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Neuron C Reference Guide Iii • Introduction to the LONWORKS Platform (078-0391-01A)
Neuron C Provides reference info for writing programs using the Reference Guide Neuron C programming language. 078-0140-01G Echelon, LONWORKS, LONMARK, NodeBuilder, LonTalk, Neuron, 3120, 3150, ShortStack, LonMaker, and the Echelon logo are trademarks of Echelon Corporation that may be registered in the United States and other countries. Other brand and product names are trademarks or registered trademarks of their respective holders. Neuron Chips and other OEM Products were not designed for use in equipment or systems, which involve danger to human health or safety, or a risk of property damage and Echelon assumes no responsibility or liability for use of the Neuron Chips in such applications. Parts manufactured by vendors other than Echelon and referenced in this document have been described for illustrative purposes only, and may not have been tested by Echelon. It is the responsibility of the customer to determine the suitability of these parts for each application. ECHELON MAKES AND YOU RECEIVE NO WARRANTIES OR CONDITIONS, EXPRESS, IMPLIED, STATUTORY OR IN ANY COMMUNICATION WITH YOU, AND ECHELON SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of Echelon Corporation. Printed in the United States of America. Copyright © 2006, 2014 Echelon Corporation. Echelon Corporation www.echelon.com Welcome This manual describes the Neuron® C Version 2.3 programming language. It is a companion piece to the Neuron C Programmer's Guide. -
Geotagging: an Innovative Tool to Enhance Transparency and Supervision
Geotagging: An Innovative Tool To Enhance Transparency and Supervision Transparency through Geotagging Noel Sta. Ines Geotagging Geotagging • Process of assigning a geographical reference, i.e, geographical coordinates (latitude and longitude) + elevation - to an object. • This could be done by taking photos, nodes and tracks with recorded GPS coordinates. • This allows geo-tagged object or SP data to be easily and accurately located on a map. WhatThe is Use Geotagging and Implementation application in of the Geo Philippines?-Tagging • A revolutionary and inexpensive approach of using ICT + GPS applications for accurate visualization of sub-projects • Device required is only a GPS enabled android cellphone, and access to freely available apps • Easily replicable for mainstreaming to Government institutions & CSOs • Will help answer the question: Is the right activity implemented in the right place? – (asset verification tool) Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 1: Visualization of a farm-to-market road in a conflict area: showing specific location, ground distance, track / alignment, elevation profile, ground photos (with coordinates, date and time taken) of Farm-to-market Road, i.e. baseline information + Progress photos + 3D visualization Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 2: Location and Visualization of rehabilitation of a city road in Earthquake damaged-area in Tagbilaran, Bohol, Philippines by the auditors and the citizen volunteers Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. 3 Visualization of a water supply project showing specific location, elevation profile from the source , distribution lines and faucets, and ground photos of community faucets Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. -
Navigation Techniques in Augmented and Mixed Reality: Crossing the Virtuality Continuum
Chapter 20 NAVIGATION TECHNIQUES IN AUGMENTED AND MIXED REALITY: CROSSING THE VIRTUALITY CONTINUUM Raphael Grasset 1,2, Alessandro Mulloni 2, Mark Billinghurst 1 and Dieter Schmalstieg 2 1 HIT Lab NZ University of Canterbury, New Zealand 2 Institute for Computer Graphics and Vision Graz University of Technology, Austria 1. Introduction Exploring and surveying the world has been an important goal of humankind for thousands of years. Entering the 21st century, the Earth has almost been fully digitally mapped. Widespread deployment of GIS (Geographic Information Systems) technology and a tremendous increase of both satellite and street-level mapping over the last decade enables the public to view large portions of the 1 2 world using computer applications such as Bing Maps or Google Earth . Mobile context-aware applications further enhance the exploration of spatial information, as users have now access to it while on the move. These applications can present a view of the spatial information that is personalised to the user’s current context (context-aware), such as their physical location and personal interests. For example, a person visiting an unknown city can open a map application on her smartphone to instantly obtain a view of the surrounding points of interest. Augmented Reality (AR) is one increasingly popular technology that supports the exploration of spatial information. AR merges virtual and real spaces and offers new tools for exploring and navigating through space [1]. AR navigation aims to enhance navigation in the real world or to provide techniques for viewpoint control for other tasks within an AR system. AR navigation can be naively thought to have a high degree of similarity with real world navigation. -
Geotagging Photos to Share Field Trips with the World During the Past Few
Geotagging photos to share field trips with the world During the past few years, numerous new online tools for collaboration and community building have emerged, providing web-users with a tremendous capability to connect with and share a variety of resources. Coupled with this new technology is the ability to ‘geo-tag’ photos, i.e. give a digital photo a unique spatial location anywhere on the surface of the earth. More precisely geo-tagging is the process of adding geo-spatial identification or ‘metadata’ to various media such as websites, RSS feeds, or images. This data usually consists of latitude and longitude coordinates, though it can also include altitude and place names as well. Therefore adding geo-tags to photographs means adding details as to where as well as when they were taken. Geo-tagging didn’t really used to be an easy thing to do, but now even adding GPS data to Google Earth is fairly straightforward. The basics Creating geo-tagged images is quite straightforward and there are various types of software or websites that will help you ‘tag’ the photos (this is discussed later in the article). In essence, all you need to do is select a photo or group of photos, choose the "Place on map" command (or similar). Most programs will then prompt for an address or postcode. Alternatively a GPS device can be used to store ‘way points’ which represent coordinates of where images were taken. Some of the newest phones (Nokia N96 and i- Phone for instance) have automatic geo-tagging capabilities. These devices automatically add latitude and longitude metadata to the existing EXIF file which is already holds information about the picture such as camera, date, aperture settings etc. -
