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[Special Effects] 2DLDA_PK_LDA_for_feature_extraction DL : 0: These are the codes in \"A note on two-dimensional linear discrimant analysis\", Pattern Recognition Letter In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.
Update : 2008-10-13 Size : 12.31kb Publisher : ruan
[Special Effects] 2DLDA_PK_LDA_for_feature_extraction DL : 0: These are the codes in "A note on two-dimensional linear discrimant analysis", Pattern Recognition Letter In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.
Update : 2025-02-19 Size : 12kb Publisher : ruan
[Special Effects] cls_AVIA DL : 0: 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.
Update : 2025-02-19 Size : 2.13mb Publisher : 陈礼升
[Other] lunwen DL : 0: 支持向量机的几篇比较好的论文。不是用单纯的支持向量机做系统仿真，而是对支持向量机的改进，和其它智能方法和支持向量机相结合。-Support Vector Machines few papers compare well. Instead of using a simple support vector machine to do system simulation, but the improved support vector machines, and other intelligent methods and support vector machine to combine.
Update : 2025-02-19 Size : 4.03mb Publisher : maoxuefei
[Multimedia program] imageProcessing DL : 0: In this project we have implemented a method for recognition of Printed Characters by extracting some features from the gray scale image using a Bank of Gabor Filters. In this project we initially segment the given word image into words and words into characters. We apply these characters to a bank of Gabor Filters .From the output of these Gabor Filters we extract features namely normalized Centroid ,Normalized area .We form a vector using these parameters and then compare these vectors with the reference vectors which were computed earlier using reference characters. Finally we print the character corresponding to the reference vector which is closest to the computed vector. - In this project we have implemented a method for recognition of Printed Characters by extracting some features from the gray scale image using a Bank of Gabor Filters. In this project we initially segment the given word image into words and words into characters. We apply these characters to a bank of Gabor Filters .From the output of these Gabor Filters we extract features namely normalized Centroid ,Normalized area .We form a vector using these parameters and then compare these vectors with the reference vectors which were computed earlier using reference characters. Finally we print the character corresponding to the reference vector which is closest to the computed vector.
Update : 2025-02-19 Size : 195kb Publisher : prasad
[SCM] Yang2008_A_study_of_inverse_short-time_fourier_tra DL : 0: We propose a new vector formulation of STFT. We derive a family of inverse STFT estimators and a least squares one. We discuss their relationship and compare their performance with respect to both additive and multiplicative modifications to STFT. The influence of window, overlap, and zero-padding are investigated as well.
Update : 2025-02-19 Size : 91kb Publisher : wan xinru
[Other] shiliangkongzhixuexibiji DL : 0: 矢量控制学习笔记，可以比较系统的学习矢量控制-Vector control study notes, you can compare the system of learning vector control
Update : 2025-02-19 Size : 563kb Publisher : 张莹
[Special Effects] gabor DL : 0: Gabor小波变换代码用于局部特征提取使用，又相当好的效果-Gabor texture descriptor have gained much attention for different aspects of computer vision and pattern recognition. Recently, on the rayleigh nature of Gabor filter outputs Rayleigh model Gabor texture descriptor is proposed. In this paper, we investigate the performance of these two Gabor texture descriptor in texture classification. We built a texture classification system based on BPNN, and use the corresponding feature vector from traditional Gabor texture descriptor or Rayleigh model one as input of BPNN. We use three datasets from the Brodatz album database. For all the three datasets, the original texture images are subdivided into non-overlapping samples of size 32 × 32. 50 of the total samples are used for training and the rest are used for testing. We compare the system training time and recognition accuracy between two Gabor texture descriptor. The experimental results show that, it takes more time when using Rayleigh model Gabor texture descriptor than tr
Update : 2025-02-19 Size : 16kb Publisher : 力量
[Software Engineering] ReseacrhOfAPPlieationOfEmbeddedSPeeehRecognitionon DL : 0: 本文首先介绍了目前语音识别的发展现状和主要手段，分析了语音识别中所采用的主要特征参数和比较前沿的研究方向，另外着重讲解了语音识别中最常用隐马尔可夫H(MM)模型，及应用广泛的矢量量化方法(VQ)。接着介绍了嵌入式平台，从软、硬件方面着重介绍了与语音识别相关部分的设计包括硬件及相关驱动程序设计，最后介绍了系统实现方法与测试结果。 -This paper describes the current status of the development of speech recognition and the main means of analysis used in speech recognition parameters and compare the main features cutting-edge research, while focused on explaining the most commonly used in speech recognition, Hidden Markov H (MM) model of , and the widely used vector quantization (VQ). Then introduced the embedded platform, from hardware and software aspects of highlighting the relevant parts of speech recognition with the design, including hardware and associated drivers for the design, finally introduced a system implementation method and test results.
