Abstract: The study of dielectric properties in a binary mixture of liquids is very useful to understand the liquid structure, molecular interaction, dynamics, and kinematics of the mixture. Time-domain reflectometry (TDR) is a powerful tool for studying the cooperation and molecular dynamics of the H-bonded system. Here we discuss the basic calibration and normalization procedure for TDR measurements. Our aim is to explain different types of error occur during TDR measurements and how to minimize it.
Abstract: This paper presents an original application of multiple criteria decision making analysis theory to the evaluation of aircraft selection problem. The selection of an optimal, efficient and reliable fleet, network and operations planning policy is one of the most important factors in aircraft selection problem. Given that decision making in aircraft selection involves the consideration of a number of opposite criteria and possible solutions, such a selection can be considered as a multiple criteria decision making analysis problem. This study presents a new integrated approach to decision making by considering the multiple criteria utility theory and the maximal regret minimization theory methods as well as aircraft technical, economical, and environmental aspects. Multiple criteria decision making analysis method uses different normalization techniques to allow criteria to be aggregated with qualitative and quantitative data of the decision problem. Therefore, selecting a suitable normalization technique for the model is also a challenge to provide data aggregation for the aircraft selection problem. To compare the impact of different normalization techniques on the decision problem, the vector, linear (sum), linear (max), and linear (max-min) data normalization techniques were identified to evaluate aircraft selection problem. As a logical implication of the proposed approach, it enhances the decision making process through enabling the decision maker to: (i) use higher level knowledge regarding the selection of criteria weights and the proposed technique, (ii) estimate the ranking of an alternative, under different data normalization techniques and integrated criteria weights after a posteriori analysis of the final rankings of alternatives. A set of commercial passenger aircraft were considered in order to illustrate the proposed approach. The obtained results of the proposed approach were compared using Spearman's rho tests. An analysis of the final rank stability with respect to the changes in criteria weights was also performed so as to assess the sensitivity of the alternative rankings obtained by the application of different data normalization techniques and the proposed approach.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Multimodal biometric systems integrate the data presented by multiple biometric sources, hence offering a better performance than the systems based on a single biometric modality. Although the coupling of biometric systems can be done at different levels, the fusion at the scores level is the most common since it has been proven effective than the rest of the fusion levels. However, the scores from different modalities are generally heterogeneous. A step of normalizing the scores is needed to transform these scores into a common domain before combining them. In this paper, we study the performance of several normalization techniques with various fusion methods in a context relating to the merger of three unimodal systems based on the face, the palmprint and the fingerprint. We also propose a new adaptive normalization method that takes into account the distribution of client scores and impostor scores. Experiments conducted on a database of 100 people show that the performances of a multimodal system depend on the choice of the normalization method and the fusion technique. The proposed normalization method has given the best results.