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I-2/5 Creation In Java Using Barcode generation for Java . UCC - 13 Encoder In .NET Using Barcode creator for .If you run this example using the mobile emulator, you get the result shown at the top of Figure 14-4As you can see, the stage is initially 240 pixels wide and 320 pixels high Now select View, Orientation, Rotate Counterclockwise to simulate the user changing the orientation of the device As you can see at the bottom of Figure 14-4, this causes the width and height variables of the stage to be updated to match the new aspect of the screen Resizability In some cases, it is not appropriate to allow the user to resize the stageYou saw an example of this in 4, where we wanted the size of the stage to always exactly match that of its background image By default, resizing is allowed but you can prevent it by setting the resizable variable to falseThis stops the user resizing or maximizing the stage, but it will still change size if the values of its width and height variables are modified by application code.Related: EAN-8 Generator .NET , UPC-E Generation .NET , .NET ISBN Generation



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7 Apr 2015 ... Download this app from Microsoft Store for Windows 10 , Windows 8.1. See screenshots, read the latest customer reviews, and compare ratings ...

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Optical Character Recognition ( OCR ) for Windows 10 - Windows Blog
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FreeOCR is Optical Character Recognition Software for Windows and ... This framework is included with Windows Vista, 7 ,8 so only may need installing on XP.

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OCR SOFTWARE - Microsoft Community
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} During the form's Load event, this code captures the delta between the width of the text box and the width of the client rectangle so that when the form's size is changed, we can reset the width of the text box to maintain the difference in width as a constant Keeping this difference constant means keeping the distance between the right edge of the text box a fixed number of ixels from the right edge of the form Keeping an edge of a control a constant distance away from its container edge is called anchoring By default, all controls are anchored to the top and left edges of their containers We're accustomed to Windows moving our child controls to keep this anchoring intact as the container's left or top edge changes However, Windows does only so much It doesn't resize our controls to anchor them to other edges Fortunately, WinForms does, without requiring you to write the manual anchoring code just shown You can change the edges that a control is anchored to by changing the Anchor property to any bitwise combination of the values in the AnchorStyles enumeration: enum AnchorStyles { None, Left, // default Top, // default Bottom, Right, } Getting our text box to resize as the form is resized is a matter of changing the Anchor property to include the right edge as well as the left and the top edges Using the Property Browser, you even get a fancy drop-down editor, as shown in Figure 219.

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Using Barcode generation for ASPNET Control to generate, create QR Even though Windows provides built-in support for anchoring to the top and left edges, anchoring does not have to include the left or top edges at all For example, it's common to anchor a modal dialog's OK and Cancel buttons to only the bottom and right edges so that these buttons stay at the bottom-right corner as the dialog is resized, but aren't resized themselves Resizing of a ontrol happens if you have two opposing edges selected If you don't have either of the opposing edges selected, neither left nor right, then the control will not be resized in that dimension but will maintain the same proportion of space between the opposing edges The middle square in Figures 220 and 221 shows this behavior as well as several other anchoring combinationsRelated: .

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Early results of the application of the PSO to type I environments (refer to Section A9) with small spatial severity showed that the PSO has an implicit ability to track changing optima [107, 228, 385] Each particle progressively converges on a point on the line that connects its personal best position with the global best position [863, 870] The trajectory of a particle can be described by a sinusoidal wave with diminishing amplitude around the global best position [651, 652] If there is a small change in the location of an optimum, it is likely that one of these oscillating particles will discover the new, nearby optimum, and will pull the other particles to swarm around the new optimum However, if the spatial severity is large, causing the optimum to be displaced outside the radius of the contracting swarm, the PSO will fail to locate the new optimum due to loss of diversity In such cases mechanisms need to be employed to increase the swarm diversity Consider spatial changes where the value of the optimum remains the same after the change, ie f (x (t)) = f (x (t + 1)), with x (t) = x (t + 1) Since the tness remains the same, the global best position does not change, and remains at the old optimum Similarly, if f (x (t)) > f (x (t+1)), assuming minimization, the global best position will also not change Consequently, the PSO will fail to track such a changing minimum This problem can be solved by re-evaluating the tness of particles at time t + 1 and updating global best and personal best positions However, keep in mind that the same problem as discussed above may still occur if the optimum is displaced outside the radius of the swarm One of the goals of optimization algorithms for dynamic environments is to locate the optimum and then to track it The self-adaptation ability of the PSO to track optima (as discussed above) assumes that the PSO did not converge to an equilibrium state in its rst goal to locate the optimum When the swarm reaches an equilibrium (ie converged to a solution), vi = 0 The particles have no momentum and the contributions from the cognitive and social components are zero The particles will remain in this stable state even if the optimum does change In the case of dynamic environments, it is possible that the swarm reaches an equilibrium if the temporal severity is low (in other words, the time between consecutive changes is large) It is therefore important to read the literature on PSO for dynamic environments with his aspect always kept in mind The next aspects to consider are the in uence of particle memory, velocity clamping and the inertia weight The question to answer is: to what extent do these parameters and characteristics of the PSO limit or promote tracking of changing optima These aspects are addressed next: Each particle has a memory of its best position found thus far, and velocity is adjusted to also move towards this position Similarly, each particle retains information about the global best (or local best) position When the environment changes this information becomes stale If, after an environment change, particles are still allowed to make use of this, now stale, information, they are.

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FreeOCR is a free Optical Character Recognition Software for Windows and supports scanning from most Twain scanners and can also open most scanned ...












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