Optimal Classifier Based Spectrum Sensing in Cognitive Radio Wireless Systems Siddharth Sharma Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur - 208016‚ India +91-9997773460 Aditya K. Jagannatham Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur - 208016‚ India +91-512-2597494 sharmas@iitk.ac.in ABSTRACT In this work‚ we present and investigate the performance of novel classification schemes for spectrum sensing in cooperative
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Linear Programming: Using the Excel Solver Outline: We will use Microsoft Excel Solver to solve the four LP examples discussed in last class. 1. The Product Mix Example The Outdoor Furniture Corporation manufactures two products: benches and picnic tables for use in yards and parks. The firm has two main resources: its carpenters (labor) and a supply of redwood for use in the furniture. During the next production period‚ 1200 hours of manpower are available under a union agreement. The firm
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explanatory variables. In short‚ it is the ratio of ESS to TSS. (l) It is the standard deviation of the Y values about the estimated regression line. (m) BLUE means best linear unbiased estimator‚ that is‚ a linear estimator that is unbiased and has the least variance in the class of all such linear unbiased estimators. (n) A statistical procedure of testing statistical hypotheses. (o) A test of significance based on the t distribution. (p) In a one-tailed test
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9/20/2011 RBI- has been a pretty important and valuable statistic…however recently the RBI has been becoming more and more discredited. 9/22/2011 How We Know What Isn’t So 1. Misperception of Random Events - Hot hand fallacy – Most statistical analysis shows that it’s not true. The evidence shows that the hot hand idea is false and that each shot is independent from the past shot. -Ex: Checked the statistics of the Philadelphia 76ers 1980 season and there was no correlation between
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Semester SY 2011-2012 Ms. derpina derp TABLE OF CONTENTS TITLE PAGE ACKNOWLEDGEMENT ii TOPICS Simple Discount 1 Simple Interest 2 Four types of Interest available 3 Compounded Amount and Compound Interest 4 Linear Programming Problems * Maximization 6 * Minimization 8 Forecasting by Trend Projection 10 Acknowledgement I would like to thank God for guiding and giving me motivation to do this math research paper; my friends for
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inequality [pic] Q.5 (a) The value of personal computer is decreasing linearly over time. Two points indicate its price at two different times: After one year Price= Rs. 45‚000 After two years Price= Rs. 40‚000 i) Determine a linear equation
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correlated‚ we didn’t eliminate them.Overall‚ we just eliminate several outliers of this data set. 2. Preliminary Models We use many models to make classification and prediction. The three models are multiple linear regression‚ classification tree and neural network. 2.1 Multiple linear regressions Based on
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constrains are x1 ≥0‚ x2 ≥0 Maximize the profit function: p = 3x1 + 5x2 2. What are the advantages of Linear programming techniques? Ans. Advantages— 1. The linear programming technique helps to make the best possible use of available productive resources (such as time‚ labour‚ machines etc.) 2. It improves the quality of decisions. The individual who makes use of linear programming methods becomes more objective than subjective. 3. It also helps in providing better tools
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Case Problem 2: Phoenix Computer Phoenix Computer manufactures and sells personal computers directly to customers. Orders are accepted by phone and through the company’s website. Phoenix will be introducing several new laptop models over next few months and management recognizes a need to develop technical support personnel to specialize in the new laptop systems. One option being considered is to hire new employees and put them through a three-month training program. Another option is to put
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ILP Problem Formulation Ajay Kr. Dhamija (N-1/MBA PT 2006-09) Abstract Integer linear programming is a very important class of problems‚ both algorithmically and combinatori- ally.Following are some of the problems in computer Science ‚relevant to DRDO‚ where integer linear Pro- gramming can be e®ectively used to ¯nd optimum so- lutions. 1. Pattern Classi¯cation 2. Multi Class Data Classi¯cation 3. Image Contrast Enhancement Pattern Classi¯cation is being extensively used for automatic
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