Algorytm genetyczny do składania powierzchni z fragmentów i jego zastosowania w kartografii

Genetic algorithms represent an up-to-date method of process optimization, where other solutions have failed or haven't given any satisfactory results. One of these processes is puzzle solving, where fragments have to be placed into the defined shape in such a way so that no fragment should mutually overlay and the whole shape area will be filled with all of these fragments. A genetic algorithm solving this task including an exact formulation and a definition of the initial conditions based on cluster analysis has been described in this paper. The algorithm efficiency will be tested in diploma works in Institute of Geodesy, Faculty of Civil Engineering, University of Technology, Brno. The results will be used in the application for cartograms creation.Algorytmy genetyczne reprezentują nowoczesne metody optymalizacji procesów, dla których inne rozwiązania zawiodły lub nie dały satysfakcjonujących rezultatów. Jednym z takich procesów jest rozwiązywanie układanek - puzli, w których fragmenty muszą być wstawione w zdefiniowany kształt w ten sposób, aby żadne się nawzajem nie nakładały, a kształt zawierał wszystkie zadane fragmenty. Praca niniejsza zawiera opis algorytmu genetycznego rozwiązującego takie zadanie wraz ze ścisłą formułą rozwiązania oraz definicją warunków początkowych, bazującą na analizie skupień. Skuteczność algorytmu będzie testowana w pracy dyplomowej w Instytucie Geodezji na Wydziale Budownictwa, Politechniki w Brnie. Rezultaty zostaną wykorzystane przy tworzeniu kartogramów

The Hardware Accelerator SFDL/SCL

This paper presents a new multiprocessor architecture for modelling and simulation of digital circuits. To speed up the simulation process a special static algorithm for dividing modelled circuit components into equivalent classes (before the simulation starts) has been designed. In components of one class events will never appear at the same time. The number of equivalent classes is practically greater than or at least equal to the number of processors (n); therefore the classes are reduced into n groups. The main criteria in this process are: minimum data transmissions among processors and maximum usage of processors. All information (tables, programs) about elements of one group are stored in local memories of the processor assigned for that group. As the distribution into classes and groups is an NP-complete time consuming problem, 2 heuristic algorithms have been created for its solution. The first one is based on colouring of oriented graphs equivalent to modelled circuits, the other one uses matrix calculus. The results of simulation experiments proved that the designed multiprocessor architecture can speed up the simulation process by more than two orders in comparison with the classical simulation method

The Bay of Naples

This paper draws a general picture of the Greek colonization of Campania between the 8th and 7th century BC and especially of Pithekoussai and Cumae, starting from the archaeological evidenceIl contributo presenta un quadro d'insieme della colonizzazione greca della Campania tra l'VIII e il VII secolo a.C., focoalizzandosi su Pithekoussai e Cuma, a partire dall'evidenza archeologic