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Ali, F, and R Pavlidis Syntactic Recognition of Hand-written Numerals IEEE Transactions on Systems, Man, and Cybernetics 7, no 7 (1977) Boldridge, AG, and RW Freund United States Patent No 4,035,768 (issued 12 July 1977) Personal Identification Apparatus Assigned to Veripen Incorporated, New York, New York CynerSign Inc http://wwwcadixcommy/ products/csign/cybersignhtml Fejfar, A, and W Haberman Automatic Identification of Personnel Through Speaker and Signature Verification System Description and Testing, Proceedings of Carnahan Conference on Crime Countermeasures July 1977 Oxford, UK Gaines, R, W Lisowski, S Press, and N Shapiro Authentication by Keystroke Timing, RAND Report R-256-NSF 1980 RAND Corporation Gupta, G, and A McCabe A Review of Dynamic Handwritten Signature Verification 1997 James Cook University Townsville, Australia Interlink Electronics, Inc http://wwwinterlinkeleccom Joyce, R, and G Gupta Identity Authentication Based on Keystroke Latencies Communications of the ACM 33 (February 1990): 168 176 Leclerc, F, and R Plamondon Automatic Signature Verification: The State of the Art 1989 1993 International Journal on Pattern Recognition and Artificial Intelligence 8, no 3 (1994): 643 660 Leggett, J, and G Williams Verifying Identity via Keystroke Characteristics International Journal Man-Machine Studies 1 (1988): 67 76 Mayer, A, F Monroe, M Reiter, and A Rubin The Design and Analysis of Graphical Passwords New York University Bell Labs, Lucent Technologies, AT&T Labs-Research Monrose, F, and A Rubin Keystroke Dynamics as a Biometric for Authentication Future Generation Computer Systems 16 (2000): 351 359 Monrose, F, M Reiter, and S Wetzel Password Hardening Based on Keystroke Dynamics 2001 Bell Labs, Lucent Technology Murray Hill, NJ Published online Springer-Verlag

1382 Conclusions and Recommendations on the Use of the Methods Given in this All of the methods presented in this chapter use a single degree-of-freedom dynamic model Such a model is adequate for almost all well-designed systems because their highest signi cant frequency of excitation at maximum operating speed is usually well below the natural frequency of the model This is, in turn, well below the second and higher natural frequencies of the real system Therefore, the dynamic ampli cation by the upper modes is minimal The models are also all linear, which is accurate for almost all real systems, although the method of Sec 135 does allow optimization using some nonlinear criteria The sections below present a review of the features of each of the methods discussed in Secs 134 through 137 and give recommendations on how best to use them Guidelines for choosing the best method are given and their limitations are detailed 13821 Cam Synthesis Using Trigonometric Series (Sec 134) This section presents a good general procedure for designing cam pro les to minimize residual vibration A major advantage of this method is that the optimization can be done over any contiguous range of operating speeds the user desires This feature is important because many real systems operate over a range of speeds Even constant speed systems still must vary in speed for startup and shutdown Also, there is always uncertainty in the design parameters, particularly stiffness and mass This uncertainty is equivalent to an uncertainty in speed This section also presents a method to optimize a design for one speed (tuned cam) This is never recommended, however, even for constant-speed systems, because of all the factors noted above For some cams designed for just one speed, even small variations in speed or parameters can cause large variations in vibration Therefore, optimization should always be done over a wide enough range of speeds to allow for both the operating speed range and parameter uncertainties It is not necessary to optimize over the range of speeds well below the maximum speed, since the energy input and primary excitation frequencies will be much lower there However, including this low-end speed range in the optimization will have little effect on the optimal solution anyway, precisely because of its low effect on vibration 13822 Cam Synthesis Using Optimal Control Theory (Sec 135) There are two signi cant features of this procedure for optimizing cam design The rst is that a theoretically exact optimal solution is obtained, subject to only a few constraints; it is not restricted to a user-de ned set of basic functions, such as the trigonometric series used in Sec 134 The method constrains the solution by requiring the rst four derivatives of the follower lift to be zero at each end of the lift event and does not allow discontinuities in the acceleration at the follower or the cam These restrictions unnecessarily constrain the solution, not allowing the acceleration discontinuities at the ends of the lift event that were found to be optimal in Sec 134 The second feature of this procedure is the ability to perform this optimization for nonlinear optimization criteria This second feature is greatly limited in its usefulness, however, because the nonlinear optimization cannot include cam preload Preload is important in optimization for stress, for example, due to the highly nonlinear relationship between stress and load.

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is one of the reasons some people who do unit testing or TDD dislike singletons; they act as a public shared resource that is static, and it s hard to override them. Avoiding static methods altogether may be too difficult, but trying to minimize the number of singletons or static methods in your application will make things easier for you while testing.

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