Description: 问题描述：
给定n个石子，其重量分别a1,a2,a3...an,要求将其划分成m份，每一份的划分费 义为这份石了中最大重量与最小重量的差的平方。总划分费用等丁m份划分费用之和。
编程任务
对于给定的n个石子，求一种划分方案，使得总划分费用最小。-Description of the problem: Given n-stones, its weight, respectively a1, a2, a3 ... an, asked that it be divided into m copies, each of the division of costs defined as the weight of the stone of the largest and the smallest weight difference the square. Small m the total costs were divided into the sum of the cost. Programming tasks for a given n-stones, seek a kind of division, making the total cost of the smallest division. Platform: |
Size: 756736 |
Author:伍超洋 |
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Description: 问题描述：给定n个石子，其重量为a1,a2...,an,要求将其划分为m堆，每一份划分的费用定义为这堆石头中最大重量与最小重量的差的平方。总划分费用等于各堆费用之和。
输入：n m 及a1,a2...,an
输出：sum
问题描述：给定n个石子，其重量为a1,a2...,an,要求将其划分为m堆，每一份划分的费用定义为这堆石头中最大重量与最小重量的差的平方。总划分费用等于各堆费用之和。
输入：n m 及a1,a2...,an
输出：sum
-Description of the problem: Given n-stones, the weight of a1, a2 ..., an, requests that it be divided into m heap, the cost of each division is defined as the weight of this pile of stone, the largest and the smallest weight of the square of the difference . Divide the total cost is equal to the cost of each stack and the. Input: nm and a1, a2 ..., an output: sum description of the problem: Given n-stones, the weight of a1, a2 ..., an, requests that it be divided into m piles, each division of the cost of is defined as the weight of this pile of stone, the largest and the smallest weight of the square of the difference. Divide the total cost is equal to the cost of each stack and the. Input: nm and a1, a2 ..., an output: sum Platform: |
Size: 15360 |
Author:Adler.C |
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Description: 包括kf，ekf，pf，upf可以自己定制模型参数，完成滤波-ReBEL currently contains most of the following functional units which can be used for state-, parameter- and joint-estimation:
Kalman filter
Extended Kalman filter
Sigma-Point Kalman filters (SPKF)
Unscented Kalman filter (UKF)
Central difference Kalman filter (CDKF)
Square-root SPKFs
Gaussian mixture SPKFs
Iterated SPKF
SPKF smoothers
Particle filters
Generic SIR particle filter
Gaussian sum particle filter
Sigma-point particle filter
Gaussian mixture sigma-point particle filter
Rao-Blackwellized particle filters
The italicized algorithms above are not fully functional yet (or included in the current release), but will be in the next or future releases. The code is designed to be as general, modular and extensible as possible, while at the same time trying to be as computationally efficient as possible. It has been tested with Matlab 7.2 (R2006a).
Platform: |
Size: 1608704 |
Author:zhangsimin |
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Description: 利用KLT跟踪算法进行兴趣点选取和跟踪。
KLT跟踪算法的原始思想是在研究不同图像之间的匹配问题时，通过计算两个平移窗口的灰度残差，并寻找最小化残差SSD(sum of square difference)来实现匹配的。但是这个过程是没有效率的，因此KLT算法进行了优化。在这个过程中，KLT算法使用泰勒展开直接计算平移矢量，而不需要通过遍历进行搜索。
-KLT tracking algorithm to select a point of interest and tracking. KLT tracking algorithm original idea is to study the matching between different images, by calculating the two translational window gray residuals and seeks to minimize the residual SSD (sum of square Difference) matching. But this process is not efficient, KLT algorithm has been optimized. In this process, the KLT algorithm uses the Taylor expansion of the direct calculation of the translation vector, without the need to search by traversing. Platform: |
Size: 2219008 |
Author: |
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Description: 计算了0.001-0.999之间数字的平方和，并给出图形，并与matlab自身的函数power进行了比较，给出了差值的图形-Calculates the square sum of the Numbers between 0.001 0.999, and the graphics is given, and with the function of matlab power are compared, and the difference of the graphics is presented Platform: |
Size: 15360 |
Author:shujian |
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Description: Detect tampering in low quality image. Part of a image is extracted , compressed at different quality original image reinserted in to original image.
Calculate sum of square difference between manipulated image and different resaved version compressed at different JPEG quality.
Tampered region is detected by resaving the image at a multitude of JPEG qualities and detect the spatially localized local minima. These minima, called JPEG ghosts.
The Results shows that for higher quality difference between manipulated image and resaved version gives higher accuracy
-Detect tampering in low quality image. Part of a image is extracted , compressed at different quality original image reinserted in to original image.
Calculate sum of square difference between manipulated image and different resaved version compressed at different JPEG quality.
Tampered region is detected by resaving the image at a multitude of JPEG qualities and detect the spatially localized local minima. These minima, called JPEG ghosts.
The Results shows that for higher quality difference between manipulated image and resaved version gives higher accuracy
Platform: |
Size: 1024 |
Author:jahnavi |
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Search - sum of square difference