Video data analytics is rapidly evolving and transforming the way we live in urban environments. In Video Data Analytics for Smart City Applications: Methods and Trends, data science experts present a comprehensive review of the latest advances and trends in video analytics technologies and their extensive applications in smart city planning and engineering. The book covers a wide range of topics including object recognition, action recognition, violence detection, and tracking, exploring deep learning approaches and other techniques for video data analytics. It also discusses the key enabling technologies for smart cities and homes and the scope and application of smart agriculture in smart cities. Moreover, the book addresses the challenges and security issues in terahertz band for wireless communication and the empirical impact of AI and IoT on performance management. One contribution also provides a review of the progress in achieving the Jal Jeevan Mission Goals for institutional capacity building in the Indian State of Chhattisgarh. For researchers, computer scientists, data analytics professionals, smart city planners and engineers, this book provides detailed references for further reading and demonstrates how technologies are serving their use-cases in the smart city. The book highlights the advances and trends in video analytics technologies and extensively addresses key themes, making it an essential resource for anyone looking to gain a comprehensive understanding of video data analytics for smart city applications.

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care. Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science. Key topics covered in the book: - An introduction to the concept of the Internet of Medical Things (IoMT). - Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data. - A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs). - Artificial Intelligence and the Internet of Medical Things. - A review of new machine learning and AI solutions in different medical areas. - A Deep Learning based solution to optimize obstacle recognition for visually impaired patients. - A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications. - Deep Learning for brain tumor detection. - Blockchain and patient data management.

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems. The book also presents solutions for analyzing complex patterns in user data and improving productivity. Readers will be able to understand the technology that drives big data solutions in HCI projects and understand its capacity in transforming an organization.The book also helps the reader to understand HCI system design and explains how to evaluate an application portfolio that can be used when selecting pilot projects. This book is a resource for researchers, students, and professionals interested in the fields of HCI, artificial intelligence, data analytics, and computer engineering.

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI) and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 of which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery: AI's Impact on Biology and Energy Management AI and CI in Physical Sciences and Predictive Modeling Computational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers.

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics. This volume bridges the gap between data science and interdisciplinary research, making it a valuable resource for researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.

Amazon Web Services: A Comprehensive Guide for Beginners and Advanced Users is your go-to companion for learning and mastering AWS. It presents 10 easy-to-read chapters that build a foundation for cloud computing while also equipping readers with the skills necessary to use AWS for commercial projects. Readers will learn how to use AWS cloud computing services for seamless integrations, effective monitoring, and optimizing cloud-based web applications. What you will learn from this guide: 1. Identity and Access Management in AWS: Learn about IAM roles, security of the root account, and password policies, ensuring a robust foundation in access management. 2. Amazon EC2 Instance: Explore the different types of EC2 instances, pricing strategies, and hands-on experiences to launch, manage, and terminate EC2 instances effectively. This knowledge will help to make informed choices about pricing strategies. 3. Storage Options and Solutions: A detailed examination of storage options within Amazon EC2 instances. Understanding Amazon Elastic Block Store (EBS), Amazon Elastic File Storage (EFS), and more, will enhance your ability to handle data storage efficiently. 4. Load Balancing and Auto Scaling: Learn about different types of load balancers and how auto-scaling groups operate, to master the art of managing varying workloads effectively. 5. Amazon Simple Storage Service (S3): Understand S3 concepts such as buckets, objects, versioning, storage classes, and practical applications. 6. AWS Databases and Analytics: Gain insights into modern databases, AWS cloud databases, and analytics services such as Amazon Quicksight, AWS Glue, and Amazon Redshift. 7. Compute Services and Integrations: Understand the workings of Docker, virtual machines, and various compute services offered by AWS, including AWS Lambda and Amazon Lightsail, Amazon MQ and Amazon SQS. 8. Cloud Monitoring: Understand how to set up alarms, analyze metrics, and ensure the efficient monitoring of your cloud environment using Amazon CloudWatch and CloudTrail. Key Features: Comprehensive Introduction to Cloud Computing and AWS Guides readers to the complete set of features in AWS Easy-to-understand language and presentation with diagrams and navigation guides References for further reading Whether you're a student diving into cloud specialization as part of your academic curriculum or a professional seeking to enhance your skills, this guide provides a solid foundation for learning the potential of the AWS suite of applications to deploy cloud computing projects.

