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with each of the algorithms is determined. Figure 13.13 shows a comparison of the performance of the two algorithms. RGWH provides a slightly better performance in lower bandwidths. Example 13.5: CN-TOAG Algorithm for TOA Indoor Geolocation A closest-neighbor TOA grid (CN-TOAG) is presented in [Kan04b]. This algorithm takes advantage of the fact that for any given point in an area covered by a number of RPs, we know the exact value of the TOA expected from all the RPs. Consider a mobile location in an area and its measured distance from RFs to be di (1 i N ), where N is the number of RPs that the mobile can see. Let D represent the vector of TOA range measurements from RPs, and let Z represent the vector of the TOA-based range measurements expected at a certain point, r = (x, y), in that area. We call Z the range signature associated with point r. An estimate of the user s location, r, can be obtained by nding that point r where Z most closely approximates D. If we de ne an error
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12 discusses in-context reference update holders. These are the pointers in the assembly that hold the reference information. These holders are hidden by default and do not enable any real functionality, but they do serve as a reminder that the assembly has in-context references and can be queried to tell you what parts the in-context relations go between. n
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4.3.1.2.2 Distributed DCA Algorithms In contrast to centrally controlled algorithms, distributed algorithms are the least complex DCA techniques, in which the same algorithmis used byeach mobileor base station in order to determine the best channel for setting up a call. Each mobile and/or base station makes channelallocation decisions independentlyusing the same algorithm hence the name distributed algorithms. The algorithmic decisions usuare ally based on the interference measurements made by the mobile or the base station. These algorithms are easy to implement, and they perform well for low-slot occupancy systems. However, in high-load systemstheir performance is degraded. Distributed algorithms require less signaling than centralized algorithms. However, the allocation is generally suboptimal owing to their locally based decisions. One real advantage of distributed algorithms is that base stations can easily be added, moved, or removed because the system automatically reorganizes and reconfigures itself. However, the cost of this flexibility is that the local decision making generally leads to a suboptimal channel allocation solution and to a higher probability of interference in neighbouring cells. Furthermore, generally distributed algorithms are based on signal strength measurements and estimates of interference. However, these interference estimates can sometimesbe poor, which can leadto bad channel allocation decisions. When a new allocation is made,the co-channel interference it inflicts may lead to an ongoingcall to experience low-service quality, often termed a service interruption. If a service interruption leads to the ongoing call being terminated prematurely, this is referred to deadlock [310]. Successive service interruptions are termed as instability. A further problem with distributed algorithms isthat the same channel can allocated at the same timeto two or more different be
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information that you want clients to see if they are browsing through your Web page's code. It is good habit to include comments (whether they are client -side or server-side comments) within your code to explain what is going on with the code and what sort of functionality the page provides. This is necessary because after you finish building the page, you will probably not be the one who maintains the page and its code years from now. Including comments make it a lot easier on the next developers who are told to add or fix something. With proper comments, they won't have to spend so much time trying to figure out what you were doing. The Meta tags follow the HTML comments. There are a number of Meta tags, but two in particular are important for getting your site listed on search engines: the Meta description tag and the keywords tag. Did you ever wonder how some search engines provide all that page information, or how they even find the pages you are looking for when you type "VB .NET" into the search engine They do it with the help of these tags. The search engines look for these tags when they parse the page, and log both the page's description and keywords to use within their search engine. So it is important to craft these tags with some thought if getting into a search engine is important for the page's promotion. After the Meta tags are the script tags. For your script tag, you chose the language of the script to be VB .NET, but you can also choose a number of other languages to use for your script. You are also telling the server that you want to process this particular script on the server before the page is rendered. In your page construction, you would put all of our VB .NET code between these scripts. You will see this in action in the next chapter. The final tag in the sample head section is the style tag. In this example, you use Cascading Style Sheets to set style attributes to particular HTML tags. Body section The second section is the body section of the document. It is necessary because the page you build won't display anything without it. The body section is constructed using opening and closing <body> tags. <html> <head> </head> <body> <!--Body Section goes here--> </body> </html> The document's presentation piece is placed between the body tags. One of the simplest ways to provide content to display within the body section is to put static text between the tags. In most cases, though, you will mark up the text with some formatting to make it a little more presentable to the user who is coming to your page. A number of tags are used to change the format of the text that you place within the body tags. If you are unsure about which tags to use, there are plenty of excellent books on this subject that you can refer to. It is important to understand that these formatting HTML tags are constructed using the same principles that are used to lay out the head and body sections of the document. For most HTML opening tags, there is a closing tag. <b>Hello</b> John! In this example, you use the HTML bold tag to bold the Hello text. This simple example shows the following:
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Correlation coef cient of the envelope as a function of normalized frequency spacing.
Tour Entity BaseCampID(FK) 1 1 2 3 4 Tour Appalachian Trail Blue Ridge Parkway Hike Outer Banks Lighthouses Bahamas Dive Amazon Trek Gauley River Rafting BaseCamp Entity BaseCampID (PK) 1 2 3 4 5 Name Ashville Cape Hatteras Freeport Ft. Lauderdale West Virginia
The Now property returns a date value containing the current date and time of your system. Console.WriteLine(Now) ' Retuns 9/7/2001 6:59:40 AM
In addition to the standard comparison operators, which are no doubt familiar, SQL provides four special comparison operators: between, in, like, and is. The first three are explained in this section. Testing for nulls using the is keyword, and handling nulls, are explained in 8, Using Expressions and Scalar Functions. The best way to find a thing is to look for it, rather than to first eliminate everything it isn t. It s far easier to locate a business in a city than it is to prove that the business doesn t exist. The same is true of database searches. Proving that a row meets a condition is faster than first eliminating every row that doesn t meet that condition. In general, restating a negative where condition as a positive condition will improve performance.
The case table is defined by a single dimension and its related measure groups. When additional data mining attributes are needed, add them via a nested table. When selecting mining structure keys for a relational table, the usual choice is the primary key of the table. Choose mining structure keys from dimension data at the highest (least granular) level possible. For example, generating a quarterly forecast will require that quarter to be chosen as the key time attribute, not the time dimension s key (which is likely day or hour). Data and content type defaults will tend to be less reliable for cube data, so review and adjust type properties as needed. Some dimension attributes based on numeric or date data may appear to the data mining interface with a text data type. A little background is required to understand why this happens: When a dimension is built, it is required to have the Key column property specified. Optionally, a Name column property can also be specified, giving the key values friendly names for the end user (e.g., June 2005 instead of 2005-06-01T00:00:00). However, data mining will use the Name column s data type instead of the Key column s data type, often resulting in unexpected text data types showing up in a mining structure. Sometimes the text data type works fine, but for other cases, especially Key Time or Key Sequence attributes, it can cause the mining structure not to build or to behave incorrectly once built. Resolving this issue requires either removing the Name column property from the dimension attribute or adding the same column to the dimension a second time without using the Name column property. If a second copy of the attribute is required, it can be marked as not visible to avoid confusing end users. The portion of cube data to be used for training is defined via the mining structure s cube slice. Adjust this slice to exclude cases that should not be used in training (e.g., discontinued products and future time periods). Consider reserving a portion of the cube data for model evaluation (e.g., train a forecast on 18 of the last 24 months of data and compare actual and forecast values for the final six months). A Lift Chart cannot be run against cube test data, so model evaluation will either require test data in a relational table, or some strategy that does not rely on the tools of the Mining Accuracy Chart. Using a cube as a mining data source can be very effective, providing access to what is often large quantities of data for training and testing, and providing the ability to create a dimension or even an entire cube based on the trained model.
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