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Description: 模拟信号转换成数字形式,必须要经过量化才能实现。标量量化的方法包括均匀量化和非均匀量化两种,这两种方法都会带来精度的损失。文中探讨了模拟信源量化的最优算法,并用C语言实现了高斯信源在不同量化电平下均匀量化和非均匀量化的最优步长的数值计算。通过对计算结果进行比较,给出了非均匀量化优于均匀量化的具体结论。-Analog signals into digital form, you must go through quantification can be achieved. Scalar uniform quantization methods include two kinds of quantitative and non-uniform quantization, these two methods will bring about the loss of accuracy. This paper discusses the analog source to quantify the optimal algorithm, and use C language implementation of the Gaussian source at different quantization levels under the uniform quantizer and the optimal non-uniform quantization step size of the numerical calculation. Calculated by comparing the results given the non-uniform quantization is superior to uniform quantization of the specific conclusions.
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Size: 726016 |
Author: 刘杰 |
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Description: Function to find SNR gain for a Stochastic Resonance detector and hence compute the optimal value of gamma, which is the quantizer threshold.
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Size: 1024 |
Author: Suresh |
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Description: matlab实验报告 学习A律PCM量化器的matlab代码-Programmable test report: Learning A law PCM quantizer matlab code
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Size: 1024 |
Author: hanhan |
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Description: 编制LBG算法程序，以语音信号的短时平均能量为例，训练一个5比特码书，并给出量化器的失真特性。-Procedures for the preparation of LBG algorithm to short-time average energy of speech signals, for example, training for a 5-bit code book, and gives quantizer distortion characteristics.
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Size: 1024 |
Author: 侍建超 |
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Description: Example of codification of a JPEG block (DCT,Quantizer,ZigZag,Huffman)
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Size: 5120 |
Author: Jorge |
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Description: 一种代码激励线性预测编码器，其特征在于：具有声道信息生成器、声音功率量化器、自适应码表-A code excited linear predictive coding device, characterized in that: a channel information generator, the sound power quantizer, adaptive code table
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Size: 4096 |
Author: 王凤 |
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Description: Compute the output of a general delta-sigma modulator with input u,
a structure described by ABCD, an initial state x0 (default zero) and
a quantizer with a number of levels specified by nlev.
Multiple quantizers are implied by making nlev an array,
and multiple inputs are implied by the number of rows in u.
Alternatively, the modulator may be described by an NTF.
The NTF is zpk object. (The STF is assumed to be 1.)
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Size: 2048 |
Author: hamdi |
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Description: Determine the SNR for a quadrature delta-sigma modulator using simulations.
The modulator is described by a noise transfer function (ntf)
and the number of quantizer levels (nlev).
The ntf/stf may be given in ABCD form.
The band of interest is defined by the oversampling ratio (R)
and the center frequency (f0).
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Size: 1024 |
Author: hamdi |
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Description: Determine the SNR for a delta-sigma modulator by using simulations.
The modulator is described by a noise transfer function (ntf)
and the number of quantizer levels (nlev).
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Size: 2048 |
Author: hamdi |
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Description: 主要是ezw编码过程中的量化器的编写代码，大家可以参考一下。-Encoding process is mainly ezw quantizer in the code, we can refer to.
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Size: 1024 |
Author: 周周 |
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Description: 使用Opencv库，编写256位矢量量化器-256-level vector quantizer in opencv
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Size: 1024 |
Author: 佑礼 |
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Description: 关于信号输入均匀量化器的信号幅度，用MATLAB实现量化过程-Uniform quantizer on the input signal amplitude, using MATLAB to quantify the process of achieving
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Size: 15360 |
Author: 陈晋芳 |
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Description: 关于信号输入均匀量化器的信号幅度，用MATLAB实现量化过程-Uniform quantizer on the input signal amplitude, using MATLAB to quantify the process of achieving
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Size: 60416 |
Author: 陈晋芳 |
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Description: 关于信号输入均匀量化器的信号幅度，用MATLAB实现量化过程-Uniform quantizer on the input signal amplitude, using MATLAB to quantify the process of achieving
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Size: 1024 |
Author: 陈晋芳 |
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Description: 关于信号输入均匀量化器的信号幅度，用MATLAB实现量化过程-Uniform quantizer on the input signal amplitude, using MATLAB to quantify the process of achieving
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Size: 4096 |
Author: 陈晋芳 |
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Description: 关于信号输入均匀量化器的信号幅度，用MATLAB实现量化过程-Uniform quantizer on the input signal amplitude, using MATLAB to quantify the process of achieving
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Size: 8192 |
Author: 陈晋芳 |
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Description: 均匀PCM编码程序，输入模拟信号，输出均匀量化的数字信号-Uniform PCM coding process, the input analog signal, digital signal output uniform quantizer
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Size: 1024 |
Author: 燕子 |
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Description: a novel arrangement for quantizer levels
in the Quantization Index Modulation (QIM) method is proposed.
Due to perceptual advantages of logarithmic quantization, and in
order to solve the problems of a previous logarithmic quantizationbased
method
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Size: 1225728 |
Author: SKP |
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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
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Size: 4096 |
Author: sun |
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Description: constructive role of noise in signal detection from parallel arrays of quantizers.-A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers,in which all the quantizer outputs are added to produce the array output.s(t)=s1(t)with prior probability P0,or s(t)=s0(t)with prior probability P1=1-P0 input signal-noise mixture x(t)=s(t)+n(t) x(t) is observed by means of a parallel array of M threshold comparators. yi(t)=U[x(t)+wi(t)-theta],i=1,2...M.wi(t) is threshold noise.Y(t)=y1(t)+y2(t)+...yM(t), the array output Y(t) is meansured at T distinct times.the plot is fthe theoretical value of minimal detection error probability Per varied with sigma.
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Size: 1024 |
Author: shasha |
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