Turku Centre for Computer Science

About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.

An Object-Based Approach to the B Formal Method

Abstract: In this paper, we describe an approach to the design of distributed systems that integrate object-oriented methods (OOM) and the non object-oriented B formal method. Our goal is to retain some OOM advantages and produce a flexible and reliable specification, and through the use of our example, we show how this is achieved. We prove formally that our design meets its informal specification with the help of B-Toolkit Release 3.3.1. We illustrate the approach by the B specification of A Computerized Visitor Information System (ACVIS). Using Object Modeling Technique diagrams allows us to make ACVIS more readable and open for changes.

Optimization of multi-photon event discrimination levels using Poisson statistics.

Abstract: In applications where random multi-photon events must be distinguishable from the background, detection of the signals must be based on either analog current measurement or photon counting and multi-level discrimination of single and multi-photon events. In this paper a novel method for optimizing photomultiplier (PMT) pulse discrimination levels in single- and multi-photon counting is demonstrated. This calibration method is based on detection of photon events in coincidence to short laser pulses. The procedure takes advantage of Poisson statistics of single- and mult-iphoton signals and it is applicable to automatic calibration of photon counting devices on production line. Results obtained with a channel photomultiplier (CPM) are shown. By use of three parallel discriminators and setting the discriminator levels according to the described method resulted in a linear response over wide range of random single- and multi-photon signals.

In-place algorithms for sorting problems

Abstract: An algorithm is said to operate in-place if it uses only a constant amount of extra memory for storing local variables besides the memory reserved for the input elements. In other words, the size of the extra memory does not grow as the number of input elements, n, gets larger, but it is bounded by a constant. An algorithm reorders the input elements stably if the original relative order of equal elements is retained.In this thesis, we devise in-place algorithms for sorting and related problems. We measure the efficiency of the algorithms by calculating the number of element comparisons and element moves performed in the worst case in the following. The amount of index manipulation operations is closely related to these quantities, so it is omitted in our calculations. When no precise figures are needed, we denote the sum of all operations by a general expression "time". The thesis consists of five separate articles, the main contributions of which are described below.We construct algorithms for stable partitioning and stable selection which are the first linear-time algorithms being both stable and in-place concurrently. Moreover, we define problems stable unpartitioning and restoring selection and devise linear-time algorithms for these problems. The algorithm for stable un-partitioning is in-place while that for restoring selection uses O(n) extra bits. By using these algorithms as subroutines we construct an adaption of Quicksort that sorts a multiset stably in O(Σki = 1 mi log(n/mi)) time where mi is the multiplicity of ith distinct element for i = 1,.., k. This is the first in-place algorithm that sorts a multiset stably in asymptotically optimal time.We present in-place algorithms for unstable and stable merging. The algorithms are asymptotically more efficient than earlier ones: the number of moves is 3(n + m)+o(m) for the unstable algorithm, 5n+12m+o(m) for the stable algorithm, and the number of comparisons at most m(t + 1) + n/2t + o(m) comparisons where m ≤ n and t = [log(n/m)]. The previous best results were 1.125(n + m) + o(n) comparisons and 5(n + in) + o(n) moves for unstable merging, and 16.5(n + in) + o(n) moves for stable merging.Finally, we devise two in-place algorithms for sorting. Both algorithms are adaptions of Mergesort. The first performs n log2 n + O(n) comparisons and e n loge n + O(n log log n) moves for any fixed 0 n loge n + O(n) comparisons and fewer than O(n log n/log log n) moves. This is the first in-place sorting algorithm that performs o(n log n) moves in the worst case while guaranteeing O(n log n) comparisons.