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Smart Buildings Digitalization IoT and Energy Efficient Smart Buildings Architecture and Applications

Systems Engineering Holistic Life Cycle Architecture Modeling and Design with Real-World Applications

Systems Engineering Holistic Life Cycle Architecture Modeling and Design with Real-World Applications

This book provides a guide for systems engineering modeling and design. It focuses on the design life cycle with tools and application-based examples of how to design a system focusing on incorporating systems principles and tools to ensure system integration. It provides product-based and service system examples to understand the models tools and activities to be applied to design and implement a system. The first section explains systems principles models and architecture for systems engineering lifecycle models and the systems architecture. Further sections explain systems design development and deployment life cycle with applications and tools and advanced systems engineering topics. Features: Focuses on model-based systems engineering and describes the architecture of the systems design models. Uses real-world examples to corroborate different and disparate systems engineering activities. Describes and applies the Vee systems engineering design methodology with cohesive examples and applications of designing systems. Discusses culture change and the skills people need to design and integrate systems. Shows detailed and cohesive examples of the systems engineering tools throughout the systems engineering life cycle. This book is aimed at graduate students and researchers in systems engineering modeling and simulation any major engineering discipline industrial engineering and technology. | Systems Engineering Holistic Life Cycle Architecture Modeling and Design with Real-World Applications

GBP 115.00
1

Advances in Traffic Transportation and Civil Architecture Proceedings of the 5th International Symposium on Traffic Transportation and Civil

Continua With the Houston Problem Book

Introduction to Design for Civil Engineers

FinFET Devices for VLSI Circuits and Systems

Warp Knitted Fabrics Construction

Heating with Wolves Cooling with Cacti Thermo-bio-architectural Framework (ThBA)

Principles of Verilog Digital Design

Deep Learning in Time Series Analysis

Deep Learning in Time Series Analysis

Deep learning is an important element of artificial intelligence especially in applications such as image classification in which various architectures of neural network e. g. convolutional neural networks have yielded reliable results. This book introduces deep learning for time series analysis particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein. An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning including students engineers researchers and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis. | Deep Learning in Time Series Analysis

GBP 115.00
1

Binary Neural Networks Algorithms Architectures and Applications

Binary Neural Networks Algorithms Architectures and Applications

Deep learning has achieved impressive results in image classification computer vision and natural language processing. To achieve better performance deeper and wider networks have been designed which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context Binary Neural Networks: Algorithms Architectures and Applications will focus on CNN compression and acceleration which are important for the research community. We will describe numerous methods including parameter quantization network pruning low-rank decomposition and knowledge distillation. More recently to reduce the burden of handcrafted architecture design neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification speech recognition object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification speech recognition object detection and more | Binary Neural Networks Algorithms Architectures and Applications

GBP 110.00
1

Spectral Multi-Detector Computed Tomography (sMDCT) Data Acquisition Image Formation Quality Assessment and Contrast Enhancement

Spectral Multi-Detector Computed Tomography (sMDCT) Data Acquisition Image Formation Quality Assessment and Contrast Enhancement

X-ray computed tomography (CT) has been one of the most popular diagnostic imaging modalities for decades in the clinic for saving patients’ lives or improving their quality of life. This book is an introductory one-stop shop for technological and clinical topics in multi-detector computed tomography (MDCT). Starting with MDCT’s fundamentals in physics and mathematics the book provides an in-depth introduction to its system architecture and imaging chain signal detection via energy-integration and photon-counting mechanisms clinical application-driven scan modes and protocols analytic and iterative image reconstruction solutions and spectral imaging – the latest technological advancement in MDCT. The book extends its coverage on image quality assessment under the theory of signal detection and statistical decision. In recognition of its clinical relevance for conspicuity enhancement in angiographic and parenchymal imaging applications the book features a chapter dedicated to the fundamental (chemical physical and physicochemical) properties and clinical administration of iodinated contrast agent. The book ends with an outlook of the contrast agents that are novel in material and delivery and their synergy with spectral MDCT to elevate CT’s contrast resolution in cardiovascular neurovascular and oncologic applications. This book will be an invaluable reference for researchers engineers radiological physicians and technologists and graduate and senior undergraduate students. Features Provides an accessible introduction to the subject Up to date with the latest advances in emerging technologies and procedures Provides a historical overview of CT technology | Spectral Multi-Detector Computed Tomography (sMDCT) Data Acquisition Image Formation Quality Assessment and Contrast Enhancement

GBP 99.99
1

Smart Manufacturing Factory Artificial-Intelligence-Driven Customized Manufacturing

Concise Chemical Thermodynamics

Concise Chemical Thermodynamics

While we all live our lives in designed landscapes of various types only on occasion do we consider what these landscapes mean to us and how they have acquired that significance. Can a landscape architect or garden designer really imbue new settings with meaning or does meaning evolve over time created by those who perceive and use these landscapes? What role does the selection and arrangement of plants and hard materials play in this process and just where does the passage of time enter into the equation? These questions collectively provide the core material for Meaning in Landscape Architecture and Gardens a compendium of four landmark essays written over a period of twenty years by leading scholars in the field of landscape architecture. New commentaries by the authors accompany each of the essays and reflect on the thinking behind them as well as the evolution of the author�s thoughts since their original publication. Although the central theme of these writings is landscape architecture broadly taken the principal subject of several essays and commentaries is the garden a subject historically plentiful in allusions and metaphors. As a whole Meaning in Landscape Architecture and Gardens offers the general reader as well as the professional a rich source of ideas about the designed landscape and the ways by which we perceive consider react and dwell within them � and what they mean to us. The essays have been perennial favorites in landscape courses since their original publication in Landscape Journal. Bringing them together � bolstered by the new commentaries � creates a book valuable to all those creating gardens and landscapes as well as those teaching and studying these subjects. | Concise Chemical Thermodynamics

GBP 175.00
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Internet of Things Security and Privacy Practical and Management Perspectives

Advancing Computational Intelligence Techniques for Security Systems Design

Surface Engineering Methods and Applications

Surface Engineering Methods and Applications

Surface engineering is considered an important aspect in the reduction of friction and wear. This reference text discusses a wide range of surface engineering technologies along with applications in a comprehensive manner. The book describes various methods in surface engineering technology with a thorough explanation of various aspects of each process that comes under this domain. Apart from an enhanced explanation of the process and its attributes this book also gives insight into the types of materials applications and optimization of surface engineering techniques. It discusses important topics including surface engineering of the functionality of graded materials materials characterization processing of biomaterials design surface modification technologies and process control smart manufacturing artificial intelligence and machine learning applications. The book: discusses computational and simulation analyses for better selection of process parameters covers optimizations of processes with state-of-the-art technologies discusses applications of surface engineering in medical agricultural architecture engineering and allied sectors covers processing techniques of biomaterials in surface engineering The text is useful for senior undergraduate graduate students and academic researchers working in diverse areas such as industrial and production engineering mechanical engineering materials science and manufacturing science. It covers a hybrid process for surface modification modeling techniques and issues in surface engineering. | Surface Engineering Methods and Applications

GBP 120.00
1

Introduction to Sustainability for Engineers

Natural Gas Installations and Networks in Buildings

Smart Computing and Self-Adaptive Systems

Advanced Materials and Manufacturing Processes

Fourier Theory in Optics and Optical Information Processing

Blockchain Technology Fundamentals Applications and Case Studies

Big Data for Entrepreneurship and Sustainable Development