A method for fingerprint image identification based on Gabor filter and power spectrum

This work presents a fingerprint identification scheme using a coefficient map based on Gabor filter and power spectrum. This process uses Poincaré index in order to locate fingerprint core and delta points and the images are aligned through these singular points before matching. From the core point, this technique divides an input image into quadrants, where information about texture orientation on three Gabor-filter directions is extracted. In the frequency domain, the analysis is based on the power spectrum, aiming towards reducing computational time. Results illustrate the method efficiency and accuracy.     

A Hybrid Intelligent Morphological Approach for Stock Market Forecasting

In this paper, a hybrid intelligent morphological approach is presented for stock market forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a correct time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. Initially, the proposed method chooses the most tuned prediction model for time series representation, then it performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed method using four real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.     

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

SUTIL – Network selection based on utility function and integer linear programming

This work presents SUTIL, a mechanism for network selection in the context of next generation networks (NGN). SUTIL selection mechanism prioritizes networks with higher relevance to the application and lower energy consumption and it enables full and seamless connectivity to mobile user devices and applications. Consequently, SUTIL contributes to realize the vision of ubiquitous computing, in which services, devices, and sensor-enriched environments interact anytime, anywhere to accomplish human designed tasks. The provided solution is based on utility function and integer linear programming and it aims at: (i) maximizing the user satisfaction while meeting application QoS and (ii) minimizing the energy consumption of devices when connecting to a target network. The solution is global since it considers for a given base station all devices that are simultaneously candidate for handoff. Simulation results showed the benefits of SUTIL usage in NGN environments.     

A multi-granular linguistic model for management decision-making in performance appraisal

The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.     

Gas–liquid dynamics at low Reynolds numbers in pillared rectangular micro channels

Most heterogeneously catalyzed gas–liquid reactions in micro channels are chemically/kinetically limited because of the high gas–liquid and liquid–solid mass transfer rates that can be achieved. This motivates the design of systems with a larger surface area, which can be expected to offer higher reaction rates per unit volume of reactor. This increase in surface area can be realized by using structured micro channels. In this work, rectangular micro channels containing round pillars of 3 μm in diameter and 50 μm in height are studied. The flow regimes, gas hold-up, and pressure drop are determined for pillar pitches of 7, 12, 17, and 27 μm. Flow maps are presented and compared with flow maps of rectangular and round micro channels without pillars. The Armand correlation predicts the gas hold-up in the pillared micro channel within 3% error. Three models are derived which give the single-phase and the two-phase pressure drop as a function of the gas and liquid superficial velocities and the pillar pitches. For a pillar pitch of 27 μm, the Darcy-Brinkman equation predicts the single-phase pressure drop within 2% error. For pillar pitches of 7, 12, and 17 μm, the Blake-Kozeny equation predicts the single-phase pressure drop within 20%. The two-phase pressure drop model predicts the experimental data within 30% error for channels containing pillars with a pitch of 17 μm, whereas the Lockhart–Martinelli correlation is proven to be non-applicable for the system used in this work. The open structure and the higher production rate per unit of reactor volume make the pillared micro channel an efficient system for performing heterogeneously catalyzed gas–liquid reactions.     

A UML/OCL framework for the analysis of graph transformation rules

In this paper we present an approach for the analysis of graph transformation rules based on an intermediate OCL representation. We translate different rule semantics into OCL, together with the properties of interest (like rule applicability, conflicts or independence). The intermediate representation serves three purposes: (1) it allows the seamless integration of graph transformation rules with the MOF and OCL standards, and enables taking the meta-model and its OCL constraints (i.e. well-formedness rules) into account when verifying the correctness of the rules; (2) it permits the interoperability of graph transformation concepts with a number of standards-based model-driven development tools; and (3) it makes available a plethora of OCL tools to actually perform the rule analysis. This approach is especially useful to analyse the operational semantics of Domain Specific Visual Languages. We have automated these ideas by providing designers with tools for the graphical specification and analysis of graph transformation rules, including a back-annotation mechanism that presents the analysis results in terms of the original language notation.     

SVM practical industrial application for mechanical faults diagnostic

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine‘s vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM’s it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

Us and them: a social capital perspective on the relationship between the business and IT departments

In this paper, a social capital perspective is applied to the relationship between the IT department and the Business organization. IT and Business are conceptualized as different occupational communities, with different understandings of their work. Our focus is on the level of social capital and the process of knowledge sharing between these occupational communities. We analyze the role that these factors play in reaching a mutual understanding within the process of IS development, and the influence this has on the perceived performance of the IT organization. Our study, combining qualitative and quantitative methods, points out that a lack of social capital (structural, relational as well as cognitive) can serve as an explanation for the often problematic relationship between these communities. Our analyses also show that social capital is especially relevant for the Business organization's perception of IT performance, whereas the IT department's perception is that performance is primarily dependent on the exchange of information.