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GAUSSIAN

GAUSSIAN是一個(gè)功能強(qiáng)大的量子化學(xué)綜合軟件包。其可執(zhí)行程序可在不同型號(hào)的大型計(jì)算機(jī)，超級(jí)計(jì)算機(jī)，工作站和個(gè)人計(jì)算機(jī)上運(yùn)行，并相應(yīng)有不同的版本。高斯功能：過渡態(tài)能量和結(jié)構(gòu)、鍵和反應(yīng)能量、分子軌道、原子電荷和電勢、振動(dòng)頻率、紅外和拉曼光譜、核磁性質(zhì)、極化率和超極化率、熱力學(xué)性質(zhì)、反應(yīng)路徑，計(jì)算可以對(duì)體系的基態(tài)或激發(fā)態(tài)執(zhí)行。可以預(yù)測周期體系的能量，結(jié)構(gòu)和分子軌道。因此，GAUSSIAN可以作為功能強(qiáng)大的工具，用于研究許多化學(xué)領(lǐng)域的課題，例如取代基的影響，化學(xué)反應(yīng)機(jī)理，勢能曲面和激發(fā)能等等。常常與gaussview連用。

  • 介紹回歸問題中高斯過程的應(yīng)用

    介紹回歸問題中高斯過程的應(yīng)用，C. E. Rasmussen & C. K. I. Williams, GAUSSIAN Processes for Machine Learning,

    標(biāo)簽： 回歸 高斯 過程

    上傳時(shí)間： 2017-07-25

    上傳用戶：skfreeman

  • The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic syst

    The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-GAUSSIAN control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-GAUSSIAN controller are solutions to what probably are the most fundamental problems in control theory.

    標(biāo)簽： filter efficient estimates recursive

    上傳時(shí)間： 2017-08-06

    上傳用戶：風(fēng)之驕子

  • Consider a BPSK and a QPSK system for the following two cases: 1) The probability that the symbol 1

    Consider a BPSK and a QPSK system for the following two cases: 1) The probability that the symbol 1 is sent and the probability that the symbol 0 is sent are all the same. 2) The probability that the symbol 1 is sent is two times than the probability that the symbol 0 is sent. Assume that the noise is GAUSSIAN distributed with mean=0 and  2 = 1.

    標(biāo)簽： probability following the Consider

    上傳時(shí)間： 2017-08-15

    上傳用戶：凌云御清風(fēng)

  • 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.

    標(biāo)簽： SiftGPU implementation processes parallely

    上傳時(shí)間： 2013-11-27

    上傳用戶：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.

    標(biāo)簽： distributions Implements Bernoulli logistic

    上傳時(shí)間： 2013-12-26

    上傳用戶：xiaoyunyun

  • This is an analog signal communication simulator, usign frequency modulation. It is designed in MATL

    This is an analog signal communication simulator, usign frequency modulation. It is designed in MATLAB-Simulink. The communications channel beetween the transmitter and the reciever is supposed to be affected by additive white GAUSSIAN noise.

    標(biāo)簽： communication modulation frequency simulator

    上傳時(shí)間： 2013-12-25

    上傳用戶：王楚楚

  • 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

    標(biāo)簽： Application Estimation Algorithm Parameter

    上傳時(shí)間： 2017-09-27

    上傳用戶：dianxin61

  • EDA分布估計(jì)算法經(jīng)典論文

    壓縮包中有5篇論文，分別為《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》這是一篇博士論文，較為詳細(xì)的介紹了各種EDA算法；《Anisotropic adaptive variance scaling for GAUSSIAN estimation of distribution algorithm》《Enhancing GAUSSIAN Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based GAUSSIAN Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing GAUSSIAN Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二階混合矩的高斯分布估計(jì)算法》介紹了一些基于EDA的創(chuàng)新算法。

    標(biāo)簽： EDA 分布估計(jì)算法 論文

    上傳時(shí)間： 2020-05-25

    上傳用戶：duwenhao

  • A Foundation in Digital Communication

    Without conceding a blemish in the first edition, I think I had best come clean and admit that I embarked on a second edition largely to adopt a more geometric approach to the detection of signals in white GAUSSIAN noise. Equally rigorous, yet more intuitive, this approach is not only student-friendly, but also extends more easily to the detection problem with random parameters and to the radar problem

    標(biāo)簽： Communication Foundation Digital in

    上傳時(shí)間： 2020-05-26

    上傳用戶：shancjb

  • Adaptive Antennas and Receivers

    Homogeneous Partitioning of the Surveillance Volume discusses the implementation of the first of three sequentially complementary approaches for increasing the probability of target detection within at least some of the cells of the surveillance volume for a spatially nonGAUSSIAN or GAUSSIAN “noise” environment that is temporally GAUSSIAN. This approach, identified in the Preface as Approach A, partitions the surveillance volume into homogeneous contiguous subdivisions.

    標(biāo)簽： Receivers Adaptive Antennas and

    上傳時(shí)間： 2020-05-26

    上傳用戶：shancjb

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