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.
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.
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.
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.
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!
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.
