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Noise refers to the various forms of unwanted signals that are considered loud, unpleasant or disruptive. It can be intended or unintended. Both sound and noise are vibrations through a medium such as air or water. They are distinguished when the brain receives and perceives a sound. It can be measured on the basis of the sound wave's amplitude and frequency. A sound level meter is used to measure sound in the air. In engineering, noise refers to undesirable residual electronic noise signals that generate acoustic noise. This kind of noise is usually measured using A-weighting or ITU-R 468 weighting techniques. This book elucidates the concepts and innovative models around prospective developments with respect to analysis and mitigation of sound. Most of the topics introduced herein cover new techniques and the applications of this field in a multidisciplinary manner. This book will help new researchers by foregrounding their knowledge in this branch.
The adverse impacts from excess noise on human health and daily activities have accelerated at an alarming rate over the last few decades. This has prompted significant research into noise attenuation and mitigation of these unwanted effects. This book is a collection of works from eminent researchers from around the world, who address the aforementioned issues. It provides the most up-to-date information on current work being conducted in the field of noise pollution and is of value to a wide range of students, engineers, scientists and industry consultants who wish to further understand current methodologies and emerging concepts.
(Cont.) Existing techniques have prohibitively long simulation times and are only suitable for final verification. Determination of substrate noise coupling during the design phase would be extremely beneficial to circuit designers who can incorporate the effect of the noise and re-design accordingly before fabrication. This would reduce the turn around time for circuits and prevent costly redesign. SNAT can be used at any stage of the design cycle to accurately predict (less than 12% error when compared to measurements) the substrate noise performance of any digital circuit with a large degree of computational efficiency.
Written By A Noted Authority In The Subject Area, This Book Is A Comprehensive Study Of The Theory And Practical Application Of Noise Reduction To Numerous Fields. It May Be Used As A Reference By Scientists And Engineers Or In A Senior-Undergraduate Or Graduate-Level Course. The First Six Chapters Deal With The Basic Mechanisms Of Sound Absorption By Which Acoustic Energy Is Converted Into Heat In Viscous And Thermal Boundaries In A Sound Field. The Second Part Covers Duct Attenuators With A Discussion Of How Their Performance Is Described And Measured. The Main Part Of Each Chapter Is Planned To Be Descriptive, And Contains Numerical Results That Should Be Of Direct Interest For Design Work. Mathematical Analysis Is Placed At The End Of The Chapters.
This technical book focuses on the acoustic analysis of weak points in a comprehensible manner. This reliably helps the designer and acoustician to understand the noise development of machines and systems and to develop suitable primary and secondary noise reduction measures. Selected application examples from practice support the user in developing his own ideas for the implementation of product-related noise reductions.
Physical Design for 3D Integrated Circuits reveals how to effectively and optimally design 3D integrated circuits (ICs). It also analyzes the design tools for 3D circuits while exploiting the benefits of 3D technology. The book begins by offering an overview of physical design challenges with respect to conventional 2D circuits, and then each chapter delivers an in-depth look at a specific physical design topic. This comprehensive reference: Contains extensive coverage of the physical design of 2.5D/3D ICs and monolithic 3D ICs Supplies state-of-the-art solutions for challenges unique to 3D circuit design Features contributions from renowned experts in their respective fields Physical Design for 3D Integrated Circuits provides a single, convenient source of cutting-edge information for those pursuing 2.5D/3D technology.
This book explores the challenges and presents best strategies for designing Through-Silicon Vias (TSVs) for 3D integrated circuits. It describes a novel technique to mitigate TSV-induced noise, the GND Plug, which is superior to others adapted from 2-D planar technologies, such as a backside ground plane and traditional substrate contacts. The book also investigates, in the form of a comparative study, the impact of TSV size and granularity, spacing of C4 connectors, off-chip power delivery network, shared and dedicated TSVs, and coaxial TSVs on the quality of power delivery in 3-D ICs. The authors provide detailed best design practices for designing 3-D power delivery networks. Since TSVs occupy silicon real-estate and impact device density, this book provides four iterative algorithms to minimize the number of TSVs in a power delivery network. Unlike other existing methods, these algorithms can be applied in early design stages when only functional block- level behaviors and a floorplan are available. Finally, the authors explore the use of Carbon Nanotubes for power grid design as a futuristic alternative to Copper.
Written by a noted authority in the subject area, this book is a comprehensive study of the theory and practical application of noise reduction to numerous fields. It may be used as a reference by scientists and engineers or in a senior-undergraduate or graduate-level course. The first six chapters deal with the basic mechanisms of sound absorption by which acoustic energy is converted into heat in viscous and thermal boundaries in a sound field. The second part covers duct attenuators with a discussion of how their performance is described and measured. The main part of each chapter is planned to be descriptive, and contains numerical results that should be of direct interest for design work. Mathematical analysis is placed at the end of the chapters.
Coding theory provides techniques which can ensure the error free transmission and storage of data. This data is often used as input to various algorithms that run on hardware. Any information about the algorithm can be useful in helping augment coding theory techniques to protect the data. This thesis studies the performance of two algorithms under noise and how coding theory techniques might be used to mitigate the effects of noise. The first part of the thesis discusses the effects of noise on the decoding hardware. In particular, the effect of noise due to radiation on low density parity code (LDPC) decoders is studied. The arrival and duration of errors induced by radiation events is also modeled. We accomplish this by proposing a multi-state radiation channel. This model accounts for the duration and dependence of the noise due to radiation. This model also subsumes some previously studied cases and allows for a more refined analysis. We introduce a corresponding LDPC combined Gallager B/E decoder and perform a density evolution analysis to characterize the idealized decoder performance. We also present results in the finite length case. The second part of the thesis discusses the effects of noisy feature data on the performance of the linear regression algorithm. Machine learning requires a large amount of data to train the learning algorithms. This data must be protected from noise when it is stored or transmitted. Until now, most techniques protect the data agnostic of the application for which the data is to be use. We study the effects of Gaussian noise in the feature data on the output of linear regression. We present coding theoretic techniques to reduce the effects of Gaussian noise on the output of the regression algorithm. We use the expected square loss to measure the effects of noise on the output of regression using repetition coding. We present a technique to optimally allocate units of redundancy to different features to minimize the expected loss given the regression coefficients. We also use submodular optimization to jointly optimize the regression parameters and redundancy allocation at the training stage of regression algorithm. We demonstrate the advantage of our technique in optimizing the redundancy allocation for protecting features.