Sensor-based navigating mobile robots for people with disabilities

People with severe physical disabilities need help with everyday tasks, such as getting dressed, eating, brushing their teeth, scratching themselves, drinking, etc. They also need support to be able to work. They are usually helped by one or more persona

The World of Hyperbooks: Contemporary Artists’ Publications in Digital Time

This thesis explores how contemporary artists use the form of the book to reflect on a world of speed and hyper-culture brought about by digitization. It highlights three recent multimedia projects by artists Neta Bomani, Stephanie Syjuco, and Katie Paterson. Not one of these artists creates a singular “book” in the traditional sense. Rather they use aspects of the book to reflect upon changes in perceptions of time due to technological advancements in publishing. Each of the artists selected dwell on issues of time—be it archival time, the rapid influx of information online, or the deep time of the environment—and binds these problems into physical and digital media. Contemporary artists’ publications encompass a much broader world of issues that require an awareness of the non-physical book as much as the traditional one. Therefore, this thesis serves to expand upon the definition of what an “artists’ publications'' can be and how artists use the format to combat issues that have arisen from our digital, hypercultural condition. Keywords: Digital Time; Artists' Publications; Contemporary Art; Neta Bomani; Stephanie Syjuco; Katie Paterson; Internet Ar

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Automated Operational Modal Analysis of a Cable-Stayed Bridge

© 2017 American Society of Civil Engineers. Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented

Promoting Innovation and Economic Growth: The Special Problem of Digital Intellectual Property

There has been an explosion in the popularity of downloading and transmitting high-value digital content, triggered by the growth of the Internet and the evolution of peer-to-peer systems. At the same time, there is a substantial disconnect between public attitudes toward copyright and the letter of the law, and growing concern among copyright-holders over the erosion of their rights. The National Academy of Sciences has identified the phenomenon at the center of these developments and labeled it the "digital dilemma": The same technologies that allow the creation and manipulation of digital content (as well as its perfect reproduction and nearly free distribution) can also be used to prevent access to digital content. The result is a major policy debate between those who seek to protect their rights in digital content and those concerned about the public access to content that has traditionally been guaranteed under copyright law. In this emerging digital world, what, if anything, should be done to ensure that authors, artists, songwriters, and musicians have adequate incentives to create content? And what, if anything, should be done to protect the public's access rights, developed in the physical world, in order to encourage innovation and dissemination and to enhance the public domain? This report from the Digital Connections Council (DCC) of the Committee for Economic Development presents a different view of this "digital dilemma." Because of CED's mission to foster economic growth, the DCC has focused on the economic impact of copyright protection in the digital age and the potential economic effects of proposals for change. The report briefly explores the history of copyright law, revealing that legal protection of the rights of creators has always been explicitly balanced against protection of ongoing innovation. The DCC brings the perspective of the second innovator -- the creator of new social value based on existing copyrighted works -- to bear, noting that every creator owes a debt to what has come before. For this reason, our intellectual property systems are based on providing incentives to both create new material and to make such material open to the public for use for subsequent creation. The report then discusses current proposals for legislative and regulatory change, focusing on requests by the content distribution industries for technical copy protection mandates. Such mandates would have substantial effects on the information technology and consumer electronics industries in this country, on innovation, and on the economic growth that stems from the freedom to innovate

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given