Efficiency Comparison of DFT/IDFT Algorithms by Evaluating Diverse Hardware : Implementations,Parallelization Prospectsand Possible Improvements

In this paper we investigate various algorithms for performing Fast Fourier Transformation (FFT)/Inverse Fast Fourier Transformation (IFFT), and proper techniquesfor maximizing the FFT/IFFT execution speed, such as pipelining or parallel processing, and use of memory structures with pre-computed values (look up tables -LUT) or other dedicated hardware components (usually multipliers). Furthermore, we discuss the optimal hardware architectures that best apply to various FFT/IFFT algorithms, along with their abilities to exploit parallel processing with minimal data dependences of the FFT/IFFT calculations. An interesting approach that is also considered in this paper is the application of the integrated processing-in-memory Intelligent RAM (IRAM) chip to high speed FFT/IFFT computing. The results of the assessment study emphasize that the execution speed of the FFT/IFFT algorithms is tightly connected to the capabilities of the FFT/IFFT hardware to support the provided parallelism of the given algorithm. Therefore, we suggest that the basic Discrete Fourier Transform (DFT)/Inverse Discrete Fourier Transform (IDFT) can also provide high performances, by utilizing a specialized FFT/IFFT hardware architecture that can exploit the provided parallelism of the DFT/IDF operations. The proposed improvements include simplified multiplications over symbols given in polar coordinate system, using sinе and cosine look up tables,and an approach for performing parallel addition of N input symbols.

Practical Aspects of Genetic Algorithms’ Implementation in Life Cycle Management of Electrotechnical Equipment

Some practical aspects of Genetic algorithms’ implementation regarding to life cycle management of electrotechnical equipment are considered.

Förderung von Gewerbeneuansiedlungen im Wirtschaftsraum Blankenburg (Harz) unter Berücksichtigung der bestehenden gewerblichen Cluster

Ziel der Arbeit war es, den Wirtschaftsraum Blankenburg (Harz) durch die Bildung und Förderung von gewerblichen Clustern attraktiver zu gestalten. In diesem Rahmen wurde zunächst die Entwicklung von Gewerbe- und Industrieflächen mithilfe der Bauleitplanung in Theorie und Praxis beschrieben. Die anschließende Untersuchung des Bundeslandes Sachsen-Anhalt und im speziellen der Stadt Blankenburg (Harz) bildeten die Datengrundlage für die weiteren Arbeitsschritte. Um die Ergebnisse dieser Standortanalyse in einen direkten Zusammenhang mit tatsächlichen Eindrücken der ansässigen Unternehmen bringen zu können, wurde eine schriftliche Unternehmensbefragung durchgeführt. Diese thematisierte vor allem die Einschätzung der Standortfaktoren in Blankenburg (Harz). Die im Zwischenfazit beschriebene Wertung, dass die Stadt derzeit nur wenige Anreize für Unternehmensneuansiedlungen bietet, war Ausgangspunkt für die Theorie der Clusterpolitik. Dabei wurden zunächst gewerbliche Cluster aus den ansässigen Unternehmen gebildet. Daraus resultierten die Wirtschaftsbereiche, deren Unternehmen für eine Ansiedlung in Blankenburg besonders in Frage kommen. Da diese Betriebe feste Anforderungen an Gewerbe- oder Industrieflächen in Bezug auf die Größe, die planungsrechtlichen Beschränkungen und die infrastrukturelle Anbindung stellen, konnten die vorhandenen Freiflächen im Rahmen einer Mikroanalyse hinsichtlich ihrer Eignung für Neuansiedlungen untersucht werden.

Creating and using a non-dedicated HPC cluster with ParallelKnoppix

This note describes ParallelKnoppix, a bootable CD that allows econometricians with average knowledge of computers to create and begin using a high performance computing cluster for parallel computing in very little time. The computers used may be heterogeneous machines, and clusters of up to 200 nodes are supported. When the cluster is shut down, all machines are in their original state, so their temporary use in the cluster does not interfere with their normal uses. An example shows how a Monte Carlo study of a bootstrap test procedure may be done in parallel. Using a cluster of 20 nodes, the example runs approximately 20 times faster than it does on a single computer.

ParallelKnoppix - Rapid deployment of a linux cluster for MPI parallel processing using non-dedicated computers

This note describes ParallelKnoppix, a bootable CD that allows creation of a Linux cluster in very little time. An experienced user can create a cluster ready to execute MPI programs in less than 10 minutes. The computers used may be heterogeneous machines, of the IA-32 architecture. When the cluster is shut down, all machines except one are in their original state, and the last can be returned to its original state by deleting a directory. The system thus provides a means of using non-dedicated computers to create a cluster. An example session is documented.

Can genetic algorithms explain experimental anomalies? An application to common property resources

It is common to find in experimental data persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm, where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments.

Regional decomposition of CO2 emissions in the world: a cluster analysis

The purpose of this paper is to study the possible differences among countries as CO2 emitters and to examine the underlying causes of these differences. The starting point of the analysis is the Kaya identity, which allows us to break down per capita emissions in four components: an index of carbon intensity, transformation efficiency, energy intensity and social wealth. Through a cluster analysis we have identified five groups of countries with different behavior according to these four factors. One significant finding is that these groups are stable for the period analyzed. This suggests that a study based on these components can characterize quite accurately the polluting behavior of individual countries, that is to say, the classification found in the analysis could be used in other studies which look to study the behavior of countries in terms of CO2 emissions in homogeneous groups. In this sense, it supposes an advance over the traditional regional or rich-poor countries classifications .

Two algorithms to find a power integral basis

"Vegeu el resum a l'inici del fitxer adjunt."

Do creative industries cluster? mapping creative local production systems in Italy

An important debate on the role of creativity and culture as factors of local economic development is distinctly emerging. Despite the emphasis put on the theoretical definition of these concepts, it is necessary to strengthen comparative research for the identification and analysis of the kind of creativity embedded in the territory as well as its determinants. Creative local production systems are identified in Italy and Spain departing from local labour markets as territorial units, and focusing on two different kinds of creative

A Model-to-model analysis of the repeated Prisoners' Dilemma: genetic algorithms vs. evolutionary dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenar

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Cluster algebras, quiver representations and triangulated categories

This is an introduction to some aspects of Fomin-Zelevinsky’s cluster algebras and their links with the representation theory of quivers and with Calabi-Yau triangulated categories. It is based on lectures given by the author at summer schools held in 2006 (Bavaria)and 2008 (Jerusalem). In addition to by now classical material, we present the outline of a proof of the periodicity conjecture for pairs of Dynkin diagrams (details will appear elsewhere) and recent results on the interpretation of mutations as derived equivalences.