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[REU 94] R EUSENS E, Partitioning complexity issue for iterated functions systems based image coding , in Proceedings f the Seventh European Signal Processing Conference (Edinburgh, Scotland), vol 1, p 171 174, September 1994 [RIN 94] R INALDO R, Z AKHOR A, Inverse and approximation problem for two-dimensional fractal sets , IEEE Transactions on Image Processing, vol 3, no 6, p 802 820, 1994 [RIN 95] R INALDO R, C ALVAGNO G, Image coding by block prediction of multiresolution subimages , IEEE Transactions on Image Processing, p 909 920, July 1995 [SAU 94] S AUPE D, H AMZAOUI R, A review of the fractal image compression literature , Computer Graphics, vol 28, no 4, p 268 276, 1994 [SAU 95] S AUPE D, Accelerating fractal image compression by multi-dimensional nearest neighbor search , in S TORER JA, C OHN M (Eds), Proceedings of the Data Compression Conference (DCC 95, Institute for Information Technology, Freiburg University), IEEE Computer Society Press, March 1995 [SIM 95] S IMON B, Explicit link between local fractal transform and multiresolution transform , in ICIP (Washington DC, USA), vol 1, p 278 281, 1995 [THO 95] T HOMAS L, D ERAVI F, Region-based fractal image compression using heuristic search , IEEE Transactions on Image Processing, vol 4, no 6, p 832 838, 1995 [TRI 93] T RICOT C, Courbes et dimension fractale, Springer-Verlag, 1993 [TRU 00] T RUONG TK, J ENG JH, R EED IS, L EE PC, L I AQ, A fast encoding algorithm for fractal image compression using the DCT inner product , IEEE Transactions on Image Processing, vol 9, no 4, p 529 535, 2000 [VIN 93] V INES G, Signal modelling with iterated function systems, PhD Thesis, Georgia Institute of Technology, May 1993 [VRS 90] V RSCAY ER, Moment and collage methods for the inverse problem of fractal construction with iterated function systems , in Fractal 90 conference, June 1990 [VRS 99] V RSCAY ER, S AUPE D, Can one break the collage barrier in fractal image coding , in Fractals in Engineering, Springer-Verlag, 1999 [WALLA 91] WALLACE GK, The JPEG still picture Communications of the ACM, vol 34, no 4, p 30 44, 1991 compression standard ,. We do not explicitly consider such secondary effects in the table 746 Route-Quality Estimation Once the individual link qualities are estimated, the next problem is how to combine them to reach a measure of an overall route quality The quality of the route depends upon the quality of the individual links present in the route The multihop route can be imagined as pipe, with each link representing a part of the pipe with constant diameter which represents the link quality The overall throughput of the route is the amount of data that can be pushed in the pipe It can be seen that the throughput is affected by the bottleneck link which has the least diameter Multiple routes may exist between a given source and destination The routing protocol is responsible for the choice of the route which can support maximum throughput for the connection. VS .NET applications. Example: A Console Control Handler. 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Yang, Wang, and Kravets [43] suggest that isotonicity is an important property for combining the link quality into a single route quality Isotonocity refers to the property that the cost of a route strictly increases when it is pre xed with additional hops For example, consider two routes between node A and node B, say R1 and R2 as shown in Figure 72 Let the combined link metrics of R1 and R2 be represented as r1 and r2 Let there be another path that is pre xed to route A B, say from node X to A denoted by X A B Consider the two routes between X and B in X A B, one passing through R1 and the other through R2 Let us denote them by X R1 and X R2 , respectively, and denote their metrics by xr1 and xr2 , respectively. identi ed which sections of the code are worthy of optimization, it is not worth spending extra effort ne-tuning the code When you have pro led your code, you will know what to optimize and can consider the tips above Then is the time to take another look at the code and see if the general rules I ve mentioned here (minimizing stack usage, releasing heap memory early and protecting it against leaks, and coding ef ciently when the compiler cannot make assumptions for you) can be applied And nally, you may nd it useful to look at the assembler listing of your code You can generate this using the Symbian OS command line abld tool, the use of which will be described in detail in your SDK documentation abld listing generates an assembler listing le for the source les for a project For a project with a single source le, testfilecpp, abld listing winscw udeb creates an assembler listing le (testfilelstWINSCW) for the code in testfilecpp You can inspect this to nd out which optimizations have been applied by the compiler, before coding the optimization ourself The listing will also give you an idea of which code idioms are particularly expensive in terms of the number of instructions required. 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