The State Data Center of Iowa and the Iowa Commission on the Status of African-Americans in Iowa: 2008

To recall and celebrate the positive contributions to our nation made by people of African descent, American historian Carter G. Woodson established Black History Week beginning on Feb. 12, 1926. In 1976, as part of the nation’s bicentennial, the week was expanded into Black History Month. This report gives data information about African-Americans in Iowa.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Open African Reference Pathology Data Center

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Infraestrutura de data center e suas tendências com foco em eficiência energética

With the Big Data development and the growth of cloud computing and Internet of Things, data centers have been multiplying in Brazil and the rest of the world. Designing and running this sites in an efficient way has become a necessary challenge and to do so, it's essential a better understanding of its infrastructure. Thus, this paper presents a bibliography study using technical concepts in order to understand the specific needs related to this environment and the best forms address them. It discusses the data center infrastructure main systems, methods to improve their energy efficiency and their future trends

Infraestrutura de data center e suas tendências com foco em eficiência energética

With the Big Data development and the growth of cloud computing and Internet of Things, data centers have been multiplying in Brazil and the rest of the world. Designing and running this sites in an efficient way has become a necessary challenge and to do so, it's essential a better understanding of its infrastructure. Thus, this paper presents a bibliography study using technical concepts in order to understand the specific needs related to this environment and the best forms address them. It discusses the data center infrastructure main systems, methods to improve their energy efficiency and their future trends

Introduction to the Software-defined Radio Approach

Telecommunications have been in constant evolution during past decades. Among the technological innovations, the use of digital technologies is very relevant. Digital communication systems have proven their efficiency and brought a new element in the chain of signal transmitting and receiving, the digital processor. This device offers to new radio equipments the flexibility of a programmable system. Nowadays, the behavior of a communication system can be modified by simply changing its software. This gave rising to a new radio model called Software Defined Radio (or Software-Defined Radio - SDR). In this new model, one moves to the software the task to set radio behavior, leaving to hardware only the implementation of RF front-end. Thus, the radio is no longer static, defined by their circuits and becomes a dynamic element, which may change their operating characteristics, such as bandwidth, modulation, coding rate, even modified during runtime according to software configuration. This article aims to present the use of GNU Radio software, an open-source solution for SDR specific applications, as a tool for development configurable digital radio.

Il problema del risparmio energetico nei data center

Con la crescente diffusione del web e dei servizi informatici offerti via internet, è aumentato in questi anni l’utilizzo dei data center e conseguentemente, il consumo di energia elettrica degli stessi. Il problema ambientale che comporta l’alto fabbisogno energetico, porta gli operatori di data center ad utilizzare tecniche a basso consumo e sistemi efficienti. Organizzazioni ambientali hanno rilevato che nel 2011 i consumi derivanti dai data center raggiungeranno i 100 milioni di kWh, con un costo complessivo di 7,4 milioni di dollari nei soli Stati Uniti, con una proiezione simile anche a livello globale. La seguente tesi intende valutare le tecniche in uso per diminuire il consumo energetico nei data center, e quali tecniche vengono maggiormente utilizzate per questo scopo. Innanzitutto si comincerà da una panoramica sui data center, per capire il loro funzionamento e per mostrare quali sono i componenti fondamentali che lo costituiscono; successivamente si mostrerà quali sono le parti che incidono maggiormente nei consumi, e come si devono effettuare le misurazioni per avere dei valori affidabili attraverso la rilevazione del PUE, unità di misura che valuta l’efficienza di un data center. Dal terzo capitolo si elencheranno le varie tecniche esistenti e in uso per risolvere il problema dell’efficienza energetica, mostrando alla fine una breve analisi sui metodi che hanno utilizzato le maggiori imprese del settore per risolvere il problema dei consumi nei loro data center. Lo scopo di questo elaborato è quello di capire quali sono le tecniche e le strategie per poter ridurre i consumi e aumentare l’efficienza energetica dei data center.

