This paper explores the feasibility of using magnetoresponsive silicone as the primary mechanism for generating vibrotactile feedback in haptic interfaces. The distinctive feature of this research lies in the integration of magnetoresponsive silicone, a flexible material that responds to electromagnetic fields to produce localized vibrations. Preliminary experiments evaluate the performance of these actuators, focusing on their ability to produce controlled vibrations across a range of frequencies and amplitudes relevant to human tactile perception. Building on this foundation, we introduce the VibroFlex Pad, a haptic interface featuring a magnetoresponsive silicone sheet and an array of electromagnets. The VibroFlex Pad demonstrates its versatility in generating varied tactile effects and simulating dynamic wavelike movements across its surface. To assess the VibroFlex Pad's effectiveness, a user study was conducted, separately evaluating tactile accuracy, overall performance, and user comfort. The findings suggest that the VibroFlex Pad offers reliable and precise vibrotactile feedback, highlighting its potential to enhance wearable haptic technologies and improve the user experience in a variety of applications.

Given a permutation of n elements, stored as an array, we address the problem of replacing the permutation by its kth power. We aim to perform this operation quickly using o(n) bits of extra storage. To this end, we first present an algorithm for inverting permutations that uses O(lg^2 n) additional bits and runs in O(n lg n) worst case time. This result is then generalized to the situation in which the permutation is to be replaced by its kth power. An algorithm whose worst case running time is O(n lg n) and uses O(lg^2 n + min{k lg n, n^{3/4 + epsilon}}) additional bits is presented.

A nonlinear auditory model is appraised in terms of its ability to encode speech formant frequencies in the fine time structure of its output. It is demonstrated that groups of model auditory nerve (AN) fibres with similar interpeak intervals accurately encode the resonances of synthetic three-formant syllables, in close agreement with physiological data. Acoustic features are derived from the interpeak intervals and used as the input to a hidden Markov model-based automatic speech recognition system. In a digits-in-noise recognition task, interval-based features gave a better performance than features based on AN firing rate at every signal-to-noise ratio tested.