Description: BP神经网络的C语言实现 BP神经网络解决异或问题 canny源代码 HMM的C语言实现 isodata K-MEANS 车牌识别系统 矢量量化的C语言实现 -Neural Network C language BP neural network solution differences or problems canny source HMM C language isodata K-MEANS License Plate Recognition System Vector Quantization C Language Platform: |
Size: 794624 |
Author:liang |
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Description: 把目标的每个颜色子空间量化为k份后，建立目标模型，在该模型上利用meanshift进行搜索找到最佳位置。-The target for each color sub-space quantization for k copies of, the establishment of the target model, in which the model meanshift use search to find the best location. Platform: |
Size: 1024 |
Author:张瑞娟 |
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Description: 基于等均值等方差的图象矢量量化Matlab程序-Based on the mean variance of the image, such as vector quantization Matlab procedures Platform: |
Size: 69632 |
Author:管军斌 |
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Description: pcm均匀pcm与非均匀pcm
一。产生长度为500的零均值，单位方差的高斯随机变量序列，用均匀pcm的方法用16电平进行量化：1）求所得的SQNR，该序列的前5个值，相应的量化值和相应的码字。2）画出量化误差（定义为输入值和量化值之间的差），同时 画出量化值作为输入值的函数的图。3）用128量化电平数重做2）题， 比较结果。-pcm uniform pcm with a non-uniform pcm. Have a length of 500 zero-mean, unit variance Gaussian random variables with a uniform method of pcm level by 16 to quantify: 1) demand from SQNR, the sequence of the first five values, the corresponding quantization value and the corresponding code word. 2) draw quantization error (defined as the import value and quantify the value of the difference between), at the same time draw to quantify the value of the import value as a function of the map. 3) 128 to quantify the number of redo-level 2) title, the results of the comparison. Platform: |
Size: 4096 |
Author:jin |
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Description: 给出了矢量量化编码全搜索和均值不等式删除法两种算法的源代码，并比较了运行速度。-Vector quantization coding gives the full-search and mean inequality law two algorithms to delete the source code, and compare the operating speed. Platform: |
Size: 2238464 |
Author:陈礼升 |
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Description: In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.-In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined. Platform: |
Size: 147456 |
Author:prasad |
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Description: This function is for training a codebook for vector quantization. The data set is split to two clusters, first, and the mean of each cluster is found (centroids). The disttance of each vector from these centroids is found and each vector is associated with a cluster. The mean of vectors of each cluster replaces the centroid first. If the total distance is not improved substantially, The centroids are each split to two. This goes on untill the required number of clusters is reached and the improvement is not substantial.
Platform: |
Size: 4096 |
Author:veeresh |
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Description: 脉冲编码调制（PCM，Pulse Code Modulation）是一种将模拟语音信号转换成数字信号的编码方式，从数学上来看，量化过程就是把一个连续幅度值的无限数集合映射成一个离散幅度值的有限数集合，编码就是用一组二进制数来表示这些映射后的有限数。
国际标准化的PCM采用折叠二进制码，符合长途电话质量。根据量化方法的不同，PCM 可分为均匀量化PCM 和对数PCM 两大类，对数PCM中，A律和μ律编码都已被定为国际标准。
本程序实现了A律、μ律以及均匀量化三种PCM，并带有GUI界面，方便使用。可以计算编码后的码率、运行时间以及最小均方误差。-Pulse Code Modulation (PCM, Pulse Code Modulation) is an analog voice signal into digital signal encoding, from the mathematical point of view, the process is to quantify the magnitude of the value of an infinite number of continuous mapping into a discrete set of the limited range of values number set, code is a set of binary digits, said after a limited number of these maps.
By folding the International Standardization of PCM binary code, in line with long-distance telephone quality. According to the different quantitative methods, PCM can be divided into uniform quantization PCM PCM and the number of two categories, on the number of PCM, A law and μ-law code have been designated as international standards.
A law implemented in this program, μ law and the uniform quantization of three PCM, with a GUI interface, easy to use. Encoded bit rate can be calculated, running time and minimum mean square error.
Platform: |
Size: 904192 |
Author:Sariel |
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Description: 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列
为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。
-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors.
