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Museums and Interactive Virtual Learning
Museums and Interactive Virtual Learning provides informal educators with practical resources that will help them to build dynamic digital engagement experiences within their own cultural organizations. Presenting vignettes from experienced museum educators and end users, as well as scientific data and practical resources, the book highlights the mutual benefits that Interactive Virtual Learning (IVL) programs offer to the museum and those visiting from a distance.Chapters mirror the step-by-step process of developing reputable IVL programs and emphasize how important it is for cultural organizations to encourage cross-departmental collaboration, if they wish to ensure that their programs align with the overall goals of the organization.Providing a thorough overview of the technologies, budget, marketing and staff requirements, the authors offer a realistic depiction of the work involved in building content for digital engagement.Emphasizing the importance of assessing existing programming, the book shows how institutions can adapt content to fit a virtual format and create inclusive digital engagement opportunities that reach local, national, and international audiences. Museums and Interactive Virtual Learning is an essential guide for professionals who are tasked with interpreting the content of a cultural organization and building lasting digital engagement opportunities.It will be particularly useful to those looking to reach diverse audiences.
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Collision Detection in Interactive 3D Environments
The heart of any system that simulates the physical interaction between objects is collision detection-the ability to detect when two objects have come into contact.This system is also one of the most difficult aspects of a physical simulation to implement correctly, and invariably it is the main consumer of CPU cycles.Practitioners, new to the field or otherwise, quickly discover that the attempt to build a fast, accurate, and robust collision detection system takes them down a long path fraught with perils and pitfalls unlike most they have ever encountered.Without in-depth knowledge and understanding of the issues associated with engineering a collision detection system, the end of that path is an abyss that has swallowed many a good programmer!Gino van den Bergen's new book is the story of his successful journey down that path.The outcome is his well-known collision detection system, the SOftware Library for Interference Detection (SOLID).Along the way, he covers the topics of vector algebra and geometry, the various geometric primitives of interest in a collision system, the powerful method of separating axes for the purposes of intersection testing, and the equally powerful Gilbert-Johnson-Keerthi (GJK) algorithm for computing the distance between convex objects.But this book provides much more than a good compendium of the ideas that go into building a collision system.The curse of practical computational geometry is floating-point arithmetic.Algorithms with straightforward implementations when using exact arithmetic can have catastrophic failures in a floating-point system.Specifically, intersection and distance algorithms implemented in a floating-point system tend to fail exactly in the most important case in a collision system-when two objects are just touching.Great care must be taken to properly handle floating-point round off errors.Gino's ultimate accomplishment in this book is his presentation on how to correctly implement the GJK distance algorithm in the presence of single-precision floating-point arithmetic. And what better way to illustrate this than with a case study, the final chapter on the design and implementation of SOLID. About the CD-ROMThe companion CD-ROM includes the full C++ source code of SOLID 3.5 as well as API documentation in HTML and PDF formats.Both single (32bit) and double (64bit) precision versions of the SOLID SDK plus example programs can be compiled for Linux platforms using GNU g++ version 2.95 to 3.3 and for Win32 platforms using Microsoft Visual C++ version 6.0 to 7.1.Use of the SOLID source code is governed by the terms of either the GNU GPL or the Trolltech QPL (see CD-ROM documentation for details). About the AuthorGino van den Bergen is a game developer living and working in The Netherlands.He is the creator of SOLID and holds a Ph.D. in computing science from Eindhoven University of Technology.Gino implemented collision detection and physics in NaN Technologies' Blender, a creation suite for interactive 3D content.
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Deep Learning for Crack-Like Object Detection
Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc.However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging.During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. This book discusses crack-like object detection problem comprehensively.It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods.It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method.It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.
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Deep Learning in Diabetes Mellitus Detection and Diagnosis
Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject.This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus.This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions.It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning. Features:Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosisAssist scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equationsInvolves exceptional subject coverage and includes the principles needed to understand deep learning
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What is the detection limit or limit of detection?
The detection limit, also known as the limit of detection, is the lowest concentration of a substance that can be reliably detected by a particular analytical method. It represents the smallest amount of analyte that can be distinguished from the background noise of the measurement. Detection limits are important in determining the sensitivity and reliability of an analytical technique, as they indicate the smallest quantity of a substance that can be accurately measured. Scientists strive to minimize detection limits in order to improve the accuracy and precision of their measurements.
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How does a smoke detector with optical detection technology work?
A smoke detector with optical detection technology works by using a light source and a sensor. The light source emits a beam of light into a chamber within the detector. When smoke particles enter the chamber, they scatter the light, causing it to hit the sensor. The sensor then triggers the alarm to go off, alerting occupants of the presence of smoke. This technology is particularly effective at detecting smoldering fires that produce a lot of smoke before bursting into flames.