Spectrum sensing su piattaforma software defined radio: Implementazione e test su stick dvb-t

Spectrum sensing su piattaforma software defined radio: Implementazione e test su stick dvb-t

Emulazione distribuita di reti di telecomunicazioni su piattaforma mininet

Dopo aver introdotto i concetti di Software Defined Networking, il protocollo Openflow ed il software di emulazione di reti Mininet, vengono mostrati lo svolgimento ed i risultati di una serie di test effettuati su reti emulate, mettendo in pratica i concetti precedentemente introdotti. Infine si sono utilizzate le conoscenze apprese per sviluppare una rete distribuita su più piattaforme Mininet.

Virtualizzazione di funzioni di rete su piattaforme per cloud computing

Questo documento affronta le novità ed i vantaggi introdotti nel mondo delle reti di telecomunicazioni dai paradigmi di Software Defined Networking e Network Functions Virtualization, affrontandone prima gli aspetti teorici, per poi applicarne i concetti nella pratica, tramite casi di studio gradualmente più complessi. Tali innovazioni rappresentano un'evoluzione dell'architettura delle reti predisposte alla presenza di più utenti connessi alle risorse da esse offerte, trovando quindi applicazione soprattutto nell'emergente ambiente di Cloud Computing e realizzando in questo modo reti altamente dinamiche e programmabili, tramite la virtualizzazione dei servizi di rete richiesti per l'ottimizzazione dell'utilizzo di risorse. Motivo di tale lavoro è la ricerca di soluzioni ai problemi di staticità e dipendenza, dai fornitori dei nodi intermedi, della rete Internet, i maggiori ostacoli per lo sviluppo delle architetture Cloud. L'obiettivo principale dello studio presentato in questo documento è quello di valutare l'effettiva convenienza dell'applicazione di tali paradigmi nella creazione di reti, controllando in questo modo che le promesse di aumento di autonomia e dinamismo vengano rispettate. Tale scopo viene perseguito attraverso l'implementazione di entrambi i paradigmi SDN e NFV nelle sperimentazioni effettuate sulle reti di livello L2 ed L3 del modello OSI. Il risultato ottenuto da tali casi di studio è infine un'interessante conferma dei vantaggi presentati durante lo studio teorico delle innovazioni in analisi, rendendo esse una possibile soluzione futura alle problematiche attuali delle reti.

A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification

The 5th generation of mobile networking introduces the concept of “Network slicing”, the network will be “sliced” horizontally, each slice will be compliant with different requirements in terms of network parameters such as bandwidth, latency. This technology is built on logical instead of physical resources, relies on virtual network as main concept to retrieve a logical resource. The Network Function Virtualisation provides the concept of logical resources for a virtual network function, enabling the concept virtual network; it relies on the Software Defined Networking as main technology to realize the virtual network as resource, it also define the concept of virtual network infrastructure with all components needed to enable the network slicing requirements. SDN itself uses cloud computing technology to realize the virtual network infrastructure, NFV uses also the virtual computing resources to enable the deployment of virtual network function instead of having custom hardware and software for each network function. The key of network slicing is the differentiation of slice in terms of Quality of Services parameters, which relies on the possibility to enable QoS management in cloud computing environment. The QoS in cloud computing denotes level of performances, reliability and availability offered. QoS is fundamental for cloud users, who expect providers to deliver the advertised quality characteristics, and for cloud providers, who need to find the right tradeoff between QoS levels that has possible to offer and operational costs. While QoS properties has received constant attention before the advent of cloud computing, performance heterogeneity and resource isolation mechanisms of cloud platforms have significantly complicated QoS analysis and deploying, prediction, and assurance. This is prompting several researchers to investigate automated QoS management methods that can leverage the high programmability of hardware and software resources in the cloud.

Service-centric Networking Extensions

This paper discusses several issues of Service-Centric Networking (SCN) as an extension of the Information-Centric Networking (ICN) paradigm. SCN allows extended caching, where not exactly the same content as requested can be read from caches, but similar content can be used to produce the content requested, e.g., by filtering or transcoding. We discuss the issue of naming and routing for general dynamic services for both tightly coupled and decoupled ICN approaches. Challenges and solutions for service management are identified, in particular for composed services, which allow distributed in-network processing of service requests. We introduce the term Software-Defined Service-Centric Networking as an extension of Software-Defined Networking. A prototype implementation for SCN proofs its validity and feasibility and underlines its potential benefits.

Debian Hamradio and Software Defined Radio : Overview of the Debian Ham blend, ham radio and software defined radio

Non peer reviewed