Update : 2025-02-19 Size : 5.75mb Publisher : fff
[matlab] LMSandRLSusingMATLAB DL : 0: 用ＭＡＴＬＡＢ实现ＬＭＳ算法和ＲＬＳ算法权矢量的比较-LMS algorithm using MATLAB and the RLS algorithm to achieve the right to compare the vector
Update : 2025-02-19 Size : 3kb Publisher : 王冰冰
[Software Engineering] PWM DL : 0: 关于各种电机的PWM程序：工程1，采用C语言完成的主要功能有 1：用定时器1中断让LED闪烁； 2：用定时器2的比较单元产生一路PWM； 3：用EVB模块产生6路PWM；另外四个汇编程序依次分别是： SVPWM（软件法）程序，交流异步矢量程序，直流双极性双闭环可逆控制程序，和采样SPWM程序。 -PWM motor on a variety of programs: Project 1, using C language to complete the main functions 1: The Timer 1 interrupt enable LED blinking 2: Timer 2 compare unit produces all the way PWM 3: EVB module produces 6 Road PWM Followed by four other assembler are: SVPWM (software method) program, Induction vector process Double Closed Loop DC bipolar reversible control procedures, And sampling SPWM procedures.
Update : 2025-02-19 Size : 80kb Publisher : bullwell
[Other] 2_1 DL : 0: 创建子VI计算两个输入向量A和B内积。要求程序能够判断两个向量的元素个数是否相等，相等则计算内积，否则利用beep.vi报警并且弹出对话框提示。将VI计算结果和数学函数的计算结果做比较，仔细检查计算程序。-Calculated to create sub-VI A and B are two input vector inner product. Required procedures to determine the number of elements of two vectors are equal, equal, the calculation of the product, or use the alarm and pop-up dialog box prompts beep.vi. The VI calculations and mathematical functions to compare the results, carefully check the computer program.
Update : 2025-02-19 Size : 9kb Publisher : 小李
[Other] 2_12VI DL : 0: 创建子VI计算两个输入向量A和B内积。要求程序能够判断两个向量的元素个数是否相等，相等则计算内积，否则利用beep.vi报警并且弹出对话框提示。将VI计算结果和数学函数的计算结果做比较，仔细检查计算程序。-Calculated to create sub-VI A and B are two input vector inner product. Required procedures to determine the number of elements of two vectors are equal, equal, the calculation of the product, or use the alarm and pop-up dialog box prompts beep.vi. The VI calculations and mathematical functions to compare the results, carefully check the computer program.
Update : 2025-02-19 Size : 4kb Publisher : 小李
[Speech/Voice recognition/combine] 19_M-W-K-D-D_SPA2010_final DL : 0: This paper presents results of speaker recognition experiments using short Polish sentences. We developed and analyzed various vector quantization representations in order to first maximize identification effectiveness and second to compare VQ (vector quantization) and GMM (Gaussian mixture model) approaches. For the research and experiments we created and exploited database, containing specially prepared short speech sequences.
