Mobile collaborative cloudless computing

Although the computational power of mobile devices has been increasing, it is still not enough for some classes of applications. In the present, these applications delegate the computing power burden on servers located on the Internet. This model assumes an always-on Internet connectivity and implies a non-negligible latency. The thesis addresses the challenges and contributions posed to the application of a mobile collaborative computing environment concept to wireless networks. The goal is to define a reference architecture for high performance mobile applications. Current work is focused on efficient data dissemination on a highly transitive environment, suitable to many mobile applications and also to the reputation and incentive system available on this mobile collaborative computing environment. For this we are improving our already published reputation/incentive algorithm with knowledge from the usage pattern from the eduroam wireless network in the Lisbon area

Addressing the Elephant in the Cloudless Sky:Designing a Commonised Mobile Network Infrastructure

Whilst the notion of a creating a ‘cloudless sky’ by moving control and processing to the edge of the network, i.e. mobile devices, provides greater agency to users over the handling of their data, it still exists within a privately controlled networked infrastructure. Infrastructure can thus be considered the elephant in the room or in this case cloudless sky. In this paper, we address the elephant by considering a more radical and perhaps utopian perspective of providing greater user agency over the networked infrastructure itself

Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis

Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements across large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements in Colombia that have emerged between 2015 to 2020. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform

What you see is not always what you get: A qualitative, comparative analysis of ex ante visualizations with ex post photography of landscape and architectural projects

This study presents a qualitative, comparative analysis of ex ante visualizations, created during planning and design phases, with ex post photography of landscape and architectural projects. Visualizations play an increasingly important role as decision-making tools in the planning process and are expected to successfully communicate proposals to both experts and laypersons. Outside of the wind farm industry there is a lack of detailed guidance for those creating landscape visualizations and currently no method of analyzing the accuracy of visualizations exists. In a world where we increasingly rely on information communicated in a visual manner itis imperative that potential viewers are provided with clues to enable them to distinguish between what is real and what is not. This study analyses a selection of visualizations from a cross section of landscape and architectural projects and reveals reoccurring patterns of inconsistencies in the depiction of content elements. The control of production through agreed guidelines proposed by previously published research could have both positive and negative effects for the future of visualization production. This research proposes that the starting point for honest communication lies in transparency, in both production techniques and presentation to clients, stakeholders and the public. There is scope for more in depth image analysis of a larger body of projects that may reveal more detailed findings that could contribute to future guideline discussions

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Spectral Detection of Human Skin in VIS-SWIR Hyperspectral Imagery without Radiometric Calibration

Many spectral detection algorithms require precise ground truth measurements that are hand-selected in the image to apply radiometric calibration, converting image pixels into estimated reflectance vectors. That process is impractical for mobile, real-time hyperspectral target detection systems, which cannot empirically derive a pixel-to-reflectance relationship from objects in the image. Implementing automatic target recognition on high-speed snapshot hyperspectral cameras requires the ability to spectrally detect targets without performing radiometric calibration. This thesis demonstrates human skin detection on hyperspectral data collected at a high frame rate without using calibration panels, even as the illumination in the scene changes. Compared to an established skin detection method that requires calibration panels, the illumination-invariant methods in this thesis achieve nearly as good detection performance in sunny scenes and superior detection performance in cloudy scenes