Unit Iv -Multimedia File Handling
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Wayne Community College 2009-2010 Strategic Plan End-Of-Year Report Table of Contents
Wayne Community College INSTITUTIONAL EFFECTIVENESS THROUGH PLAN & BUDGET INTEGRATION 2009-2010 Strategic Plan End-of-Year Report Wayne Community College 2009-2010 Strategic Plan End-of-Year Report Table of Contents Planning Group 1 – President Foundation Institutional Advancement Planning Group 2 – VP Academic Services Academic Skills Center Ag & Natural Resources Allied Health Arts & Sciences Business Administration Cooperative Programs Dental Engineering & Mechanical Studies Global Education Information Systems & Computer Technology Language & Communication Library Mathematics Medical Lab Sciences Nursing Pre-Curriculum Public Safety Public Services Science SJAFB Social Science Transportation Planning Group 3 – VP Student Services VP Student Services Admissions & Records Financial Aid Student Activities Student Development Planning Group 4 – VP Educational Support Services VP Educational Support Services Campus Information Services Educational Support Technologies Facilities Operations Information Technology Security Planning Group 5 – VP Continuing Education VP Continuing Education Basic Skills Business & Industry Center Occupational Extension WCC PLANNING DOCUMENT 2009-2010 Department: Foundation Long Range Goal #8: Integrate state-of-practice technology in all aspects of the college’s programs, services, and operations. Short Range Goal #8.2: Expand and improve program accessibility through technology. Objective/Intended Outcome: The Foundation has experienced phenomenal growth in the last three years. With the purchase of the Raisers Edge Software, we have been able to see a direct increase in our revenues. In order to sustain this level of growth, The Foundation either needs to hire extra manpower or purchase additional Raiser’s Edge software to support our growth. 1. Raiser’s Edge NetSolutions: Enhance the Foundation office’s fundraising abilities. The Foundation would be able to accept online donations, reservations for golf tournament, gala, arts and humanities programs and reach out to alumni. -
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. -
Intel Corporation 2000 Annual Report
silicon is in 2000 Annual Report i n t e l .c o m i n t c . c o m Intel facts and figures Net revenues Diluted earnings per share Dollars in billions Dollars, adjusted for stock splits 35 1.6 33.7 1.51 30 29.4 1.2 26.3 25 25.1 Intel revenues 1.05 20.8 20 grew 15% in 2000, 0.97 0.86 0.8 giving us our 14th 16.2 15 0.73 consecutive year of 11.5 10 0.50 0.4 8.8 revenue growth. 0.33 0.33 5.8 5 4.8 0.12 0.16 0 0 91 92 93 94 95 9697 98 99 00 91 92 93 94 95 9697 98 99 00 Geographic breakdown of 2000 revenues Return on average stockholders’ equity Percent Percent 100 40 38.4 35.5 35.6 33.3 North America 41% Intel has 30 75 30.2 experienced strong 27.3 28.4 26.2 international growth, 21.6 20 50 with 59% of revenues 20.4 Asia-Pacific 26% outside North America in 2000. 10 25 Europe 24% 0 Japan 9% 91 92 93 94 95 9697 98 99 00 0 Capital additions to property, Stock price trading ranges by fiscal year plant and equipment † Dollars, adjusted for stock splits Dollars in millions 75 8,000 Capital invest- 6,674 ments reflect Intel’s 6,000 50 commitment to building leading-edge manu- 4,501 4,000 4,032 facturing capacity for 3,550 3,403 25 3,024 state-of-the-art 2,441 2,000 silicon products. -
Intel Corporation Annual Report 1999