Basics of Python Programming: A Quick Guide for Beginners is an essential companion to mastering the Python programming language. The book presents information about Python in 12 structured chapters with a strong emphasis on fundamentals and practical information. Starting with basic operators, functions and expressions, contents explain file handling, exception handling and modules. The book concludes with advanced topics such as object oriented programming and machine learning. Key Features: Fundamental Focus: Covers the core concepts of Python programming to build a strong foundation in python programming in an easy-to-understand format. Practical Demonstrations: Learn by doing. This textbook includes hands-on practical demonstrations that reinforce your understanding of Python concepts. IDE Guidance: Includes programming and installation guidance for Python-supporting Integrated Development Environments (IDEs). Explores Python Frameworks: Introduces Python frameworks such as Matplotlib, TensorFlow, PyTorch, Scikit-Learn, and NLTK for complex projects. Python for Machine Learning: Gives a preliminary understanding of Python for machine learning tasks for data science and AI applications. Basics of Python Programming: A Quick Guide for Beginners is the perfect starting point for aspiring students, programmers and tech enthusiasts. Whether you're a student looking to build a solid foundation in Python or an industry professional venturing into machine learning and artificial intelligence, this textbook has you covered.

6G Wireless Communications and Mobile Networking introduces the key technologies behind 6G wireless communication and mobile networking to the reader. The book starts with a general vision of 6G technology, which includes the motivation that drives 6G research, the international organizations working on 6G standardization and recent progress in 6G research. Separate chapters on millimeter-wave and terahertz-wave technologies in 6G, the development of latest 6G antenna technology as well as related wireless communication applications are included in the contents. The book also provides details about the 6G network layer, such as self-organizing network driven by network slicing, software-defined networking and network function virtualization. Finally, it covers some popular research topics, including the challenges and solutions to massive 6G IoT networks, 6G cloud/edge computing and big data systems that may appear in the foreseeable future. </p> Key Features: </p> - Provides a complete introduction to 6G vision and technology </p> - Consists of both basic theories and frontier technologies </p> - Separate chapters on key topics such as 6G physical layers, millimeter wave and terahertz technology and advanced antenna arrays </p> - Covers future trends and applications such as intelligent management systems, 6G IoT networks, cloud/edge computing and big data applications </p> This focused reference will significantly enhance the knowledge of engineering students and apprentices involved in the field of telecommunications. Readers interested in cutting-edge wireless networking technologies will also benefit from the information provided.

This e-book provides several state-of-the-art analog circuit design techniques. It presents both empirical and theoretical materials for system-on-a-chip (SOC) circuit design. Fundamental communication concepts are used to explain a variety of topics including data conversion (ADC, DAC, Σ-Δ oversampling data converters), clock data recovery, phase-locked loops for system timing synthesis, supply voltage regulation, power amplifier design, and mixer design. This is an excellent reference book for both circuit designers and researchers who are interested in the field of design of analog communication circuits for SOC applications.

This book is tailored to fulfil the requirements in the area of the signal processing in communication systems. The book contains numerous examples, solved problems and exercises to explain the methodology of Fourier Series, Fourier Analysis, Fourier Transform and properties, Fast Fourier Transform FFT, Discrete Fourier Transform DFT and properties, Discrete Cosine Transform DCT, Discrete Wavelet Transform DWT and Contourlet Transform CT. The book is characterized by three directions, the communication theory and signal processing point of view, the mathematical point of view and utility computer programs. The contents of this book include chapters in communication system and signals, Fourier Series and Power Spectra, Fourier Transform and Energy Spectra, Fourier Transform and Power Spectra, Correlation Function and Spectral Density, Signal Transmission and Systems, Hilbert Transform, Narrow Band-Pass Signals and Systems and Numerical Computation of Transform Coding. This book is intended for undergraduate students in institutes, colleges, universities and academies who want to specialize in the field of communication systems and signal processing. The book will also be very useful to engineers of graduate and post graduate studies as well as researchers in research centers since it contains a great number of mathematical operations that are considered important in research results.