3.3 Fractal dimension: Hausdorff’s dimension is the generally used represention for fractal dimension .Considering an object that possesses an Euclidean dimension R‚the Hausdorff’s fractal dimension F0 can be computed by the following expression: F0 = lim┬(e→0)log〖N(e〗 )/log〖e⁻¹〗 where N(e) is the counting of hyper-cubes of dimension R and length e that fill the object. But here fractal dimension is obtained using box counting algorithm .[15] 3.4 GLCM is the widely used statistical
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public class JavaApplicationStates { //Multi-dimensional array that stores state and state information private String state[][] = { {"ALABAMA"‚ "Nothern Flicker"‚ "Camellia"}‚ {"ALASKA"‚ "Willow Ptarmigan"‚ "Forget-me-not"}‚ {"ARIZONA"‚ "Cactus Wren"‚ "Saguaro Cactus Blossom"}‚ {"ARKANSAS"‚ "Northern Mockingbird"‚ "Apple Blossom"}‚ {"CALIFORNIA"‚ "California Quail"‚ "California Poppy"}‚ {"COLORADO"‚ "Lark Bunting"‚ "Rocky Mountain Columbine"}
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EXPERIMENT NO 2 FACT TABLE FOR UNIVERSITY DATABASE AIM:-Creation of dimension table and fact table for University Database. THEORY:- DIMENSION TABLE In data warehousing‚ a dimension table is one of the set of companion tables to a fact table. The fact table contains business facts or measures and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables‚ the dimension tables contain descriptive attributes (or fields) which are typically
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Instrumental texture analysis Texture profile analysis (TPA) Texture profile analysis was performed as per the method used by Reddy and Khairnar (2015). The size of the cutlet used for TPA (two-cycle compression test) was 3.0 cm x 4.0 cm (diameter x height). TPA was carried out using a Taxt-plus Texture Analyzer (Stable Micro Systems Ltd.‚ Surrey‚ UK)‚ attached with a 50 kg load cell. A 75 mm diameter compression platen was used with a pre test speed of 1 mm/ sec; test speed of 1 mm/sec and post-test
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LADDER DIAGRAM: FIGURE 53 LADDER PROGRAM FOR THE SHIFT AND CLOCK PROGRAM THE SIMULATION OPTION IS SELECTED: Figure 54 SIMULATING THE LADDER PROGRAM ERROR AND DEBUGGING: Once the simulation has started it checks for the errors in the ladder diagram‚ address etc. if there is an error‚ the output cannot be processed until the error is rectified. REMOVING THE DEFECTIVE PRODUCTS: Figure 55 REMOVING THE DETECTED BOTTEL 1 Figure 56 REMOVING THE DEFECTED BOTTLE 2 INPUT
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Advance Analytics Internship Coding Challenge Sai Charan Thotapalli 01/25/2015 Data description First‚ is need to know the amount of information this analysis will involve‚ in this section a general review of data. Number of rows‚ this mean the number of observations to be analysed. 21‚061 observations are found. ## [1] 21061 Number of columns‚ this mean the number of variables to be analysed. ## [1] 12 The original names of the variables. ## [1] ## [4] ## [7] ## [10] "day" "platform" "orders"
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which as a result is costing money in terms of what our company could be making. First we could be wasting much valuable time in trying to figure out what values to use for price‚ advertising expenditures‚ and personal expenditures‚ when a simple regression analysis of our demand model could tell us if any of those factors actually have an effect on our profits at all‚ and how much those factors affect our business. Second we could be use the data to optimize our profits resulting in more money. Our
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ASSIGNMENT NO: 2 Name: ATIYA SALEEM ROLL NO: 10-SE-19(M) SUBJECT: TECHNICAL REPORT WRITTING SUBMITTED TO: SIR KASHIF TOPIC: MY SECRET TALENT DATE: 26-11-2012 MIRPURE UNIVERSITY OF SCIENCE AND TECHNOLOGY (MUST) A.J&K MY SECRET TALENT When we talk about talent then first question that comes in our mind is “what is talent?” .Talent is any natural ability
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Chapter 7 Discussion of Test Results 7.1 Synchronization on unloaded networks First considering the result for test case 1.1‚ a low average value is revealed‚ if compared to the accuracy stated for NTP in the literature. This low average should be taken with a grain of salt however‚ as this is not an absolute value as shortly explained in the results of the tests. Therefore‚ oscillations between -20 ms and 20 ms would for example result in an average difference of 0 ms‚ why the average difference
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The first thing to note is that the votes for Latino/Hispanic English are more evenly distributed across both scales‚ compared to AAE’s peak towards the high end of the scales. Still‚ the average rank numbers for Latino/Hispanic English are 4.5 (correct-ness) and 3.7 (pleasantness). This yields the biggest difference between correctness and pleasantness in this study (0.8 discrepancy). While participants perceive this dialect to be almost as correct/incorrect as AAE‚ Latino/Hispanic English is ranked
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