Multimode interference filter to solve degradation on coupler common-mode rejection

After quantifying degradation of a common mode rejection ratio (CMRR) 3dB-coupler due to excitation of TE01 mode, a novel compact circuit including multimode interference (MMI) coupler+bend+MMI+filter (CBF) is proposed. We show a CBF circuit has better CMRR at the expense of moderate loss. A complete tolerance analysis to main geometrical parameters has also been carried out.Publicad

Bases de datos avanzadas

Departament d' Enginyeria i Ciència dels Computadors. Codi d'assignatura: II5

Una Experiencia en el uso de metodologías activas en la asignatura Arquitectura de Computadores

Este trabajo presenta un análisis de la experiencia en el uso de la metodología de Aprendizaje Basada en Proyectos (ABP) en la asignatura Arquitectura de Computadores del Grado en Informática de la Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU). De este estudio se desprende que un 95,7% del alumnado está satisfecho con esta metodología docente y continuaría en futuros cursos utilizándola. Además, el estudio realizado revela que esta metodología ayuda a la capacitación transversal del alumnado, no supone aparentemente ningún sobreesfuerzo y tiene efectos positivos en las calificaciones del alumnado.SUMMARY -- This paper presents an analysis of the experience in using the project-based learning (PBL) methodology in Computer Architecture subject of the Degree in Informatics Engineering offered in the University of the Basque Country (UPV/EHU). This study shows that 95.7% of the students are satisfied with the teaching methodology, and they would continue using it in future courses. In addition, the study shows that this methodology contributes to the students' cross training, apparently does not generate any great increment in the students' effort and has positive effects on the obtained grade

Experiencia de implantación de la asignatura de Proyecto de Final de Grado en Ingeniería Informática dentro de un contexto empresarial real

Ponència presentada a les XXIII Jornadas sobre la Enseñanza Universitaria de la Informática, celebrades a Cáceres del 5 al 7 de juliol de 2017El Grado en Ingeniería Informática de nuestra universidad integra el proyecto de final de grado y la realización de prácticas externas en una sola asignatura de 18 créditos. Los estudiantes deben realizar 300 horas de prácticas en una empresa durante las que desarrollan un proyecto de naturaleza profesional propuesto por la empresa y relacionado con la especialidad que están cursando. Cada proyecto es supervisado por un profesional de la empresa y un tutor académico. Finalmente, los estudiantes deben redactar una memoria técnica y realizar una presentación oral ante un tribunal universitario. En el artículo se analizan los resultados de los tres primeros cursos de implantación de la asignatura y se extraen conclusiones desde los siguientes puntos de vista: La formación de los estudiantes. La participación de las empresas del entorno. El proceso de evaluación. La gestión, organización y coordinación de la asignatura. Desde el principio este modelo se valoró muy positivamente desde el punto de vista de la formación del estudiantado. Aunque la valoración de las empresas está más dividida, pues exige una implicación mucho mayor por parte de las mismas, hemos conseguido oferta suficiente para todos nuestros estudiantes. El aspecto que más ha cambiado a lo largo de estos tres años es el sistema de evaluación, en el que se contemplan aspectos relacionados con la integración del estudiante en la empresa, la calidad del trabajo desarrollado, la redacción de la memoria y la presentación oral. La gestión de la asignatura también se ha ido adaptando a las dificultades que entraña gestionar los proyectos propuestos por las empresas, asignar proyectos y tutores a los estudiantes y coordinar estudiantes, empresas, profesores tutores y tribunales de evaluación.In the Bachelor's Degree in Computer Engineering of our university, the final project and the student work placement are integrated in a single subject of 18 ECTS credits. Students must spend 300 hours in a company with the purpose of developing, with the guidance of a professional supervisor, a project related to their specialty. To complete the project, each student must write a technical report under the supervision of an academic tutor and present it to an examining board. In this paper we analyze the results of the first three academic years of the subject and we extract conclusions from four different points of view: • The academic training of the students. • The participation of companies of the surroundings. • The evaluation process. • The management, organization and coordination of the subject. This new model shows important advantages and, as a major result, it enhances the academic training of students. The company opinions about the model are diverse and, in general, it requires very active involvement and more efforts of all the participants in its development. During these three years, we have offered enough placements for all our students in the computer science departments of relevant companies of our surroundings. The evaluation system is the aspect of the subject that has needed more revisions as it requires to combine different criteria related to the integration of the student in the company, the results of the project and the quality of the report and the oral presentation. For the coordinators of the subject, the main challenges have been the collection and validation of the different projects proposed by the company supervisors, the projects assignment to both academic tutors and students, and the planning of examining boards

Modeling Analytical Streams for Social Business Intelligence

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector

Quality Indicators for Social Business Intelligence

Comunicació presentada a 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (Granada, Spain, 22-25 Oct. 2019)The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator

Quality management in social business intelligence projects

Social networks have become a new source of useful information for companies. Increasing the value of social data requires, first, assessing and improving the quality of the relevant data and, subsequently, developing practical solutions that apply them in business intelligence tasks. This paper focuses on the Twitter social network and the processing of social data for business intelligence projects. With this purpose, the paper starts by defining the special requirements of the analysis cubes of a Social Business Intelligence (SoBI) project and by reviewing previous work to demonstrate the lack of valid approaches to this problem. Afterwards, we present a new data processing method for SoBI projects whose main contribution is a phase of data exploration and profiling that serves to build a quality data collection with respect to the analysis objectives of the project

A Data Quality Multidimensional Model for Social Media Analysis

Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.Funding for open access charge: CRUE-Universitat Jaume

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems