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Description: 重读linux 2.4.2o所写的笔记，可以作为入门参考
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Size: 534113 |
Author: sciwander |
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Description: 求解tsp问题的局部优化算法-the local optimization algorithm
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Size: 45056 |
Author: 曲晓威 |
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Description: 重读linux 2.4.2o所写的笔记，可以作为入门参考-Rereading linux 2.4.2o written notes, can be used as a reference entry
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Size: 533504 |
Author: sciwander |
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Description: 本文介绍了JPEG2000的静态图像压缩技术在TI TMS320DM642 DSP上的实现过程；对压缩技术核心小波算法结构进行改进，对硬件特点的程序结构、具体代码及总线读写等方面的改进作了详细描述．改进后的软件系统，对于1 024×768像素大小的图像、数据存储空间仅需6 KB，使得在资源有限的DSP芯片上能够运行JPEG2000图像压缩技术；编码速度相对于未改进的提高了l2～ 2O倍，可迭到3帧／s；压缩比为O．5 bit／p时，峰值信噪比可达到3O．-This paper introduces the JPEG2000 still image compression technology in the TI TMS320DM642 DSP Implementation process on wavelet compression technology is the core algorithm to improve the structure of the hardware features of the process structure, read and write the specific code and bus improvements, such as was described in detail . The improved software system, for 1 024 × 768 pixel image size, data storage space only 6 KB, allows limited resources to run the DSP chip JPEG2000 image compression technology encoding speed is not improved relative to the improvement of the l2 ~ 2O-fold, again and again to be 3/s compression ratio for O. 5 bit/p, the peak signal to noise ratio can be achieved 3O.
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Size: 243712 |
Author: H Simon |
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Description: 脱壳的人都知道这东西的用处，就是下载的地方太少，网上有的多是1.13版的，这个版本的极少。 -BB
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Size: 29696 |
Author: 林天发 |
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Description: conversion byte 2o tuple
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Size: 10240 |
Author: lim |
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Description: 在草图符号的自适应学习中，不同用户的训练样本数量可能不同，保持在不同样本数量下良好的学习效
果成为需要解决的一个重要问题．提出一种自适应的草图符号识别方法，该方法采用与训练样本个数相关的分类
器组合策略将模板匹配方法和SVM统计分类方法进行了高效组合．它通过利用支持小样本学习的模板匹配方法
和支持大量样本学习的SVM 方法，并同时利用草图符号中的在线信息和离线信息，实现了不同样本个数下自适应
的符号学习和识别．基于该方法，文中设计并实现了支持自适应识别的草图符号组件．最后，利用扩展的PIBG
Toolkit开发出原型系统IdeaNote．评估表明，该方法可以在24类草图符号分别使用1到2O个训练样本时具有较
高的识别正确率和较好的时间性能．-In the sketch symbol adaptive learning, the number of training samples of different users may be different, to keep the number of samples under different good learning effect
Fruit become an important issue to be resolved. The draft plan proposed an adaptive character recognition method using the number associated with the classification of training samples
Combination strategy will be the template matching method and SVM classification method was efficient statistical combination. It supports a small sample study by using a template matching method
And support a large number of samples to learn the SVM method, and sketch symbols while using online information and offline information and achieve a number of different samples of adaptive
Learning and recognition of symbols. Based on this method, the paper designed and implemented to support adaptive sketch recognition symbol components. Finally, using the extended PIBG
Toolkit developed a prototype system IdeaNote. Is shown th
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Size: 626688 |
Author: 郭事业 |
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Description: 2o Abrir o módulo Federal --> Obrigaç õ es de Tributos Federais
3o Executar SQLMonitor.exe
4o No aplicativo Oracle_monitor, flegue a opç ã o saffdir.exe
5o Volte no módulo e realize o procedimento
6o Salve o resultado no bloco de notas e nos envie
Caso tenha alguma dúvida, entre em contato.
Atenciosamente,
Shirley Silva
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Size: 15493120 |
Author: guillmes |
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