Still Image File Format Comparison
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Javascript and the DOM
Javascript and the DOM 1 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento The web architecture with smart browser The web programmer also writes Programs which run on the browser. Which language? Javascript! HTTP Get + params File System Smart browser Server httpd Cgi-bin Internet Query SQL Client process DB Data Evolution 3: execute code also on client! (How ?) Javascript and the DOM 1- Adding dynamic behaviour to HTML 3 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Example 1: onmouseover, onmouseout <!DOCTYPE html> <html> <head> <title>Dynamic behaviour</title> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> </head> <body> <div onmouseover="this.style.color = 'red'" onmouseout="this.style.color = 'green'"> I can change my colour!</div> </body> </html> JAVASCRIPT The dynamic behaviour is on the client side! (The file can be loaded locally) <body> <div Example 2: onmouseover, onmouseout onmouseover="this.style.background='orange'; this.style.color = 'blue';" onmouseout=" this.innerText='and my text and position too!'; this.style.position='absolute'; this.style.left='100px’; this.style.top='150px'; this.style.borderStyle='ridge'; this.style.borderColor='blue'; this.style.fontSize='24pt';"> I can change my colour... </div> </body > JavaScript is event-based UiEvents: These event objects iherits the properties of the UiEvent: • The FocusEvent • The InputEvent • The KeyboardEvent • The MouseEvent • The TouchEvent • The WheelEvent See https://www.w3schools.com/jsref/obj_uievent.asp Test and Gym JAVASCRIPT HTML HEAD HTML BODY CSS https://www.jdoodle.com/html-css-javascript-online-editor/ Javascript and the DOM 2- Introduction to the language 8 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento JavaScript History • JavaScript was born as Mocha, then “LiveScript” at the beginning of the 94’s. -
Binary Image Compression Using Neighborhood Coding
BINARY IMAGE COMPRESSION USING NEIGHBORHOOD CODING Tiago B. A. de Carvalho1, Tsang Ing Ren1, George D.C. Cavalcanti1, Tsang Ing Jyh2 1Center of Informatics, Federal University of Pernambuco, Brazil. 2Alcatel-Lucent, Bell Labs, Belgium E-mail: 1{tbac,tir,gdcc}@cin.ufpe.br, [email protected] ABSTRACT Bitmap image format requires a reasonable great amount of computer memory, since for each pixel it is necessary a Compression plays an important role in the storage and set of bits to represent it, and a relatively small image can transmission of digital image. Binary image compression is contain millions of pixels. Even a binary image that uses just of essential value for document imaging. Here, we propose a a bit per pixel can demand a large amount of disk space. novel compression technique based on the concept of There are dozens of bitmap image formats that use neighborhood coding, which codes each pixel of an image compression techniques, e.g., TIFF, GIF, PNG, JBIG and according to the number of neighbor pixels in different JPEG. The TIFF format can also use different types of directions. The proposed technique is a lossless compression compression methods such as CCITT Group 3 or Group 4. scheme, which also uses run-length encoding (RLE) and We propose a novel lossless binary image compression Huffman coding. We evaluated and compared this method to technique based on neighborhood coding scheme described several image file format, using test images taken from the in [2]. The codification starts by transforming each pixel of MPEG-7 core experiment CE-shape and the binary image the image into a vector. -
MSD FATFS Users Guide