clients networking and communications intel.com 1999 annual report the building blocks of the internet economy intc.com server platforms solutions and services 29.4 30 2.25 90 2.11 26.3 1.93 25.1 1.73 20.8 20 1.45 1.50 60 16.2 1.01 11.5 10 0.75 30 8.8 0.65 0.65 High 5.8 4.8 3.9 0.31 Close 0.24 0.20 Low INTEL CORPORATION 1999 0 0 0 90 91 92 93 94 95 96 97 98 99 90 91 92 93 94 95 96 97 98 99 90 91 92 93 94 95 96 97 98 99 Net revenues Diluted earnings per share Stock price trading ranges (Dollars in billions) (Dollars, adjusted for stock splits) by fiscal year (Dollars, adjusted for stock splits) 3,111 1999 facts and figures 3,000 45 2,509 Intel’s stock 38.4 2,347 35.5 35.6 price has risen 33.3 2,000 28.4 30 1,808 27.3 at a 48% 26.2 21.2 21.6 20.4 1,296 1,111 970 compound 1,000 15 780 618 517 annual growth 0 rate in the 0 90 91 92 93 94 95 96 97 98 99 90 91 92 93 94 95 96 97 98 99 Research and development Return on average (Dollars in millions, excluding purchased last 10 years. stockholders’ equity in-process research and development) (Percent) 9.76 4,501 9 Japan 4,500 7% 4,032 7.05 3,550 3,403 3,024 5.93 6 Asia- 3,000 Pacific North 5.14 23% America 2,441 43% 1,9 33 3.69 2.80 3 1,500 1,228 2.24 Machinery 948 & equipment 1.63 1.35 Europe 680 1.12 27% Land, buildings & improvements 0 0 90 91 92 93 94 95 96 97 98 99 90 91 92 93 94 95 96 97 98 99 Book value per share Geographic breakdown of 1999 revenues Capital additions to property, at year-end (Percent) plant and equipment† (Dollars, adjusted for stock splits) (Dollars in millions) Past performance does not guarantee future results. -
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. -
(12) United States Patent (10) Patent No.: US 7,020,893 B2 Connelly (45) Date of Patent: Mar
US007020893B2 (12) United States Patent (10) Patent No.: US 7,020,893 B2 Connelly (45) Date of Patent: Mar. 28, 2006 (54) METHOD AND APPARATUS FOR 5,945,988 A 8, 1999 Williams et al. CONTINUOUSLY AND 5,977.964. A 11/1999 Williams et al. OPPORTUNISTICALLY DRIVING AN 5.991,841 A 11/1999 Gafken et al. OPTIMIAL BROADCAST SCHEDULE BASED 6,002,393 A 12/1999 Hite et al. ON MOST RECENT CLIENT DEMAND FEEDBACK FROMA DISTRIBUTED SET OF (Continued) BROADCAST CLIENTS FOREIGN PATENT DOCUMENTS (75) Inventor: Jay H. Connelly, Portland, OR (US) WO WO 99,65237 12/1999 (73) Assignee: Intel Corporation, Santa Clara, CA (Continued) (US) OTHER PUBLICATIONS (*) Notice: Subject to any disclaimer, the term of this Intel: Intel Architecture Labs. Internet and Broadcast: The patent is extended or adjusted under 35 Key To Digital Convergence. Utilizing Digital Technology U.S.C. 154(b) by 882 days. to Meet Audience Demand, 2000, pp. 1-4. (21) Appl. No.: 09/882,487 (Continued) Filed: Jun. 15, 2001 Primary Examiner Ngoc Vu (22) (74) Attorney, Agent, or Firm—Blakely, Sokoloff, Taylor & (65) Prior Publication Data Zafman LLP US 20O2/O194598 A1 Dec. 19, 2002 (57) ABSTRACT (51) Int. C. HO)4N 7/173 (2006.01) Abroadcast method and system for continuously and oppor (52) U.S. Cl. ............................ 725/97; 725/91. 725/95; tunistically driving an optimal broadcast schedule based on 725/114; 725/121 most recent client demand feedback from a distributed set of (58) Field of Classification Search .................. 725/95, broadcast clients. The broadcast system includes an opera 725/97, 98, 105,109, 118, 121, 46,91, 92, tion center that broadcasts meta-data to a plurality of client 725/114, 120 systems. -
Ultra-Wideband Research and Implementation
UltraWideband Research and Implementation Senior Capstone Project 2007 Final Report by: Jarrod Cook Nathan Gove Project Advisors: Dr. Brian Huggins Dr. In Soo Ahn Dr. Prasad Shastry Abstract Ultra‐wideband (UWB) is a wireless transmission standard that will soon revolutionize consumer electronics. UWB is interesting because of its inherent low power consumption, high data rates of up to 480 Mbps, and large spatial capacity1. Furthermore, the power spectral density is low enough to prevent interference with other wireless services. This new standard has recently been approved by the FCC for unlicensed use and has been gaining interest throughout the consumer electronics