Covariance-based least-squares filtering algorithm under Markovian measurement delays

This paper addresses the least-squares linear filtering problem of signals from measurements which may be randomly delayed by one or two sampling times. The delays are modelled by a homogeneous discrete-time Markov chain to capture the dependence between them. Assuming that the evolution equation generating the signal is not available and that only the first- and second-order moments of the processes involved in the observation model are known, a recursive filtering algorithm is derived using an innovation approach. Recursive formulas for the filtering error variances are also obtained to measure the precision of the proposed estimators.This research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-52291-P)

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor systems with random uncertainties both in the sensor outputs and during the transmission connections. The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal, and delays may occur during transmission. These uncertainties are commonly described by means of independent Bernoulli random variables. In the present paper, the model is generalised in two directions: (i) at each sensor, the degradation in the measurements is modelled by sequences of random variables with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous and consecutive sampling times, and that the evolution of the signal process is unknown, we address the problem of signal estimation in terms of covariances, using the following distributed fusion method. First, the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then, the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical simulation example.Ministerio de Economía, Industria y CompetitividadEuropean Union (EU) MTM2017-84199-PAgencia Estatal de Investigació

Maritime-oriented foragers during the Late Pleistocene on the eastern costa del sol (Southeast Iberia): Cueva Victoria (Málaga, Spain)

The Mediterranean coast of Spain is marked by several clusters of Palaeolithic sites: to the south of the Pyrenees, in the area around the Ebro River, in the central part, and on the south coast, one of the southernmost regions in Europe. The number of sites is small compared with northern Iberia, but like that region, the Palaeolithic occupations are accompanied by several rock art ensembles. The archaeological material (both biotic and abiotic resources) and radiocarbon dates presented here were obtained during archaeological fieldwork of professor J. Fortea in the Late Pleistocene deposits in Cueva Victoria, located near the modern coastline and about 150 km north of the Strait of Gibraltar. In the three occupation phases, marine resources were acquired by shell-fishing (focusing almost exclusively on the clam Ruditapes decussatus), fishing, and the use of beached marine mammals. This contrasts with the limited data about the exploitation of terrestrial resources by hunting and gathering animals and plants. The study is completed by the study of artefacts (lithic and bone industry and objects of adornment) that help to understand the subsistence strategies of the cave occupants and enable a comparison with other groups inhabiting the Mediterranean coasts of the Iberian Peninsula during Greenland Interstadial 1, between ca. 15.1 and 13.6 cal BP.This work was supported by the University of Salamanca GIR PREHUSAL, the Ministry of Science and Innovation-Spanish Government (PaleontheMove-PID2020-114462GB-I00), the Universidad Nacional de Educación a Distancia (Madrid) and Dirección General de Universitat, Investigacio i Ciencia of the Valencian Regional Government (Project Aico/2020/97).Peer reviewe

Estimación lineal en sistemas estocásticos con parámetros distribuidos mediante observaciones inciertas

Univ. Granada, Departamento de Estadística e Investigación Operativa. Leída 23-03-9

The Waring Distribution as a Low-Frequency Prediction Model: A Study of Organic Livestock Farms in Andalusia

Although the numbers are relatively small with respect to non-organic livestock, the importance of organic livestock farms lies in their sustainable coexistence with the natural environment and in the high-quality food products obtained. In this type of production, no artificial chemicals or genetically modified organisms are used, therefore there will be less impact on the environment and, in most cases, native breeds are employed. This paper describes a geostatistical study of organic livestock farms in Andalusia (southern Spain), conducted using information from the 2009 Agricultural Census, by classes of livestock. This region currently records the highest output in Spain for organic livestock farming. The number of farms was fitted according to the univariate generalizedWaring distribution, which is presented as a means of analyzing this type of discrete measurement, using agricultural or livestock data. The Waring distribution is used when the frequency of occurrence of a phenomenon is very low and allows one to divide the variance. The most important outcome of this study is the finding that livestock data variability is mainly due to external factors such as the proneness component of the variance.Faculty of Social and Legal Sciences (Melilla)Department of Statistics and Operational ResearchResearch Group "Survival Analysis and Probability Distributions"Office for Political Science and Research, through the project "Social-Labour Statistics and Demography" at the University of Granada (Spain

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

This paper addresses the linear least-squares estimation of a signal from measurements subject to stochastic sensor gain degradation and random delays during the transmission. These uncertainty phenomena, common in network systems, have traditionally been described by independent Bernoulli random variables.Wepropose a model that is more general and therefore has greater applicability to real-life situations. The model has two particular characteristics: firstly, the sensor gain degradation is represented by a white sequence of random variables with values in [0,1]; in addition, the absence or presence of delays in the transmission is described by a homogeneous three-state Markov chain, which reflects a possible correlation of delays at different sampling times. Furthermore, assuming that the measurement noise is one-step correlated, we obtain recursive prediction, filtering and fixed-point smoothing algorithms using the first and second-order moments of the signal and the processes present in the observation model. Simulation results for a scalar signal are provided to illustrate the feasibility of the proposed algorithms, using the estimation error variances as a measure of the quality of the estimators.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

MATERIALES INTERACTIVOS PARA LA RESOLUCIÓN DE UN PROBLEMA DE PROGRAMACIÓN LINEAL USANDO EL MÉTODO SIMPLEX

In this paper we present educational interactive materials for a pleasant learning of the resolution of a Linear Programming Problem using the Simplex method. First of them provides, through an example, the steps involved in the graphic resolution of a Linear Programming Problem with two variables. The following materials correspond to the different steps to solve a Linear Programming Problem: Transformation of a Linear Problem to standard form, Construction of the first Simplex table and Simplex Algorithm.En este trabajo presentamos materiales docentes interactivos para un aprendizaje ameno de la resolución de un problema de Programación Lineal mediante el método Simplex. En el primero se proporciona, mediante un ejemplo, los pasos a seguir en la resolución gráfica de un problema de Programación Lineal con dos variables. Los siguientes materiales se corresponden con las distintas fases a seguir desde el planteamiento de un problema de Programación Lineal hasta su resolución: Transformación de un problema de Programación Lineal general a forma estándar, Construcción de la primera tabla del Simplex y Algoritmo Simplex

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

This paper focuses on the distributed fusion estimation problem in which a signal transmitted over wireless sensor networks is subject to deception attacks and random delays. We assume that each sensor can suffer attacks that may corrupt and/or modify the output measurements. In addition, communication failures between sensors and their local processors can delay the receipt of processed measurements. The randomness of attacks and transmission delays is modelled by different Bernoulli random variables with known probabilities of success. According to these characteristics of the sensor networks and assuming that the measurement noises are cross-correlated at the same time step between sensors and are also correlated with the signal at the same and subsequent time steps, we derive a fusion estimation algorithm, including prediction and filtering, using the distributed fusion method. First, for each sensor, the local least-squares linear prediction and filtering algorithm are derived, using a covariance-based approach. Then, the distributed fusion predictor and the corresponding filter are obtained as the matrix-weighted linear combination of corresponding local estimators, checking that the mean squared error is minimised. A simulation example is then given to illustrate the effectiveness of the proposed algorithms

Proyecto de capacitación virtual en semiología neurológica

Depto. de MedicinaFac. de MedicinaTRUEsubmitte