Lifetime maximization for beamforming applications in wireless sensor networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If data is to be sent to a far-away base station, collaborative beamforming by the sensors may help to dis- tribute the load among the nodes and reduce fast battery depletion. However, collaborative beamforming techniques are far from opti- mality and in many cases may be wasting more power than required. In this contribution we consider the issue of energy efficiency in beamforming applications. Using a convex optimization framework, we propose the design of a virtual beamformer that maximizes the network's lifetime while satisfying a pre-specified Quality of Service (QoS) requirement. A distributed consensus-based algorithm for the computation of the optimal beamformer is also provided

Implementation of intelligent data acquisition system for ITER fast controllers using RIO devices.

A basic requirement of the data acquisition systems used in long pulse fusion experiments is the real time physical events detection in signals. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. This type of applications can be implemented in ITER using fast controllers. ITER is standardizing the architectures to be used for fast controller implementation. Until now the standards chosen are PXIe architectures (based on PCIe) for the hardware and EPICS middleware for the software. This work presents the methodology for implementing data acquisition and pre-processing using FPGA-based DAQ cards and how to integrate these in fast controllers using EPICS.

Sintetizador analógico en Arduino

Este proyecto consiste en el diseño y construcción de un sintetizador basado en el chip 6581 Sound Interface Device (SID). Este chip era el encargado de la generación de sonido en el Commodore 64, ordenador personal comercializado en 1982, y fue el primer sintetizador complejo construido para ordenador. El chip en cuestión es un sintetizador de tres voces, cada una de ellas capaz de generar cuatro diferentes formas de onda. Cada voz tiene control independiente de varios parámetros, permitiendo una relativamente amplia variedad de sonidos y efectos, muy útil para su uso en videojuegos. Además está dotado de un filtro programable para conseguir distintos timbres mediante síntesis sustractiva. El sintetizador se ha construido sobre Arduino, una plataforma de electrónica abierta concebida para la creación de prototipos, consistente en una placa de circuito impreso con un microcontrolador, programable desde un PC para que realice múltiples funciones (desde encender LEDs hasta controlar servomecanismos en robótica, procesado y transmisión de datos, etc.). El sintetizador es controlable vía MIDI, por ejemplo, desde un teclado de piano. A través de MIDI recibe información tal como qué notas debe tocar, o los valores de los parámetros del SID que modifican las propiedades del sonido. Además, toda esa información también la puede recibir de un PC mediante una conexión USB. Se han construido dos versiones del sintetizador: una versión “hardware”, que utiliza el SID para la generación de sonido, y otra “software”, que reemplaza el SID por un emulador, es decir, un programa que se comporta (en la medida de lo posible) de la misma manera que el SID. El emulador se ha implementado en un microcontrolador Atmega 168 de Atmel, el mismo que utiliza Arduino. ABSTRACT. This project consists on design and construction of a synthesizer which is based on chip 6581 Sound Interface Device (SID). This chip was used for sound generation on the Commodore 64, a home computer presented in 1982, and it was the first complex synthesizer built for computers. The chip is a three-voice synthesizer, each voice capable of generating four different waveforms. Each voice has independent control of several parameters, allowing a relatively wide variety of sounds and effects, very useful for its use on videogames. It also includes a programmable filter, allowing more timbre control via subtractive synthesis. The synthesizer has been built on Arduino, an open-source electronics prototyping platform that consists on a printed circuit board with a microcontroller, which is programmable with a computer to do several functions (lighting LEDs, controlling servomechanisms on robotics, data processing or transmission, etc.). The synthesizer is controlled via MIDI, in example, from a piano-type keyboard. It receives from MIDI information such as the notes that should be played or SID’s parameter values that modify the sound. It also can receive that information from a PC via USB connection. Two versions of the synthesizer have been built: a hardware one that uses the SID chip for sound generation, and a software one that replaces SID by an emulator, it is, a program that behaves (as far as possible) in the same way the SID would. The emulator is implemented on an Atmel’s Atmega 168 microcontroller, the same one that is used on Arduino.

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Low-resource language recognition using a fusion of phoneme posteriorgram counts, acoustic and glottal-based i-vectors

This paper presents a description of our system for the Albayzin 2012 LRE competition. One of the main characteristics of this evaluation was the reduced number of available files for training the system, especially for the empty condition where no training data set was provided but only a development set. In addition, the whole database was created from online videos and around one third of the training data was labeled as noisy files. Our primary system was the fusion of three different i-vector based systems: one acoustic system based on MFCCs, a phonotactic system using trigrams of phone-posteriorgram counts, and another acoustic system based on RPLPs that improved robustness against noise. A contrastive system that included new features based on the glottal source was also presented. Official and postevaluation results for all the conditions using the proposed metrics for the evaluation and the Cavg metric are presented in the paper.

Processing and validation of JEFF3-3.1.2 Cross-section Library into Various Formats: ACE, PENDF, GENDF, MATXSR and BOXER.

Following the processing and validation of JEFF-3.1 performed in 2006 and presented in ND2007, and as a consequence of the latest updated of this library (JEFF-3.1.2) in February 2012, a new processing and validation of JEFF-3.1.2 cross section library is presented in this paper. The processed library in ACE format at ten different temperatures was generated with NJOY-99.364 nuclear data processing system. In addition, NJOY-99 inputs are provided to generate PENDF, GENDF, MATXSR and BOXER formats. The library has undergone strict QA procedures, being compared with other available libraries (e.g. ENDF/B-VII.1) and processing codes as PREPRO-2000 codes. A set of 119 criticality benchmark experiments taken from ICSBEP-2010 has been used for validation purposes.

Cross-Products LASSO

Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.

Efficient Monte Carlo optimization for multi-label classifier chains

Multi-label classification (MLC) is the supervised learning problem where an instance may be associated with multiple labels. Modeling dependencies between labels allows MLC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies. On the one hand, the original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors down the chain. On the other hand, a recent Bayes-optimal method improves the performance, but is computationally intractable in practice. Here we present a novel double-Monte Carlo scheme (M2CC), both for finding a good chain sequence and performing efficient inference. The M2CC algorithm remains tractable for high-dimensional data sets and obtains the best overall accuracy, as shown on several real data sets with input dimension as high as 1449 and up to 103 labels.