices or vectors. Briefly, SLRA is defined as follows. Given an initial matrix with a certain structure (for example, Hankel), the aim is to find a matrix of specified lower rank that approximates this initial matrix, whilst maintaining the initial structure. We demonstrate that the optimization problem arising is typically very difficult; in particular, the objective function is multiextremal even in simple cases. We also look at different methods of solving the SLRA problem. We show that some traditional methods do not even converge to a locally optimal matrix.

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.

In the protocol conformance testing, many existing test methods can effectively detect the possible faults of the implementation under test. However, it is difficult to diagnose the found faults in terms of the test results. This paper presents a diagnosable input/output (DIO) sequence, to differentiate a state from other states under a given condition. We further propose a two-tier protocol conformance testing and diagnosing method based on DIO sequences. The proposed method can effectively detect and diagnose the possible faults of the implementation of a protocol.

This study uses the r-theta transformation technique to map a fingerprint image to the straight-line signals. Subsequently, the “vector magnitude invariant transform” technique is applied to them to generate an invariant magnitude for person identification. This technique can solve the image rotation problem. Various vertical magnitude strips are generated to deal with the image-shifting problem. The algorithm developed in this study can precisely classify the fingerprint images.

The goal of the paper is to get a method of Lithuanian speech diphthong modelling. We use a formant-based synthesizer for this modelling. The second order quasipolynomial has been chosen as the formant model in time domain. A general diphthong model is a multi-input and single-output (MISO) system, that consists of two parts where the first part corresponds to the first vowel of the diphthong and the second one – to the other vowel. The system is excited by semi-periodic impulses with a smooth transition from one vowel to the other. We derived the parametric input-output equations in the case of quasipolynomial formants, defined a new notion of the convoluted basic signal matrix, derived parametric minimization functional formulas for the convoluted output data. The new formant parameter estimation algorithm for convoluted data, based on Levenberg–Marquardt approach, has been derived and its stepwise form presented. Lithuanian diphthong /ai/ was selected as an example. This diphthong was recorded with the following parameters: PCM 48 kHz, 16 bit, stereo. Two characteristic pitches of the vowels /a/ and /i/ have been chosen. Equidistant samples of these pitches have been used for estimating parameters of MISO formant models of the vowels. Transition from the vowel /a/ to the vowel /i/ was achieved by changing excitation impulse amplitudes by the arctangent law. The method was audio tested, and the Fourier transforms of the real data and output of the MISO model have been compared. It was impossible to distinguish between the real and simulated diphthongs. The magnitude and phase responses only have shown small differences.

Electronic commerce (e-commerce) is a relatively new, emerging and constantly changing area of business management and information technology. One of the technological innovations in banking, finance and e-commerce is the electronic cash (e-cash) transfer system. E-cash transfer systems refers to the technological breakthrough that enables us to perform financial transactions electronically. In this paper we propose a secure e-cash transfer system based on the elliptic curve cryptography. In order to protect the honest participants of the e-cash system we use an elliptic curve blind signature scheme and also we need a trusted third party to trace the criminals.

In this paper we have proposed a novel method for image denoising using local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule. The algorithm performs separable column-wise and row-wise image denoising (i.e., independently by rows and by columns), combining the obtained results into the final image estimate. The newly developed method performs competitively among recently published state-of-the-art denoising methods in terms of the peak signal-to-noise ratio (PSNR), even outperforming them for small to medium noise variances for images that are piecewise constant along their rows and columns.