industry. The main goal of the project is to increase interest in UWB technology, and to explore its usefulness. The history and benefits of UWB will be discussed, followed by the project goals, implementation, and results. Texas Instruments DSP platforms were used in conjunction with Simulink and Code Composter Studio to implement the scaled‐down baseband modulation scheme. Also, RF microwave components were donated from Hittite Corporation to perform quadrature modulation and up conversion to a frequency of 3.4 GHz. 1 Spatial capacity is defined as bits/sec/square‐meter. UWB is projected at having 6 devices working simultaneously within a 10 m range [16]. ii. Acknowledgements There are some people that need to be acknowledged for their work with this project. They include, but are not limited to: • Luke Vercimak and Karl Weyeneth for work on their 2006 Senior Capstone Project, Software Defined Radio. • Dr. Brian Huggins, Dr. In Soo Ahn and Dr. Prasad Shastry for advising, guidance and general encouragement throughout. -
Microprocessor Packaging (Intel)
MicroprocessorMicroprocessor PackagingPackaging The Key Link in the Chain Koushik Banerjee Technical Advisor Assembly Technology Development Intel Corporation 1 MicroprocessorsMicroprocessors 1971 2001 2 GlobalGlobal PackagingPackaging R&DR&D China Shanghai Philippines Cavite Arizona Chandler Malaysia Penang VirtualVirtual ATDATD 3 MainMain R&DR&D facilityfacility inin Chandler,Chandler, AZAZ 4 Computing needs driving complexity First to introduce organic in mainstream CPUs First to introduce flip chip in Intel 486TM Pentium®mainstreamPentium® Pro Pentium® CPUs II Pentium® III Pentium® 4 Itanium® Processor Processor Processor Processor Processor Processor Processor 25 MHz 1.0+ GHz Ceramic Wire-bond To Organic To Flip Chip 5 LookingLooking aheadahead …… ComplexityComplexity andand ChallengesChallenges toto supportsupport Moore’sMoore’s LawLaw 1. Silicon to package interconnect 2. Within package interconnect 3. Power management 4. Adding more functionality GoalGoal :: BringBring technologytechnology innovationinnovation intointo HighHigh volumevolume manufacturingmanufacturing atat aa LOWLOW COSTCOST 6 SiliconSilicon ?? PackagePackage RelationshipRelationship AnatomyAnatomy 101101 Silicon Processor: The “brain” of the computer (generates instructions) Packaging: The rest of the body (Communicates instructions to the outside world, adds protection) NoNo PackagePackage == NoNo ProductProduct !! Great Packaging = Great Products !! Great Packaging = Great Products !! 7 TheThe KeyKey LinkLink inin thethe ChainChain OpportunityOpportunity Transistor-to-Transistor -
Configuring and Scheduling an Eager-Writing Disk Array
Configuring and Scheduling an Eager-Writing Disk Array for a Transaction Processing Workload Chi Zhang∗ Xiang Yu∗ Arvind Krishnamurthyy Randolph Y. Wang∗ Abstract cult challenges to storage systems than office work- loads. These applications exhibit little locality or Transaction processing applications such as those sequentiality; a large percentage of the I/O requests exemplified by the TPC-C benchmark are among the are writes, many of which are synchronous; and most demanding I/O applications for conventional there may be little idle time. storage systems. Two complementary techniques exist to improve the performance of these systems. Traditional techniques that work well for office Eager-writing allows the free block that is closest to workloads tend to be less effective for these transac- a disk head to be selected for servicing a write re- tion processing applications. Memory caching pro- quest, and mirroring allows the closest replica to be vides little relief in the presence of poor locality and selected for servicing a read request. Applied indi- a small read-to-write ratio. As disk areal density im- vidually, the