By Sue Whitesides (auth.), Peter Eades, Tadao Takaoka (eds.)
This publication constitutes the refereed lawsuits of the twelfth foreign convention on Algorithms and Computation, ISAAC 2001, held in Christchurch, New Zealand in December 2001.
The sixty two revised complete papers offered including 3 invited papers have been conscientiously reviewed and chosen from a complete of 124 submissions. The papers are geared up in topical sections on combinatorial new release and optimization, parallel and allotted algorithms, graph drawing and algorithms, computational geometry, computational complexity and cryptology, automata and formal languages, computational biology and string matching, and algorithms and knowledge buildings.
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Extra resources for Algorithms and Computation: 12th International Symposium, ISAAC 2001 Christchurch, New Zealand, December 19–21, 2001 Proceedings
This approach has advantages in terms of flexibility, scalability, and low cost. The advantage of using a cluster of workstations as the computational platform is that a cluster of a large number of workstations is easily available. A disadvantage is that there may be many users running unrelated tasks on the workstations so that the available computing resource for each task fluctuates in an unpredictable manner. Furthermore, communication between workstations is relatively slow. Although the performance of generalized cross-validation (GCV)–based threshold selection scheme is excellent, it is costly from CPU time viewpoint when implemented sequentially.
As far as the structure of computation is concerned, the DWT is same as an octave-band filter bank. The filter bank has a regular structure; it is easily implemented by repeated application of identical cells Fig. 1 . It is computationally efficient. The decomposition of real-time Fig. 1 Basic computational cell of (a) the DWT, (b) the inverse DWT (a) DWT Cell (b) DWT Cell 40 3 Finite Precision Error Modeling and Analysis signal into wavelet coefficients involves FIR filtering and its reconstruction involves IIR filtering.
6. Collection of results by master node from slaves. Next, the performance measures used in our experiment speedup and efficiency are defined. 2 Speedup Factor One of the most important criteria and a very commonly used factor, determining the usefulness of a parallel algorithm, is the speedup factor. The speedup factor S is defined as the ratio of wall clock execution time needed whether only one processor is used to that needed when P processors are used in parallel . The speedup achieved by using a parallel network over a single processor is given by !
Algorithms and Computation: 12th International Symposium, ISAAC 2001 Christchurch, New Zealand, December 19–21, 2001 Proceedings by Sue Whitesides (auth.), Peter Eades, Tadao Takaoka (eds.)