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Feb 21, 2016 · Hi friends, This is a screencast to the tutorial available at this url - http://tphangout.​com/?p=18.Duration: 4:12 Posted: Feb 21, 2016

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connects p = 128 processors, calculate the number of links, the diameter, and the bisection width for the following topologies: (a) Fully connected, cyclic grid (2D mesh), cyclic torus (3D mesh), and hypercube (b) Crossbar, butter y, and binary tree Also, calculate the number of switches for each network 26 A butter y network is a blocking network (Section 222), which means that certain combinations of source destination connections are mutually exclusive Figure 210 depicts a 3D butter y network with eight source and destination lines Which of the following connection pairs block each other: 2 6 and 7 1, 3 4 and 2 7, 5 0 and 0 5, 3 5 and 6 7, 1 1 and 3 0, 4 2 and 1 3 27 28 Parallel programming implies the parallelization of a problem (Section 23) Describe the three main areas of the parallelization process Four quite common decomposition techniques are studied in Section 242: data decomposition, recursive decomposition, exploratory decomposition, and speculative decomposition Which of these techniques seems indicated for parallelizing the following algorithms and applications: Quicksort (Cormen et al [42]), chess game, fast fourier transform (FFT) (Cormen et al [42]), and path nding algorithm Brie y describe how you would decompose these problems In Section 251, three different types of data dependence are discussed: ow dependence, antidependence, and output dependence Consider the following fragment of a program: 1: b = a 3 2: a = b 7 3: c = a + b - d 4: a = c - 11 5: d = a - b + c Identify all dependences and classify their types Exercise 29 asks you to identify and classify the data dependences in a small code fragment In loops, data dependence usually arises through the access of array elements (Section 252) Consider the following loop: for i = 3 to 50 do S: A(i) = B(2 i) + 1 T: B(i+1) = A(i-1) + A(i+1) U: C(2 i) = A(i-2) + E(i+2) V: D(i) = C(2 i+1) + B(i+1) end for (a) Without considering the subscripts of the array variables, which of the array variables A, B, C, D, and E are potentially involved in the dependences among the tasks.



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Cloud Vision API Client Library for Java | Google Developers
Cloud Vision API : Integrates Google Vision features, including image labeling, face, logo, and landmark detection, optical character recognition ( OCR ), and detection of explicit content, ... Read the Developer's Guide for the Google API Client Library for Java . ... See all versions available on the Maven Central Repository.

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Comparison of optical character recognition software - Wikipedia
From Wikipedia, the free encyclopedia. Jump to navigation Jump to search. This comparison of optical character recognition software includes: OCR engines, that do the .... Debian manual page for Cuneiform for Linux version 1.1.0 ; ^ " OCR SDK Language Packages Download ". Dynamsoft.com. Retrieved 2013-09-12.

It is easy to see from Eq. (8.5) that, if process X has spectral density function (8.9), its variance will be equal to (8.7). As o 3 0, from Eq. (8.9) we obtain f o $ const o1 b . We use f x $ g x in the sense that limx3I f x =g x 1. Now we conclude that an exactly second-order self-similar process can be de ned via its autocorrelation function (8.6), variance of the sum (8.7), or spectral density function (8.9), and all these de nitions are equivalent. Meanwhile, for the de nition of an asymptotically second-order self-similar process, it is important to de ne the kind of characteristic to be used. We will use the following de nition. De nition 8.2.2. A stationary process X . . . ; X 1 ; X0 ; X1 ; . . . with nite mean m EXi < I and variance s2 Var Xi < I is called asymptotically second-order self-similar with parameter 0 < b < 1, if

8:10

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com.asprise.ocr.Ocr maven / gradle build tool code. The class is part of the package ➦ Group: com.asprise.ocr ➦ Artifact: java-ocr-api ➦ Version: 15.3.0.3.

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for all k 1; 2; . . . . The sense of this de nition is that, for suf ciently large m, the processes X m will have the same autocorrelation function equal to (8.6). m As we can see from Eqs. (8.1) and (8.5), Var X0 is a double integral of the autocorrelation function or an integral of the spectral density function. These m relations can clarify the behavior of r k , Var X0 , and f o obtained from the real traf c measurements. Usually self-similarity of the data traf c is established by m analyzing the traf c variance Var X0 [5, 12], but the behavior of the variance m Var X0 can be close to a self-similar one, while the behavior of the autocorrelation can be quite different from the theoretical one since the autocorrelation function is a m second derivative of the variance Var X0 . It is also important that, as can be seen from Eq. (8.5), even small harmonics at low frequency can have a dramatic in uence m on the behavior of Var X0 in a suf cient region of m values. 8.3 MODEL OF SELF-SIMILAR TRAFFIC

(b) Identify all intraiteration dependences and their types (c) Identify all interiteration dependences and their types What is the distance of each dependence For nonuniform dependences, give the minimum and the maximum distance 211 Dependence analysis of nested loops is a straightforward generalization of the dependence analysis of single loops Essentially, the index variable becomes an index vector and the dependence distance becomes a dependence vector (Section 252) Consider the following example of a double loop: for i = 1 to 10 do for j = 1 to 10 do S: A(i+1,j-1) = B(i,j) + E(i+1,j+1) T: B(i,j-1) = A(i,j) + 1 U: C(2 i+1,j) = A(i+1,j-1) + 4 V: D(i,j) = C(i,j-1) - 1 end for end for (a) Identify all dependences and their types What is the distance vector of each dependence For nonuniform dependences, give the minimum and the maximum distance for each component of the vector (b) Can the loop order be swapped, that is, the rst loop iterates over j and the second over i Control dependence can be converted into data dependence, using a when operator as shown in Section 253 Convert the control dependences in the following code fragment into data dependences 1: if u > 5 then 2: v = u - 5 3: else if u < 2 then 4: v = u + 2 5: else 6: w = u - 2 7: end if

Consider a Poission process on the time axis with intensity l: Let y . . . ; y 1 ; y0 ; y1 ; . . . ;

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OCR with Java and Tesseract – Brandsma Blog
7 Dec 2015 ... 3.2: Import the required jars . a) Right click the Tess4J project and select ' Properties'. tesseract05. b) Select Java build path –> Libraries .












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