903 research outputs found

    Fast non-iterative algorithm for 3D point-cloud holography

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    Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, exceeding 100,000x at 512x512 array format.Comment: 22 pages, 11 figures, manuscript and supplemen

    3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

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    We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

    Supporting Multi-Cloud in Serverless Computing

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    Serverless computing is a widely adopted cloud execution model composed of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) offerings. The increased level of abstraction makes vendor lock-in inherent to serverless computing, raising more concerns than previous cloud paradigms. Multi-cloud serverless is a promising emerging approach against vendor lock-in, yet multiple challenges must be overcome to tap its potential. First, we need to be aware of both the performance and cost of each FaaS provider. Second, a multi-cloud architecture must be proposed before deploying a multi-cloud workflow. Domain-specific serverless offerings must then be integrated into the multi-cloud architecture to improve performance or save costs. Moreover, dealing with serverless offerings from multiple providers is challenging. Finally, we require workload portability support for serverless multi-cloud. In this paper, we present a multi-cloud library for cross-serverless offerings. We develop the End Analysis System (EAS) to support comparison among public FaaS providers in terms of performance and cost. Moreover, we design proof-of-concept multi-cloud architectures with domain-specific serverless offerings to alleviate problems such as data gravity. Finally, we deploy workloads on these architectures to evaluate several public FaaS offerings.Comment: Accepted for the 15th IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC'22 Companion

    Advanced virtual reality applications and intelligent agents for construction process optimisation and defect prevention

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    Defects and errors in new or recently completed construction work continually pervade the industry. Whilst inspection and monitoring processes are established vehicles for their 'control', the procedures involved are often process driven, time consuming, and resource intensive. Paradoxically therefore, they can impinge upon the broader aspects of project time, cost and quality outcomes. Acknowledging this means appreciating concatenation effects such as the potential for litigation, impact on other processes and influence on stakeholders' perceptions—that in turn, can impede progress and stifle opportunities for process optimisation or innovation. That is, opportunities relating to for example, logistics, carbon reduction, health and safety, efficiency, asset underutilisation and efficient labour distribution. This study evaluates these kinds of challenge from a time, cost and quality perspective, with a focus on identifying opportunities for process innovation and optimisation. It reviews—within the construction domain—state of the art technologies that support optimal use of artificial intelligence, cybernetics and complex adaptive systems. From this, conceptual framework is proposed for development of real-time intelligent observational platform supported by advanced intelligent agents, presented for discussion. This platform actively, autonomously and seamlessly manages intelligent agents (Virtual Reality cameras, Radio-Frequency Identification RFID scanners, remote sensors, etc.) in order to identify, report and document 'high risk' defects. Findings underpin a new ontological model that supports ongoing development of a dynamic, self-organised sensor (agent) network, for capturing and reporting real-time construction site data. The model is a 'stepping stone' for advancement of independent intelligent agents, embracing sensory and computational support, able to perform complicated (previously manual) tasks that provide optimal, dynamic, and autonomous management functions

    Visualizations for an Explainable Planning Agent

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    In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

    Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

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    With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

    Web-Scale Discovery and Federated Search

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    In stark contrast to the library card catalogs of old, today’s library search interfaces offer much more than one-dimensional, item-specific searching. Users are now engaged in a process of discovery in which they are empowered to control not only the sources of content being searched, but also the context into which information is delivered, and the platform onto which information is synthesized. By eliminating the barriers to information discovery, law libraries can position themselves as true partners in this process, defining their mission in new ways, and providing critical services in an ever-complex information ecosystem

    Data Placement in Object Storage Based Multiple Containers in Cloud Environment

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    Cloud computing is an Internet based processing where virtual shared servers give programming and different resources. Cloud storage is only capacity of information on outsider cloud servers. The benefits are boundless capacity, backup and recovery. The bad marks are specialized issues, cost and absence of backing in security. In This paper, we made to build an application for cloud security in IBM bluemix cloud to partition the data and storing them into multiple containers of object storage. Object storage is a resource which is used in IBM bluemix cloud to store a data. Hence the data is retrieve when needed by merging it. An proposed efficient data placement algorithm is used. This will consider how to place the files efficiently to the containers in object storage. Beside, the files will merge when client needs it back. So some additional algorithms is also used for partitioning and merging of files. Our goal is to achieve good security for cloud storage system, through proposed algorithm by using multiple containers of object storage in cloud
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