Download Free Signalling Under Uncertainty Book in PDF and EPUB Free Download. You can read online Signalling Under Uncertainty and write the review.

This book is devoted to some of the problems encountered in the theory of sophisticated signals used in radar. The term sophisticated signal is under stood to mean a signal for which the product of the signal duration by the spectrum width substantially exceeds unity. Although it is impossible to draw an exact borderline between simple and sophisticated signals, the term "sophisticated signal" is sufficient to define one of the principal characteristics of modern radar. Recently, various sophisticated signals (frequency-modulated pulses, coded groups, phase-modulated signals, etc.) have found use in radar. This makes it possible to improve the resolution, to ensure simultaneous measurements of the range and range rate of a target, to elecrically scan over finite angular dimensions, etc. Although the realization of such potentialities is associated with substantial difficulties, one can say with certainty that "classical" radar technology, which uses simple signals at constant frequency and duty cycle, yields to more complex methods based on the use of wide-band signals of the sophisticated structure. The properties of radar signals, which characterize the measurement of a target's range and range rate, are described by the Woodward ambiguity function. The role of this function is similar to that of the antenna pattern, i.e., the ambiguity function defines the accuracy and resolution of the range and range rate measurements to the same extent as the antenna pattern de fines the accuracy and resolution of the azimuth and elevation measurements.
A method to find and connect the small data clues that show what the future’s big picture will look like. “Strategy decisions are like playing high-stakes blackjack, and scanning is the technique for counting cards. Martin Schwirn isn’t a pro gambler, but an expert in scanning.” —Bill Ralston, cofounder of Strategic Business Insights and author of Scenario Planning Handbook An organization’s future success depends on their decision makers’ ability to anticipate changes and disruptions in the marketplace. But how do you get information about tomorrow today? How can your decisions today account for tomorrow’s uncertainties? Small Data, Big Disruptions presents a tool kit to foresee coming changes: Understand why big data will not help you with understanding tomorrow’s disruptions. The future starts with small data—first. Learn the proven 4-step process to capture small data that help envision the future. See examples of how the process anticipated major disruptions. Implement the process in your organization and learn how to initiate meaningful actions. Small Data, Big Disruptions provides the information you need to anticipate the future, understand tomorrow’s market dynamics, and make the necessary decisions to meet the future on your terms. Small Data, Big Disruptions lets you exploit the period between the moment you could know about emerging disruptions and the moment most everybody will know about it. It’s the difference between being ahead of the curve and struggling to catch up.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
This paper considers the signalling aspect of monetary policy. We introduce a heuristic framework for the study of signal uncertainty, and use this to analyse the signal uncertainty implicit in the communications of the Bank of England's Monetary Policy Committee (MPC). Our findings suggest that frequencies of key terms expressing signal uncertainty in MPC minutes may either reflect the degree of confidence implicit in MPC deliberations, or offer evidence for the presence of an irreducible kind of signal uncertainty that shows up as white noise, casting doubt on the soundness of the various qualitative uncertainty indices found in the literature.
Signal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems. In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.
Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication theory more than fifty years ago. Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.
Despite the extensive body of evidence that informs regulatory decisions on pharmaceutical products, significant uncertainties persist, including the underlying variability in human biology, factors associated with the chemistry of a drug, and limitations in the research and clinical trial process itself that might limit the generalizability of results. As a result, regulatory reviewers are consistently required to draw conclusions about a drug's safety and efficacy from imperfect data. Efforts are underway within the drug development community to enhance the evaluation and communication of the benefits and risks associated with pharmaceutical products, aimed at increasing the predictability, transparency, and efficiency of pharmaceutical regulatory decision making. Effectively communicating regulatory decisions necessarily includes explanation of the impact of uncertainty on decision making. On February 12 and May 12, 2014, the Institute of Medicine's Forum on Drug Discovery, Development, and Translation held public workshops to advance the development of more systematic and structured approaches to characterize and communicate the sources of uncertainty in the assessment of benefits and risks, and to consider their implications for pharmaceutical regulatory decisions. Workshop presentations and discussions on February 12 were convened to explore the science of identifying and characterizing uncertainty in scientific evidence and approaches to translate uncertainties into decisions that reflect the values of stakeholders. The May 12 workshop presentations and discussions explored tools and approaches to communicating about scientific uncertainties to a range of stakeholders in the drug development process. Characterizing and Communicating Uncertainty in the Assessment of Benefits and Risks of Pharmaceutical Products summarizes the presentation and discussion of both events. This report explores potential analytical and communication approaches and identifies key considerations on their development, evaluation, and incorporation into pharmaceutical benefit- risk assessment throughout the entire drug development lifecycle.