International audienceThis work presents a set of cascade high gain predictors to reconstruct the vector state of triangular nonlinear systems with delayed output. By using a Lyapunov-Krasvoskii approach, simple sufficient conditions ensuring the exponential convergence of the observation error towards zero are given. All predictors used in the cascade have the same structure. This feature will greatly improve the easiness of their implementation. This result is illustrated by some simulations
International audienceThis article proposes a high-gain observer design for a class of nonlinear systems with multiple known time-varying delays intervening in the states and the inputs. In the free delay case, the class of systems under consideration coincides with a canonical form characterising a class of multi-output nonlinear systems, which are observable for any input. The underlying high-gain design has been mainly motivated by its inherent simplicity from both design and implementation points of view. Indeed, the observer gain is determined from an explicit resolution of a time-invariant Lyapunov algebraic equation up to the specification of a single design parameter. An academic observation problem is addressed to illustrate the effectiveness of the proposed observer
Keystroke dynamics is a viable and practical way as an addition to security for identity verification. It can be combined with passphrases authentication resulting in a more secure verification system. This paper presents a new soft biometric approach for keystroke dynamics. Soft biometrics traits are physical, behavioral or adhered human characteristics, which have been derived from the way human beings normally distinguish their peers (e.g. height, gender, hair color etc.). Those attributes have a low discriminating power, thus not capable of identification performance. Additionally, they are fully available to everyone which makes them privacy-safe. Thus, in this study, it consists of extracting information from the keystroke dynamics templates with the ability to recognise the hand(s) used (i.e. one/two hand(s)); the gender; the age category; and the handedness of a user when he/she types a given password or passphrase on a keyboard. Experiments were conducted on a keystroke dynamics database of 110 users and our experimental results show that the proposed methods are promising.
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