Freescale MSD FATFS Users Guide Document Number: MSDFATFSUG Rev. 0 02/2011 How to Reach Us: Home Page: www.freescale.com E-mail: [email protected] USA/Europe or Locations Not Listed: Freescale Semiconductor Technical Information Center, CH370 1300 N. Alma School Road Chandler, Arizona 85224 +1-800-521-6274 or +1-480-768-2130 [email protected] Europe, Middle East, and Africa: Information in this document is provided solely to enable system and Freescale Halbleiter Deutschland GmbH software implementers to use Freescale Semiconductor products. There are Technical Information Center no express or implied copyright licenses granted hereunder to design or Schatzbogen 7 fabricate any integrated circuits or integrated circuits based on the 81829 Muenchen, Germany information in this document. +44 1296 380 456 (English) +46 8 52200080 (English) Freescale Semiconductor reserves the right to make changes without further +49 89 92103 559 (German) notice to any products herein. Freescale Semiconductor makes no warranty, +33 1 69 35 48 48 (French) representation or guarantee regarding the suitability of its products for any particular purpose, nor does Freescale Semiconductor assume any liability [email protected] arising out of the application or use of any product or circuit, and specifically disclaims any and all liability, including without limitation consequential or Japan: incidental damages. “Typical” parameters that may be provided in Freescale Freescale Semiconductor Japan Ltd. Semiconductor data sheets and/or specifications can and do vary in different Headquarters applications and actual performance may vary over time. All operating ARCO Tower 15F parameters, including “Typicals”, must be validated for each customer 1-8-1, Shimo-Meguro, Meguro-ku, application by customer’s technical experts. -
OSLC Architecture Management Specification 3.0 Project Specification Draft 01 17 September 2020
Standards Track Work Product OSLC Architecture Management Specification 3.0 Project Specification Draft 01 17 September 2020 This stage: https://docs.oasis-open-projects.org/oslc-op/am/v3.0/psd01/architecture-management-spec.html (Authoritative) https://docs.oasis-open-projects.org/oslc-op/am/v3.0/psd01/architecture-management-spec.pdf Previous stage: N/A Latest stage: https://docs.oasis-open-projects.org/oslc-op/am/v3.0/architecture-management-spec.html (Authoritative) https://docs.oasis-open-projects.org/oslc-op/am/v3.0/architecture-management-spec.pdf Latest version: https://open-services.net/spec/am/latest Latest editor's draft: https://open-services.net/spec/am/latest-draft Open Project: OASIS Open Services for Lifecycle Collaboration (OSLC) OP Project Chairs: Jim Amsden ([email protected]), IBM Andrii Berezovskyi ([email protected]), KTH Editor: Jim Amsden ([email protected]), IBM Additional components: This specification is one component of a Work Product that also includes: OSLC Architecture Management Version 3.0. Part 1: Specification (this document). architecture-management- spec.html OSLC Architecture Management Version 3.0. Part 2: Vocabulary. architecture-management-vocab.html OSLC Architecture Management Version 3.0. Part 3: Constraints. architecture-management-shapes.html OSLC Architecture Management Version 3.0. Machine Readable Vocabulary Terms. architecture-management- vocab.ttl OSLC Architecture Management Version 3.0. Machine Readable Constraints. architecture-management-shapes.ttl architecture-management-spec Copyright © OASIS Open 2020. All Rights Reserved. 17 September 2020 - Page 1 of 19 Standards Track Work Product Related work: This specification is related to: OSLC Architecture Management Specification Version 2.0. -
Towards the Second Edition of ISO 24707 Common Logic
Towards the Second Edition of ISO 24707 Common Logic Michael Gr¨uninger(with Tara Athan and Fabian Neuhaus) Miniseries on Ontologies, Rules, and Logic Programming for Reasoning and Applications January 9, 2014 Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 1 / 20 What Is Common Logic? Common Logic (published as \ISO/IEC 24707:2007 | Information technology Common Logic : a framework for a family of logic-based languages") is a language based on first-order logic, but extending it in several ways that ease the formulation of complex ontologies that are definable in first-order logic. Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 2 / 20 Semantics An interpretation I consists of a set URI , the universe of reference a set UDI , the universe of discourse, such that I UDI 6= ;; I UDI ⊆ URI ; a mapping intI : V ! URI ; ∗ a mapping relI from URI to subsets of UDI . Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 3 / 20 How Is Common Logic Being Used? Open Ontology Repositories COLORE (Common Logic Ontology Repository) colore.oor.net stl.mie.utoronto.ca/colore/ontologies.html OntoHub www.ontohub.org https://github.com/ontohub/ontohub Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 4 / 20 How Is Common Logic Being Used? Ontology-based Standards Process Specification Language (ISO 18629) Date-Time Vocabulary (OMG) Foundational UML (OMG) Semantic Web Services Framework (W3C) OntoIOp (ISO WD 17347) Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 -
APL Literature Review
APL Literature Review Roy E. Lowrance February 22, 2009 Contents 1 Falkoff and Iverson-1968: APL1 3 2 IBM-1994: APL2 8 2.1 APL2 Highlights . 8 2.2 APL2 Primitive Functions . 12 2.3 APL2 Operators . 18 2.4 APL2 Concurrency Features . 19 3 Dyalog-2008: Dyalog APL 20 4 Iverson-1987: Iverson's Dictionary for APL 21 5 APL Implementations 21 5.1 Saal and Weiss-1975: Static Analysis of APL1 Programs . 21 5.2 Breed and Lathwell-1968: Implementation of the Original APLn360 25 5.3 Falkoff and Orth-1979: A BNF Grammar for APL1 . 26 5.4 Girardo and Rollin-1987: Parsing APL with Yacc . 27 5.5 Tavera and other-1987, 1998: IL . 27 5.6 Brown-1995: Rationale for APL2 Syntax . 29 5.7 Abrams-1970: An APL Machine . 30 5.8 Guibas and Wyatt-1978: Optimizing Interpretation . 31 5.9 Weiss and Saal-1981: APL Syntax Analysis . 32 5.10 Weigang-1985: STSC's APL Compiler . 33 5.11 Ching-1986: APL/370 Compiler . 33 5.12 Ching and other-1989: APL370 Prototype Compiler . 34 5.13 Driscoll and Orth-1986: Compile APL2 to FORTRAN . 35 5.14 Grelck-1999: Compile APL to Single Assignment C . 36 6 Imbed APL in Other Languages 36 6.1 Burchfield and Lipovaca-2002: APL Arrays in Java . 36 7 Extensions to APL 37 7.1 Brown and Others-2000: Object-Oriented APL . 37 1 8 APL on Parallel Computers 38 8.1 Willhoft-1991: Most APL2 Primitives Can Be Parallelized . 38 8.2 Bernecky-1993: APL Can Be Parallelized . -
PDF Image JBIG2 Compression and Decompression with JBIG2 Encoding and Decoding SDK Library | 1
PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 1 JBIG2 is an image compression standard for bi-level images developed by the Joint bi-level Image Expert Group. It is suitable for lossless compression and lossy compression. According to the group’s press release, in its lossless mode, JBIG2 usually generates files that are one- third to one-fifth the size of the fax group 4 and twice the size of JBIG, which was previously released by the group. The double-layer compression standard. JBIG2 was released as an international standard ITU in 2000. JBIG2 compression JBIG2 is an international standard for bi-level image compression. By segmenting the image into overlapping and/or non-overlapping areas of text, halftones and general content, compression techniques optimized for each content type are used: *Text area: The text area is composed of characters that are well suited for symbol-based encoding methods. Usually, each symbol will correspond to a character bitmap, and a sub-image represents a character or text. For each uppercase and lowercase character used on the front face, there is usually only one character bitmap (or sub-image) in the symbol dictionary. For example, the dictionary will have an “a” bitmap, an “A” bitmap, a “b” bitmap, and so on. VeryUtils.com PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 1 PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 2 *Halftone area: Halftone areas are similar to text areas because they consist of patterns arranged in a regular grid. -
Using Common Logic
