Recent innovations in modern radar for designing transmitted waveforms, coupled with new algorithms for adaptively selecting the waveform parameters at each time step, have resulted in improvements in tracking performance. Of particular interest are waveforms that can be mathematically designed to have reduced ambiguity function sidelobes, as their use can lead to an increase in the target state estimation accuracy. Moreover, adaptively positioning the sidelobes can reveal weak target returns by reducing interference from stronger targets. The manuscript provides an overview of recent advances in the design of multicarrier phase-coded waveforms based on Bjorck constant-amplitude zero-autocorrelation (CAZAC) sequences for use in an adaptive waveform selection scheme for mutliple target tracking. The adaptive waveform design is formulated using sequential Monte Carlo techniques that need to be matched to the high resolution measurements. The work will be of interest to both practitioners and researchers in radar as well as to researchers in other applications where high resolution measurements can have significant benefits. Table of Contents: Introduction / Radar Waveform Design / Target Tracking with a Particle Filter / Single Target tracking with LFM and CAZAC Sequences / Multiple Target Tracking / Conclusions
The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.
Food Safety is an increasingly important issue. Numerous foodcrises have occurred internationally in recent years (the use ofthe dye Sudan Red I; the presence of acrylamide in various friedand baked foods; mislabelled or unlabelled genetically modifiedfoods; and the outbreak of variant Creutzfeldt-Jakob disease)originating in both primary agricultural production and in the foodmanufacturing industries. Public concern at these and other eventshas led government agencies to implement a variety of legislativeactions covering many aspects of the food chain. This book presents and compares the HACCP and ISO 22000:2005food safety management systems. These systems were introduced toimprove and build upon existing systems in an attempt to addressthe kinds of failures which can lead to food crises. Numerouspractical examples illustrating the application of ISO 22000 to themanufacture of food products of animal origin are presented in thisextensively-referenced volume. After an opening chapter whichintroduces ISO 22000 and compares it with the well-establishedHACCP food safety management system, a summary of internationallegislation relating to safety in foods of animal origin ispresented. The main part of the book is divided into chapters whichare devoted to the principle groups of animal-derived foodproducts: dairy, meat, poultry, eggs and seafood. Chapters are alsoincluded on catering and likely future directions. The book is aimed at food industry managers and consultants;government officials responsible for food safety monitoring;researchers and advanced students interested in food safety.
The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.
The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.
Recent innovations in modern radar for designing transmitted waveforms, coupled with new algorithms for adaptively selecting the waveform parameters at each time step, have resulted in improvements in tracking performance. Of particular interest are waveforms that can be mathematically designed to have reduced ambiguity function sidelobes, as their use can lead to an increase in the target state estimation accuracy. Moreover, adaptively positioning the sidelobes can reveal weak target returns by reducing interference from stronger targets. The manuscript provides an overview of recent advances in the design of multicarrier phase-coded waveforms based on Bjorck constant-amplitude zero-autocorrelation (CAZAC) sequences for use in an adaptive waveform selection scheme for mutliple target tracking. The adaptive waveform design is formulated using sequential Monte Carlo techniques that need to be matched to the high resolution measurements. The work will be of interest to both practitioners and researchers in radar as well as to researchers in other applications where high resolution measurements can have significant benefits. Table of Contents: Introduction / Radar Waveform Design / Target Tracking with a Particle Filter / Single Target tracking with LFM and CAZAC Sequences / Multiple Target Tracking / Conclusions
More than half a century after the imposition of the dictatorship of the Colonels in Greece, a number of questions related to its nature, development and demise remains understudied and feebly answered. One of the most interesting -yet understudied- incidents of the dictatorship is its ill-fated self-transformation attempt into some form of civilian rule in 1973: the so-called 'Markezinis experiment', after the politician who assumed the task of heading the transition government and lead to elections. The whole venture lasted a mere eight weeks, faced heavy opposition from both the opposition elites and the civil society and eventually collapsed by a military hard-liners' coup. The story of this failed attempt raises a series of questions: what was the nature of the dictatorship of the Colonels, and why did it take it six years to seek some form of civilianisation? Were the intentions of Papadopoulos and Markezinis sincere, and were the politicians of the opposition right to refuse to legitimise the 'experiment'? What was the Polytechnic students' uprising role in the demise of the 'experiment'? Was there an American reaction, and was it the main reason for the collapse of the transition, as Markezinis claimed? The book seeks to address the above questions, and argues that the failure of the 'Markezinis experiment' paved the way for the actual transition of 1974 as it happened. The research is supported by foreign (British and American) archival resources, as well as by private archives and personal interviews. The book concludes by briefly seeking to trace some potential alternative paths for the failed self- transformation attempt, and by accounting for the long-term consequences of the failure of the 'Markezinis experiment'."--Bloomsbury Publishing.
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