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Machine Learning Techniques for Assistive Robotics
Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.
Data-Intensive Computing in Smart Microgrids
Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generatio...
Building Climate Resilience in Agriculture
This volume discusses the need to adopt Climate-Resilient Agriculture (CRA) practices to address the increasing global impact that climate change has on agricultural productivity and agriculture-dependent communities. This approach applies technological, policy and economic measures to achieve sustainable agricultural growth in the sectors of grain, fruit, vegetable, fiber, feed, livestock, fisheries and forestry, with the ultimate goal of adapting and building resilience to climate change. The book also uses GIS, crop modeling and remote sensing techniques for future climate resilience applications in agriculture, and covers pest control measures that avoid the use of pesticides to boost crop and livestock productivity for improved food security. The book will be of interest to researchers and students in environmental science, climate science, sustainability and agriculture, as well as policy makers and environmental organizations.
Cotton Production and Uses
This book provides a comprehensive and systematic overview of the recent developments in cotton production and processing, including a number of genetic approaches, such as GM cotton for pest resistance, which have been hotly debated in recent decades. In the era of climate change, cotton is facing diverse abiotic stresses such as salinity, drought, toxic metals and environmental pollutants. As such, scientists are developing stress-tolerant cultivars using agronomic, genetic and molecular approaches. Gathering papers on these developments, this timely book is a valuable resource for a wide audience, including plant scientists, agronomists, soil scientists, botanists, environmental scientists and extention workers.
Agronomic Crops
Agronomic crops have provided food, beverages, fodder, fuel, medicine and industrial raw materials since the beginning of human civilization. More recently, agronomic crops have been cultivated using scientific rather than traditional methods. However, in the current era of climate change, agronomic crops are suffering from different environmental stresses that result in substantial yield loss. To meet the food demands of the ever-increasing global population, new technologies and management practices are being adopted to boost yields and maintain productivity under both normal and adverse conditions. Further, in the context of sustainable agronomic crop production, scientists are adopting n...
Solar Energy
This book covers challenges and opportunities related to solar-energy based systems. It covers a wide variety of topics related to solar energy, including applications-based systems such as solar thermal systems that are focused on drying, desalination, space cooling, refrigeration, and processing; recent advances in solar cells (DSSC) and photovoltaics; technologies for storage of energy (both sensible heating as well as latent heating); and the design of concentrated solar receivers. The information is presented in the context of the overall global energy utilization, and the role of solar energy has been highlighted. The contents of this book will be of interest to researchers, professionals, and policymakers alike.
