Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

High dimensional data analysis for anomaly detection and quality improvement

Analysis of large-scale high-dimensional data with a complex heterogeneous data structure to extract information or useful features is vital for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. Chapter 3 focuses on detecting anomalies from high-dimensional data. Traditionally, most of the image-based anomaly detection methods perform denoising and detection sequentially, which affects detection accuracy and efficiency. In this chapter, A novel methodology, named smooth-sparse decomposition (SSD), is proposed to exploit regularized high-dimensional regression to decompose an image and separate anomalous regions simultaneously by solving a large-scale optimization problem. Chapter 4 extends this to spatial-temporal functional data by extending SSD to spatiotemporal smooth-sparse decomposition (ST-SSD), with a likelihood ratio test to detect the time of change accurately based on the detected anomaly. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are also developed. The proposed methodology is also applied to tonnage signals, rolling inspection data and solar flare monitoring. Chapter 5 considers the adaptive sampling problem for high-dimensional data. A novel adaptive sampling framework, named Adaptive Kernelized Maximum-Minimum Distance is proposed to adaptively estimate the sparse anomalous region. The proposed method balances the sampling efforts between the space filling sampling (exploration) and focused sampling near the anomalous region (exploitation). The proposed methodology is also applied to a case study of anomaly detection in composite sheets using a guided wave test. Chapter 6 explores the penalized tensor regression to model the tensor response data with the process variables. Regularized Tucker decomposition and regularized tensor regression methods are developed, which model the structured point cloud data as tensors and link the point cloud data with the process variables. The performance of the proposed method is evaluated through simulation and a real case study of turning process optimization.Ph.D

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

Structural Health Monitoring Damage Detection Systems for Aerospace

This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation