14 research outputs found

    Google Custom Search: The Power of Google with a Librarian\u27s Touch

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    In this lighting session, we\u27ll look at Google Custom Search, its features, and how it can be used at your library. Why fight the inevitability your users will begin their search with Google, when you can take control of it. With Google Custom Search, you can customize a Google Search, giving your users the familiar Google experience but allowing you to curate the sites and/or topics it searches

    What’s New? Deploying a Library New Titles Page with Minimal Programming

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    With a new titles web page, a library has a place to show faculty, students, and staff the items they are purchasing for their community. However, many times heavy programing knowledge and/or a LAMP stack (Linux, Apache, MySQL, PHP) or APIs separate a library’s data from making a new titles web page a reality. Without IT staff, a new titles page can become nearly impossible or not worth the effort. Here we will demonstrate how a small liberal arts college took its acquisition data and combined it with a Google Sheet, HTML, and a little JavaScript to create a new titles web page that was dynamic and engaging to its users

    Lightning Round: Freeze-Frame: Archiving the Dynamic Web with Webrecorder.io

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    Archiving dynamic web content can be difficult especially while maintaining its GUI (graphical user interface). Modern websites have moved beyond static HTML pages to include videos, interactive JavaScript, or even individualized social media feeds - which makes archiving them even more difficult. Webrecoder.io is a free web-based tool that can quickly archive all of these types of content and more

    Digital Possibilities: vHMML

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    Part of the Digital Technologies in the Humanities and Social Sciences Conference hosted by CSB/SJU Librarie

    Digital Possibilities: Python

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    Part of the Digital Technologies in the Humanities and Social Sciences Conference hosted by CSB/SJU Librarie

    Creating an OER Toolkit: Offering Customized Solutions and Reducing Barriers in a Small Liberal Arts College

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    From the Chapter introduction: Colleges and universities are increasingly engaging with open access (OA), open educational resources (OERs), and open textbooks. At Macalester College, an exclusively undergraduate institution of roughly 2,000 students, we have long been involved with OA initiatives. We believe strongly in the free and fair exchange of information, which is the foundation of open access. In this chapter we focus on our experience supporting OERs, which, according to the Hewlett Foundation (n.d.), are “teaching, learning and research materials in any medium—digital or otherwise—that reside in the public domain or have been released under an open license that permits no-cost access, use, adaptation and redistribution by others with no or limited restrictions” (“Open Educational Resources: OER Defined”). Our background with OA initiatives has served as the foundation for our foray into OERs and open textbooks

    Full-Field 3D Experimental Modal Analysis from Dynamic Point Clouds Measured Using a Time-of-Flight Imager

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    The ability to measure static, high-resolution 3D point cloud data has existed for multiple decades and has been used to great benefit in both civil and mechanical engineering applications. Recently, time-of-flight imagers have emerged that are capable of measuring 3D dynamic point clouds at rates as high as 30 point cloud captures per second with resolutions approaching the millimeter scale. Conventional modal analysis utilizes contact measurements that are captured in the Lagrangian (i.e., material) coordinate system. Imager measurements such as used for DIC are captured in what is approximately an Eulerian frame of reference. However, oftentimes the imager measurements are captured in a small-motion, sub-pixel regime and can be assumed to be captured in a Lagrangian reference frame. As a result, most experimental modal identification algorithms are designed to operate on data captured in a Lagrangian reference frame. Measurements of 3D point clouds of vibrating structures do not necessarily fit into either an Eulerian or Lagrangian framework, particularly in the case where motion of the structure is large. An additional feature of these measurements is that the number of points measured on the structure can change significantly through time as a result of occlusions, the change in angle of the structure, or simply noise in the measurement. This feature of point clouds is significantly different from imagers and contact sensors in which the dimensionality of the measurements does not change through time. In this work we present the first known technique for extracting structural dynamics information from dynamic point clouds

    3D structural vibration identification from dynamic point clouds

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    Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure\u27s displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems
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