Temperature Sensing Using Colloidal-Core Photonic Crystal Fiber

We report on a temperature sensor based on the monitoring of the luminescence spectrum of CdSe/ZnS nanocrystals, dispersed in mineral oil and inserted into the core of a photonic crystal fiber. The high overlap between the pump light and the nanocrystals as well as the luminescence guiding provided by the fiber geometry resulted in relatively high luminescence powers and improved optical signal-to-noise ratio (OSNR). Also, both core end interfaces were sealed so as to generate a more stable and robust waveguide structure. Temperature sensitivity experiments indicated a 70 pm/degrees C spectral shift over the 5 degrees C to 90 degrees C range.

Chaotic encryption method based on life-like cellular automata

A chaotic encryption algorithm is proposed based on the "Life-like" cellular automata (CA), which acts as a pseudo-random generator (PRNG). The paper main focus is to use chaos theory to cryptography. Thus, CA was explored to look for this "chaos" property. This way, the manuscript is more concerning on tests like: Lyapunov exponent, Entropy and Hamming distance to measure the chaos in CA, as well as statistic analysis like DIEHARD and ENT suites. Our results achieved higher randomness quality than others ciphers in literature. These results reinforce the supposition of a strong relationship between chaos and the randomness quality. Thus, the "chaos" property of CA is a good reason to be employed in cryptography, furthermore, for its simplicity, low cost of implementation and respectable encryption power. (C) 2012 Elsevier Ltd. All rights reserved.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

IDM - A New Parallel Methodology to Calculate the Determinant of Matrices of the Order n, with Computational Complexity O(n)

This paper presents a new parallel methodology for calculating the determinant of matrices of the order n, with computational complexity O(n), using the Gauss-Jordan Elimination Method and Chio's Rule as references. We intend to present our step-by-step methodology using clear mathematical language, where we will demonstrate how to calculate the determinant of a matrix of the order n in an analytical format. We will also present a computational model with one sequential algorithm and one parallel algorithm using a pseudo-code.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

Novel Sealing Technique for Practical Liquid-Core Photonic Crystal Fibers

In this letter, we describe a simple and effective technique to prevent evaporation in liquid-core photonic crystal fibers (PCFs). The technique consists of using a micropipette to deploy a micro-droplet of an ultraviolet curable polymer adhesive in both core inputs. After it is cured, the adhesive creates sealing polymer plugs with quite satisfactory insertion loss (overall optical transmission of about 15%). Processed fibers remained liquid-filled for at least six weeks. From a practical point of view, we conducted a supercontinuum generation experiment in a water-core PCF to demonstrate a 120-minute spectral width stability and the ability to withstand at least 3-mW average power at the sealed fiber input. Similar experiments carried out with nonsealed fibers produced supercontinuum spectra lasting no longer than 10 minutes, with average powers kept below 0.5 mW to avoid thermally induced evaporation.

Analysis of nonlinear dynamics of vocal folds using high-speed video observation and biomechanical modeling

Primary voice production occurs in the larynx through vibrational movements carried out by vocal folds. However, many problems can affect this complex system resulting in voice disorders. In this context, time-frequency-shape analysis based on embedding phase space plots and nonlinear dynamics methods have been used to evaluate the vocal fold dynamics during phonation. For this purpose, the present work used high-speed video to record the vocal fold movements of three subjects and extract the glottal area time series using an image segmentation algorithm. This signal is used for an optimization method which combines genetic algorithms and a quasi-Newton method to optimize the parameters of a biomechanical model of vocal folds based on lumped elements (masses, springs and dampers). After optimization, this model is capable of simulating the dynamics of recorded vocal folds and their glottal pulse. Bifurcation diagrams and phase space analysis were used to evaluate the behavior of this deterministic system in different circumstances. The results showed that this methodology can be used to extract some physiological parameters of vocal folds and reproduce some complex behaviors of these structures contributing to the scientific and clinical evaluation of voice production. (C) 2010 Elsevier Inc. All rights reserved.

