Reconfigurable Flood Wall Inspired by Architected Origami

Recent interest in the art of origami has opened a wide range of engineering applications and possibilities. Shape changing structures based on origami have had a large influence on the drive for efficient, sustainable engineering solutions. However, development in novel macro-scale utilization is lacking compared to the effort towards micro-scale devices. There exists an opening for environmentally actuated structures that improve quality for life of humans and the natural environment. Specifically, resilient infrastructure systems could potentially benefit from the tailorable properties and programmable reconfiguration of origami-inspired designs. The realm of flood protection and overall water resources management creates a unique opportunity for adaptable structures. A flood protection system, or flood wall, is one application of the origami technique. In many situations, flood protection is visually displeasing and hinders an otherwise scenic natural environment within a cityscape. By applying a permanent, adaptable protection system in flood-prone areas, not only will general aesthetics be conserved, but quick deployment in disaster situations will be ensured. With a rapidly changing climate and an increase in storm disaster events, an efficient flood-protection system is vital. In this study, simple rigid flood barriers are compared to adaptable wall systems that utilize multi-stable configurations. The flood event is characterized by a surcharge of water that is suddenly introduced–like that of a flash flood–and sustained at steady-state. Small-scale prototypes are tested in a hydraulic flume and compared to a numerical simulation for validation.Ohio State University College of Engineering Undergraduate Research ScholarshipNo embargoAcademic Major: Civil Engineerin

Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition

Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.Comment: Accepted to WACV 202

Network Coding-Based Next-Generation IoT for Industry 4.0

Industry 4.0 has become the main source of applications of the Internet of Things (IoT), which is generating new business opportunities. The use of cloud computing and artificial intelligence is also showing remarkable improvements in industrial operation, saving millions of dollars to manufacturers. The need for time-critical decision-making is evidencing a trade-off between latency and computation, urging Industrial IoT (IIoT) deployments to integrate fog nodes to perform early analytics. In this chapter, we review next-generation IIoT architectures, which aim to meet the requirements of industrial applications, such as low-latency and highly reliable communications. These architectures can be divided into IoT node, fog, and multicloud layers. We describe these three layers and compare their characteristics, providing also different use-cases of IIoT architectures. We introduce network coding (NC) as a solution to meet some of the requirements of next-generation communications. We review a variety of its approaches as well as different scenarios that improve their performance and reliability thanks to this technique. Then, we describe the communication process across the different levels of the architecture based on NC-based state-of-the-art works. Finally, we summarize the benefits and open challenges of combining IIoT architectures together with NC techniques

Design and analysis of peer 2 peer operating system

The peer to peer computing paradigm has become a popular paradigm for deploying distributed applications. Examples: Kadmelia, Chord, Skype, Kazaa, Big Table. Multiagent systems have become a dominant paradigm within AI for deploying reasoning and analytics applications. Such applications are compute-intensive. In disadvantaged networks the ad-hoc architecture is the most suitable one. Examples: military scenarios, disaster scenarios. We combine the paradigms of peer-to-peer computing, multiagent systems, cloud computing, and ad-hoc networks to create the new paradigm of ad-hoc peer-to-peer mobile agent cloud (APMA cloud) that can provide the computing power of a cloud in “disadvantaged” regions (e.g., through RF using a router or GPRS) – To this end we have designed and implemented a peer to peer operating system –PPOS that can leverage the computing power of such a cloud

Governance Framework for Cloud Computing

In the current era of competitive business worldand stringent market share and revenue sustenance challenges,organizations tend to focus more on their core competencies ratherthan the functional areas that support the business. However,traditionally this has not been possible in the IT management areabecause the technologies and their underlying infrastructures aresignificantly complex thus requiring dedicated and sustained inhouse efforts to maintain IT systems that enable core businessactivities. Senior executives of organisations are forced in manycases to conclude that it is too cumbersome, expensive and timeconsuming for them to manage internal IT infrastructures. Thistakes the focus away from their core revenue making activities.This scenario facilitates the need for external infrastructurehosting, external service provision and outsourcing capability.This trend resulted in evolution of IT outsourcing models. Theauthors attempted to analyse the option of leveraging the cloudcomputing model to facilitate this common scenario. This paperinitially discusses the characteristics of cloud computing focusingon scalability and delivery as a service. The model is evaluatedusing two case scenarios, one is an enterprise client with30,000 worldwide customers followed by a small scale subjectmatter expertise through small to medium enterprise (SME)organisations. The paper evaluates the findings and developsa governance framework to articulate the value propositionof cloud computing.. The model takes into consideration thefinancial aspects, and the behaviors and IT control structures ofan IT organisation

Towards building a Deep Learning based Automated Indian Classical Music Tutor for the Masses

Music can play an important role in the well-being of the world. Indian classical music is unique in its requirement for rigorous, disciplined, expert-led training that typically goes on for years before the learner can reach a reasonable level of performance. This keeps many, including the first author of this paper, away from mastering the skill. The problem is particularly compounded in rural areas, where the available expertise may be limited and prohibitively expensive, but the interest in learning classical music still prevails, nevertheless. Machine Learning has been complementing, enhancing, and replacing many white-collar jobs and we believe it can help with this problem as well. This paper describes efforts at using Machine Learning techniques, particularly, Long Short-Term Memory for building a system that is a step toward provisioning an Indian Classical Music Tutor for the masses. The system is deployed in the cloud using orchestrated containerization for potential worldwide access, load balancing, and other robust features

A Middleware framework for self-adaptive large scale distributed services

Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.Postprint (published version

Neural text line extraction in historical documents: a two-stage clustering approach

Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.Das Erschließen des unermesslichen Wissens, welches in unzähligen gescannten historischen Dokumenten verborgen liegt, bildet die Motivation für die vorgelegte Dissertation. Durch das Verknüpfen moderner Verfahren des maschinellen Lernens und der klassischen Bildverarbeitung wird in dieser Arbeit ein vollautomatisches Verfahren zur Extraktion von Textzeilen aus historischen Dokumenten entwickelt. Die Qualität wird auf verschiedensten Datensätzen im Vergleich zu anderen Ansätzen nachgewiesen. Das Verfahren wird bereits durch eine Vielzahl von Forschern verschiedenster Disziplinen genutzt