Asoke k nandi (39 Ergebnisse)

hardcover. Zustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

xii, 416 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: New.

Zustand: New. In English.

Zustand: new.

Hardback. Zustand: New. Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiersLists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparisonGives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systemsIncludes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book.…

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New. In English.

Zustand: New.

Zustand: New.

Zustand: New. In English.

Hardback. Zustand: New. Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors-such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression-to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods. …

Zustand: New.

Hardcover. Zustand: Brand New. 404 pages. 9.75x6.75x1.25 inches. In Stock.

Hardcover. Zustand: Brand New. 1st edition. 184 pages. 10.00x7.00x0.50 inches. In Stock.

Buch. Zustand: Neu. Neuware - Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: - Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiers - Lists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparison - Gives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systems - Includes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book.…

Zustand: New.

Hardcover. Zustand: Brand New. 368 pages. 10.10x7.26x0.96 inches. In Stock.

Hardback. Zustand: New. Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiersLists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparisonGives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systemsIncludes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book.…

Hardcover. Zustand: Brand New. 404 pages. 9.75x6.75x1.25 inches. In Stock.

Zustand: New.

Buch. Zustand: Neu. Neuware - Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. - Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoring¿guiding readers from the basics of rotating machines to the generation of knowledge using vibration signals - Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs - Features learning algorithms that can be used for fault diagnosis and prognosis - Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.…

Hardback. Zustand: New. Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors-such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression-to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods. …

Zustand: New. In English.