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This de nition is valid only for 0 < s < 1 nd 0 < s + s < 1 The general case is slightly more complex and will not be dealt with here 2-microlocal spaces, as opposed to H lder space, require two exponents (s, s ) in their de nition While p is de ned as the sup of exponents , such that f belongs to Cx0 , we cannot proceed in the same way to de ne 2-microlocal exponents Instead, we de ne in the abstract s,s plan (s, s ) the 2-microlocal frontier of f at x0 as the curve (s, sup{s , f Cx0 }) It is not hard to show that this curve is well de ned, concave and decreasing Its intersection with the s axis is exactly l Moreover, under the hypothesis that f possesses a minimum global regularity, p is the intersection of the frontier with the line s + s = 0 The 2-microlocal frontier thus allows us to re-interpret the two exponents within a uni ed framework The main advantage of the frontier is that it completely describes the evolutions of p under integro-differentiation of arbitrary order: indeed, an integro-differentiation of order simply shifts the frontier by along the s axis Thus, 2-microlocal analysis provides extremely rich information on the regularity of a function around a point To conclude this brief presentation, let us mention that algorithms exist which make it possible to numerically estimate the 2-microlocal frontier They often allow us to calculate the values of p and l more precisely than a direct method (various estimation methods for the exponents and the 2-microlocal frontier are proposed in FracLab) Furthermore, it is possible to develop a 2-microlocal formalism [LEV 04a] which presents strong analogies with the multifractal formalism (see below) 136 An example: analysis of stock market price As an illustration of some of the notions introduced above, we use this section to detail a simpli ed example of the fractal analysis of a signal based on H lder exponents Our purpose is not to develop a complete application (this would require a whole chapter) but instead to demonstrate how we calculate and use the information provided by local regularity analysis in a practical scenario The signal is a stock market log Financial analysis offers an interesting area of application for fractal methods (see 13 for a detailed study) We will consider the evolution of the Nikkei index between January 1, 1980 and November 5, 2000 These signals comprise 5,313 values and are presented in Figure 15 As with many stock market accounts, it is extremely irregular We calculate the local H lder exponent of the logarithm of this signal, which is the quantity on which nancial analysts work The exponent is calculated by a direct application of De nition 13: at each point x, we nd, for increasing values of , one couple (y, z) for which the signal oscillation in a ball centered in x of radius is attained A bilogarithmic regression between the vector of the values found for the oscillation and the distances |y z| is then performed (see the FracLab manual for more details on the procedure) As Figure 16 shows, most local exponents are comprised between 0 and 1, with. public void actionPerformed(ActionEvent e) { dlistaddElement(textgetText()); //add text from the field listsetListData(dlist); //send a new Vector to the list listrepaint(); //and tell it to redraw } 6 Pixels. . multiple times to get multiple formats of the barcode image onto . to call the Windows API OpenClipboard, EmptyClipboard adn then make multiple calls .Related: Related: . 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