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Languages : en
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Author: Nikolai Kolev
Publisher: BoD – Books on Demand
ISBN: 9533073454
Category : Technology & Engineering
Languages : en
Pages : 338
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Book Description
The book is an edited collection of research articles covering the current state of sonar systems, the signal processing methods and their applications prepared by experts in the field. The first section is dedicated to the theory and applications of innovative synthetic aperture, interferometric, multistatic sonars and modeling and simulation. Special section in the book is dedicated to sonar signal processing methods covering: passive sonar array beamforming, direction of arrival estimation, signal detection and classification using DEMON and LOFAR principles, adaptive matched field signal processing. The image processing techniques include: image denoising, detection and classification of artificial mine like objects and application of hidden Markov model and artificial neural networks for signal classification. The biology applications include the analysis of biosonar capabilities and underwater sound influence on human hearing. The marine science applications include fish species target strength modeling, identification and discrimination from bottom scattering and pelagic biomass neural network estimation methods. Marine geology has place in the book with geomorphological parameters estimation from side scan sonar images. The book will be interesting not only for specialists in the area but also for readers as a guide in sonar systems principles of operation, signal processing methods and marine applications.
Author: Vincent Myers
Publisher: Cambridge Scholars Publishing
ISBN: 1443861529
Category : Technology & Engineering
Languages : en
Pages : 296
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This book consists of the proceedings of the International Conference on Detection and Classification of Underwater Targets which took place in Brest, France, in October 2012. This collection of academic papers represents the current state of the art of research and development in the areas of sensor technology, processing, modeling and automation for the purpose of detecting and classifying objects in the underwater environment, written by leading researchers in government, industry and academia. These articles should be of interest not only to those working on underwater target detection, but also to researchers in the related fields of remote sensing, robotic perception and medical imaging.
Author: Guoliang Chen
Publisher: Springer
ISBN: 9811064423
Category : Computers
Languages : en
Pages : 639
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This book constitutes the refereed proceedings of the 8th International Symposium on Parallel Architecture, Algorithm and Programming, PAAP 2017, held in Haikou, China, in June 2017. The 50 revised full papers and 7 revised short papers presented were carefully reviewed and selected from 192 submissions. The papers deal with research results and development activities in all aspects of parallel architectures, algorithms and programming techniques.
Author: Shin, Jane Shin
Publisher:
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Category :
Languages : en
Pages : 0
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By utilizing onboard sensors such as side-scan or forward-looking sonar, autonomous underwater robots can perform many useful tasks, such as exploring and searching for targets in underwater environments. In order to recognize and classify objects with high confidence, however, these mobile sensors must obtain multiple looks or "views" for each target using different positions and orientations that allow for a different interpretation based on local occlusions and environmental conditions. As a result, when tasked with classifying many targets, the mobile sensor must find the most efficient path through multiple configurations in an effort to reduce the cost and time required by each underwater mission. This dissertation presents a novel and general approach, referred to as informative multi-view planning (IMVP), that simultaneously determines the most informative sequence of views and the shortest path between them. The approach is demonstrated both in simulations and sea tests using an unmanned underwater vehicle (UUV) equipped with a side-scan sonar (SSS) and engaged in underwater multi-target classification. Both simulation and experimental results show that IMVP achieves excellent classification performance while reducing the total time required by the mission by up to half the time required by state-of-the-art multi-view path planning methods. One reason is that IMVP utilizes knowledge of the automatic target recognition (ATR) algorithm, as well as prior measurements, in order to determine the most informative views. Additionally, by using knowledge of the target location and field-of-view (FOV) geometry, IMVP is able to find the shortest path between them by solving a traveling salesman problem with neighborhoods (TSPN). In this dissertation, a novel physics-inspired algorithm based on Lin-Kernighan heuristic (LKH) is developed for searching for the optimal TSPN path for multiple non-disjoint neighborhoods. It is shown that the LKH algorithm is able to decrease the computational complexity of TSPN solutions by leveraging the intersections of valuable neighborhoods using computational geometry constructs known as coverage cones. When compared to state-of-the-art TSPN algorithms, the proposed method is able to find shorter paths with either comparable or reduced computation. The advantages of the LKH algorithm are found to become more significant as the number of intersecting neighborhoods increases, thus also allowing the mobile sensor to observe multiple targets from a single configuration.
Author: Vinit Kumar Gunjan
Publisher: Springer Nature
ISBN: 3031430093
Category : Technology & Engineering
Languages : en
Pages : 337
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This book provides a systematic and comprehensive overview of cognitive intelligence and AI-enabled IoT ecosystem and machine learning, capable of recognizing the object pattern in complex and large data sets. A remarkable success has been experienced in the last decade by emulating the brain–computer interface. It presents the applied cognitive science methods and AI-enabled technologies that have played a vital role at the core of practical solutions for a wide scope of tasks between handheld apps and industrial process control, autonomous vehicles, IoT, intelligent learning environment, game theory, human computer interaction, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The book contains contents highlighting artificial neural networks that are analogous to the networks of neurons that comprise the brain and have given computers the ability to distinguish an image of a cat from one of a coconut, to spot pedestrians with enough accuracy to direct a self-driving car, and to recognize and respond to the spoken word. The chapters in this book focus on audiences interested in artificial intelligence, machine learning, fuzzy, cognitive and neurofuzzy-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behavior, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step execution and explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning, artificial intelligence, and cognitive science such as healthcare products, AI-enabled IoT, gaming, medical, and engineering. Overall, this book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and academics in the field of machine learning and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains.
Author: Quanxing Lu
Publisher:
ISBN:
Category :
Languages : en
Pages : 78
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Book Description
This thesis presents an approach of classifying multiple targets of interest in minimum time with satisfactory confidence by an imaging sensor on an underwater robot. The overall goal is achieved by sequentially solving a single target classification problem and a global target ordering problem. First, a multi-view single-target classification algorithm is developed based on the POMDP framework, which incorporates a deep convolutional neural network and a support vector machine as the observation model. The classification algorithm allows the underwater robot to adaptively select its next configuration state near the target of interest in order to maximize the increase of classification confidence. Next, a traveling salesman algorithm is used to generate the global target visiting order. Simulation results of an unmanned underwater vehicle equipped with a side-scan sonar validate the effectiveness of the proposed algorithm and demonstrates the ability to find significantly shorter path for multi-view based multi-target classification.
Author: Harold Eugene Edgerton
Publisher: Prentice Hall
ISBN:
Category : Sonar
Languages : en
Pages : 316
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Book Description
Examines many kinds of sonar recorders, depth finders, and side-scan sonars that proliferated in the marketplace and predicts additional equipment and uses to be developed.
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Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hype spectral data), and scales well to large problem sizes. Afreet combines an SVM with a sophisticated spatio-spectral feature construction mechanism that allows it to classify spectrally ambiguous pixels. We demonstrate the effectiveness of the system by applying Afreet to several broad area classification problems in remote sensing, and provide a comparison with conventional maximum likelihood classification.
Author: Anil Chaudhary
Publisher: Springer Nature
ISBN: 9811501289
Category : Computers
Languages : en
Pages : 206
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Book Description
These proceedings gather cutting-edge papers exploring the principles, techniques, and applications of Microservices in Big Data Analytics. The ICETCE-2019 is the latest installment in a successful series of annual conferences that began in 2011. Every year since, it has significantly contributed to the research community in the form of numerous high-quality research papers. This year, the conference’s focus was on the highly relevant area of Microservices in Big Data Analytics.