A Matlab version of the face detection algorithm can be downloaded
from http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do?objectId=13701&objectType=FILE. Platform: |
Size: 1397760 |
Author:霞 |
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Description: 为在极低速率下实现高质量的语音编码,提出了一
种新的有效的线谱对( LSP)参数量化算法——P-RS- MSM Q
算法。此算法以多帧联合矩阵量化作为基本框架,引入了基
于超级帧模式的均值去除和帧间预测策略、 矩阵分裂和子矩
阵多级量化策略 同时提出了基于语音帧短时谱能量的帧
内加权和基于超级帧中各子帧重要性的帧间加权策略等。-Very low rates in high-quality speech coding, a new and effective LSP (LSP) parameter quantization algorithm- P-RS-MSM Q algorithm. This algorithm to quantify the multi-frame as the basic framework of the joint matrix, the introduction of super-frame model based on the mean removal and interframe prediction strategy, matrix splitting and multi-level quantization matrices the same time frame was proposed based on short-term spectrum of speech frame energy weighted and based on the sub-frame super-frame, the importance of inter-weighted strategies. Platform: |
Size: 171008 |
Author:杨亚欣 |
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Description: 一。产生长度为500的零均值，单位方差的高斯随机变量序列，用均匀pcm的方法用16电平进行量化：1）求所得的SQNR，该序列的前5个值，相应的量化值和相应的码字。2）画出量化误差（定义为输入值和量化值之间的差），同时 画出量化值作为输入值的函数的图。3）用128量化电平数重做2）题， 比较结果。
二。产生一个长度为500，按N(O,1)分布的随机变量序列，分别用16，128量化电平数和u=255的u律非线性进行量化，画出每种情况下量化器的误差和输入-输出关系，并求SQNR.
三。长度为500的非平稳序列a由两部分组成：前20个样本是按照均值为零和方差为400的高斯随机变量产生的，其余480个样本是根据均值和方差为1的高斯随机变量产生的，对这个序列分别用均匀pcm和非均匀pcm方法进行128电平量化，试比较两种情况下所得到的SQNR。
-One. Have a length of 500 zero mean, unit variance Gaussian random variables with uniform pcm way to quantify the level with a 16: 1) Find the income SQNR, the sequence of the first 5 values, the corresponding quantization value and the corresponding codeword. 2) Draw the quantization error (defined as the input value and quantify the difference between the values), and draw quantitative values as a function of input graph. 3) redo 128 the number of quantization level 2) title, compare the results.
II. Produce a length of 500, according to N (O, 1) distributed random variables, respectively 16,128 and the number of quantization level of u u = 255 to quantify non-linear law, draw each case quantizer error and input- output relationships, and seek SQNR.
III. Length of the non-stationary series is a 500 consists of two parts: the first 20 samples in accordance with zero mean and variance of 400 generated Gaussian random variable, and the remaining 480 samples are based o Platform: |
Size: 4096 |
Author:sun |
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Description: 语音信号处理矢量量化的LBG算法，又称K-mean 算法-in speech signal process vector quantization technology using LBG algorithm with matlab language Platform: |
Size: 1024 |
Author:renfangqin |
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Description: 基于邻域均值关系的Contourlet 域量化水印算法-Quantization watermarking scheme based on neighboring mean value relation in Contourlet transform domain. Platform: |
Size: 556032 |
Author:xiaozhang |
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Description: 数字图像隐藏及提取研究分析论文、文献等。主要研究算法为LSB算法和DCT奇偶量化、DCT均值量化。-Digital Image Information Hiding and Extraction of analytical papers, literature. The main algorithm for the LSB algorithm and DCT parity quantization, DCT mean quantization. Platform: |
Size: 4916224 |
Author:TomCat |
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Description: 下载的基于均值量化各种应用的音频水印，共11篇-Download the audio watermarking based on mean quantization variety of applications, a total of 11 Platform: |
Size: 3516416 |
Author:李 |
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Description: This m-file applies uniform quantization on a given audio signal called music_ee6424.wav
It then plots waveforms and gives the Mean Square Quantization Error (MSQE) Platform: |
Size: 1024 |
Author:Darsh |
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Description: 本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors. Platform: |
Size: 1561600 |
Author:吴绪周 |
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Search - mean quantization