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The eMoviX project lets you create your own customized, micro MoviX2 version that can boot up and play your personal video content. The fact that eMoviX software is so small (under 10MB) allows most of the software to be contained on the initial RAM disk (initrd.gz). Once the RAM disk is loaded into memory and the kernel is booted, eMoviX has only to access the CD or DVD to get the video content. The eMoviX software incorporates a RAM disk that was built inside a User-Mode Linux session (http://usermodelinux.org) using Debian GNU/Linux packages. Roberto De Leo created eMoviX to contain only those components that were needed for the dedicated media player itself, as well as some components needed to support the player (such as extra drivers for audio and remote controls). eMoviX includes BusyBox (www.busybox.net), a single utility that includes many common Linux commands. The parts needed to make the CD bootable come from the SYSLINUX Project (http://syslinux.zytor.com). As for the Linux kernel, the 2.4.22 kernel is included with the version of eMoviX that comes with this book. MPlayer (http://mplayerhq.hu) is the multimedia player used with eMoviX, and Advanced Linux Sound Architecture, ALSA, (www.alsa-project.org) is the sound system. When you or your friends want to play back your bootable movies, MPlayer plays in fullscreen mode. You can use your keyboard to control the playback (later in this chapter, Table 5-1 provides an MPlayer cheat sheet to detail how to use the keys). Limitations on what you can play with your eMoviX bootable movies are based on the codecs and video file formats that MPlayer supports and the size constraints of the medium (CD or DVD) you choose to use. In other words, the hardest part of this project can be getting the video you want to include onto your computer.
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You can create instances of the Dateobject by using any of the constructors listed in Table 6.4. Table 6.4: Date Constructor Methods Method Date() Usage Creates an instance of the Date object with the current date and time. Date(year, month, day, hours, Creates a date instance with the date specified through the minutes, seconds, milliseconds) parameters passed in the constructor. Date(dateString) Creates an instance of the Date object with the date specified in the dateString parameter. The format of this parameter is "month, day, year hours:minutes:seconds".
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where Rij for i; j = 1, 2, 3 are the line resistances of phases 1, 2, and 3 when i j or the resistance between lines i and j when i 6 j. Generalizing the results obtained, it is possible to build an equation to calculate the alterations of power losses in the lines of the subtransmission system regarding the use of a DG source located in the distribution system belonging to the arbitrary SS m as 8 9 2 3 ! n n n n n < = X X X X X 2 Rml Im 4 Ii Rij Rmj 2Im Rml 5 d P m 3 Im : ; i 1 j 1 j 1 l 1 l 1
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CHAPTER
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Predictive analytics refers to a series of techniques concerned with making more informed decisions based on an analysis of historical data. These methods are used throughout the industries of science and business. For example, pharmaceutical companies use these techniques to assess the safety of potential drugs before testing them in human clinical trials, and marketing organizations use these models to predict which customers will buy a speci c product. This section will use the following example to illustrate the process of building and using predictive models. A telecommunications company wishes to identify and prioritize all customers they believe are likely to change to another service provider. In an attempt to avoid losing business, the company will offer new products or new service options to these customers. Over the years, the organization collected customer data monthly that included decisions to switch to another service provider. Table 4.1 is an example of the information collected on customers, where each observation relates to a speci c customer in one month. The data includes a variable, churn, where a 1 indicates a switch to a new service provider and a 0 indicates a continuation of service. The table also includes the speci c month the observation was recorded (month), the age of the customer (age), the annual income of the customer (income), the number of months they have been a customer (customer length), the gender of the customer (gender), the number of calls made that month (monthly calls), and the number of calls made to the customer service department (service requests). This data will be used to build a model attempting to understand any relationships between (1) the customer data collected and (2) whether the customer changes service provider. The telecommunications company would like to use the model in their marketing department to identify customers likely to switch. Since the company has limited resources, it would also like to prioritize the customers most likely to switch. The company has data for the current month, which is shown in Table 4.2. The data for the current month is the same type of data used to build the model, except that churn is currently unknown. The model created from the historical data can then be used with the new data to make a series of predictions. Table 4.3 is an example of a table where two columns have been added for the values generated by the
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javac classpath .:$DOM4J_HOME/dom4j-full.jar xslt_test
=ROUND(123.37,-1)
Saturating power fluxdensity dB(W/m 2)
Figure 10.2 Client/server network.
=(PMT(C7/12,C6,-(C4-(C4*C5)))*C6)-(C4-(C4*C5))
Exhibit 9.1 (Continued) SP800-66, Resource Guide for Implementing the Health Insurance Portability and Accountability Act (HIPAA) Security Rule Texas Department of Information Resources, Practices for Protecting Information Resources Assets U.S. Department of Commerce, EU Safe Harbor Privacy Principles Policy and Procedures: Relevant and Complementary Publications 12 CFR Part 30, Appendix A, II (Operational and Managerial Standards), B AICPA, SAS 94 (AU Section 319), Effect of Information Technology on the Auditor s Consideration of Internal Control in a Financial Statement Audit Basel II BCI, Good Practice Guidelines Bill 198, Ontario, Canada, Keeping the Promise for a Strong Economy Act (Budget Measures) CERT, OCTAVE Clinger Cohen Act CMS, CSR CMS, Information Security ARS, Version 2.0 CMS, Integrated IT Investment & System Life Cycle Framework CMS, Policy for the Information Security Program CMS, SSP Methodology, 2003 COBIT 4.1, Control Objectives, Management Guidelines, Maturity Models, IT Governance Institute COSO, ERM Framework COSO, Integrated Framework DoD Policy 8500 DCID 6/3 EU Directive on Privacy and Electronic Communications 2002/58/EC (Article 4, 6) FFIEC, Operations FIPS 199, Security Categorization of Federal Information FISMA GAO/AIMD-12.19.6, Federal Information System Controls Audit Manual GLBA, Privacy of Financial Information HIPAA HKICPA, Internal Control and Risk Management A Basic Framework IIA, GTAG, Guide 1: Information Technology Controls IIA, GTAG, Guide 2: Change and Patch Management Controls: Critical for Organizational Success Internal Control 2006: The Next Wave of Certi cation Guidance for Management ISO 15489.2-2002, Records Management Part 2: Guidelines ISO 15489-1:2001, Information and Documentation Records Management Part 1 ISO 17799:2000, Information Technology Security Techniques Code of Practice for Information Security Management ISO 27001:2005, Information
javax.xml.soap.Node. Represents the DOM representation of a node in an XML document and provides methods for manipulating a DOM tree like adding, setting the values of a node, and removing a node from the tree. javax.xml.soap.Text. Represents the value of node as a text object.
An Introductory VBA Function Example
The payload of the RTP packets consists of short blocks of text that are defined for backward-compatibility using the ITU-T recommendation T.140 for text transmission. An example for the RTP packet carrying the text/red and text/t140 payloads with one redundant block is shown in Figure 17.3.
pm = 1 pu pd From (12.88) and (12.89) we get the following conditions that must be satis ed at all times for the probabilities to remain between 0 and 1: 0 mt2 t + Nmt t + 1 2N 2 (12.90)
3.2 Baseband signal processing analogue
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