الصفحة 25
الصفحة 25
Developments in Language Theory ; 11th International Conference, DLT 2007, Turku, Finland, July 3-6, 2007, Proceedings
It addresses all important issues in language theory including grammars, acceptors and transducers for words, trees and graphs; algebraic theories of automata; relationships to cryptography, concurrency, complexity theory and logic; bioinspired computing, and quantum computing.
Development and evaluation of setup strategies in printed circuit board assembly
The last decade has seen a rapid extension of electronic control devices for various types of technical products. In modern electronics manufacturing, highly automated assembly systems are used to mount the electronic components onto the printed circuit boards (PCBs). Maintaining high production flexibility in order to meet the desired product variety and, at the same time, achieving high utilization rates of the capital-intensive production equipment can only be achieved by applying highly sophisticated planning and control strategies for the operation of modern placement machines. Ihsan Onur Yilmaz develops a novel group setup strategy which integrates multiple problems of the PCB assembly, especially in a medium-variety production environment. At the core of his principle approach are the identification of similarities between different types of PCBs and the generation of PCB clusters upon which group setup strategies are based. The developed setup strategies are also innovative in the sense that they integrate the optimization of detailed machine operations. This integration has not been achieved in the classical approaches which primarily rely on statistical clustering techniques.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Designing green landscapes
This book presents the latest thinking in adaptive management for forest ecosystems. Based on the ‘multiple path’ principle, this approach links species choice and silvicultural methods with changing demands and changing environmental conditions, to ensure continuous adaptation, often several times within the lifetime of a tree. The ‘multiple path’ principle at the core of this approach represents a robust theoretical framework for designing forested landscapes. It provides a logical basis both for coordinating spatial objectives and for integrating varied forms of expertise; it limits planning horizons to realistic timeframes; and it allows for forecasts based on current real attributes of spatially explicit land parcels. This is in stark contrast with traditional forestry practices which simply assess the forest resource at regular time intervals and prescribe standard management schedules for specific forest types.
Design, Automation, and Test in Europe : The Most Influential Papers of 10 Years Date
The Design, Automation and Test in Europe (DATE) conference celebrated in 2007 its tenth anniversary. This provides an excellent historical overview of the evolution of a domain that contributed substantially to the growth and competitiveness of the circuit electronics and systems industry.
Design of Systems on a Chip : Design and Test
Design of Systems on a Chip: Design&Test is the second of two volumes addressing the design challenges associated with new generations of the semiconductor technology. The various chapters are the compilations of tutorials presented at workshops in the recent years by prominent authors from all over the world. Technology, productivity and quality are the main aspects under consideration to establish the major requirements for the design and test of upcoming systems on a chip. In particular this second book include contributions on three different, but complementary axes: core design, computer-aided design tools and test methods. A collection of chapters deal with the heterogeneity aspect of core designs, showing the diversity of parts that may share the same substrate in a state-of-the-art system on a chip.
Design Automation Methods and Tools for Microfluidics-Based Biochips
Design Automation Methods and Tools for Microfluidics-Based Biochips deals with all aspects of design automation for microfluidics-based biochips. Experts have contributed chapters on many aspects of biochip design automation. Topics covered include: device modeling; numerical methods and simulation tools; physical design and module placement.
Design and Optimization of Passive UHF RFID Systems
Radio Frequency Identification (RFID) is an automatic identification method, relying on storing and remotely retrieving data using devices called RFID tags or transponders. An RFID tag is an object that can be attached to or incorporated into a product, animal, or person for the purpose of identification using radio waves. Chip-based RFID tags contain silicon chips and antennas. Active tags require an internal power source, while passive tags do not.
Deployment and operation of complex software in heterogeneous execution environments : The SODALITE approach
This book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring.
Deploying .NET Applications : Learning MSBuild and ClickOnce
Whether building a console application, a web service, or a smart client, you eventually need to distribute your finished work. Deploying .NET Applications is a complete guide to delivering applications built with .NET. Packed with hands-on guidance, practical examples, and war stories from the authors many experiences with deployment scenarios, this book provides everything you need to know. The book begins by introducing the deployment problem, then examines why deployment is an engineering problem for organizations. Subsequent chapters provide detail about deploying each type of application, then discuss automated deployments.
Dental Image Analysis for Disease Diagnosis
This book provides an overview of computational approaches to medical image examination and analysis in oral radiology utilizing dental radiograph to detect and diagnose dental caries in cases of decayed teeth. The book also presents a novel multiphase level set method for automatic segmentation of dental radiographs.
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 pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
Deep learning and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
Declarative programming for knowledge management ; 16th International conference on applications of declarative programming and knowledge management, INAP 2005, Fukuoka, Japan, October 22-24, 2005. Revised Selected Papers
Presents a selection of papers presented at the 16th Inter- tional Conference on Applications of Declarative Programming and Knowledge Management, INAP 2005,held in October 2005 at Waseda University, Fukuoka, Japan. These papers re?ect a snapshot of ongoing research and current app- cations in knowledge management and declarative programming.
Decision Procedures : An Algorithmic Point of View
Concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research.
Dataset Studio
Data is the new oil, which means that AI engineers can face difficulties in locating suitable datasets. Dataset Studio is a comprehensive platform designed to support AI engineers in the creation and optimization of datasets. This project offers a diverse range of services that encompass data collection, data augmentation, and data classification. As a result, this software empowers engineers by automatically generating structured data through the utilization of advanced tools and AI techniques. By automating the laborious tasks of manual data collection and extraction, Dataset Studio effectively streamlines the workflow for AI engineers, enabling them to save valuable time and focus on the more intricate aspects of dataset development and refinement.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
