An Immersive Multi-Party Conferencing System for Mobile Devices Using 3D Binaural Audio

[EN] The use of mobile telephony, along with the widespread of smartphones in the consumer market, is gradually displacing traditional telephony. Fixed-line telephone conference calls have been widely employed for carrying out distributed meetings around the world in the last decades. However, the powerful characteristics brought by modern mobile devices and data networks allow for new conferencing schemes based on immersive communication, one the fields having major commercial and technical interest within the telecommunications industry today. In this context, adding spatial audio features into conventional conferencing systems is a natural way of creating a realistic communication environment. In fact, the human auditory system takes advantage of spatial audio cues to locate, separate and understand multiple speakers when they talk simultaneously. As a result, speech intelligibility is significantly improved if the speakers are simulated to be spatially distributed. This paper describes the development of a new immersive multi-party conference call service for mobile devices (smartphones and tablets) that substantially improves the identification and intelligibility of the participants. Headphone-based audio reproduction and binaural sound processing algorithms allow the user to locate the different speakers within a virtual meeting room. Moreover, the use of a large touch screen helps the user to identify and remember the participants taking part in the conference, with the possibility of changing their spatial location in an interactive way.This work has been partially supported by the government of Spain grant TEC-2009-14414-C03-01 and by the new technologies department of TelefónicaAguilera Martí, E.; López Monfort, JJ.; Cobos Serrano, M.; Macià Pina, L.; Martí Guerola, A. (2012). An Immersive Multi-Party Conferencing System for Mobile Devices Using 3D Binaural Audio. Waves. 4:5-14. http://hdl.handle.net/10251/57918S514

Indicator-based MONEDA: A Comparative Study of Scalability with Respect to Decision Space Dimensions

Proceedings of: 2011 IEEE Congress on Evolutionary Computation (CEC), New Orleans, LA, June 5-8 2011The multi-objective neural EDA (MONEDA) was proposed with the aim of overcoming some difficulties of current MOEDAs. MONEDA has been shown to yield relevant results when confronted with complex problems. Furthermore, its performance has been shown to adequately adapt to problems with many objectives. Nevertheless, one key issue remains to be studied: MONEDA scalability with regard to the number of decision variables. In this paper has a two-fold purpose. On one hand we propose a modification of MONEDA that incorporates an indicator-based selection mechanism based on the HypE algorithm, while, on the other, we assess the indicator-based MONEDA when solving some complex two-objective problems, in particular problems UF1 to UF7 of the CEC 2009 MOP competition, configured with a progressively-increasing number of decision variables.This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-02.Publicad

Moving away from error-based learning in multi-objective estimation of distribution algorithms

Proceedings of: 12th annual conference on Genetic and evolutionary computation (GECCO '10). Portland, Oregon, USA, July 7-11, 2010.In this work we analyze the model-building issue and the requirements it imposes on the learning paradigm being used. We argue that error-based learning, the class of learning most commonly used in MOEDAs, is responsible for current MOEDA underachievement. We present ART as a viable alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume based selector as described for the HypE algorithm. We experimentally show that thanks to MARTEDA's novel model-building approach and an indicator-based population ranking the algorithm it is able to outperform similar MOEDAs and MOEAs.This work was supported in part by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, DPS2008-07029-C02-02 and CAM CONTEXTS S2009/TIC-1485.Publicad

A multi-agent system for managing adverse weather situations on the road network

The development of traffic management and control strategies to improve traffic flows and road safety is necessary due to the high dynamism of traffic flows. The use of distributed intelligent systems can help the traffic organizations and the road operators to cope with possible incidents on the road network, especially when the incidents are related to adverse meteorological conditions. In that case, the probability of road accidents is increased due to the difficulty of driving under bad weather conditions. So, if the operators detect any meteorological incident, they must decide how to deal with it in order to improve traffic safety. In this paper we introduce a new multiagent system (MAS) to support traffic management when there appear meteorological problems in the road network. MAS technology helps to deal with the specific characteristics of traffic domain. The proposed MAS is able to work in two ways: a) coordinately, where all the agents work to solve weather problems in large networks and b) locally, where due to communications breakdown small groups of agents work together to inform road users about weather problems. The MAS has a rule-based system to deal with the meteorological data and decide the actions to take in front of any meteorological issue. This expert system also controls the quality of the data, improving the road operator confidence in the decisions taken by the expert system. However, weather sensors can provide wrong data, due to several factors (hardware failure, climate factors, etc.) so the rule based system controls these provided data by applying specific coherence and correlation rules to improve the quality of the taken decisions

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by the Brazilian National Council for Scientific and Technological Development projects CNPq BJT 407851/2012-7 and CNPq PVE 314017/2013-5 and projects MINECO TEC 2012-37832-C02-01, CICYT TEC 2011-28626-C02-02.Publicad

