The increasing popularity of object-oriented programming languages, design methods, database managers, and other technologies has challenged software development project managers with a new set of rules. Project managers need to reexamine their standard methods for planning and controlling projects to adapt to the new rules for development. This book combines the perspectives of project management and systems theory to provide a unique look at managing object-oriented projects. Software engineers and project managers working with object technology will obtain essential tools for managing any software project and will learn how to apply those tools specifically to managing object-oriented software projects.
This guidebook provides an integrated, cohesive system of project management that aligns directly with the technology it manages. Organized into self-contained sections, this book permits you to access the project management objects you need. In addition, it provides examples of what to do and what not to do using real-life examples from the author's experience.
+ Provides the methods necessary to productively manage object-oriented software development
+ Contains real-world examples that illustrate how all of the different objects work
+ Consists of self-contained sections that can be referred to when the reader needs information regarding a specific aspect of project management

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Whether building a relational, object-relational, or object-oriented database, database developers are increasingly relying on an object-oriented design approach as the best way to meet user needs and performance criteria. This book teaches you how to use the Unified Modeling Language-the official standard of the Object Management Group-to develop and implement the best possible design for your database.

Inside, the author leads you step by step through the design process, from requirements analysis to schema generation. You'll learn to express stakeholder needs in UML use cases and actor diagrams, to translate UML entities into database components, and to transform the resulting design into relational, object-relational, and object-oriented schemas for all major DBMS products.
* Teaches you everything you need to know to design, build, and test databases using an OO model.
* Shows you how to use UML, the accepted standard for database design according to OO principles.
* Explains how to transform your design into a conceptual schema for relational, object-relational, and object-oriented DBMSs.
* Offers practical examples of design for Oracle, SQL Server, Sybase, Informix, Object Design, POET, and other database management systems.
* Focuses heavily on re-using design patterns for maximum productivity and teaches you how to certify completed designs for re-use.

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

Diese Hardcover-Ausgabe ist Teil der TREDITION CLASSICS. Der Verlag tredition aus Hamburg veroffentlicht in der Buchreihe TREDITION CLASSICS Werke aus mehr als zwei Jahrtausenden. Diese waren zu einem Grossteil vergriffen oder nur noch antiquarisch erhaltlich. Mit TREDITION CLASSICS verfolgt tredition das Ziel, tausende Klassiker der Weltliteratur verschiedener Sprachen wieder als gedruckte Bucher zu verlegen - und das weltweit Die Buchreihe dient zur Bewahrung der Literatur und Forderung der Kultur. Sie tragt so dazu bei, dass viele tausend Werke nicht in Vergessenheit geraten

For 'Recent Progress in Brain and Cognitive Engineering' Brain and Cognitive Engineering is a converging study field to derive a better understanding of cognitive information processing in the human brain, to develop "human-like" and neuromorphic artificial intelligent systems and to help predict and analyze brain-related diseases. The key concept of Brain and Cognitive Engineering is to understand the Brain, to interface the Brain, and to engineer the Brain. It could help us to understand the structure and the key principles of high-order information processing on how the brain works, to develop interface technologies between a brain and external devices and to develop artificial systems that can ultimately mimic human brain functions. The convergence of behavioral, neuroscience and engineering research could lead us to advance health informatics and personal learning, to enhance virtual reality and healthcare systems, and to "reverse engineer" some brain functions and build cognitive robots. In this book, four different recent research directions are presented: Non-invasive Brain-Computer Interfaces, Cognitive- and Neural-rehabilitation Engineering, Big Data Neurocomputing, Early Diagnosis and Prediction of Neural Diseases. We cover numerous topics ranging from smart vehicles and online EEG analysis, neuroimaging for Brain-Computer Interfaces, memory implantation and rehabilitation, big data computing in cultural aspects and cybernetics to brain disorder detection. Hopefully this will provide a valuable reference for researchers in medicine, biomedical engineering, in industry and academia for their further investigations and be inspiring to those who seek the foundations to improve techniques and understanding of the Brain and Cognitive Engineering research field.

