Current and Nascent SETI Instruments

Here we describe our ongoing efforts to develop high-performance and sensitive instrumentation for use in the search for extra-terrestrial intelligence (SETI). These efforts include our recently deployed Search for Extraterrestrial Emissions from Nearby Developed Intelligent Populations Spectrometer (SERENDIP V.v) and two instruments currently under development; the Heterogeneous Radio SETI Spectrometer (HRSS) for SETI observations in the radio spectrum and the Optical SETI Fast Photometer (OSFP) for SETI observations in the optical band. We will discuss the basic SERENDIP V.v instrument design and initial analysis methodology, along with instrument architectures and observation strategies for OSFP and HRSS. In addition, we will demonstrate how these instruments may be built using low-cost, modular components and programmed and operated by students using common languages, e.g. ANSI C.Comment: 12 pages, 5 figures, Original version appears as Chapter 2 in "The Proceedings of SETI Sessions at the 2010 Astrobiology Science Conference: Communication with Extraterrestrial Intelligence (CETI)," Douglas A. Vakoch, Edito

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Machine learning for early detection of traffic congestion using public transport traffic data

The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte és proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trànsit urbà