A5005927883- Computational Geometry and Mesh Generation
- Seismic Performance and Analysis
- graph theory and CDMA systems
- Algorithms and Data Compression
- Seismic Waves and Analysis
- Seismology and Earthquake Studies
- Geological Modeling and Analysis
- Complexity and Algorithms in Graphs
- Structural Health Monitoring Techniques
- Tunneling and Rock Mechanics
- Optimization and Packing Problems
- earthquake and tectonic studies
- Scientific Computing and Data Management
- Geotechnical Engineering and Analysis
- Interconnection Networks and Systems
- Advanced Graph Theory Research
- Optimization and Search Problems
- Simulation and Modeling Applications
- Geotechnical and Geomechanical Engineering
- Fluid Dynamics Simulations and Interactions
- Earthquake and Tsunami Effects
- Geotechnical and construction materials studies
- Advanced Data Processing Techniques
- Digital Image Processing Techniques
- Underwater Acoustics Research
University of Canterbury
2004-2024
This paper discusses simulated ground motion intensity, and its underlying modelling assumptions, for great earthquakes on the Alpine Fault. The simulations utilise latest understanding of wave propagation physics, kinematic earthquake rupture descriptions three-dimensional nature Earth's crust in South Island New Zealand. effect hypocentre location is explicitly examined, which found to lead significant differences intensities (quantified form peak velocity, PGV) over northern half...
Given an array of positive and negative values, we consider the problem K maximum sums. When overlapping property needs to be observed, previous algorithms for sum are not directly applicable. We designed O(K * n) algorithm subsequences problem. This was then modified solve subarrays in n/sup 3/) time. Finally, present a VLSI with steps circuit size O(n/sup 2/), which is cost-optimal parallelisation sequential algorithm.
The maximum subarray problem is to find the contiguous array elements having largest possible sum. We extend this K subarrays. For general subarrays where overlapping allowed, Bengtsson and Chen presented O(min{K+nlog2n,nK}) time algorithm for one-dimensional case, which finds unsorted Our in sorted order with improved complexity of O ((n + K) log K). two-dimensional we introduce two techniques that establish O(n3) subcubic time.
Matrix multiplication is a fundamental mathematical operation that has numerous applications across most scientific fields. Cannon's distributed algorithm to multiply two n-by-n matrices on dimensional square mesh array with n2 cells takes exactly 3n − 2 communication steps complete. We show it possible perform matrix in just 1.5n 1 of the same size, thus halving number required.
The maximum subarray problem is to find the array portion that maximizes sum of elements in it. For K disjoint subarrays, Ruzzo and Tompa gave an O(n) time solution for one-dimension. This is, however, difficult extend two-dimensions. While a trivial O(Kn 3 ) easily obtainable two-dimensional size n × n, little study has been undertaken improve complexity. We first propose O(n + log K) asymptotically equivalent Tompa's when sorted order needed. Based on this, we achieve Kn 2 n) cubic ≤ n/ n....
In this paper, we present a parallel algorithm for the maximum convex sum (MCS) problem, which is generalization of subarray (MSA) problem. MSA find rectangular portion within given data array that maximizes in it. The MCS problem to shape instead sum. For O(n) time algorithms are already known on an (n, n) 2D processors. achieve same bound We provide rigorous proofs correctness our based Hoare logic and also some experimental results gathered from Blue Gene/P super computer.
We present efficient sequential and parallel algorithms for the maximum sum (MS) problem, which is to maximize of some shape in data array. deal with two MS problems; subarray (MSA) problem convex (MCS) problem. In MSA we find a rectangular part within given array that maximizes it. The MCS rather than sum. Thus, generalization MSA. For O ( n ) time are already known on an , 2D processors. improve communication steps from 2 − 1 n, optimal. achieve asymptotic bound provide rigorous proofs...