Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Compilation of thesis abstracts, September 2009

NPS Class of September 2009This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452751

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

NASA Tech Briefs, December 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Adaptive sampling by histogram equalization: theory, algorithms, and applications, 2007

We present the investigation of a novel, progressive, adaptive sampling scheme. This scheme is based on the distribution of already obtained samples. Even spaced sampling of a function with varying slopes or degrees of complexity yields relatively fewer samples from the regions of higher slopes. Hence, a distribution of these samples will exhibit a relatively lower representation of the function values from regions of higher complexity. When compared to even spaced sampling, a scheme that attempts to progressively equalize the histogram of the function values results in a higher concentration of samples in regions of higher complexity. This is a more efficient distri-bution of sample points, hence the term adaptive sampling. This conjecture is confirmed by numerous examples. Compared to existing adaptive sampling schemes, our approach has the unique ability to efficiently obtain expensive samples from a space with no prior knowledge of the relative levels of variation or complexity in the sampled function. This is a requirement in numerous scientific computing applications. Three models are employed to achieve the equalization in the distribution of sampled function values: (1) an active-walker model, containing elements of the random walk theory, and the motion of Brownian particles, (2) an ant model, based on the simulation of the behavior of ants in search of resources, and (3) an evolutionary algorithm model. Their performances are compared on objective basis such as entropy measure of information, and the Nyquist-Shannon minimum sampling rate for band-limited signals. The development of this adaptive sampling scheme was informed by a need to effi-ciently synthesize hyperspectral images used in place of real images. The performance of the adaptive sampling scheme as an aid to the image synthesis process is evaluated. The synthesized images are used in the development of a measure of clutter in hyperspectral images. This process is described, and the results are presented