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.
標簽： Communication Principles Simulation Systems of
上傳時間： 2020-05-31
上傳用戶：shancjb
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
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.
標簽： Stochastic Geometry Networks Wireless Volume and
上傳時間： 2020-06-01
上傳用戶：shancjb
統(tǒng)計學習基礎:數(shù)據(jù)挖掘、推理與預測介紹了這些領域的一些重要概念。盡管應用的是統(tǒng)計學方法，但強調(diào)的是概念，而不是數(shù)學。許多例子附以彩圖。《統(tǒng)計學習基礎:數(shù)據(jù)挖掘、推理與預測》內(nèi)容廣泛，從有指導的學習（預測）到無指導的學習，應有盡有。包括神經(jīng)網(wǎng)絡、支持向量機、分類樹和提升等主題，是同類書籍中介紹得最全面的。計算和信息技術的飛速發(fā)展帶來了醫(yī)學、生物學、財經(jīng)和營銷等諸多領域的海量數(shù)據(jù)。理解這些數(shù)據(jù)是一種挑戰(zhàn)，這導致了統(tǒng)計學領域新工具的發(fā)展，并延伸到諸如數(shù)據(jù)挖掘、機器學習和生物信息學等新領域。許多工具都具有共同的基礎，但常常用不同的術語來表達。【內(nèi)容推薦】《統(tǒng)計學習基礎:數(shù)據(jù)挖掘、推理與預測》試圖將學習領域中許多重要的新思想?yún)R集在一起，并且在統(tǒng)計學的框架下解釋它們。隨著計算機和信息時代的到來，統(tǒng)計問題的規(guī)模和復雜性都有了急劇增加。數(shù)據(jù)存儲、組織和檢索領域的挑戰(zhàn)導致一個新領域“數(shù)據(jù)挖掘”的產(chǎn)生。數(shù)據(jù)挖掘是一個多學科交叉領域，涉及數(shù)據(jù)庫技術、機器學習、統(tǒng)計學、神經(jīng)網(wǎng)絡、模式識別、知識庫、信息提取、高性能計算等諸多領域，并在工業(yè)、商務、財經(jīng)、通信、醫(yī)療衛(wèi)生、生物工程、科學等眾多行業(yè)得到了廣泛的應用。【作者簡介】Trevor Hastie,Robert Tibshirani和Jerome Friedman都是斯坦福大學統(tǒng)計學教授，并在這個領域做出了杰出的貢獻。Hastie和Tibshirani提出了廣義和加法模型，并出版專著“Generalized Additive Models”。Hastie的主要研究領域為：非參數(shù)回歸和分類、統(tǒng)計計算以及生物信息學、醫(yī)學和工業(yè)的特殊數(shù)據(jù)挖掘問題。他提出主曲線和主曲面的概念，并用S-PLUS編寫了大量統(tǒng)計建模軟件。Tibshirani的主要研究領域為：應用統(tǒng)計學、生物統(tǒng)計學和機器學習。他提出了套索的概念，還是“An Introduction to the Bootstrap”一書的作者之一。Friedman是CART、MARS和投影尋蹤等數(shù)據(jù)挖掘工具的發(fā)明人之一。他不僅是位統(tǒng)計學家，而且是物理學家和計算機科學家，先后在物理學、計算機科學和統(tǒng)計學的一流雜志上表發(fā)論文80余篇。
標簽： 統(tǒng)計
上傳時間： 2022-05-04
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