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Description: GPS卫星数据分析，包括C/A码分析，P1、P2码分析，D1、D2分析，周跳数据分析，图形仿真 等等，相关的数据算法 ！ 比较不错！-GPS satellite data analysis, including the C/A code analysis, P1, P2 code analysis, D1, D2, Zhou jump data analysis, graphics simulation, etc., relevant data algorithm! Quite good!
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Size: 4833280 |
Author: lxr |
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Description: 这是仿真跳时超宽带的功率普密度分析的源代码，它是研究超宽带的基础。-This is the simulation of UWB jump at the Cape power density analysis of source code, it is research-based broadband.
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Size: 2048 |
Author: 王国芳 |
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Description: 我们给出一个模板 和一幅图象 。不难发现原图中左边暗，右边亮，中间存在着一条明显的边界。进行模板操作后的结果如下： 。
可以看出，第3、4列比其他列的灰度值高很多，人眼观察时，就能发现一条很明显的亮边，其它区域都很暗，这样就起到了边沿检测的作用。
为什么会这样呢？仔细看看那个模板就明白了，它的意思是将右邻点的灰度值减左邻点的灰度值作为该点的灰度值。在灰度相近的区域内，这么做的结果使得该点的灰度值接近于0；而在边界附近，灰度值有明显的跳变，这么做的结果使得该点的灰度值很大，这样就出现了上面的结果。
这种模板就是一种边沿检测器，它在数学上的涵义是一种基于梯度的滤波器，又称边沿算子，你没有必要知道梯度的确切涵义，只要有这个概念就可以了。梯度是有方向的，和边沿的方向总是正交(垂直)的，例如，对于上面那幅图象的转置图象，边是水平方向的，我们可以用梯度是垂直方向的模板 检测它的边沿。
例如，一个梯度为45度方向模板 ，可以检测出135度方向的边沿。-we give a template and an image. It is not difficult to find the maximum were left dark, right-liang, in the middle there is a clear boundary. After the template for the operation results are as follows :. Can be seen, three, four out other than the gray value is much higher, eye observation, we can obviously found a bright side. Other regions are dark, and this has played a role in the detection of 2500. Why is this the case? A closer look at the template on which to understand it. It means the right to the point o gray minus left point as a gray value of the point of gray values. In a similar gray area, do so as a result of the point of gray values close to 0; And near the border. gray values jump significantly changed, the results do make the point very gray value, and this appeared to
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Size: 9216 |
Author: 李涯 |
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Description: MCRGSA------组播路由问题遗传模拟退火算法
％M-----------遗传算法进化代数
％N-----------种群规模，取偶数
％Pm----------变异概率调节参数
％K-----------同一温度下状态跳转次数
％t0----------初始温度
％alpha-------降温系数
％beta--------浓度均衡系数
％ROUTES------备选路径集
％Num---------到各节点的备选路径数目
％Cost--------费用邻接矩阵
％Source------源节点标号
％End---------目的节点标号组成的向量
％MBR---------各代最优路径编码-MCRGSA------ Multicast Routing genetic simulated annealing% M----------- genetic algorithm algebra% N----------- population size, even take Pm----------% probability parameter variation%----------- same temperature K Jump under a number of state----------% t0 initial temperature cooling% alpha------- beta coefficient%-------- balanced concentration coefficient% ROUTES------ Alternative Path Set% Enable--------- nodes to the number of alternative paths%-------- Cost adjacent costs Matrix% Source------ source node labeling% End--------- destination node labeling Group Vector%% of the MBR--------- generations optimal path coding
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Size: 1024 |
Author: 程爱华 |
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Description: This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
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Size: 958464 |
Author: 大辉 |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 16384 |
Author: 徐剑 |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 220160 |
Author: 晨间 |
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Description: This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
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Size: 348160 |
Author: 晨间 |
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Description: 一个可逆跳转蒙特卡罗采样（RJMCMC）算法详细程序，内附相关论文，对照论文看算法，便于理解。包含多种运动方式（增加，减少，分裂，合成，更新）-A Reversible Jump Monte Carlo sampling (RJMCMC) algorithm detailed procedures, enclosing the relevant papers, watch the control thesis algorithm, easy to understand. Contains a wide range of movement (increase, decrease, fragmentation, synthesis, update)
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Size: 656384 |
Author: 颜靖华 |
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Description: 无线传感器网络路由算法，采用最短路径，先搜索节点的一跳范围的距离，再搜索两跳的距离，仿真效果良好。-Wireless sensor network routing algorithm, using the shortest path first jump of the scope of the search node distance, and then search for the two jump distance, simulation results were very good.
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Size: 14336 |
Author: 电信科学 |
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Description: 随机跳跃法实现最优化问题实例程序（MATLAB code）-A program using random jump method to achieve optimization (MATLAB code)
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Size: 6144 |
Author: Lizzer Wee |
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Description:
本程序是使用MATLAB语言采用迎风格式解对流方程，对流方程在工程上有很广泛的应用，而迎风格式的精度比较高还包括用跳点格式解扩散方程的初值问题，所以有两个程序
-This procedure is to use the MATLAB language solution using upwind convection equation, the convection equations in engineering, there is a wide range of applications, and the accuracy of upwind scheme also includes a relatively high point format with jump-diffusion equation solution of the initial value problem, so there are two procedures
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Size: 1024 |
Author: 马冠男 |
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Description: 主要介绍了dvhop算法的源代码，先计算平均每跳的距离，再根据跳数求出锚节点与未知节点的距离，再用三边测量法求出未知节点坐标-Dvhop algorithm introduces the source code, to calculate the average distance of each jump, then jump a few calculated in accordance with anchor nodes and unknown distance from the node, and then calculated trilateration unknown node coordinates
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Size: 1024 |
Author: 闫军 |
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Description: 基于模拟退火的粒子群算法，模拟退火算法在搜索过程中具有该概率突跳的能力，能够有效地避免搜索过程陷入局部极小解。-Based on simulated annealing particle swarm optimization, simulated annealing algorithm in the search process has a sudden jump in the probability of the capacity, which can effectively avoid the search process into a local minimum solution.
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Size: 3072 |
Author: cuiping5122 |
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Description: 通过灰度化、分段灰度拉伸、中值滤波、边缘检测和二值化等方式对车牌图像进行预处理，然后基于灰阶跳变定位车牌。-By gray, fractional gray stretch, median filtering, edge detection and binarization, etc. on the license plate image is preprocessed, and then jump on gray positioning plate.
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Size: 5528576 |
Author: 沈静 |
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Description: 求系统单位给定阶跃响应的性能指标（超调量，峰值时间，调整时间 .....），其中，
y，t表示系统节约相应的函数值与其对应时间,函数返回的是阶跃响应的第一(正向)波峰值b1、 阶跃响应的第二(正向)波峰值b2,阶跃响应的超调量sigma,阶跃响应的衰减比n,阶跃响应的衰减率pusi
阶跃响应的衰减震荡周期T,阶跃响应的振荡频率f.-Demand system units for a given step response performance (overshoot, peak time, adjust the time .....), which, y, t that the system saves the corresponding function value and its corresponding time, the function returns the order Jump to respond to the first (positive) wave peak b1, step response of the second (positive) wave peak b2, step response overshoot sigma, step response of the attenuation ratio of n, the step response of the attenuation rate pusi step response of the decay vibration cycle T, the step response of the oscillation frequency f.
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Size: 1024 |
Author: hua gong |
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Description: Manchester码的编码规则是，如果原始数据为“1”，将其编码成“10”；如果原始数据为“0”，将其编码成“01”。这种编码的特点是每个码元中间都有跳变，低频能量较少，便于接收端提取时钟信息。-Manchester, the coding rule is, if the original data is " 1" , be coded " 10" if the original data is " 0" , be coded " 01." This code is characterized by the middle of each symbol has a jump, low energy less easy to extract the clock information on the receiving end.
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Size: 3072 |
Author: 严雪峰 |
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Description: 自己编写的利用matlab实现无线自组织网AODV仿真的算法源码。
为由50个节点构成的网络。在50m×50m大小的区域内，随机的产生50个节点。由用户指定源节点和目的节点，程序负责给出从源节点到目的节点的最小路径和此路径的跳数。各个节点之间设定为只要在自己直接通信范围内的其它节点，均一跳可达。-Write your own realization of the use of matlab simulation of wireless self-organizing network algorithms AODV source. The grounds of a network of 50 nodes. Size of 50m × 50m in area, randomly generated 50 nodes. By the user to specify the source node and destination node, the program responsible for the given node from the source node to the destination path and this path is the minimum number of hops. Set between each node to communicate directly as long as within the scope of their other nodes, all jump up.
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Size: 1024 |
Author: shan5217 |
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Description: WeChat jump a high jump, the required information and code, small game.(WeChat gamehigh grades)
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Size: 581632 |
Author: 牛吓吓 |
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Description: 跳一跳辅助，微信跳一跳外挂，请按照说明自行下载解读(Jump one jump auxiliary)
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Size: 26518528 |
Author: 唐唐895 |
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