**8. References**

16 Will-be-set-by-IN-TECH

ill-posed inverse problems in general and the problem of unknown groundwater pollutant

Its ease of use and remarkable efficiency in handling complex objective functions and constraints has made simulated annealing an attractive choice for solving a wide range of complex optimization problems. However, the slow convergence and hence long time of execution of standard Boltzmann-type simulated annealing has been a constraint. Adaptive Simulated Annealing removes that constraint by making the annealing schedules decrease exponentially in annealing-time, thereby making the convergence much faster. A major difference between ASA and traditional Boltzamnn Annealing algorithms is that the ergodic sampling takes place in terms of n parameters and the cost function. In ASA the exponential annealing schedules permit resources to be spent adaptively on re-annealing and on pacing the convergence in all dimensions, ensuring ample global searching in the first phases of

Another major advantages of using Adaptive Simulated Annealing is also the fact that the parameters of algorithm are adjusted adaptively and hence the solutions do not vary widely if parameter values are changed within reasonable limits. This is in contrast with Genetic Algorithm where even minor changes to parameters such as mutation probability, cross over

A linked simulation-optimization method for source identification was developed based on adaptive simulated annealing. It was applied to an illustrative study area. The results obtained were compared with those obtained using genetic algorithm, a more commonly used optimization approach. It is evident from the limited numerical experiments that adaptive simulated annealing algorithm based solutions converge to the actual source fluxes faster than genetic algorithm based solutions. This results in substantial saving in computational time. The source fluxes identified by using adaptive simulated annealing are closer to actual fluxes when compared to the results obtained using genetic algorithm, even when the observation data are erroneous and the hydro-geological parameters are uncertain. It can be concluded that adaptive simulated annealing is computationally more efficient for use in simulation-optimization based methods for identification of unknown groundwater pollutant sources, specially in a time constrained environment. Use of ASA has the potential to reduce CPU time required for solution by an order of magnitude. However, with very large number of iterations in the linked simulation-optimization approach, it is possible that the solutions obtained using GA could converge to a marginally better solution compared to that ASA based algorithm. However, it appears that ASA based solutions converge very close to the optimal solution using only a small fraction of iterations required while using GA. The relevance of contaminant monitoring locations is demonstrated. Further studies are required to develop dedicated monitoring networks which can increase the efficiency of

probability or population size causes a significant difference in the solutions.

source characterization in particular.

**7. Conclusion**

source identification.

Manish Jha and Bithin Datta

*James Cook University, Townsville and CRC CARE, Adelaide, Australia*

**Author details**

search and ample quick convergence in the final phases[15].


URL: *http://dx.doi.org/10.1007/b11442*


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© 2012 Shi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Shi et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Simulated Annealing for Fast Motion** 

The promising video coding standard, H.264/AVC [1], is developed by the Join Video Team of ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG). By utilizing several new techniques, such as advanced intra predictions, variable block size ME, integer transformation, in-loop deblocking filter, H.264/AVC has achieved significant compression gain compared with previous video coding standards. It is now widely applied to many types of visual services, for example Digital Multimedia Broadcasting, Mobile Phone, and High Definition (HD) video delivery. In the near future, holography video and Super-HD video are expected to hit consumer market. These kinds of large sized video contents require higher coding efficiency while keeping the encoder complexity within an acceptable level. Therefore, new techniques are needed to reduce the computational complexity so that various real time video encoder and delivery services for

In particular, Block-Matching Motion Estimation (BMME) with Full Search (FS) algorithm [2] is the main computational burden in H.264/AVC due to exhaustively search all possible blocks within the search window using Lagrangian multiplier. Although FS algorithm can obtain the optimum motion vector (MV) in most cases, it consumes more than 80% of the total computational complexity. Thus, a fast and efficient motion estimation algorithm is required for H.264/AVC. Recently, two major approaches were researched to overcome this problem. One employs fast mode decision algorithms to skip unnecessary block modes in variable block checking process [3, 4]. The other one utilizes Fast Motion Estimation (FME)

Various algorithms have been proposed to reduce search points for FME Search algorithm. Motion adaptive search (MAS) [5] utilized the motion activity information to adjust the search strategy. In Variable Step Search (VSS) algorithm [6], motion search range is

**Estimation Algorithm in H.264/AVC** 

Zhiru Shi, W.A.C. Fernando and A. Kondoz

Additional information is available at the end of the chapter

the large sized video contents could be feasible.

searching algorithms to reduce unnecessary search points [5-11].

http://dx.doi.org/10.5772/50974

**1. Introduction** 
