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Compression of color images
Published in Sharma Gaurav, Digital Color Imaging Handbook, 2017
As an example of how energy compaction can be useful, let us consider the example in Figure 8.2. Recall that an orthogonal transformation is a simple rotation of the input space. Refer to Figure 8.2, where two contiguous samples in an image are being computed. For every pair of pixels, one point
The strategy design of ‘wide area ZigBee’ network and its main mathematic problem
Published in International Journal of Electronics, 2022
To analyse the performance of WA-ZigBee, some excellent advantage of multilayer independent networks is listed below. In a serial structural system, its system MTBF is smaller than its every element MTBFi. It is explained in Equation 2.In a complex system, if their elements were independent, its system error ‘e’ should be the sum of square root of every component (‘ei’ or ‘ej’) error. And if it were correlation, for example, in an uncertain system, it is better to select absolute value stands for its every relative error. So, its error sum of an independent complex system is smaller than a correlation system, there is correlation coefficient η ≠ 0 in Equation 3.Meanwhile, it is simpler to representation a static network. For, in a linear system, orthogonal transformation and Steepest Descent Method is a normal way to represent and approximate a system. For example, in a linear algebra, an orthogonal transformation is a linear transformation T:V → V on a real inner product space V, which preserves the inner product. That is, for each pair u, v of elements of V, we haveSince the lengths of vectors and the angles between them are defined through the inner product, orthogonal transformations preserve lengths of vectors and angles between them.And to approach the change of network topology, it is also easier than other network. Gradient descent is a first order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient of the function at the current point. If the instead one takes steps proportional to the positive of the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent.