Download or read book Sensor Data Understanding written by Marcin Grzegorzek and published by Logos Verlag Berlin GmbH. This book was released on 2017 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid development in the area of sensor technology has been responsible for a number of societal phenomena like UGC (User Generated Content) or QS (Quantified Self). Machine learning algorithms benefit a lot from the availability of such huge volumes of digital data. For example, new technical solutions for challenges caused by the demographic change (ageing society) can be proposed in this way, especially in the context of healthcare systems in industrialised countries. The goal of this book is to present selected algorithms for Visual Scene Analysis (VSA, processing UGC) as well as for Human Data Interpretation (HDI, using data produced within the QS movement) and to expose a joint methodological basis between these two scientific directions. While VSA approaches have reached impressive robustness towards human-like interpretation of visual sensor data, HDI methods are still of limited semantic abstraction power. Using selected state-of-the-art examples, this book shows the maturity of approaches towards closing the semantic gap in both areas, VSA and HDI.
Download or read book Sensor Data Analysis and Management written by A. Suresh and published by John Wiley & Sons. This book was released on 2021-11-11 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.
Download or read book Understanding Smart Sensors written by Randy Frank and published by Artech House. This book was released on 2013 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, Understanding Smart Sensors is the most complete, up-to-date, and authoritative summary of the latest applications and developments impacting smart sensors in a single volume. This thoroughly expanded and revised edition of an Artech bestseller contains a wealth of new material, including critical coverage of sensor fusion and energy harvesting, the latest details on wireless technology, the role and challenges involved with sensor apps and cloud sensing, greater emphasis on applications throughout the book, and dozens of figures and examples of current technologies from over 50 companies. This edition provides you with knowledge regarding a broad spectrum of possibilities for technology advancements based on current industry, university and national laboratories R & D efforts in smart sensors. Updated material also identifies the need for trusted sensing, the efforts of many organizations that impact smart sensing, and more. Utilizing the latest in smart sensor, microelectromechanical systems (MEMS) and microelectronic research and development, you get the technical and practical information you need keep your designs and products on the cutting edge. Plus, you see how network (wired and wireless) connectivity continues to impact smart sensor development. By combining information on micromachining and microelectronics, this is the first book that links these two important aspects of smart sensor technology so you don't have to keep multiple references on hand. This comprehensive resource also includes an extensive list of smart sensor acronyms and a glossary of key terms. With an effective blend of historical information and the latest content, the third edition of Understanding Smart Sensors provides a unique combination of foundational and future-changing information.
Download or read book Measurement Data Analysis and Sensor Fundamentals for Engineering and Science written by Patrick F. Dunn and published by CRC Press. This book was released on 2019-02-20 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: A combination of two texts authored by Patrick Dunn, this set covers sensor technology as well as basic measurement and data analysis subjects, a combination not covered together in other references. Written for junior-level mechanical and aerospace engineering students, the topic coverage allows for flexible approaches to using the combination book in courses. MATLAB® applications are included in all sections of the combination, and concise, applied coverage of sensor technology is offered. Numerous chapter examples and problems are included, with complete solutions available.
Download or read book Data Mining Techniques in Sensor Networks written by Annalisa Appice and published by Springer Science & Business Media. This book was released on 2013-09-12 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.
Download or read book Data Analytics and Applications of the Wearable Sensors in Healthcare written by Shabbir Syed-Abdul and published by MDPI. This book was released on 2020-06-17 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research articles, we have organized the book to show various aspects covered in this field, such as eHealth, technology-integrated research, prediction models, rehabilitation studies, prototype systems, community health studies, ergonomics design systems, technology acceptance model evaluation studies, telemonitoring systems, warning systems, application of sensors in sports studies, clinical systems, feasibility studies, geographical location based systems, tracking systems, observational studies, risk assessment studies, human activity recognition systems, impact measurement systems, and a systematic review. We would like to take this opportunity to invite high quality research articles for our next Special Issue entitled “Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases” as a part of Sensors journal.
Download or read book Sensor Analysis for the Internet of Things written by Michael Stanley and published by Springer Nature. This book was released on 2022-06-01 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals. Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types. We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.
Download or read book Tracking and Sensor Data Fusion written by Wolfgang Koch and published by Springer Science & Business Media. This book was released on 2013-09-20 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor Data Fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying real-world phenomenon is achieved. Typically, this insight is either unobtainable otherwise or a fusion result exceeds what can be produced from a single sensor output in accuracy, reliability, or cost. This book provides an introduction Sensor Data Fusion, as an information technology as well as a branch of engineering science and informatics. Part I presents a coherent methodological framework, thus providing the prerequisites for discussing selected applications in Part II of the book. The presentation mirrors the author's views on the subject and emphasizes his own contributions to the development of particular aspects. With some delay, Sensor Data Fusion is likely to develop along lines similar to the evolution of another modern key technology whose origin is in the military domain, the Internet. It is the author's firm conviction that until now, scientists and engineers have only scratched the surface of the vast range of opportunities for research, engineering, and product development that still waits to be explored: the Internet of the Sensors.
Download or read book Smart Sensor Networks written by Umang Singh and published by Springer Nature. This book was released on 2021-09-01 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Sensor Data Analysis and Management written by A. Suresh and published by John Wiley & Sons. This book was released on 2021-11-22 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.
Download or read book Understanding Real world Phenomena from Human generated Sensor Data written by Theofania Kleio Tsapeli and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sensor Data Fusion Analysis for Broad Applications written by Natividad Duro Carralero and published by . This book was released on 2024-07-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, there are many fields of application where different sensors are used to collect sensitive data. A good analysis of this data allows for improving the performance of a system, as well as making it more efficient and secure. New technologies have made it increasingly possible to analyze larger amounts of data, which has allowing for the area of sensor data fusion analysis to undergo exponential growth. The objective of this reprint is to immerse the reader in the latest advances in this area, showing applications in very different fields that demonstrate its relevance.
Download or read book Making Sense of Sensors written by Omesh Tickoo and published by Apress. This book was released on 2016-12-30 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make the most of the common architectures used for deriving meaningful data from sensors. This book provides you with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical examples and pointers to more information are used to outline the key aspects of Multimodal recognition. The book concludes with a discussion on relationship extraction, knowledge representation, and management. In today’s world we are surrounded by sensors collecting various types of data about us and our environments. These sensors are the primary input devices for wearable computers, IoT, and other mobile devices. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts. What You'll Learn Look at the general architecture for sensor based data Understand how data from common domains such as inertial, visual and audio is processed Master multi-modal recognition using multiple heterogeneous sensors Transition from recognition to knowledge through relationship understanding between entities Leverage different methods and tools for knowledge representation and management Who This Book Is For New college graduates and professionals interested in acquiring knowledge and the skills to develop innovative solutions around today's sensor-rich devices.
Download or read book Sensor Data Analysis and Information Extraction for Structural Health Monitoring written by Linjun Yan and published by . This book was released on 2006 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, advances in sensing techniques, internet technologies, and wireless communications are increasingly facilitating and allowing practical deployment of large and dense sensor networks for structural health monitoring. Thus, it is vital to develop efficient techniques to process and analyze the massive amount of sensor data in order to extract essential information on the monitored structures. The efforts in this dissertation are mainly dedicated to this direction, including studies on structural damage identification and traffic pattern recognition. In these studies, traditional analysis tools for structural engineering (e.g., finite element (FE) based simulation) are utilized, and the potential of machine learning techniques is extensively explored. Using different strategies, three structural damage identification approaches (referred to as approach A, B, and C, respectively) are developed in this dissertation. Both approaches A and B adopt decentralized analysis frameworks, which define substructures in the monitored system according to the sensor spatial distribution. Within approach A, each substructure is represented by a dynamic model, and the system properties are periodically identified based on sensor measurements for monitoring purposes. Herein, approach A is applied to seismic downhole data to monitor changes in soil layer properties. As for approach B, a neural network is developed for each substructure to predict the dynamic response at a selected sensor location from measurements of neighboring sensors. Thus, the dynamic characteristics of the substructure are represented by the network, and changes in the statistical distribution of network prediction error are evaluated and utilized as a damage indicator. Three applications of approach B are presented, two based on experimental data and one of bridge pier damage identification based on simulation data. Approach C adopts a statistical pattern recognition paradigm. Within this framework, a range of damage patterns of interest is provided by numerical simulation, the Principal Components Analysis (PCA) technique is employed for feature extraction, and a neural network is developed for damage pattern identification. Herein, approach C is also applied to the bridge pier damage identification problem. Finally, the combination of neural networks and PCA is also employed to develop a strain-based vehicle classification approach, based on a unique strain-video dataset.
Download or read book Faulty and Missing Sensor Data Analysis written by Duk-Jin Kim and published by . This book was released on 2013 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Daily human monitoring systems, however, have many challenges. One of the challenges is the sensor locations and the number of the sensor deployment. The location of the sensors must be accurate enough to diagnose the disorder, yet the collected physiological data from the on-body sensors should not be biased because of the discomfort of too many sensors being deployed. Another challenge is the fault and noise tolerance of the sensor data. Because of the nature of the body sensor networks (deploying sensors on the human body), the sensor failure or noise data cannot be avoided. Analyzing the biased or corrupted physiological data from the pervasive system leads to an erroneous diagnosis.
Download or read book High Spatial Resolution Remote Sensing written by Yuhong He and published by CRC Press. This book was released on 2018-06-27 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation. To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions: What are the challenges of using new sensors and new platforms? What are the cutting-edge methods for fine-level information extraction from high spatial resolution images? How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes? The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.
Download or read book Data Management and Data Analysis Techniques for Wireless Sensor Networks written by Song Lin and published by . This book was released on 2007 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt:
