Ink Blotting One method for escaping from a maze is via ‘ink-blotting’. In this method your starting square is marked with the number ‘1’. All free, valid squares north, south, east and west around the number ‘1‘ are marked with a number ‘2’. In the next step, all free, valid squares around the two are marked with a ‘3’ and the process is repeated iteratively until : The exit is found (a free square other than the starting position is reached on the very edge of the maze), or, No more free squares are available, and hence no exit is possible.
標(biāo)簽： method ink-blotting Blotting escaping
上傳時(shí)間： 2014-12-03
上傳用戶：123啊
均值漂移算法的詳細(xì)介紹，論證均值漂移算法的收斂性，介紹mean-shift算法在圖像分割，目標(biāo)跟蹤領(lǐng)域的應(yīng)用
上傳時(shí)間： 2016-03-04
上傳用戶：jing911003
The Fuzzy Clustering and Data Analysis Toolbox is a collection of Matlab functions. Its propose is to divide a given data set into subsets (called clusters), hard and fuzzy partitioning mean, that these transitions between the subsets are crisp or gradual.
標(biāo)簽： Clustering collection functions Analysis
上傳時(shí)間： 2016-03-19
上傳用戶：1427796291
Noncoherent receivers are attractive for pulsed UWB systems due to the implementation simplicity. To alleviate the noise effect in detecting UWB PPM signals, this letter proposes a simple yet flexible weighted noncoherent receiver structure, which adopts a square-law integrator multiplied with a window function.
標(biāo)簽： implementation Noncoherent attractive simplicity
上傳時(shí)間： 2013-12-01
上傳用戶：wys0120
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標(biāo)簽： CENTRES KMEANS OPTIONS cluster
上傳時(shí)間： 2014-01-07
上傳用戶：zhouli
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標(biāo)簽： multidimensional estimation algorithm Gaussian
上傳時(shí)間： 2013-12-03
上傳用戶：我們的船長(zhǎng)
％ decode with soft-input viterbi algorithm 硬判決 ％ //k=4,r=1/2 ％輸入數(shù)據(jù)為軟信息，并且數(shù)據(jù)為均值為1的BPSK調(diào)制，如果均值為MEAN,那么62,63,103,104行應(yīng)做相應(yīng)修改
標(biāo)簽： soft-input algorithm viterbi decode
上傳時(shí)間： 2014-10-28
上傳用戶：aig85
The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標(biāo)簽： Matlab-interfaces inference interface the
上傳時(shí)間： 2016-08-27
上傳用戶：gxrui1991
ClustanGraphics聚類分析工具。提供了11種聚類算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of Squares Flexible (ß space distortion parameter) Density (or k-linkage, density-seeking mode analysis)
標(biāo)簽： ClustanGraphics Complete Neighbor Linkage
上傳時(shí)間： 2014-01-02
上傳用戶：003030
A Module-based Wireless Node (MW-Node) is a Node with wireless and mobile capabilities added by means of modules. It is not a new node object derived from Node. Rather it is a new layout of mostly existing components. Rationale for this new design has been presented in [1]. The MW-Node provides a flexible support for wireless and mobile networking and in particular: support for multiple interfaces/multiple channels, and a common basis for the implementation of wireless routing protocols.
標(biāo)簽： Node Module-based capabilities Wireless
上傳時(shí)間： 2013-12-26
上傳用戶：大三三
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