Update : 2025-02-19 Size : 252kb Publisher : Tomasz
[VHDL-FPGA-Verilog] Estimating19609112312005 DL : 0: Simple exercise that calculate the Taylor expansion of the exponential function. Input variables: degree N vector of evaluation points, x At each step plots the Taylor polynomial and compare with the real function function y=taylor_exp(N,x) printf("Order of the expansion: d ", N) size(x) y=ones(size(x)) plot(x,y,"r-",x,exp(x),"b-") legend("n=0,exp(x)") for n=1:N y+=(1/factorial(n))*(x.^n) plot(x,y,"r-",x,exp(x),"b-") xlabel("x") ylabel("f(x)") legend("approx","exp(x)") pause end endfunction - Simple exercise that calculate the Taylor expansion of the exponential function. Input variables: degree N vector of evaluation points, x At each step plots the Taylor polynomial and compare with the real function function y=taylor_exp(N,x) printf("Order of the expansion: d ", N) size(x) y=ones(size(x)) plot(x,y,"r-",x,exp(x),"b-") legend("n=0,exp(x)") for n=1:N y+=(1/factorial(n))*(x.^n) plot(x,y,"r-",x,exp(x),"b-") xlabel("x") ylabel("f(x)") legend("approx","exp(x)") pause end endfunction
Update : 2025-02-19 Size : 4kb Publisher : ali
[Embeded Linux] misc2 DL : 0: Simple exercise that calculate the Taylor expansion of the exponential function. Input variables: degree N vector of evaluation points, x At each step plots the Taylor polynomial and compare with the real function function y=taylor_exp(N,x) printf("Order of the expansion: d ", N) size(x) y=ones(size(x)) plot(x,y,"r-",x,exp(x),"b-") legend("n=0,exp(x)") for n=1:N y+=(1/factorial(n))*(x.^n) plot(x,y,"r-",x,exp(x),"b-") xlabel("x") ylabel("f(x)") legend("approx","exp(x)") pause end endfunction - Simple exercise that calculate the Taylor expansion of the exponential function. Input variables: degree N vector of evaluation points, x At each step plots the Taylor polynomial and compare with the real function function y=taylor_exp(N,x) printf("Order of the expansion: d ", N) size(x) y=ones(size(x)) plot(x,y,"r-",x,exp(x),"b-") legend("n=0,exp(x)") for n=1:N y+=(1/factorial(n))*(x.^n) plot(x,y,"r-",x,exp(x),"b-") xlabel("x") ylabel("f(x)") legend("approx","exp(x)") pause end endfunction
Update : 2025-02-19 Size : 9kb Publisher : ali
[AI-NN-PR] Matlab-svm-BP-compare DL : 0: 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。-SVM and BP neural networks, although non-linear regression can be used to do, but they are based on different theoretical basis, the return mechanism is not the same. SVM based on structural risk minimization theory, generally considered the generalization ability of neural networks than strong. To test this view, a support vector machine of this writing the general non-linear regression procedures and based on Matlab neural network toolbox of the BP neural network simulation module, the simulation results confirm that support vector machines do not only the generalization ability of non-linear regression in BP network, and neural networks to avoid the inherent shortcomings- the training results unstable.
Update : 2025-02-19 Size : 11kb Publisher :
[matlab] compare DL : 0: Fano编码函数说明： [next_P,next_index,code_num]=compare(current_P,current_index) 为比较函数，主要用于信源符号的分组 current_P为当前分组的信源的概率矢量-Fano coding Function Description: [next_P, next_index code_num] = the compare (current_P current_index) function, current_P packet for the source symbols for the probability of the current packet' s source vector
Update : 2025-02-19 Size : 1kb Publisher : 龙哥
[matlab] VECTOR DL : 0: 针对水声中的矢量圆阵，比较MUSIC算法与MVDR算法的分辨率和抗各向同性噪声的能力。-For underwater acoustic vector in circular array, the MUSIC algorithm with the ability to compare MVDR algorithm isotropic resolution and anti-noise.
Update : 2025-02-19 Size : 2kb Publisher : 跖草君
[Mathimatics-Numerical algorithms] compare DL : 0: 可以在容器vector中进行数据的排序及删除重复的数据。(You can sort the data in the container vector and delete the duplicate data.)
Update : 2025-02-19 Size : 5.1mb Publisher : 炎瓜瓜

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