effectiveness of each of these techniques proves at 60-100% annually [9], as memory density is limited. An eager-writing disk array (EW-Array) improves at only 40% per year, and as the amount combines these two complementary techniques. In of data in transaction processing systems continues such a system, eager-writing enables low-cost replica to grow, one can expect little improvement in cache propagation so that the system can provide excel- hit rates in the near future. Delayed write tech- lent performance for both read and write operations niques become less applicable in the presence of a while maintaining a high degree of reliability. -
Oral History Avram Miller
Oral History of Avram Miller Interviewed by: Dane Elliot Recorded: August 19, 2013 Mountain View, California CHM Reference number: X6923.2014 © 2013 Computer History Museum Oral History of Avram Miller Elliot: All right. I’m here with Avram Miller today. It’s August 19, 2013. And we’re interviewing Avram specifically on how Intel invests in their future. Miller: How Intel invested in their Future Elliot: Very good. I’m Dane Elliot, and this is: Miller: Avram Miller. Elliot: Let’s start off with a little bit of background here. Miller: Okay. Elliot: And the background that I’d like to get is education and significant contributions in your career path that led you to Intel. So education? Miller: Well, I basically am self-educated. I always say that I graduated from Avram U. I studied music when I was in high school and then I became a Merchant Seaman for a while. Eventually, I got into science. I was always interested in electronics, and I started doing science at the University of California Medical School in San Francisco. Long story short, I ended up in Holland, at the Faculty of Medicine, Erasmus University. By the time I was 28, I was in an assistant professor in Medicine (medical informatics). When I was 29, I moved with my family to Israel where I was an Adjunct Associate Professor at the Medical Faculty of the University of Tel Avi and started up a medical computer business. What’s probably most relevant to our discussion is that I returned to the United States in 1979 and joined Digital Equipment Corporation. -
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 -
Abkürzungs-Liste ABKLEX
Abkürzungs-Liste ABKLEX (Informatik, Telekommunikation) W. Alex 1. Juli 2021 Karlsruhe Copyright W. Alex, Karlsruhe, 1994 – 2018. Die Liste darf unentgeltlich benutzt und weitergegeben werden. The list may be used or copied free of any charge. Original Point of Distribution: http://www.abklex.de/abklex/ An authorized Czechian version is published on: http://www.sochorek.cz/archiv/slovniky/abklex.htm Author’s Email address: [email protected] 2 Kapitel 1 Abkürzungen Gehen wir von 30 Zeichen aus, aus denen Abkürzungen gebildet werden, und nehmen wir eine größte Länge von 5 Zeichen an, so lassen sich 25.137.930 verschiedene Abkür- zungen bilden (Kombinationen mit Wiederholung und Berücksichtigung der Reihenfol- ge). Es folgt eine Auswahl von rund 16000 Abkürzungen aus den Bereichen Informatik und Telekommunikation. Die Abkürzungen werden hier durchgehend groß geschrieben, Akzente, Bindestriche und dergleichen wurden weggelassen. Einige Abkürzungen sind geschützte Namen; diese sind nicht gekennzeichnet. Die Liste beschreibt nur den Ge- brauch, sie legt nicht eine Definition fest. 100GE 100 GBit/s Ethernet 16CIF 16 times Common Intermediate Format (Picture Format) 16QAM 16-state Quadrature Amplitude Modulation 1GFC 1 Gigabaud Fiber Channel (2, 4, 8, 10, 20GFC) 1GL 1st Generation Language (Maschinencode) 1TBS One True Brace Style (C) 1TR6 (ISDN-Protokoll D-Kanal, national) 247 24/7: 24 hours per day, 7 days per week 2D 2-dimensional 2FA Zwei-Faktor-Authentifizierung 2GL 2nd Generation Language (Assembler) 2L8 Too Late (Slang) 2MS Strukturierte