Using Common Logic = Mapping other KR techniques to CL. = Eliminating dongles. = The zero case Pat Hayes Florida IHMC John Sowa talked about this side. Now we will look more closely at this side, using the CLIF dialect. Wild West Syntax (CLIF) Any character string can be a name, and any name can play any syntactic role, and any name can be quantified. Anything that can be named can also be the value of a function. All this gives a lot of freedom to say exactly what we want, and to write axioms which convert between different notational conventions. It also allows many other 'conventional' notations to be transcribed into CLIF directly and naturally. It also makes CLIF into a genuine network logic, because CLIF texts retain their full meaning when transported across a network, archived, or freely combined with other CLIF texts. (Unlike OWL-DL and GOFOL) Mapping other notations to CLIF. 1. Description Logics Description logics provide ways to define classes in terms of other classes, individuals and properties. All people who have at least two sons enlisted in the US Navy. <this class> owl:intersectionOf [:Person _:x] _:x rdf:type owl:Restriction _:x owl:minCardinality "2"^^xsd:number _:x owl:onProperty :hasSon _:x owl:toClass _:y _:y rdf:type owl:Restriction _:y owl:hasValue :USNavy _:y owl:onProperty :enlistedIn Mapping other notations to CLIF. 1. Description Logics Classes are CL unary relations, properties are CL binary relations. So DL operators are functions from relations to other relations. (owl:IntersectionOf Person (owl:minCardinality 2 hasSon (owl:valueIs enlistedIn USNavy))) (AND Person (MIN 2 hasSon (VAL enlistedIn USNavy))) ((AND Person (MIN 2 hasSon (VAL enlistedIn USNavy))) Harry) (= 2ServingSons (AND Person (MIN 2 hasSon (VAL enlistedIn USNavy)))) (2ServingSons Harry) (NavyPersonClassification 2ServingSons) (BackgroundInfo 2ServingSons 'Classification introduced in 2003 for public relation purposes.') Mapping other notations to CLIF. -
Active Learning – Concept Maps Leighann Tomaswick & Jennifer Marcinkiewicz March 30, 2018 Cite This Resource: Tomaswick, L
Active Learning – Concept Maps LeighAnn Tomaswick & Jennifer Marcinkiewicz March 30, 2018 Cite this resource: Tomaswick, L. and Marcinkiewicz, J. (2018). Active Learning – Concept Maps. Kent State University Center for Teaching and Learning. Retrieved [todaysdate] from [insert hyperlink] What is a Concept Map? . A concept map is a visualization of knowledge that is organized by the relationships between the topics. At its core, it is made of concepts that are connected together by lines (or arrows) that are labelled with the relationship between the concepts. The concepts are usually found in circles or boxes. Concept maps are a cross disicplinary active learning technique that help students manage concepts into sub-concepts, synthesize information, see a larger picture and develop higher-order thinking skills and strategies (Lee et al, 2013). Concept maps can summarize a part of a book, connect historical events, describe how a business is run, develop a personal care plan or patient treatment, describe how the body works, or the interconnectedness of a wetland’s ecology. A concept map may be used as a pre- class assignment, small group activity, whole class activity or a way to summarize the information at the end of a class or project. Instead of reading explanations from students, concepts maps provide a way to quickly look through students thinking process and understanding of concepts. Introduction . Novak developed concepts maps as a way to prompt meaningful learning (Novak and Gowen, 1984). They were built on Ausubel’s cognitive psychology, learning happens with the assimiluation of new concepts and proposition into exisiting concepts (Novak & Musonda, 1991). -
Implementing Consolidated-Clinical Document Architecture (C-CDA) for Meaningful Use Stage 2
Implementing Consolidated-Clinical Document Architecture (C-CDA) for Meaningful Use Stage 2 ONC Implementation and Testing Division April 5, 2013 Remember how healthcare data was exchanged prior to Electronic Health Records (EHRs)? Office of the National Coordinator for 2 Health Information Technology Healthcare Data Exchange (Pre-EHRs) Vast amounts of patient data collected by clinicians Medical information such as vitals, orders, prescriptions, lab notes, discharge summaries, etc. dictated or recorded by hand All of this clinical data was stored as paper records (documents) at each point of care If patient health records needed to be