A Beginners Guide To Data Agglomeration And Intelligent Sensing PDF Download
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| Author | : Amartya Mukherjee |
| Publisher | : Academic Press |
| Total Pages | : 198 |
| Release | : 2020-02-19 |
| Genre | : Science |
| ISBN | : 0128203455 |
Download A Beginner's Guide to Data Agglomeration and Intelligent Sensing Book in PDF, ePub and Kindle
A Beginners Guide to Data Agglomeration and Intelligent Sensing provides an overview of the Sensor Cloud Platform, Converge-casting, and Data Aggregation in support of intelligent sensing and relaying of information. The book begins with a brief introduction on sensors and transducers, giving readers insight into the various types of sensors and how one can work with them. In addition, it gives several real-life examples to help readers properly understand concepts. An overview of concepts such as wireless sensor networks, cloud platforms, and device-to-cloud and sensor cloud architecture are explained briefly, as is data gathering in wireless sensor networks and aggregation procedures. Final sections explore how to process gathered data and relay the data in an intelligent way, including concepts such as supervised and unsupervised learning, software defined networks, sensor data mining and smart systems. Presents the latest advances in data agglomeration for intelligent sensing Discusses the basic concepts of sensors, real-life applications of sensors and systems, the protocols and applications of wireless sensor networks, the methodology of sensor data accumulation, and real-life applications of Intelligent Sensor Networks Provides readers with an easy-to-learn and understand introduction to the concepts of the cloud platform, Sensor Cloud and Machine Learning
| Author | : Lakhveer Singh |
| Publisher | : Academic Press |
| Total Pages | : 280 |
| Release | : 2022-11-08 |
| Genre | : Science |
| ISBN | : 0323998097 |
Download Multifaceted Bio-sensing Technology Book in PDF, ePub and Kindle
Multifaceted Bio-sensing Technology introduces the different types of biosensors, their construction materials, configurations, production methods, and their uses in bioelectrochemical fuel cells (BEFC). It focuses on recent progress in the production of biosensing platforms/interfaces, their integration, design and fabrication, and their multifaceted applications in bioelectrochemical systems. The chapters explore the integration of genetic elements such as DNA, enzymes, and whole cells within these systems, and address environmental applications including wastewater contaminant detection, toxicity, and bioremediation. Throughout, the book shows how rapid, minuscule, and affordable biocomponents can be produced for a variety of energy and environmental applications. This book provides a practical introduction to the production of biocomponents for bioelectrochemical devices and environmental monitoring, and will be a useful reference for graduates and researchers involved in the application of bioelectrochemical systems, as well as those working more broadly in bioenergy, electrochemistry, biology, environmental engineering, and multidisciplinary research across those areas. Examines the applications of biosensors in bioelectrochemical fuel cells and other fields and their integration and assembly for future uses Explores on the application of carbon nanomaterials in biosensors Presents detailed schematics and calculations that outline integrations in bioelectrochemical systems
| Author | : Parikshit N. Mahalle |
| Publisher | : CRC Press |
| Total Pages | : 138 |
| Release | : 2021-08-02 |
| Genre | : Technology & Engineering |
| ISBN | : 1000417816 |
Download The Convergence of Internet of Things and Cloud for Smart Computing Book in PDF, ePub and Kindle
This book presents the know-how of the real-time IoT application development activity including a basic understanding of the IoT architecture, use cases, smart computing, and the associated challenges in design and development of the IoT system. All the technical details related to protocol stack, technologies, and platforms used for the implementation are explained. It further includes techniques and case studies that include smart computing on the IoT–Cloud models along with test beds for experimentation purposes. The book aims at setting up the groundwork for the creation of applications that can help make day-to-day tasks simpler by meeting the needs of varied sectors like education, health care, agriculture, and so forth. Features: • Covers IoT cloud convergence with a focus on complex industrial IoT case studies. • Discusses the broad background of IoT–Cloud convergence architectures and its fundamentals along with resource provisioning mechanisms. • Emphasizes the use of context in developing context-aware IoT solutions. • Presents a novel C-model that explains the IoT application development phases. • Discusses a simplified convergence model that depicts the role of Cloud in an IoT application. This book aims at graduate students, researchers, and professionals getting started in the IoT field.
| Author | : Management Association, Information Resources |
| Publisher | : IGI Global |
| Total Pages | : 1509 |
| Release | : 2022-08-12 |
| Genre | : Social Science |
| ISBN | : 1668474654 |
Download Research Anthology on Modern Violence and Its Impact on Society Book in PDF, ePub and Kindle
Violence on both large and small scales has a tremendous impact on society. The causes and impacts of violence have been under study for decades; however, in the modern era, it is important to remain knowledgeable of the current research on violence. As tragic events flood news headlines around the world, it is essential to evaluate violence, its causes, and its impact on society in order to mitigate and prevent violence globally. The Research Anthology on Modern Violence and Its Impact on Society discusses the causes, ideologies, and perceptions of modern violence and examines its impact on society. It presents emerging research on modern violence in multiple dimensions from interpersonal to mass violence. Covering topics such as gun violence, radicalization, and victim-offender overlap, this major reference work is an essential resource for sociologists, politicians, government officials, law enforcement, community leaders, educators and administrators of both K-12 and higher education, students of higher education, psychologists, criminologists, victimologists, researchers, and academicians.
| Author | : Ayan Kumar Panja |
| Publisher | : Academic Press |
| Total Pages | : 168 |
| Release | : 2022-06-02 |
| Genre | : Technology & Engineering |
| ISBN | : 0128230738 |
Download Biomedical Sensors and Smart Sensing Book in PDF, ePub and Kindle
Biomedical Sensors and Smart Sensing: A Beginner’s Guide, a book in the 10-volume Primers in Biomedical Imaging Devices and Systems series, covers a wide range of interdisciplinary applications in imaging modalities, nuclear medicine, computed tomographic systems, x-ray systems, magnetic resonance imaging, ultrasound, and virtual reality. The series explores the essential fundamental techniques required to analyze and process signals and images for diagnosis, scientific discovery and medical applications. Volumes in this series cover a wide range of interdisciplinary areas, combining foundational content with practical case studies to demonstrate the applications of these technologies in real-world situations. In addition, the 10-volume series considers various medical devices, electronics, circuits, sensors and algorithms. Several applications ranging from basic biological science to clinical practice are included to facilitate ongoing research. Covers a variety of sensing and signal processing techniques Introduces different approaches relating to communication and intelligent data processing for early detection and prediction of diseases Includes practical case studies
| Author | : Fei Hu |
| Publisher | : CRC Press |
| Total Pages | : 676 |
| Release | : 2012-12-15 |
| Genre | : Technology & Engineering |
| ISBN | : 1439892814 |
Download Intelligent Sensor Networks Book in PDF, ePub and Kindle
Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.
| Author | : Umang Singh |
| Publisher | : Springer Nature |
| Total Pages | : 233 |
| Release | : 2021-09-01 |
| Genre | : Computers |
| ISBN | : 3030772144 |
Download Smart Sensor Networks Book in PDF, ePub and Kindle
This book provides IT professionals, educators, researchers, and students a compendium of knowledge on smart sensors and devices, types of sensors, data analysis and monitoring with the help of smart sensors, decision making, impact of machine learning algorithms, and artificial intelligence-related methodologies for data analysis and understanding of smart applications in networks. Smart sensor networks play an important role in the establishment of network devices which can easily interact with physical world through plethora of variety of sensors for collecting and monitoring the surrounding context and allowing environment information. Apart from military applications, smart sensor networks are used in many civilian applications nowadays and there is a need to manage high volume of demands in related applications. This book comprises of 9 chapters and presents a valuable insight on the original research and review articles on the latest achievements that contributes to the field of smart sensor networks and their usage in real-life applications like smart city, smart home, e-healthcare, smart social sensing networks, etc. Chapters illustrate technological advances and trends, examine research opportunities, highlight best practices and standards, and discuss applications and adoption. Some chapters also provide holistic and multiple perspectives while examining the impact of smart sensor networks and the role of data analytics, data sharing, and its control along with future prospects.
| Author | : Peter Bruce |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 395 |
| Release | : 2017-05-10 |
| Genre | : Computers |
| ISBN | : 1491952911 |
Download Practical Statistics for Data Scientists Book in PDF, ePub and Kindle
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
| Author | : Steven S. Skiena |
| Publisher | : Springer |
| Total Pages | : 456 |
| Release | : 2017-07-01 |
| Genre | : Computers |
| ISBN | : 3319554441 |
Download The Data Science Design Manual Book in PDF, ePub and Kindle
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
| Author | : Frank Hutter |
| Publisher | : Springer |
| Total Pages | : 223 |
| Release | : 2019-05-17 |
| Genre | : Computers |
| ISBN | : 3030053180 |
Download Automated Machine Learning Book in PDF, ePub and Kindle
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
