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GAUSSIAN-Pepper-Noise-Generator

  • Hardware random number generators attempt to extract randomness directly from complex physical syst

    Hardware random number generators attempt to extract randomness directly from complex physical systems. In this way they create random outputs without requiring any seed inputs. In this paper we describe how to use Physical Random Functions (or Physical Unclonable Functions, PUFs) to create a candidate hardware random number generator.

    标签: generators randomness Hardware directly

    上传时间: 2013-12-20

    上传用户:缥缈

  • 卡耐基.梅隆大学的牛发写的关于孤立点和数据清洗的文章

    卡耐基.梅隆大学的牛发写的关于孤立点和数据清洗的文章,全英文,2003年完成,Probabilistic Noise Identification and Data Cleaning,Real world data is never as perfect as we would like it to be and can often suffer from corruptions that may impact interpretations of the data, models created from the data, and decisions made based on the data. One approach to this problem is to identify and remove records that contain corruptions. Unfortunately, if only certain fields in a record have been corrupted then usable, uncorrupted data will be lost. In this paper we present LENS, an approach for identifying corrupted fields and using the remaining noncorrupted fields for subsequent modeling and analysis.

    标签: 大学 数据

    上传时间: 2017-08-29

    上传用户:thinode

  • SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussi

    SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation, SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.

    标签: SiftGPU implementation processes parallely

    上传时间: 2013-11-27

    上传用户:zhangjinzj

  • Implements mixture of binary (logistic) PCAs where pixels are modeled using Bernoulli distributions

    Implements mixture of binary (logistic) PCAs where pixels are modeled using Bernoulli distributions instead of Gaussian. The images do not need to be aligned.

    标签: distributions Implements Bernoulli logistic

    上传时间: 2013-12-26

    上传用户:xiaoyunyun

  • EM算法(英文)A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaus

    EM算法(英文)A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models

    标签: Application Estimation Algorithm Parameter

    上传时间: 2017-09-27

    上传用户:dianxin61

  • tas3204

    The TAS3204 is a highly-integrated audio system-on-chip (SOC) consisting of a fully-programmable, 48-bit digital audio processor, a 3:1 stereo analog input MUX, four ADCs, four DACs, and other analog functionality. The TAS3204 is programmable with the graphical PurePath Studio™ suite of DSP code development software. PurePath Studio is a highly intuitive, drag-and-drop environment that minimizes software development effort while allowing the end user to utilize the power and flexibility of the TAS3204’s digital audio processing core. TAS3204 processing capability includes speaker equalization and crossover, volume/bass/treble control, signal mixing/MUXing/splitting, delay compensation, dynamic range compression, and many other basic audio functions. Audio functions such as matrix decoding, stereo widening, surround sound virtualization and psychoacoustic bass boost are also available with either third-party or TI royalty-free algorithms. The TAS3204 contains a custom-designed, fully-programmable 135-MHz, 48-bit digital audio processor. A 76-bit accumulator ensures that the high precision necessary for quality digital audio is maintained during arithmetic operations. Four differential 102 dB DNR ADCs and four differential 105 dB DNR DACs ensure that high quality audio is maintained through the whole signal chain as well as increasing robustness against noise sources such as TDMA interference. The TAS3204 is composed of eight functional blocks: Clocking System Digital Audio Interface Analog Audio Interface Power supply Clocks, digital PLL I2C control interface 8051 MCUcontroller Audio DSP – digital audio processing 特性 Digital Audio Processor Fully Programmable With the Graphical, Drag-and-Drop PurePath Studio™ Software Development Environment 135-MHz Operation 48-Bit Data Path With 76-Bit Accumulator Hardware Single-Cycle Multiplier (28 × 48)

    标签: 3204 tas

    上传时间: 2016-05-06

    上传用户:fagong

  • dspc语言程序实现iir滤波器

    设计中使用的信号为 信息信号: signal=sin(2*pi*sl*n*T) 高频噪声: noise =0.5*sin(2*pi*ns1*n*T) 混合信号: x=(signal+noise) 其中sl=1000Hz,ns1=4500Hz,T=1/10000。混合信号波形为滤波器输入信号波形,信息信号波形为输出信号波形,滤波器的效果为滤除两个高频噪声。

    标签: dspc iir 语言程序 滤波器

    上传时间: 2016-05-08

    上传用户:梅浩梅浩

  • generator

    由于MyBatis属于一种半自动的ORM框架,所以主要的工作将是书写Mapping映射文件,但是由于手写映射文件很容易出错,所以查资料发现有现成的工具可以自动生成底层模型类、Dao接口类甚至Mapping映射文件。

    标签: generator

    上传时间: 2016-05-27

    上传用户:tt357788906

  • matlab-均值滤波.中值滤波

    I=imread('fig1.jpg');%从D盘名为myimages的文件夹中读取。格式为jpg的图像文件chost J=imnoise(I,'salt & pepper',0.02);%给图像加入均值为0,方差为0.02的淑盐噪声 subplot(2,4,1); imshow(I); title('原始图像'); subplot(2,4,2); imshow(J); title('加入椒盐噪声之后的图像'); %h=ones(3,3)/9; %产生3 × 3的全1数组 %B=conv2(J,h); %卷积运算 %采用MATLAB中的函数对噪声干扰的图像进行滤波 Q=wiener2(J,[3 3]); %对加噪图像进行二维自适应维纳滤波 P=filter2(fspecial('average',3),J)/255; %均值滤波模板尺寸为3 K1=medfilt2(J,[3 3]); %进行3 × 3模板的中值滤波 K2= medfilt2(J,[5 5]); %进行5 × 5模板的中值滤波 K3= medfilt2(J,[7 7]); %进行7 × 7模板的中值滤波 K4= medfilt2(J,[9 9]); %进行9 × 9模板的中值滤波 %显示滤波后的图像及标题 subplot(2,4,3); imshow(Q); title('3 × 3模板维纳滤波后的图像'); subplot(2,4,4); imshow(P); title('3 × 3模板均值滤波后的图像'); subplot(2,4,5); imshow(K1); title('3 × 3模板的中值滤波的图像'); subplot(2,4,6); imshow(K2); title('5 × 5模板的中值滤波的图像'); subplot(2,4, 7); imshow(K3); title('7 × 7模板的中值滤波的图像'); subplot(2,4,8); imshow(K4); title('9 × 9模板的中值滤波的图像');

    标签: matlab 均值滤波 中值滤波

    上传时间: 2016-06-02

    上传用户:wxcr_1

  • Microphone Arrays : A Tutorial

    This report presents a tutorial of fundamental array processing and beamforming theory relevant to microphone array speech processing. A microphone array consists of multiple microphones placed at different spatial locations. Built upon a knowledge of sound propagation principles, the multiple inputs can be manipulated to enhance or attenuate signals emanating from particular directions. In this way, microphone arrays provide a means of enhancing a desired signal in the presence of corrupting noise sources. Moreover, this enhancement is based purely on knowledge of the source location, and so microphone array techniques are applicable to a wide variety of noise types. Microphone arrays have great potential in practical applications of speech processing, due to their ability to provide both noise robustness and hands-free signal acquisition.

    标签: Microphone array Tutorial Array Signal Processing

    上传时间: 2016-06-12

    上传用户:halias