A high-ranking official in the Imperial War Office in Vienna, Raphael Georg Kiesewetter (1773-1850) is better known for his musicological activities. An accomplished amateur musician, he studied with Albrechtsberger, hosted private concerts of early music, and was closely involved in the affairs of Vienna's Society of the Friends of Music. His important collection of scores is now in the Austrian National Library. He also wrote a number of books and articles, including a pioneering study of Arabic music which was the first to use original sources, owing to the assistance of orientalist Joseph von Hammer-Purgstall. Originally published in German in 1834 and reissued here in its 1848 English translation, the present work is considered Kiesewetter's most significant and remains accessible to the general reader. Based on an evolutionary approach influenced by the Enlightenment, the book presents seventeen epochs which are named after their most characteristic composers.

The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

This book constitutes the refereed proceedings of the 28th Symposium of the German Association for Pattern Recognition, DAGM 2006. The book presents 32 revised full papers and 44 revised poster papers together with 5 invited papers. Topical sections include image filtering, restoration and segmentation, shape analysis and representation, recognition, categorization and detection, computer vision and image retrieval, machine learning and statistical data analysis, biomedical data analysis, and more.

This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.

The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intelligent" machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Der in den letzten Jahren vollzogene Dbergang yom Verkiiufer- zum Kaufermarkt zwingt viele Unternehmen des Maschinen- und Anlagenbaus ihre strategische Ausrichtung zu iiberdenken. Indikatoren des Wandlungsprozesses sind wachsende Forderungen der Kunden nach kundenspezifischen Liisungen, komplexen Produk- ten und hoher Qualitat bei kurzen Lieferzeiten, hoher Termintreue und niedrigen Preisen /l/. Um in dem geanderten Umfeld bestehen zu kiinnen, miissen sich die Unternehmen dem Kauferverhalten anpassen /2/. Die in der Vergangenheit nach rein funktionalen Grundsatzen gestalteten Organisa- tions!.trukturen in den technisch indirekt-produktiven Bereichen verursachen durch Trennung der einzelnen Fachbereiche -wie Vertrieb, Konstruktion, Produktion und Materialwirtschaft - eine Vielzahl von Schnittstellen im Auftragsdurchlauf bei einem geringen Auftrags- und Kundenbezug. Steigerungen der Auftragskomplexitat und -flexibilitat kiinnen nur durch die Bereitstellung zusatzlicher Kapazitaten im Unter- nehmen erreicht werden. Der Preisdruck yom Markt fordert deshalb Strategien, die neben einer Flexibilisierung geringere Kosten verursachen, die tagerbestande senken und eine hohe Qualitat der Produkte sicherstellen. Die Einfiihrung ganzheitlicher Strukturen in allen Auftragsabwicklungs-Bereichen mit iiberschaubaren, dezentralen und eigenverantwortlichen Einheiten bietet vielen Unternehmen die Moglichkeit, das gewiinschte Ziel einer verbesserten Lieferbereit- sehaft bei verkiirzten Durchlaufzeiten und reduzierten Bestanden zu erreichen. Hier- zu muB die Anfragen- und Auftragsbearbeitung von der Angebotserstellung bis zur Auslieferung in ihrer Aufbau- und Ablauforganisation konsequent auf die Bearbei- tung der Kundenauftrage ausgerichtet sein /3/.

Dieser Buchtitel ist Teil des Digitalisierungsprojekts Springer Book Archives mit Publikationen, die seit den Anfangen des Verlags von 1842 erschienen sind. Der Verlag stellt mit diesem Archiv Quellen fur die historische wie auch die disziplingeschichtliche Forschung zur Verfugung, die jeweils im historischen Kontext betrachtet werden mussen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen Ausrichtung vom Verlag nicht beworben.