A fitting algorithm for random modeling the PLC channel

The characteristics of the power-line communication (PLC) channel are difficult to model due to the heterogeneity of the networks and the lack of common wiring practices. To obtain the full variability of the PLC channel, random channel generators are of great importance for the design and testing of communication algorithms. In this respect, we propose a random channel generator that is based on the top-down approach. Basically, we describe the multipath propagation and the coupling effects with an analytical model. We introduce the variability into a restricted set of parameters and, finally, we fit the model to a set of measured channels. The proposed model enables a closed-form description of both the mean path-loss profile and the statistical correlation function of the channel frequency response. As an example of application, we apply the procedure to a set of in-home measured channels in the band 2-100 MHz whose statistics are available in the literature. The measured channels are divided into nine classes according to their channel capacity. We provide the parameters for the random generation of channels for all nine classes, and we show that the results are consistent with the experimental ones. Finally, we merge the classes to capture the entire heterogeneity of in-home PLC channels. In detail, we introduce the class occurrence probability, and we present a random channel generator that targets the ensemble of all nine classes. The statistics of the composite set of channels are also studied, and they are compared to the results of experimental measurement campaigns in the literature.

Auto-localization algorithm for local positioning systems

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems (LPSs), for which there are no inter-beacon distance measurements available and neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors have implemented heuristic methods based on optimization algorithms to solve the problem. However, such methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we present a new method to calculate the inter-beacon distances, and hence the beacons positions, based in the linearization of the trilateration equations into a closed-form solution which does not require any approximate initial estimation. The simulations and field evaluations show a good estimation of the beacon node positions.

A Personal Agent Architecture for Task Automation in the Web of Data. Bringing intelligence to everyday tasks

