This book is a result of the recent rapid advances in two related technologies: com- munications and computers. Over the past few decades, communication systems have increased in complexity to the point where system design and performance analysis can no longer be conducted without a significant level of computer sup- port. Many of the communication systems of fifty years ago were either power or noise limited. A significant degrading effect in many of these systems was thermal noise, which was modeled using the additive Gaussian noise channel.
标签： Communication Principles Simulation Systems of
上传时间： 2020-05-31
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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.
标签： Telecommunications Processing Signal for
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Part I provides a compact survey on classical stochastic geometry models. The basic models defined in this part will be used and extended throughout the whole monograph, and in particular to SINR based models. Note however that these classical stochastic models can be used in a variety of contexts which go far beyond the modeling of wireless networks. Chapter 1 reviews the definition and basic properties of Poisson point processes in Euclidean space. We review key operations on Poisson point processes (thinning, superposition, displacement) as well as key formulas like Campbell’s formula. Chapter 2 is focused on properties of the spatial shot-noise process: its continuity properties, its Laplace transform, its moments etc. Both additive and max shot-noise processes are studied. Chapter 3 bears on coverage processes, and in particular on the Boolean model. Its basic coverage characteristics are reviewed. We also give a brief account of its percolation properties. Chapter 4 studies random tessellations; the main focus is on Poisson–Voronoi tessellations and cells. We also discuss various random objects associated with bivariate point processes such as the set of points of the first point process that fall in a Voronoi cell w.r.t. the second point process.
标签： Stochastic Geometry Networks Wireless Volume and
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统计学习基础:数据挖掘、推理与预测介绍了这些领域的一些重要概念。尽管应用的是统计学方法，但强调的是概念，而不是数学。许多例子附以彩图。《统计学习基础:数据挖掘、推理与预测》内容广泛，从有指导的学习（预测）到无指导的学习，应有尽有。包括神经网络、支持向量机、分类树和提升等主题，是同类书籍中介绍得最全面的。计算和信息技术的飞速发展带来了医学、生物学、财经和营销等诸多领域的海量数据。理解这些数据是一种挑战，这导致了统计学领域新工具的发展，并延伸到诸如数据挖掘、机器学习和生物信息学等新领域。许多工具都具有共同的基础，但常常用不同的术语来表达。【内容推荐】《统计学习基础:数据挖掘、推理与预测》试图将学习领域中许多重要的新思想汇集在一起，并且在统计学的框架下解释它们。随着计算机和信息时代的到来，统计问题的规模和复杂性都有了急剧增加。数据存储、组织和检索领域的挑战导致一个新领域“数据挖掘”的产生。数据挖掘是一个多学科交叉领域，涉及数据库技术、机器学习、统计学、神经网络、模式识别、知识库、信息提取、高性能计算等诸多领域，并在工业、商务、财经、通信、医疗卫生、生物工程、科学等众多行业得到了广泛的应用。【作者简介】Trevor Hastie,Robert Tibshirani和Jerome Friedman都是斯坦福大学统计学教授，并在这个领域做出了杰出的贡献。Hastie和Tibshirani提出了广义和加法模型，并出版专著“Generalized additive Models”。Hastie的主要研究领域为：非参数回归和分类、统计计算以及生物信息学、医学和工业的特殊数据挖掘问题。他提出主曲线和主曲面的概念，并用S-PLUS编写了大量统计建模软件。Tibshirani的主要研究领域为：应用统计学、生物统计学和机器学习。他提出了套索的概念，还是“An Introduction to the Bootstrap”一书的作者之一。Friedman是CART、MARS和投影寻踪等数据挖掘工具的发明人之一。他不仅是位统计学家，而且是物理学家和计算机科学家，先后在物理学、计算机科学和统计学的一流杂志上表发论文80余篇。
标签： 统计
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