A. Senthil Kumar – författare

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

This book presents cutting-edge research in effective machining methods for composite materials, including metals, fiber-reinforced polymers, and alloys.By explaining how to effectively enhance machine life and optimize materials and time costs, Advances in Composite and Advanced Materials Machining enables readers to create the best possible end product. Focusing on common modern materials including novel composites, polymers, lightweight alloys, and advanced materials, the book also provides a step-by-step guide to effective machining of hard-to-cut material. It also covers trimming, milling, drilling, and other modern machining processes on fiber-reinforced polymer composites. Recent advances in drilling polymeric matrix composites, ecological machining, grinding technology, nano-machining, and intelligent machining are all covered.This book will be of interest to professionals in aerospace and automotive engineering as well as unconventional machining, advanced manufacturing processes, and nanomachining.

This book presents cutting-edge research in effective machining methods for composite materials, including metals, fiber-reinforced polymers, and alloys.By explaining how to effectively enhance machine life and optimize materials and time costs, Advances in Composite and Advanced Materials Machining enables readers to create the best possible end product. Focusing on common modern materials including novel composites, polymers, lightweight alloys, and advanced materials, the book also provides a step-by-step guide to effective machining of hard-to-cut material. It also covers trimming, milling, drilling, and other modern machining processes on fiber-reinforced polymer composites. Recent advances in drilling polymeric matrix composites, ecological machining, grinding technology, nano-machining, and intelligent machining are all covered.This book will be of interest to professionals in aerospace and automotive engineering as well as unconventional machining, advanced manufacturing processes, and nanomachining.

Fixtures are crucial to new manufacturing techniques and largely dictate the level of flexibility a manufacturing system can achieve. Advanced Fixture Design for FMS provides a systematic basis for the selection and design of fixturing systems. It gives a review of the current state of the art of flexible and reconfigurable fixturing systems. Recent developments in design methodology using CAD are analysed in depth. Fixture design is seen as an inseparable part of process planning. The primary objective of a fixture system is to ensure that the part being manufactured can be made consistently within the tolerance specified in the design. A new method of tolerance analysis is used to check the suitability of location surfaces and the sequence of operations and is explained in detail.

Himalaya, one of the global biodiversity hotspots, is the abode of a variety of flora and fauna. The Himalayan ecosystems have immense ecological, socioeconomic, and aesthetic significance as they provide a wide range of ecosystem services. The northwest Himalaya (NWH), covering three states of India viz., Uttarakhand, Himachal Pradesh, and Jammu and Kashmir, starts from the foothills of Shivaliks in the south and extends to the greater Himalaya in the north.  This region is also the source of some of the major rivers of India. With the increase in population, the NWH ecosystems have been under threat due to deforestation, loss of biodiversity, expansion of agriculture and settlement, overexploitation of natural resources, habitat loss and fragmentation, poaching, mining, construction of roads and large dams, and unplanned tourism. The Himalaya being young and geotectonically active, remains inherently unstable, fragile, and prone to natural disasters. Climate change is also likely to impact the Himalayan cryosphere drastically. Recognizing the importance of the Himalaya, a National Mission for Sustaining the Himalayan Ecosystem, one of the eight missions under the National Action Plan on Climate Change (NAPCC) of Govt. of India, to conserve biodiversity, forest cover and other ecological values in the Himalayan region has been taken up.Spaceborne remote sensing with its ability to provide synoptic and repetitive coverage has emerged as a powerful tool for assessment and monitoring of the Himalayan resources and phenomena. Indian Institute of Remote Sensing, Dehradun has taken up a number of studies in the fields of geology, water resources, forestry, agriculture, urban settlement, etc., over the last decade. The book summarises the work carried out in different disciplines, illustrated with tables and figures and a host of relevant references. It is hoped that the book serves as an excellent reference of immense value to the students, researchers, professors, scientists, professionals, and decision makers working in the NWH region.

of India, to conserve biodiversity, forest cover and other ecological values in the Himalayan region has been taken up.Spaceborne remote sensing with its ability to provide synoptic and repetitive coverage has emerged as a powerful tool for assessment and monitoring of the Himalayan resources and phenomena.