Internet está evolucionando hacia la conocida como Live Web. En esta nueva etapa en la evolución de Internet, se pone al servicio de los usuarios multitud de streams de datos sociales. Gracias a estas fuentes de datos, los usuarios han pasado de navegar por páginas web estáticas a interacturar con aplicaciones que ofrecen contenido personalizado, basada en sus preferencias. Cada usuario interactúa a diario con multiples aplicaciones que ofrecen notificaciones y alertas, en este sentido cada usuario es una fuente de eventos, y a menudo los usuarios se sienten desbordados y no son capaces de procesar toda esa información a la carta. Para lidiar con esta sobresaturación, han aparecido múltiples herramientas que automatizan las tareas más habituales, desde gestores de bandeja de entrada, gestores de alertas en redes sociales, a complejos CRMs o smart-home hubs. La contrapartida es que aunque ofrecen una solución a problemas comunes, no pueden adaptarse a las necesidades de cada usuario ofreciendo una solucion personalizada. Los Servicios de Automatización de Tareas (TAS de sus siglas en inglés) entraron en escena a partir de 2012 para dar solución a esta liminación. Dada su semejanza, estos servicios también son considerados como un nuevo enfoque en la tecnología de mash-ups pero centra en el usuarios. Los usuarios de estas plataformas tienen la capacidad de interconectar servicios, sensores y otros aparatos con connexión a internet diseñando las automatizaciones que se ajustan a sus necesidades. La propuesta ha sido ámpliamante aceptada por los usuarios. Este hecho ha propiciado multitud de plataformas que ofrecen servicios TAS entren en escena. Al ser un nuevo campo de investigación, esta tesis presenta las principales características de los TAS, describe sus componentes, e identifica las dimensiones fundamentales que los defines y permiten su clasificación. En este trabajo se acuña el termino Servicio de Automatización de Tareas (TAS) dando una descripción formal para estos servicios y sus componentes (llamados canales), y proporciona una arquitectura de referencia. De igual forma, existe una falta de herramientas para describir servicios de automatización, y las reglas de automatización. A este respecto, esta tesis propone un modelo común que se concreta en la ontología EWE (Evented WEb Ontology). Este modelo permite com parar y equiparar canales y automatizaciones de distintos TASs, constituyendo un aporte considerable paraa la portabilidad de automatizaciones de usuarios entre plataformas. De igual manera, dado el carácter semántico del modelo, permite incluir en las automatizaciones elementos de fuentes externas sobre los que razonar, como es el caso de Linked Open Data. Utilizando este modelo, se ha generado un dataset de canales y automatizaciones, con los datos obtenidos de algunos de los TAS existentes en el mercado. Como último paso hacia el lograr un modelo común para describir TAS, se ha desarrollado un algoritmo para aprender ontologías de forma automática a partir de los datos del dataset. De esta forma, se favorece el descubrimiento de nuevos canales, y se reduce el coste de mantenimiento del modelo, el cual se actualiza de forma semi-automática. En conclusión, las principales contribuciones de esta tesis son: i) describir el estado del arte en automatización de tareas y acuñar el término Servicio de Automatización de Tareas, ii) desarrollar una ontología para el modelado de los componentes de TASs y automatizaciones, iii) poblar un dataset de datos de canales y automatizaciones, usado para desarrollar un algoritmo de aprendizaje automatico de ontologías, y iv) diseñar una arquitectura de agentes para la asistencia a usuarios en la creación de automatizaciones. ABSTRACT The new stage in the evolution of the Web (the Live Web or Evented Web) puts lots of social data-streams at the service of users, who no longer browse static web pages but interact with applications that present them contextual and relevant experiences. Given that each user is a potential source of events, a typical user often gets overwhelmed. To deal with that huge amount of data, multiple automation tools have emerged, covering from simple social media managers or notification aggregators to complex CRMs or smart-home Hub/Apps. As a downside, they cannot tailor to the needs of every single user. As a natural response to this downside, Task Automation Services broke in the Internet. They may be seen as a new model of mash-up technology for combining social streams, services and connected devices from an end-user perspective: end-users are empowered to connect those stream however they want, designing the automations they need. The numbers of those platforms that appeared early on shot up, and as a consequence the amount of platforms following this approach is growing fast. Being a novel field, this thesis aims to shed light on it, presenting and exemplifying the main characteristics of Task Automation Services, describing their components, and identifying several dimensions to classify them. This thesis coins the term Task Automation Services (TAS) by providing a formal definition of them, their components (called channels), as well a TAS reference architecture. There is also a lack of tools for describing automation services and automations rules. In this regard, this thesis proposes a theoretical common model of TAS and formalizes it as the EWE ontology This model enables to compare channels and automations from different TASs, which has a high impact in interoperability; and enhances automations providing a mechanism to reason over external sources such as Linked Open Data. Based on this model, a dataset of components of TAS was built, harvesting data from the web sites of actual TASs. Going a step further towards this common model, an algorithm for categorizing them was designed, enabling their discovery across different TAS. Thus, the main contributions of the thesis are: i) surveying the state of the art on task automation and coining the term Task Automation Service; ii) providing a semantic common model for describing TAS components and automations; iii) populating a categorized dataset of TAS components, used to learn ontologies of particular domains from the TAS perspective; and iv) designing an agent architecture for assisting users in setting up automations, that is aware of their context and acts in consequence.

ParAlign: a parallel sequence alignment algorithm for rapid and sensitive database searches

There is a need for faster and more sensitive algorithms for sequence similarity searching in view of the rapidly increasing amounts of genomic sequence data available. Parallel processing capabilities in the form of the single instruction, multiple data (SIMD) technology are now available in common microprocessors and enable a single microprocessor to perform many operations in parallel. The ParAlign algorithm has been specifically designed to take advantage of this technology. The new algorithm initially exploits parallelism to perform a very rapid computation of the exact optimal ungapped alignment score for all diagonals in the alignment matrix. Then, a novel heuristic is employed to compute an approximate score of a gapped alignment by combining the scores of several diagonals. This approximate score is used to select the most interesting database sequences for a subsequent Smith–Waterman alignment, which is also parallelised. The resulting method represents a substantial improvement compared to existing heuristics. The sensitivity and specificity of ParAlign was found to be as good as Smith–Waterman implementations when the same method for computing the statistical significance of the matches was used. In terms of speed, only the significantly less sensitive NCBI BLAST 2 program was found to outperform the new approach. Online searches are available at http://dna.uio.no/search/

