The information in this book will show you how to create code that will run on all of the different Linux distributions and hardware types. It will allow you to understandhow Linux works and how to take advantage of its flexibility.
標簽： will information different create
上傳時間： 2017-02-12
上傳用戶：日光微瀾
This resource presents you with the skills you need to become the ultimate power user for ALL Linux distributions. The author provides detailed instructions for customization, optimization, troubleshooting, shortcuts, and using the best 3rd party tools.
上傳時間： 2014-01-15
上傳用戶：變形金剛
dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.
標簽： probabilistic distributed large-scale dynamical
上傳時間： 2014-01-12
上傳用戶：wangdean1101
Eclipse is the leading Integrated Development Environment (IDE) for Java, with a rich ecosystem of plug-ins and an open source framework that supports other languages and projects. You’ll fnd this reference card useful for getting started with Eclipse and exploring the breadth of its features. We rundown the Eclipse distributions and confguration options, then guide you through Views, Editors, and Perspec- tives in Workbench 101. We list the top shortcuts and toolbar actions for everyday development. And, we provide a guide to the best places for fnding plug-ins and getting involved with the Eclipse community.
標簽： Development Environment Integrated ecosystem
上傳時間： 2013-12-18
上傳用戶：wangchong
We (the Klimas family) are relative Linux newbies (with Linux since Summer 1998). We run RedHat mostly -> the solutions might not be directly applicable to other Linux distributions (although most of them probably will). Hope this helps, we try to be as practical as possible. Of course, we provide no warranty whatsoever!
標簽： Linux relative newbies Klimas
上傳時間： 2017-09-06
上傳用戶：gdgzhym
使用matlab實現gibbs抽樣，MCMC: The Gibbs Sampler 多元高斯分布的邊緣概率和條件概率 Marginal and conditional distributions of multivariate normal distribution
上傳時間： 2019-12-10
上傳用戶：real_
This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
標簽： Telecommunications Processing Signal for
上傳時間： 2020-06-01
上傳用戶：shancjb