Geotag Propagation in Social Networks Based on User Trust Model
1 Geotag Propagation in Social Networks Based on User Trust Model Ivan Ivanov, Peter Vajda, Jong-Seok Lee, Lutz Goldmann, Touradj Ebrahimi Multimedia Signal Processing Group Ecole Polytechnique Federale de Lausanne, Switzerland Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 2 We introduce users in our system for geotagging in order to simulate a real social network GPS coordinates to derive geographical annotation, which are not available for the majority of web images and photos A GPS sensor in a camera provides only the location of the photographer instead of that of the captured landmark Sometimes GPS and Wi-Fi geotagging determine wrong location due to noise http: //www.pl acecas t.net Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 3 Tag – short textual annotation (free-form keyword)usedto) used to describe photo in order to provide meaningful information about it User-provided tags may sometimes be spam annotations given on purpose or wrong tags given by mistake User can be “an algorithm” http://code.google.com/p/spamcloud http://www.flickr.com/photos/scriptingnews/2229171225 Multimedia Signal Processing Group Swiss Federal Institute of Technology Goal 4 Consider user trust information derived from users’ tagging behavior for the tag propagation Build up an automatic tag propagation system in order to: Decrease the anno ta tion time, and Increase the accuracy of the system http://www.costadevault.com/blog/2010/03/listening-to-strangers Multimedia -
UC Berkeley International Conference on Giscience Short Paper Proceedings
UC Berkeley International Conference on GIScience Short Paper Proceedings Title Tweet Geolocation Error Estimation Permalink https://escholarship.org/uc/item/0wf6w9p9 Journal International Conference on GIScience Short Paper Proceedings, 1(1) Authors Holbrook, Erik Kaur, Gupreet Bond, Jared et al. Publication Date 2016 DOI 10.21433/B3110wf6w9p9 Peer reviewed eScholarship.org Powered by the California Digital Library University of California GIScience 2016 Short Paper Proceedings Tweet Geolocation Error Estimation E. Holbrook1, G. Kaur1, J. Bond1, J. Imbriani1, C. E. Grant1, and E. O. Nsoesie2 1University of Oklahoma, School of Computer Science Email: {erik; cgrant; gkaur; jared.t.bond-1; joshimbriani}@ou.edu 2University of Washington, Institute for Health Metrics and Evaluation Email: [email protected] Abstract Tweet location is important for researchers who study real-time human activity. However, few studies have examined the reliability of social media user-supplied location and description in- formation, and most who do use highly disparate measurements of accuracy. We examined the accuracy of predicting Tweet origin locations based on these features, and found an average ac- curacy of 1941 km. We created a machine learning regressor to evaluate the predictive accuracy of the textual content of these fields, and obtained an average accuracy of 256 km. In a dataset of 325788 tweets over eight days, we obtained city-level accuracy for approximately 29% of users based only on their location field. We describe a new method of measuring location accuracy. 1. Introduction With the rise of micro-blogging services and publicly available social media posts, the problem of location identification has become increasingly important. -
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data Michael D. Lieberman, Hanan Samet, Jagan Sankaranarayanan Center for Automation Research, Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland College Park, MD 20742, USA {codepoet, hjs, jagan}@cs.umd.edu Abstract— The successful execution of location-based and feature-based queries on spatial databases requires the construc- tion of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience’s vocabulary of geographic locations, termed its Fig. 1. Locations mentioned in news articles about the May 2009 swine flu pandemic, obtained by geotagging related news articles. Large red circles spatial lexicon. In contrast to previous work, a new spatial lexicon indicate high frequency, and small circles are color coded according to recency, model is presented that distinguishes between a global lexicon of with lighter colors indicating the newest mentions. locations known to all audiences, and an audience-specific local lexicon. -
Geotagging Tweets Using Their Content
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference Geotagging Tweets Using Their Content Sharon Paradesi Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology [email protected] Abstract Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audi- ence based on its geographic location is a major chal- lenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a per- formance improvement of three orders by TwitterTag- ger compared to that of the baseline model. Introduction People use popular social networking websites such as Face- book and Twitter to share their interests and opinions with their friends and the online community. Harnessing this in- formation in real-time and showing it to the relevant audi- ence based on its geographic location is a major challenge. Figure 1: Architecture of TwitterTagger The microblogging social medium, Twitter, is used because of its relevance to users in real-time. The goal of this research is to identify the locations refer- Pipe POS tagger1. The noun phrases are then compared with enced in a tweet and show relevant tweets to a user based on the USGS database2 of locations. Common noun phrases, that user’s location. For example, a user traveling to a new such as ‘Love’ and ‘Need’, are also place names and would place would would not necessarily know all the events hap- be geotagged. To avoid this, the system uses a greedy ap- pening in that place unless they appear in the mainstream proach of phrase chunking. -
TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response Jens De Bruijn1, Hans De Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, Jeroen C.J.H
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-203 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 13 June 2017 c Author(s) 2017. CC BY 3.0 License. TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response Jens de Bruijn1, Hans de Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, Jeroen C.J.H. Aerts1 1Institute for Environmental Studies, VU University, Amsterdam, 1081HV, The Netherlands 5 2Global Facility for Disaster Reduction and Recovery, World Bank Group, Washington D.C., 20433, USA 3FloodTags, The Hague, 2511 BE, The Netherlands Correspondence to: Jens de Bruijn ([email protected]) Abstract. The availability of timely and accurate information about ongoing events is important for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, and in particular Twitter, have gained traction as 10 a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, analyses of a tweet’s text, combined with an evaluation of its metadata, can help to increase the number of geo-located tweets. This paper describes a new algorithm (TAGGS), that georeferences tweets by using the spatial information of groups of tweets mentioning the same location. This technique results in a roughly twofold increase in the number of geo-located tweets as compared to existing methods. We applied this approach 15 to 35.1 million flood-related tweets in 12 languages, collected over 2.5 years. In the dataset, we found 11.6 million tweets mentioning one or more flood locations, which can be towns (6.9 million), provinces (3.3 million), or countries (2.2 million). -
Parallel Range, Segment and Rectangle Queries with Augmented Maps
Parallel Range, Segment and Rectangle Queries with Augmented Maps Yihan Sun Guy E. Blelloch Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] Abstract The range, segment and rectangle query problems are fundamental problems in computational geometry, and have extensive applications in many domains. Despite the significant theoretical work on these problems, efficient implementations can be complicated. We know of very few practical implementations of the algorithms in parallel, and most implementations do not have tight theoretical bounds. In this paper, we focus on simple and efficient parallel algorithms and implementations for range, segment and rectangle queries, which have tight worst-case bound in theory and good parallel performance in practice. We propose to use a simple framework (the augmented map) to model the problem. Based on the augmented map interface, we develop both multi-level tree structures and sweepline algorithms supporting range, segment and rectangle queries in two dimensions. For the sweepline algorithms, we also propose a parallel paradigm and show corresponding cost bounds. All of our data structures are work-efficient to build in theory (O(n log n) sequential work) and achieve a low parallel depth (polylogarithmic for the multi-level tree structures, and O(n) for sweepline algorithms). The query time is almost linear to the output size. We have implemented all the data structures described in the paper using a parallel augmented map library. Based on the library each data structure only requires about 100 lines of C++ code. We test their performance on large data sets (up to 108 elements) and a machine with 72-cores (144 hyperthreads). -
An Introduction to Georss: a Standards Based Approach for Geo-Enabling RSS Feeds
Open Geospatial Consortium Inc. Date: 2006-07-19 Reference number of this document: OGC 06-050r3 Version: 1.0.0 Category: OpenGIS® White Paper Editors: Carl Reed OGC White Paper An Introduction to GeoRSS: A Standards Based Approach for Geo-enabling RSS feeds. Warning This document is not an OGC Standard. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting do Document type: OpenGIS® White Paper Document subtype: White Paper Document stage: APPROVED Document language: English OGC 06-050r3 Contents Page i. Preface – Executive Summary........................................................................................ iv ii. Submitting organizations............................................................................................... iv iii. GeoRSS White Paper and OGC contact points............................................................ iv iv. Future work.....................................................................................................................v Foreword........................................................................................................................... vi Introduction...................................................................................................................... vii 1 Scope.................................................................................................................................1 -
Handwritten Digit Classication Using 8-Bit Floating Point Based Convolutional Neural Networks
Downloaded from orbit.dtu.dk on: Apr 10, 2018 Handwritten Digit Classication using 8-bit Floating Point based Convolutional Neural Networks Gallus, Michal; Nannarelli, Alberto Publication date: 2018 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Gallus, M., & Nannarelli, A. (2018). Handwritten Digit Classication using 8-bit Floating Point based Convolutional Neural Networks. DTU Compute. (DTU Compute Technical Report-2018, Vol. 01). General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Handwritten Digit Classification using 8-bit Floating Point based Convolutional Neural Networks Michal Gallus and Alberto Nannarelli (supervisor) Danmarks Tekniske Universitet Lyngby, Denmark [email protected] Abstract—Training of deep neural networks is often con- In order to address this problem, this paper proposes usage strained by the available memory and computational power. of 8-bit floating point instead of single precision floating point This often causes it to run for weeks even when the underlying which allows to save 75% space for all trainable parameters, platform is employed with multiple GPUs.