Modeling and measuring the spatial relation "along": Regions, contours and fuzzy sets

The analysis of spatial relations among objects in an image is an important vision problem that involves both shape analysis and structural pattern recognition. In this paper, we propose a new approach to characterize the spatial relation along, an important feature of spatial configurations in space that has been overlooked in the literature up to now. We propose a mathematical definition of the degree to which an object A is along an object B, based on the region between A and B and a degree of elongatedness of this region. In order to better fit the perceptual meaning of the relation, distance information is included as well. In order to cover a more wide range of potential applications, both the crisp and fuzzy cases are considered. In the crisp case, the objects are represented in terms of 2D regions or ID contours, and the definition of the alongness between them is derived from a visibility notion and from the region between the objects. However, the computational complexity of this approach leads us to the proposition of a new model to calculate the between region using the convex hull of the contours. On the fuzzy side, the region-based approach is extended. Experimental results obtained using synthetic shapes and brain structures in medical imaging corroborate the proposed model and the derived measures of alongness, thus showing that they agree with the common sense. (C) 2011 Elsevier Ltd. All rights reserved.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Investigation of the Impacts of Large-Scale Wind Power Penetration on the Angle and Voltage Stability of Power Systems

The complexity of power systems has increased in recent years due to the operation of existing transmission lines closer to their limits, using flexible AC transmission system (FACTS) devices, and also due to the increased penetration of new types of generators that have more intermittent characteristics and lower inertial response, such as wind generators. This changing nature of a power system has considerable effect on its dynamic behaviors resulting in power swings, dynamic interactions between different power system devices, and less synchronized coupling. This paper presents some analyses of this changing nature of power systems and their dynamic behaviors to identify critical issues that limit the large-scale integration of wind generators and FACTS devices. In addition, this paper addresses some general concerns toward high compensations in different grid topologies. The studies in this paper are conducted on the New England and New York power system model under both small and large disturbances. From the analyses, it can be concluded that high compensation can reduce the security limits under certain operating conditions, and the modes related to operating slip and shaft stiffness are critical as they may limit the large-scale integration of wind generation.

Influence of the deposition parameters on the morphology and electrical conductivity of PANI/PSS self-assembled films

The influence of deposition parameters, namely polymer concentration and pH of the deposition solution, cleaning, and drying steps on the morphology and electrical characteristics of polyaniline and sulfonated polystyrene (PANI/PSS) nanostructured films deposited by the self-assembly technique is evaluated by UV-Vis spectroscopy, optical and atomic force microscopy, and electrical resistance measurements. It is found that stirring the cleaning solution during the cleaning step is crucial for obtaining homogenous films. Stirring of the cleaning solution also influences the amount of PANI adsorbed in the films. In this regard, the drying process seems to be less critical since PANI amount and film thickness are similar in films dried with N-2 flow or with an absorbent tissue. It is observed, however, that drying with N-2 flow results in rougher films. As an additional point, an assessment of the influence of the deposition method (manual versus mechanical) on the film characteristics was carried out. A significant difference on the amount of PANI and film thickness between films prepared by different human operators and by a homemade mechanical device was observed. The variability in film thickness and PANI adsorbed amount is smaller in films mechanically assembled. (c) 2007 Elsevier B.V. All rights reserved.

On the electrical conductivity of Ti-implanted alumina

Ion implantation of metal species into insulators provides a tool for the formation of thin, electrically conducting, surface layers with experimenter-controlled resistivity. High energy implantation of Pt and Ti into alumina accelerator components has been successfully employed to control high voltage surface breakdown in a number of cases. In the work described here we have carried out some basic investigations related to the origin of this phenomenon. By comparison of the results of alumina implanted with Ti at 75 keV with the results of prior investigations of polymers implanted with Pt at 49 eV and Au at 67 eV, we describe a physical model of the effect based on percolation theory and estimate the percolation parameters for the Ti-alumina composite. We estimate that the percolation dose threshold is about 4 x 10(16) cm(-2) and the maximum dose for which the system remains an insulator-conductor composite is about 10 x 10(16) cm(-2). The saturation electrical conductivity is estimated to be about 50 S/m. We conclude that the observed electrical conductivity properties of Ti-implanted alumina can be satisfactorily described by percolation theory. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3697900]

Nonoriented Electrical Steels

The physical origins of the magnetic properties of nonoriented electrical steels; its relations to microstructural features like grain size, nonmetallic inclusions, dislocation density distribution, crystallographic texture, and residual stresses; and its processing by cold rolling and annealing are overviewed, using quantitative relations whenever available.