MINLP Optimization Algorithm for the Synthesis of Heat and Work Exchange Networks

This paper introduces a new optimization model for the simultaneous synthesis of heat and work exchange networks. The work integration is performed in the work exchange network (WEN), while the heat integration is carried out in the heat exchanger network (HEN). In the WEN synthesis, streams at high-pressure (HP) and low-pressure (LP) are subjected to pressure manipulation stages, via turbines and compressors running on common shafts and stand-alone equipment. The model allows the use of several units of single-shaft-turbine-compressor (SSTC), as well as helper motors and generators to respond to any shortage and/or excess of energy, respectively, in the SSTC axes. The heat integration of the streams occurs in the HEN between each WEN stage. Thus, as the inlet and outlet streams temperatures in the HEN are dependent of the WEN design, they must be considered as optimization variables. The proposed multi-stage superstructure is formulated in mixed-integer nonlinear programming (MINLP), in order to minimize the total annualized cost composed by capital and operational expenses. A case study is conducted to verify the accuracy of the proposed approach. The results indicate that the heat integration between the WEN stages is essential to enhance the work integration, and to reduce the total cost of process due the need of a smaller amount of hot and cold utilities.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Intravenous catheter-associated Staphylococcus aureus bacteraemia: a common problem that can be prevented [Editorial]

No Abstract

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulae for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.

A gradient descent algorithm for minimizing amino acid coupling reactions when synthesizing cyclic-peptide libraries

Combinatorial chemistry has become an invaluable tool in medicinal chemistry for the identification of new drug leads. For example, libraries of predetermined sequences and head-to-tail cyclized peptides are routinely synthesized in our laboratory using the IRORI approach. Such libraries are used as molecular toolkits that enable the development of pharmacophores that define activity and specificity at receptor targets. These libraries can be quite large and difficult to handle, due to physical and chemical constraints imposed by their size. Therefore, smaller sub-libraries are often targeted for synthesis. The number of coupling reactions required can be greatly reduced if the peptides having common amino acids are grouped into the same sub-library (batching). This paper describes a schedule optimizer to minimize the number of coupling reactions by rotating and aligning sequences while simultaneously batching. The gradient descent method thereby reduces the number of coupling reactions required for synthesizing cyclic peptide libraries. We show that the algorithm results in a 75% reduction in the number of coupling reactions for a typical cyclic peptide library.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.

A lightweight encryption algorithm for mobile online multimedia devices

Online multimedia data needs to be encrypted for access control. To be capable of working on mobile devices such as pocket PC and mobile phones, lightweight video encryption algorithms should be proposed. The two major problems in these algorithms are that they are either not fast enough or unable to work on highly compressed data stream. In this paper, we proposed a new lightweight encryption algorithm based on Huffman error diffusion. It is a selective algorithm working on compressed data. By carefully choosing the most significant parts (MSP), high performance is achieved with proper security. Experimental results has proved the algorithm to be fast. secure: and compression-compatible.

