IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS

16

Authors: Mohid Tayyub and Gul N. Khan

Collison detection is a wide-ranging real-world application. It is one of the key components used in gaming, simulation and animation. Efficient algorithms are required for collision detection as it is repeatedly executed throughout the course of an application. Moreover, due to its computationally intensive nature researchers are investigating ways to reduce its execution time. This paper furthers those research works by devising a parallel CPU-GPU implementation of both broad and narrow phase collision detection with heterogenous workload sharing. An important aspect of co-scheduling is to determine an optimal CPU-GPU partition ratio. We also showcase a successive approximation approach for CPU-GPU implementation of collision detection. The paper demonstrates that the framework is not only applicable to CPU/GPU systems but to other system configuration obtaining a peak performance improvement in the range of 18%.

18

Authors: Joana Margarida Miguel, Maria João Varanda Pereira, Pedro Rangel Henriques and Mario Berón

The technology of nowadays allows to easily extract, store, process and use information about individuals and organizations. The increase of the amount of data collected and its value to our society was, at first, a great advance that could be used to optimize processes, find solutions and support decisions but also brought new problems related with lack of privacy and malicious attacks to confidential information. In this paper, a tool to anonymize databases is presented. It can be used by data publishers to protect information from attacks controlling the desired privacy level and the data usefulness. In order to specify these requirements a DSL (PrivasL) is used and the automatization of repository transformation, that is based on language processing techniques, is the novelty of this work.

20

Authors: Wen-Chieh Tung and Jing-Sin Liu

Coverage path planning (CPP) is a fundamental task that is conducted in many applications for machining, cleaning, mine sweeping, lawn mowing, and performing missions by using unmanned aerial vehicles such as mapping, surveillance, search and rescue, and air-quality monitoring. An approach for conducting CPP for a known environment with obstacles involves decomposing the environment into cells such that each cell can be covered individually. The visiting order of the cells can then be decided to connect those intracell paths together. Finding the shortest intercell path that visits every cell and returns to the origin cell is similar to the traveling salesman problem (TSP). However, an additional variation from TSP that should be considered is that there are multiple intracell paths for each cell. These paths result from different selections of entry and exit points in each cell and thus affect the intercell path. This integrated TSP and CPP problem is known as TSP-CPP and is similar to the TSP with neighborhoods (TSPN). To solve TSP-CPP, one must simultaneously determine the visiting order of sites with minimal repetition and the transition points of each visiting site. The current approaches for solving TSP-CPP are as follows: (i) adapting dynamic programming (DP) for TSP to TSP-CPP, which is excellent for obtaining the optimal route and (ii) determining the optimal route by conducting a brute force enumerative search on entry and exit point combinations for every cell and then solving each combination of entry and exit points with a TSP solver. For large numbers of cells, approaches (i) and (ii) both suffer from exponential complexity and are impractical for complex environments. In this study, we proposed an appropriate genetic algorithm implementation for TSP-CPP to achieve an optimal balance between time efficiency and path optimality to eliminate the curse of dimensionality in DP. Our approach is demonstrated to find the true optimal solution as DP in all simulation environments that can be solved by both DP and GA, and GA is one hundred times faster than DP approach for maps decomposed with large cell number.