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What is a halide detection?
Halide detection refers to the process of identifying and measuring the presence of halide ions, such as chloride, bromide, and iodide, in a sample. This can be done using various analytical techniques, such as ion chromatography, colorimetric assays, or electrochemical sensors. Halide detection is important in environmental monitoring, industrial processes, and medical diagnostics, as the presence of halides can indicate contamination, corrosion, or specific health conditions. Accurate and sensitive detection of halides is crucial for ensuring safety and quality in various applications.
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What is Apple's fall detection?
Apple's fall detection is a feature available on certain Apple Watch models that can automatically detect if the wearer has taken a hard fall. When a fall is detected, the watch will tap the wearer on the wrist, sound an alarm, and display an alert. If the wearer does not respond within a minute, the watch will automatically call emergency services and send a message to the wearer's emergency contacts with their location. This feature is designed to provide an added layer of safety and peace of mind for Apple Watch users, particularly for those who may be at risk of falling.
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Technologies, Innovation, and Change in Personal and Virtual Learning Environments
Over the last decade there continues to be an increase in the technology and how it affects our lives.Since then the incorporation of electronic databases and other communication tools for students, faculty and staff, virtual learning environments have become an important innovation in the student learning experience. Technologies, Innovation, and Change in Personal and Virtual Learning Environments presents a widespread collection of research on the growth, innovation and implementation of learning technologies for educators, technologists and trainers.The book is a useful source for academics and professionals interested in information and communication technologies.
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Science, Technology And Innovation Indicators : Lessons from the Development Experience in Africa
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Virus Detection
Viruses do not behave as other microbes; their life cycles require infecting healthy cells, commandeering their cellular apparatus, replicating and then killing the host cell.Methods for virus detection and identification have been developed only in the past few decades.These recently developed methods include molecular, physical, and proteomic techniques.All these approaches (Electron Microscopy, Molecular, Direct Counting, and Mass Spectrometry Proteomics) to detection and identification are reviewed in this succinct volume.It is written in approachable language with enough detail for trained professionals to follow and want to recommend to others. Key Features Covers common detection methods Reviews the history of detection from antiquity to the present Documents the strengths and weaknesses of various detection methods Describes how to detect newly discovered viruses Recommends specific applications for clinical, hospital, environmental, and public health uses
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Technology for the Language Classroom : Creating a 21st Century Learning Experience
This volume in the ALLC series offers current and soon-to-be professionals in the ESL / EFL field a comprehensive guide to how to make the best use of technology to enhance the English language learning experience.The book has a predominant focus on practical insights that are based on successful real-life experiences at the classroom and study program level, including contributions from teachers in various countries.Nevertheless, there is also a strong foundation in existing research and literature as they relate to the needs of English language teachers.To promote reflective and exploratory practice, there is plenty of 'food-for-thought' for the reader. Although pre-service and in-service teachers represent the primary audience, the book is likely to be just as useful for language program administrators, researchers, curriculum and materials writers, and e-learning developers.
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What are chemical detection reactions?
Chemical detection reactions are specific chemical reactions that are used to detect the presence of a particular substance or compound. These reactions often produce a visible color change, gas release, or precipitation, which can be easily observed and used to identify the presence of the target compound. Chemical detection reactions are commonly used in analytical chemistry, environmental monitoring, and forensic science to identify and quantify the presence of various substances in a sample. These reactions are highly specific and sensitive, allowing for accurate detection and analysis of target compounds.
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How are police detection dogs kept?
Police detection dogs are typically kept in kennels at the police department or at the handler's home. They are provided with comfortable bedding, regular exercise, and a balanced diet to keep them healthy and fit for their duties. The dogs also receive ongoing training and socialization to maintain their skills and behavior. Additionally, they receive regular veterinary care to ensure their well-being.
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Where is the magnet detection located?
The magnet detection is located in the hard drive of a computer or other electronic device. It is used to detect the presence of a magnet, which can potentially cause damage to the device or disrupt its functioning. When a magnet is detected, the device may trigger a warning or take protective measures to prevent any harm.
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What is error detection for smart systems?
Error detection for smart systems is the process of identifying and flagging any anomalies, discrepancies, or inaccuracies in the data or functioning of the system. This can involve using algorithms, sensors, or other methods to monitor and analyze the system's performance in real-time. By detecting errors early, smart systems can take corrective actions or alert human operators to intervene, ensuring the system operates reliably and accurately. This is crucial for maintaining the integrity and trustworthiness of smart systems in various applications such as autonomous vehicles, healthcare, and industrial automation.
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