A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Large monitoring networks are becoming increasingly common and can generate large datasets from thousands to millions of observations in size, often with high temporal resolution. Processing large datasets using traditional geostatistical methods is prohibitively slow and in real world applications different types of sensor can be found across a monitoring network. Heterogeneities in the error characteristics of different sensors, both in terms of distribution and magnitude, presents problems for generating coherent maps. An assumption in traditional geostatistics is that observations are made directly of the underlying process being studied and that the observations are contaminated with Gaussian errors. Under this assumption, sub–optimal predictions will be obtained if the error characteristics of the sensor are effectively non–Gaussian. One method, model based geostatistics, assumes that a Gaussian process prior is imposed over the (latent) process being studied and that the sensor model forms part of the likelihood term. One problem with this type of approach is that the corresponding posterior distribution will be non–Gaussian and computationally demanding as Monte Carlo methods have to be used. An extension of a sequential, approximate Bayesian inference method enables observations with arbitrary likelihoods to be treated, in a projected process kriging framework which is less computationally intensive. The approach is illustrated using a simulated dataset with a range of sensor models and error characteristics.

Methodology of photic stimulation revisited:updated European algorithm for visual stimulation in the EEG laboratory

Intermittent photic stimulation (IPS) is a common procedure performed in the electroencephalography (EEG) laboratory in children and adults to detect abnormal epileptogenic sensitivity to flickering light (i.e., photosensitivity). In practice, substantial variability in outcome is anecdotally found due to the many different methods used per laboratory and country. We believe that standardization of procedure, based on scientific and clinical data, should permit reproducible identification and quantification of photosensitivity. We hope that the use of our new algorithm will help in standardizing the IPS procedure, which in turn may more clearly identify and assist monitoring of patients with epilepsy and photosensitivity. Our algorithm goes far beyond that published in 1999 (Epilepsia, 1999a, 40, 75; Neurophysiol Clin, 1999b, 29, 318): it has substantially increased content, detailing technical and logistical aspects of IPS testing and the rationale for many of the steps in the IPS procedure. Furthermore, our latest algorithm incorporates the consensus of repeated scientific meetings of European experts in this field over a period of 6 years with feedback from general neurologists and epileptologists to improve its validity and utility. Accordingly, our European group has provided herein updated algorithms for two different levels of methodology: (1) requirements for defining photosensitivity in patients and in family members of known photosensitive patients and (2) requirements for tailored studies in patients with a clear history of visually induced seizures or complaints, and in those already known to be photosensitive.

Manometry-Based Cough Identification Algorithm

Gastroesophageal reflux disease (GERD) is a common cause of chronic cough. For the diagnosis and treatment of GERD, it is desirable to quantify the temporal correlation between cough and reflux events. Cough episodes can be identified on esophageal manometric recordings as short-duration, rapid pressure rises. The present study aims at facilitating the detection of coughs by proposing an algorithm for the classification of cough events using manometric recordings. The algorithm detects cough episodes based on digital filtering, slope and amplitude analysis, and duration of the event. The algorithm has been tested on in vivo data acquired using a single-channel intra-esophageal manometric probe that comprises a miniature white-light interferometric fiber optic pressure sensor. Experimental results demonstrate the feasibility of using the proposed algorithm for identifying cough episodes based on real-time recordings using a single channel pressure catheter. The presented work can be integrated with commercial reflux pH/impedance probes to facilitate simultaneous 24-hour ambulatory monitoring of cough and reflux events, with the ultimate goal of quantifying the temporal correlation between the two types of events.

Summarization of scientific documents by detecting common facts in citations

Reading scientific articles is more time-consuming than reading news because readers need to search and read many citations. This paper proposes a citation guided method for summarizing multiple scientific papers. A phenomenon we can observe is that citation sentences in one paragraph or section usually talk about a common fact, which is usually represented as a set of noun phrases co-occurring in citation texts and it is usually discussed from different aspects. We design a multi-document summarization system based on common fact detection. One challenge is that citations may not use the same terms to refer to a common fact. We thus use term association discovering algorithm to expand terms based on a large set of scientific article abstracts. Then, citations can be clustered based on common facts. The common fact is used as a salient term set to get relevant sentences from the corresponding cited articles to form a summary. Experiments show that our method outperforms three baseline methods by ROUGE metric.©2013 Elsevier B.V. All rights reserved.