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Hoang T: Advanced Techniques for Optimal
Sizing of Analog Integrated Circuits

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

Quantum Computing, Machine Learning, and Bio-inspired Optimization

Learn to secure, build, implement, and test future blockchain applications Blockchain was originally introduced in the crypto-currency of Bitcoin in order to keep track of transactions over a peer-to-peer network to prevent the double-spending of bitcoin. This distributed ledger technology, however, has developed a reputation as a disruptive technology for the transfer of values and disintermediation. Despite this reputation, blockchain is, simply, at its core, a data structure-in this case, a cryptographic hash chain of blocks of transactions-continually replicated and maintained in a distributed manner by a peer-to-peer network of hosts. Together they decide through a specific consensus protocol, what transactions can be registered or added to the blockchain. Blockchain Application Security introduces the reader to the architecture and components of blockchain, as well as blockchain and bitcoin protocol, aimed at a basic level of understanding of the technical perspective. The book discusses the benefits, the vulnerabilities, and even the design flaws found in blockchain applications. It also teaches how to manage the risks of blockchain by deciding how digital assets, data confidentiality, and data availability should be protected from unauthorized access. Blockchain Application Security readers will also find: <ul> <li>An exploration of source code and core APIs relevant to blockchain</li> <li>A glossary of common terminology used for blockchain</li> <li>Real cases of blockchain applications to illustrate points</li></ul>Blockchain Application Security is an ideal resource for application architects, software developers, security auditors, and vulnerability testers of enterprise-based blockchain applications.

How to Design Secure and Attack Resilient Blockchain Applications

Blockchain Application Security

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Foundations, Technologies, and Prospects for the Future

Autonomous Vehicle Safety Solutions

Enhancing Reflective Teaching in Higher Education

Redefining Teaching and Learning

GenAI in Higher Education

Building a Program for Strategy and Oversight

Practical AI Governance

Psychophysics and Experimental Phenomenology of Pattern Cognition

Progress In Data Science

Ubiquitous Laplacian: An Introduction To Numerical Pdes With Applications In Data Science

Sybex Study Guide

The fastest and most effective way to learn the fundamentals of Azure cloud technology and prepare for the AZ-900 certification exam The newly updated second edition of Microsoft Certified Azure Fundamentals Study Guide is an accurate and up-to-date roadmap to the AZ-900 certification exam and a reliable, effective resource for everyone seeking to learn about or reinforce their knowledge of Azure services, pricing, security and compliance, resource management, and more. In the book, experienced Microsoft Principal Customer Success Account Manager and long-time author, Jim Boyce, covers every foundational Azure competency, including: <ul> <li>General and foundational cloud concepts</li> <li>Azure Core Services, including architecture, subscription and billing options, data services, and virtual machines</li> <li>Azure Core Networking Services, like virtual networks, load balancers, and VPN gateways</li> <li>Security, compliance, privacy, and trust features</li> <li>Azure Solutions, like AI and machine learning, serverless computing, and IoT</li> <li>Azure pricing, service levels, and lifecycles</li> <li>Azure Resource creation and management</li> </ul> Ideal for technical and non-technical professionals alike, this straightforward Study Guide draws on the proven Sybex model to explain core Azure features and concepts. It will get you ready to succeed on the widely used AZ-900 certification test and elevate your understanding of Azure cloud software, improving your day-to-day performance in any role that directly or indirectly intersects with the cloud platform. Inside the book <ul> <li>End-of-chapter review questions, with answers and explanations, you can use to measure your progress and help you focus on the areas where you need to improve</li> <li>Concise and intuitive explanations of core Azure services, functions, and features</li> <li>Complimentary access to the superior Sybex online learning environment and test bank, including practice test questions, digital flashcards, and a glossary of key terms</li> </ul> With authoritative, concise coverage of all relevant cloud concepts, Azure architecture and services, and management and governance features tested by the AZ-900 certification exam, this edition of Microsoft Certified Azure Fundamentals Study Guide is the fastest, most efficient way to get up to speed on foundational Azure cloud competencies.

Exam AZ-900

MC Azure Fundamentals Study Guide

Creativity in the Age of AI

What Art Is Now

Expert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB Surrogate Modeling and Optimization explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification. The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. The book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. Sample topics discussed in Surrogate Modeling and Optimization include: <ul> <li>Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space</li> <li>Criteria for constrained and unconstrained optimization and the most important optimization theories</li> <li>Various numerical algorithms for gradient-based optimization</li> <li>Gradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information</li> <li>Detailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise</li> <li>The combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy</li> <li>Neural network models, focusing on training uncertainty and its effect on prediction uncertainty</li> <li>Efficient global optimization using either polynomial response surfaces or Kriging surrofates</li> </ul> Surrogate Modeling and Optimization is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.

Theories, Applications, and Limitations

Surrogate Modeling and Optimization

Vital information on AI, ML, and 5G, and implementing these topics for cellular network design and optimization AI for Design and Optimization of Cellular Networks focuses on the intersection of cellular communications and AI, discussing current advances, key concepts, challenges, opportunities, and lifecycle management. Divided into two parts, the first part provides the background of AI and cellular communications, exploring the challenges that are associated with creation, deployment, operation, and maintenance of systems for the design and optimization of cellular networks. The second part explains the process of network planning, design, and optimization of cellular networks, including examples and use cases in each area. The second part also discusses the application of AI to energy saving, the challenges that operators face, and a glimpse of future trends for AI, cellular networks, and their related NDO (Network Design and Optimization) activities. The book also includes coverage of AI-powered design technologies, performance characterization of advanced antenna systems, propagation predictions, and more. With heavy contribution from industry professionals at Ericsson, AI for Design and Optimization of Cellular Networks includes detailed information on: Evolution of cellular communications from the early GSM days until today, with special emphasis on the arrival of 5G and its implications compared with LTESupervised, unsupervised, semi-supervised, and reinforcement learning with an intuitive focus, covering their distinctive characteristicsDifferent types of digital twins, their fundamental properties, their suitability for design and optimization of cellular networks, and using them together with AI techniquesTransition from traditional network performance management and operations towards an AI-driven combination of control loops An informed and timely exploration of key developments and challenges by those who work on the frontline, AI for Design and Optimization of Cellular Networks is an essential resource for professionals working in cellular communications and AI, providing a one-of-a-kind blend of practical and theoretical information.

AI for Design and Optimization of Cellular Networks

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

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