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Defect-Oriented Testing for Nano-Metric CMOS VLSI Circuits
Failures of nano-metric technologies owing to defects and shrinking process tolerances give rise to significant challenges for IC testing. As the variation of fundamental parameters such as channel length, threshold voltage, thin oxide thickness and interconnect dimensions goes well beyond acceptable limits, new test methodologies and a deeper insight into the physics of defect-fault mappings are needed. In Defect-Oriented Testing for Nano-Metric CMOS VLSI Circuits state of the art of defect-oriented testing is presented from both a theoretical approach as well as from a practical point of view. Step-by-step handling of defect modeling, defect-oriented testing, yield modeling and its usage in common economics practices enables deeper understanding of concepts.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Deep learning and computer vision in remote sensing-II
Computer vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing.
Decrypted Secrets : Methods and Maxims of Cryptology
Cryptology, for millennia a "secret science", is rapidly gaining in practical importance for the protection of communication channels, databases, and software. Beside its role in computerized information systems (public key systems), more and more applications within computer systems and networks are appearing, which also extend to access rights and source file protection. The first part of this book treats secret codes and their uses - cryptography. The second part deals with the process of covertly decrypting a secret code - cryptanaly-sis - where in particular advice on assessing methods is given. The book presupposes only elementary mathematical knowledge.
Decomposition Techniques in Mathematical Programming : Engineering and Science Applications
This textbook for students and practitioners presents a practical approach to decomposition techniques in optimization. It provides an appropriate blend of theoretical background and practical applications in engineering and science, which makes the book interesting for practitioners, as well as engineering, operations research and applied economics graduate and postgraduate students. "Decomposition Techniques in Mathematical Programming" is based on clarifying, illustrative and computational examples and applications from electrical, mechanical, energy and civil engineering as well as applied mathematics and economics. It addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides rigorous decomposition algorithms as well as heuristic ones.
Decision Making under Deep Uncertainty : From Theory to Practice
Focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them.
Database performance at scale: a practical guide
Optimizing database performance at the scale required for today’s data-intensive applications often requires more than performance tuning and scaling out. This book shares commonly overlooked considerations, pitfalls, and opportunities that have helped many teams break through database performance plateaus. It’s neither a definitive guide to distributed databases nor a beginner’s resource. Rather, it’s a look at the many different factors that impact performance, and our top field-tested recommendations for navigating them. Chapter 1 provides two (fun and fanciful) tales that surface some of the many roadblocks you might face and highlight the range of strategies for navigating around them.
Data visualization and analysis in second language research
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages.
Data structure and algorithms using C++ : A practical implementation
Intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it's designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output. Keeping in mind the learner’s sentiments and requirements, the exemplary programs are narrated with a simple approach so that it can lead to creation of good programs that not only executes properly to give the output, but also enables the learners to incorporate programming skills in them. The style of writing a program using a programming language is also emphasized by introducing the inclusion of comments wherever necessary to encourage writing more readable and well commented programs. As practice makes perfect, each chapter is also enriched with practice exercise questions so as to build the confidence of writing the programs for learners.
Data parallel C++programming accelerated systems using C++ and SYCL
Full of practical advice, detailed explanations, and code examples to illustrate key topics. SYCL enables access to parallel resources in modern accelerated heterogeneous systems. Now, a single C++ application can use any combination of devices–including GPUs, CPUs, FPGAs, and ASICs–that are suitable to the problems at hand. This book teaches data-parallel programming using C++ with SYCL and walks through everything needed to program accelerated systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL. Later chapters cover advanced topics, including error handling, hardware-specific programming, communication and synchronization, and memory model considerations.
Data mining with computational intelligence
Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.
Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples—many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.
Data Mining : Foundations and Practice
This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms.
Data Integration in the Life Sciences ; 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007, Proceedings
it cover a wide spectrum of theoretical and practical issues including scienti?c work?ows, - notation in data integration, mapping and matching techniques, and modeling of life science data. It presenting research on new models, methods, or algorithms and 6 papers presenting imp- mentation of systems or experience with systems in practice.
Data Complexity in Pattern Recognition
Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.
Data and applications security XX ; 20th Annual IFIP WG 11.3 Working Conference on data and applications security, Sophia Antipolis, France, July 31-August 2, 2006, Proceedings
For 20 years, the IFIP WG 11. 3 Working Conference on Data and Appli- tions Security (DBSEC) has been a major forum for presenting originalresearch results, practical experiences, and innovative ideas in data and applications - curity.Like the previous conference, the 20th DBSEC has proved to be up to this challenge. DBSEC 2006 received 56 submissions, out of which the program committee selected22 high-qualitypaperscoveringanumber of diverseresearchtopicssuch as access control, privacy, and identity management.
Data and applications security XIX ; 19th Annual IFIP WG 11.3 working conference on data and applications security, Storrs, CT, USA, August 7-10, 2005, Proceedings
Constitutes the refereed proceedings of the 19th Annual Working Conference on Data and Applications Security held in Storrs, CT, USA, in August 2005. The papers present theory, technique, applications, and practical experience of data and application security with topics like cryptography, privacy, security planning and administration, and more.
Data and applications security and privacy XXXIV ; 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Regensburg, Germany, June 25–26, 2020, Proceedings
This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy.
Data Algorithms with Spark
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns
Cytogenetics and molecular cytogenetics
Genomic technologies provide the means of diagnosis and management of many human diseases. Without insights from cytogenetics, correct interpretation of modern high-throughput results is difficult, if not impossible. This book summarizes applications of cytogenetics and molecular cytogenetics for students, clinicians and researchers in genetics, genomics and diagnostics. The book combines the state-of-the-art knowledge and practical expertise from leading researchers and clinicians and provides a comprehensive overview of current medical and research applications of many of these technologies.
