Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter

The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast computing the likelihood of particles, a numerical fitting approach is proposed to construct the Likelihood Probability Density Function (Li-PDF) by using a comparably small number of so-called fulcrums. The likelihood of particles is thereby analytically inferred, explicitly or implicitly, based on the Li-PDF instead of directly computed by utilizing the observation, which can significantly reduce the computation and enables real time filtering. The proposed approach guarantees the estimation quality when an appropriate fitting function and properly distributed fulcrums are used. The details for construction of the fitting function and fulcrums are addressed respectively in detail. In particular, to deal with multivariate fitting, the nonparametric kernel density estimator is presented which is flexible and convenient for implicit Li-PDF implementation. Simulation comparison with a variety of existing approaches on a benchmark 1-dimensional model and multi-dimensional robot localization and visual tracking demonstrate the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a draft/preprint of one paper submitted to the IEEE Transaction

idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining

This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paperSpanish Ministry of Science projects OVAMAH (TIN 2009-13839-C03-03) and MIDAS (TIN 2010-21272-C02-01), funded by the European Regional Development Fund, projects of the Junta of Castilla and Leon BU006A08 and JCYL-2002-05; Projects of the Spanish Government SA071A08, CIT-020000-2008-2 and CIT-020000-2009-12; the Professional Excellence Program 2006-2010 IFARHU-SENACYT-Panama. The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S.A., within the framework of the project MAGNO2008 - 1028. - CENIT Project funded by the Spanish Ministry

iGenda : an event scheduler for common users and centralised systems

The world is walking towards an aged society as a consequence of the increasing rate of longevity in modern cultures. With age comes the fact that memory decreases its efficiency and memory loss starts to surge.Within this context, iGenda is a Personal Memory Assistant (PMA) designed to run on a personal computer or mobile device that tries to help final-users in keeping track of their daily activities. In addition, iGenda has included a Centralised Management System (CMS) on the side of an hospital-like institution, the CMS stands a level above the PMA and the goal is to manage the medical staff (e.g. physicians and nurses) daily work schedule taking into account the patients and resources, communicating directly with the PMA of the patient. This paper presents the platform concept, the overall architecture of the system and the key features on the different agents and components

Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study

Agriculture is the very backbone of every country. Unfortunately, agricultural sustainability is threatened by the lack of energy-efficient solutions. The threat becomes more evident with the constantly growing world population. The research community must, therefore, focus on resolving the problem of high energy consumption. This paper proposes a model of energy scheduling in agricultural contexts. Greater energy efficiency is achieved by means of PV (photovoltaics) and hydropower, as demonstrated in the conducted case study. The developed model is intended for contexts where the farm is located near a river, so the farmer can use the flowing water to produce energy. Moreover, the model has been emulated using a variety of state-of-the-art laboratory devices. Optimal energy scheduling is performed via a decision tree approach, optimizing the use of energy resources and reducing electricity costs. Finally, a realistic scenario is presented to show the technical features and the practical behaviors of each emulator when adapting the results of the decision tree. The research outcomes demonstrate the importance of the technical validation of each model. In addition, the results of the emulation reveal practical issues that had not been discovered during the theoretical study or during the simulationhe present research work was conducted and funded within the scope of the following project: Eco Rural IoT project, funded by TETRAMAX-VALUECHAIN-TTX-1, and UID/EEA/00760/2019, funded by FEDER Funds through the COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Web Page Classification with Pre-Trained Deep Convolutional Neural Networks

In this paper, we propose mining the growing amount of information present on the internet in the form of visual content. We address the problem of web page categorization based on the multimedia elements present on it. To achieve this, our framework leverages a pre-trained deep convolutional neural network model, which is used as a feature extractor for later classification. This paper presents experimental results concerning the effectiveness of different classifiers trained with features extracted at various depths of the convolutional neural network

Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare

Abstract—Ambient intelligence has acquired great importance in recent years and requires the development of new innovative solutions. This paper presents a distributed telemonitoring system, aimed at improving healthcare and assistance to dependent people at their homes. The system implements a service-oriented architecture based platform, which allows heterogeneous wireless sensor networks to communicate in a distributed way independent of time and location restrictions. This approach provides the system with a higher ability to recover from errors and a better flexibility to change their behavior at execution time. Preliminary results are presented in this paper. Index Terms—Ambient intelligence (AmI), healthcare, servicesoriented architectures (SOAs), wireless sensors networks (WSNs)

Second-order statistics analysis and comparison between arithmetic and geometric average fusion: Application to multi-sensor target tracking

Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing data, respectively. In the context of multisensor target tracking, the two most common formats of data to be fused are random variables and probability density functions, namely v-fusion and f-fusion, respectively. In this work, we analyze and compare the second-order statistics (including variance and mean square error) of AA and GA in terms of both v-fusion and f-fusion. The case of weighted Gaussian mixtures representing multitarget densities in the presence of false alarms and missed detections (whose weight sums are not necessarily unit) is also considered, the result of which turns out to be significantly different from that of a single target. In addition to exact derivation, exemplifying analyses and illustrations are also provided.This work was supported in part by the Marie Skłodowska-Curie Individual Fellowship under Grant 709267, in part by Shaanxi Natural Fund under Grant 2018MJ6048, in part by the Northwestern Polytechnical University, and in part by Junta Castilla y León, Consejería de Educación and FEDER funds under project SA267P18

VoIP: The Convergence of networks

The development of telecommunications, especially of the Internet, has made possible the use of technologies such as IP (Internet Protocol) telephony for both business and leisure. The problems arising from the diversity in number of existing telecommunication nets are motivating the study of systems to promote the homogenisation of means of voice and data transport. This paper presents the VoIP (voice over Internet Protocol) solution as a likely solution for this problem. It also develops a first approach to the concept and terminology of net convergence, and later, it establishes a comparison between IP telephony and conventional telephony. The paper also supplies a detailed analysis of requirements of IP telephony, of its legal situation and of the different standards used in its development