Cohesión, Vinculación e Integración sociales en el marco del Capital Social

Background of INCASI Project H2020-MSCA-RISE-2015 GA 691004. WP1: CompilationEl objetivo de este artículo consiste en identificar, definir y engranar en un modelo unificado conceptos tales como Interacción-relación, Capital social y otros Capitales; Cohesión, Vinculación e Integración sociales; Bonding, Bridging y Linking desde una perspectiva de redes sociales. Estos conceptos que tienen entidad propia han sido estudiados bien de manera independiente bien apareados. Además, relacionada con estos conceptos, existe una constelación de otros muchos como por ejemplo solidaridad, confianza, roles, valores comunes, reciprocidad, exclusión, inclusión y segregación. Estos conceptos se resitúan en el modelo conceptual propuesto.The aim of this paper is to identify, define and engage in a unified model related concepts such as interaction, social capital and other capitals, Cohesion and Integration Linking social Bonding, Bridging and Linking and Social Networks from the perspective of social networks. These concepts have been studied in its own right independently or paired. In addition, related to these concepts there is a constellation of many others such as solidarity, trust, roles, shared values, reciprocity, exclusion, inclusion, segregation that are being repositioned in the proposed conceptual model

A stopping criterion for multi-objective optimization evolutionary algorithms

This Paper Puts Forward A Comprehensive Study Of The Design Of Global Stopping Criteria For Multi-Objective Optimization. In This Study We Propose A Global Stopping Criterion, Which Is Terms As Mgbm After The Authors Surnames. Mgbm Combines A Novel Progress Indicator, Called Mutual Domination Rate (Mdr) Indicator, With A Simplified Kalman Filter, Which Is Used For Evidence-Gathering Purposes. The Mdr Indicator, Which Is Also Introduced, Is A Special-Purpose Progress Indicator Designed For The Purpose Of Stopping A Multi-Objective Optimization. As Part Of The Paper We Describe The Criterion From A Theoretical Perspective And Examine Its Performance On A Number Of Test Problems. We Also Compare This Method With Similar Approaches To The Issue. The Results Of These Experiments Suggest That Mgbm Is A Valid And Accurate Approach. (C) 2016 Elsevier Inc. All Rights Reserved.This work was funded in part by CNPq BJT Project 407851/2012-7 and CNPq PVE Project 314017/2013-

An information fusion framework for context-based accidents prevention

The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.This work was partially funded by CNPq BJT Project 407851/2012–7 and CNPq PVE Project 314017/2013–5

Evaluation of the Intermediate Values of the TGA Curves as Indicators of the Proximal Analysis of Biomass

[EN] Thermogravimetric analysis (TGA) is becoming popular for the evaluation of biomass to determine the content of ashes, volatiles, and fixed carbon and to simulate pyrolysis, gasification, and combustion processes. This analysis consists of heating a sample recording the weight variation as the temperature increases over time. The final temperature of the analyzes is usually set at 550 degrees C or 900 degrees C. The aim of this paper is to use the intermediate weight values obtained in short times from heating process in TGA to calculate the percentage of volatile, ash, or the residual mass remaining at the end of the experiment. Under the hypothesis that the curve does not vary when the analysis is carried out under certain conditions for the same type of biomass, these values must be similar and are related to the searched values. Nevertheless, given that the behavior of the thermogravimetric curves can be influenced by different factors, such as the species, temperature variation with time, final temperature reached, and presence of leaves, these factors are analyzed in this article. The results show models developed for the ash and volatiles determination from TGA time reduced to 75 s when a temperature increase of 200 degrees C per minute is used (CR-200 and VR-200 models). The curves obtained have R2 coefficients of between 0.75 and 0.95, being validated through independent samples. It is shown that the plot of the curve is influenced by the composition, the rate of heating and the percentage of leaves. This variability makes it necessary to select an analytical method that is efficient and as brief as possible. In this article, rapid analyses combined with the application of the equations obtained are proposed.This work was carried out within the framework of a study examining the analysis of the implementation of biomass exploitation chains in rural communities in the province of Bolívar (Ecuador) as part of the ADSIEO-COOPERATION program of the Polytechnic University of Valencia (UPV). The Ecuadorian Energy Exploitation Research Network of Biomass (ECUMASA) and the IBEROMASA Network of the Ibero-American Program of Science and Technology for Development (CYTED) participated in this program.Velázquez Martí, B.; Gaibor-Chavez, J.; López- Cortés, I.; Olivares Aguilar, LE. (2023). Evaluation of the Intermediate Values of the TGA Curves as Indicators of the Proximal Analysis of Biomass. Agronomy. 13(10). https://doi.org/10.3390/agronomy13102552131