IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety Enables readers to understand a broad area of state-of-the-art research in physical IoT-enabled security IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety describes new techniques in unobtrusive surveillance that enable people to act and communicate freely, while at the same time protecting them from malevolent behavior. It begins by characterizing the latest on surveillance systems deployed at smart campuses, miniatures of smart cities with more demanding frameworks that enable learning, social interaction, and creativity, and by performing a comparative assessment in the area of unobtrusive surveillance systems for smart campuses. A proposed taxonomy for IoT-enabled smart campus unfolds in five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, the book presents the state of the art and then makes a comparative assessment to classify the systems. Finally, the book extracts valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by unobtrusive surveillance systems for smart campuses. IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety includes specific discussion of: Smart campus's prior work taxonomies and classifications, a proposed taxonomy, and an adopted weight scoring model Personal consumer benefits and potential social dilemmas encountered when adopting an unobtrusive surveillance system Systems that focus on smart buildings, public spaces, smart lighting and smart traffic lights, smart labs, and smart campus ambient intelligence A case study of a spatiotemporal authentication unobtrusive surveillance system for smart campus safety and emerging issues for further research directions IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety is an essential resource for computer science and engineering academics, professionals, and every individual who is working and doing research in the area of unobtrusive surveillance systems and physical security to face malevolent behavior in smart campuses.

Industrial Tomography: Systems and Applications, Second Edition thoroughly explores the important techniques of industrial tomography, also discusses image reconstruction, systems, and applications. This book presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries.

Advances in Computers, Volume 124 presents updates on innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on Traffic-Load-Aware Virtual Channel Power-gating in Network-on-Chips, An Efficient DVS Scheme for On-chip Networks, A Power-Performance Balanced Network-on-Chip for Mixed CPU-GPU Systems, Routerless Networks-on-Chip, Routing Algorithm Design for Power- and Temperature-Aware NoCs, Approximate Communication for Energy-Efficient Network-on-Chip, Power-Efficient NoC Design by Partial Topology Reconfiguration, The Design of a Deflection-based Energy-efficient On-chip Network, and Power-Gating in Networks-on-Chip.

Digital transformation in organizations optimizes the business processes but also brings additional challenges in the form of security threats and vulnerabilities. Cyberattacks incur financial losses for organizations and can affect their reputations. Due to this, cybersecurity has become critical for business enterprises. Extensive technological adoption in businesses and the evolution of FinTech applications require reasonable cybersecurity measures to protect organizations from internal and external security threats. Recent advances in the cybersecurity domain such as zero trust architecture, application of machine learning, and quantum and post-quantum cryptography have colossal potential to secure technological infrastructures. Cybersecurity Issues and Challenges for Business and FinTech Applications discusses theoretical foundations and empirical studies of cybersecurity implications in global digital transformation and considers cybersecurity challenges in diverse business areas. Covering essential topics such as artificial intelligence, social commerce, and data leakage, this reference work is ideal for cybersecurity professionals, business owners, managers, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Advances in Computers, Volume 127 presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on Edge AI, Edge Computing, Edge Analytics, Edge Data Analytics, Edge Native Applications, Edge Platforms, Edge Computing, IoT, Internet of Things, etc.

Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment.

Crypto is red-hot right now.

Media outlets are giving crypto unprecedented airtime while the general public has been captivated by the staggering price rises seen across the board. When measured in US dollar terms, Bitcoin ballooned by over 10 times in the 2017 calendar year alone.

Beyond the tremendous increases in value, crypto has received so much attention because of the challenging questions it raises about money and the role of central authorities such as banks and governments - roles which were taken for granted in the past.Before the start of the crypto revolution, government-issued banknotes and coins seemed to be the only conceivable forms of money. We had never known any other way in our lifetime, nor in that of our parents.

Fast-forward to today, and many members of the crypto community ardently believe that crypto is destined to replace government-issued money, just as the personal computer replaced the typewriter. If this vision is even halfright, the implications are hard to overstate. At the very least, crypto promises to substantially weaken the monopoly power of centralized institutions.

But these are still early days for crypto. And most members of the public find crypto to be, well, cryptic. As United States Senator Thomas Carper said: "Virtual currencies, perhaps most notably Bitcoin, have captured the imagination of some, struck fear among others, and confused the heck out of the rest of us."1 Perhaps some readers can relate to that sentiment.

Truthfully, few people have an accurate understanding of how crypto works, and many are highly skeptical. The Crypto Intro has been written to explain everything and respond to the tough crypto questions.But we may be getting ahead of ourselves. Before taking a look at how crypto functions, let's make sure we understand what we're talking about.

As technology continues to develop, the healthcare industry must adapt and implement new technologies and services. Recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine must be considered to ensure patients receive the best possible care. Advancements in Bio-Medical Image Processing and Authentication in Telemedicine introduces recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine and discusses the design of high-accuracy decision support systems. Covering key topics such as artificial intelligence, medical imaging, telemedicine, and technology, this premier reference source is ideal for medical professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented -the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing.

In today's competitive market, a manager must be able to look at data, understand it, analyze it, and then interpret it to design a smart business strategy. Big data is also a valuable source of information on how customers interact with firms through various mediums such as social media platforms, online reviews, and many more. The applications and uses of business analytics are numerous and must be further studied to ensure they are utilized appropriately. Data-Driven Approaches for Effective Managerial Decision Making investigates management concepts and applications using data analytics and outlines future research directions. The book also addresses contemporary advancements and innovations in the field of management. Covering key topics such as big data, business intelligence, and artificial intelligence, this reference work is ideal for managers, business owners, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach provides a comprehensive guide for public health authorities, researchers and health professionals in psychological health. The book takes a unique approach by exploring how Artificial Intelligence (AI) and Machine Learning (ML) based solutions can assist with monitoring, detection and intervention for mental health at an early stage. Chapters include computational approaches, computational models, machine learning based anxiety and depression detection and artificial intelligence detection of mental health. With the increase in number of natural disasters and the ongoing pandemic, people are experiencing uncertainty, leading to fear, anxiety and depression, hence this is a timely resource on the latest updates in the field.

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

Advances in Computers, Volume 126 presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on VLSI for Super-Computing: Creativity in R+D from Applications and Algorithms to Masks and Chips, Bulk Bitwise Execution Model in Memory: Mechanisms, Implementation, and Evaluation, Embracing the Laws of Physics: Three Reversible Models of Computation, WSNs in Environmental Monitoring: Data Acquisition and Dissemination Aspects, Energy efficient implementation of tensor operations using dataflow paradigm for machine learning, and A Run-Time Job Scheduling Algorithm for Cluster Architectures with DataFlow Accelerators.