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[Compress-Decompress algrithms] Surfac DL : 0: We describe and demonstrate an algorithm that takes as input an unorganized set of points fx1􀀀 􀀀 xng 􀀀 IR3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance — all are inferred automatically from the data. This problem naturally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching.-We describe and demonstrate an algorithm that takes as input anunorganized set of points fx1
Date : 2025-12-29 Size : 500kb User : 李叶林
[Compress-Decompress algrithms] huise DL : 0: 灰色系统用于预测未知数据，是一种比较好的方法，此程序只需改变原始数据即可得到结果！-Gray system is used to predict the unknown data is a better method, this procedure simply by changing the raw data you can get the results!
Date : 2025-12-29 Size : 204kb User : huifei
[Compress-Decompress algrithms] AspackDie141 DL : 0: 这是一个小小的 PE 文件解压缩器 (EXE, DLL, ...) 她可以解压缩自 Aspack 2000 以后的任何 Aspack 版本. 包括: - Aspack 2000 - Aspack 2001 - Aspack 2.1 - Aspack 2.11 - Aspack 2.11c/d - Aspack 2.12 - Aspack 2.12a/b - 一些未知的版本-This is a small decompressor PE files (EXE, DLL, ...) she can extract from any subsequent Aspack Aspack 2000 version include:- Aspack 2000- Aspack 2001- Aspack 2.1- Aspack 2.11- Aspack 2.11 c/d- Aspack 2.12- Aspack 2.12a/b- some unknown version
Date : 2025-12-29 Size : 65kb User : 123yueyue
[Compress-Decompress algrithms] 30gedanpianji DL : 0: 主要内容: (1)将<AT89S51单片机试验及实践教程>里面的实验用Proteus仿真!!!!包括C语言的和汇编的!!! (2)加了注释(本人也是刚学的,可能有些错误,望提出!) 还加了部分与该实验有关的资料!! (3)将本人发现的原版中的问题加以改正.(里面还有小部分问题,目前仍然不明) -Main content: (1) inside the <AT89S51单片机试验及实践教程> experimental Proteus simulation!!!! Including the C language and assembly!!! (2) Add a comment (I was just learning, and there may be some mistakes, hope raised!) Also added a section with information relevant to the experiment!! (3) I found the problem to be corrected in the original. (There is also a small part of the problem, is still unknown)
Date : 2025-12-29 Size : 4.83mb User : hehe
[Compress-Decompress algrithms] slam-for-dumies DL : 0: The Simultaneous Localisation and Mapping (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its lo- cation within this map. A solution to the SLAM problem has been seen as a `holy grail for the mobile robotics com- munity as it would provide the means to make a robot truly autonomous.-The Simultaneous Localisation and Mapping (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its lo- cation within this map. A solution to the SLAM problem has been seen as a `holy grail for the mobile robotics com- munity as it would provide the means to make a robot truly autonomous.
Date : 2025-12-29 Size : 354kb User : sthepany07
[Compress-Decompress algrithms] Setup-Installer DL : 0: Something unknown to me
Date : 2025-12-29 Size : 2.01mb User : kiran dhore
[Compress-Decompress algrithms] sreenivas2009-icassp DL : 0: Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived from the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.
Date : 2025-12-29 Size : 346kb User : pashaa