This book covers sensors and multiple sensor systems, including sensor networks and multi-sensor data fusion. It presents the physics and principles of operation and discusses sensor selection, ratings and performance specifications, necessary hardware and software for integration into an engineering system and signal processing and data analysis. Additionally, it discusses parameter estimation, decision making and practical applications. Even though the book has all the features of a course textbook, it also contains a wealth of practical information on the subject.

This book constitutes the refereed proceedings of the 4th International Workshop on Practice and Theory in Public Key Cryptography, PKC 2001, held in Cheju Island, Korea in February 2001. The 30 revised full papers presented were carefully reviewed and selected from 67 submissions. The papers address all current issues in public key cryptography, ranging from mathematical foundations to implementation issues.

This introductory textbook on engineering system instrumentation emphasizes sensors, transducers, actuators, and devices for component interconnection. The book deals with instrumenting an engineering system through the incorporation of suitable sensors, actuators, and associated interface hardware including filters, amplifiers and other signal modifiers. In view of the practical considerations, design issues, and industrial techniques that are presented throughout the book, and in view of the simplified and snap-shot style presentation of more advanced theory and concepts, it also serves as a useful reference for engineers, technicians, project managers, and other practicing professionals in industry and in research laboratories.

This publication contains a substantial amount of detail about the broad history of the development of econometric software based on the personal recollections of many people. For economists, the computer has increasingly become the primary applied research tool, and it is software that makes the computer work.

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.

Researchers and professionals

The simulation of the vehicle’s environmental sensors, the so-called sensor simulation, is crucial for testing and validating autonomous driving. Automobile manufacturers are increasingly focusing on a standardized architecture with a high level of abstraction. In order to simulate the sensors, such as radar sensors, most realistically on a point cloud level, data-based methods are used in many cases. In general, and specifically in case of radar sensors, there are still challenges to be faced. Therefore, four research questions are addressed: Is it possible to generate synthetic training data for data-based models? Which statistical approaches are suitable to simulate radar point clouds and how shall their learning capacities be evaluated? Is there a modeling approach to circumvent the disadvantages of statistical modeling? How to tackle the statistical nature of radar sensors during validation? Die Simulation der Umfeldsensoren des Fahrzeugs, die sogenannte Sensorsimulation, ist für Test und Absicherung des autonomen Fahrens entscheidend. Die Automobilhersteller setzen dabei zunehmend auf eine standardisierte Architektur mit hohem Abstraktionsgrad. Um die Sensoren, wie z.B. Radarsensoren, möglichst realitätsnah auf Punktwolkenebene zu simulieren, werden in vielen Fällen datenbasierte Methoden eingesetzt. Im Allgemeinen und speziell im Fall von Radarsensoren gilt es noch immer zahlreiche Herausforderungen zu meistern. Daher werden in dieser Arbeit vier Forschungsfragen behandelt: Können synthetische Trainingsdaten für datenbasierte Modelle generiert werden? Welche statistischen Ansätze sind geeignet, um Radar-Punktwolken zu simulieren und wie können die Ansätze bewertet werden? Gibt es einen Modellierungsansatz, um Nachteile der statistischen Modellierung zu umgehen? Wie kann die statistische Natur bei der Validierung berücksichtigt werden?