shared between providers, they usually required manual exchange (e.g. fax, “snail mail”) • Coordination of care between providers slow, costly; patient outcomes inconsistent • Duplicative healthcare services (e.g. labs imaging) frequent Office of the National Coordinator for 3 Health Information Technology What do the 2014 Edition EHR Certification Criteria and Meaningful Use Stage 2 objectives say about Health Information Exchange? Office of the National Coordinator for 4 Health Information Technology CMS & ONC Rules: 2014 Edition EHR Certification Criteria & MU2 ONC: Standards, Implementation Specifications & Certification Criteria (S&CC) 2014 Edition • Specifies capabilities and functions that Complete EHRs and EHR Modules must perform electronically in order to be certified under the ONC HIT Certification Program Reference: ONC Health Information Technology : Standards, Implementation Specifications, -
Answers to Exercises
Answers to Exercises A bird does not sing because he has an answer, he sings because he has a song. —Chinese Proverb Intro.1: abstemious, abstentious, adventitious, annelidous, arsenious, arterious, face- tious, sacrilegious. Intro.2: When a software house has a popular product they tend to come up with new versions. A user can update an old version to a new one, and the update usually comes as a compressed file on a floppy disk. Over time the updates get bigger and, at a certain point, an update may not fit on a single floppy. This is why good compression is important in the case of software updates. The time it takes to compress and decompress the update is unimportant since these operations are typically done just once. Recently, software makers have taken to providing updates over the Internet, but even in such cases it is important to have small files because of the download times involved. 1.1: (1) ask a question, (2) absolutely necessary, (3) advance warning, (4) boiling hot, (5) climb up, (6) close scrutiny, (7) exactly the same, (8) free gift, (9) hot water heater, (10) my personal opinion, (11) newborn baby, (12) postponed until later, (13) unexpected surprise, (14) unsolved mysteries. 1.2: A reasonable way to use them is to code the five most-common strings in the text. Because irreversible text compression is a special-purpose method, the user may know what strings are common in any particular text to be compressed. The user may specify five such strings to the encoder, and they should also be written at the start of the output stream, for the decoder’s use. -
DICOM Compression 2002
The Medicine Behind the Image DDIICCOOMM CCoommpprreessssiioonn 22000022 David Clunie Director of Technical Operations Princeton Radiology Pharmaceutical Research The Medicine SScchheemmeess SSuuppppoorrtteedd Behind the Image • RLE • JPEG - lossless and lossy • JPEG-LS - more efficient, fast lossless • JPEG 2000 - progressive, ROI encoding • Deflate (zip/gzip) - for non-image objects The Medicine IInn pprraaccttiiccee mmoossttllyy …… Behind the Image • Lossless JPEG for cardiac angio – multi-frame 512x512x8, 1024x1024x10 – CD-R and on network • Lossless JPEG for CT/MR – mostly on MOD media rather than over network – 256x256 to 1024x1024, 12-16 bits • RLE/lossless/lossy JPEG for Ultrasound – 640x480 single and multiframe 8 bits gray/RGB, text The Medicine BBuutt …… Behind the Image • JPEG lossless not the most effective • JPEG lossy limited to 12 bits unsigned • Undesirable JPEG blockiness • Perception that wavelets are better • Need for better progressive encoding • Need for region-of-interest encoding The Medicine JJPPEEGG LLoosssslleessss Behind the Image • Reasonable predictive scheme – Most often only previous pixel predictor used (SV1), which is not always the best choice • No run-length mode – No way to take advantage of large background areas • Huffman entropy coder – Slow (multi-pass) The Medicine LLoosssslleessss CCoommpprreessssiioonn Behind the Image CALIC Arithmetic 3.91 JPEG2000 VM4 5x3 3.81 JPEG-LS MINE 3.81 JPEG2000 VM4 3.66 2x10 S+P Arithmetic 3.4 JPEG-LS MINE - NO 3.31 Byte All RUN NASA szip 3.09 JPEG best 3.04 JPEG SV