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A microelectromechanical-systems (MEMS)-based electromagnetically actuated loudspeaker to reduce form factor, cost, and power consumption, and increase energy efficiency in hearing-aid applications is presented. The MEMS loudspeaker has multilayer copper coils, an NiFe soft magnet on a thin polyimide diaphragm, and an NdFeB permanent magnet on the perimeter. The coil impedance is measured at 1.5 Omega, and the resonant frequency of the diaphragm is located far from the audio frequency range. The device is driven by a power-scalable, 0.25-mum complementary metal-oxide semiconductor class-D SigmaDelta amplifier stage. The class-D amplifier is formed by a differential H-bridge driven by a single bit, pulse-density-modulated SigmaDelta bitstream at a 1.2-MHz clock rate. The fabricated MEMS loudspeaker generates more than 0.8-mum displacement, equivalent to 106-dB sound pressure level (SPL), with 0.13-mW power consumption. Driven by the SigmaDelta class-D amplifier, the MEMS loudspeaker achieves measured 65-dB total harmonic distortion (THD) with a measurement uncertainty of less than 10%. Energy-efficient and cost-effective advanced hearing aids would benefit from further miniaturization via MEMS technology. The results from this study appear very promising for developing a compact, mass-producible, low-power loudspeaker with sufficient sound generation for hearing-aid applications.

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To investigate how the sensorimotor systems of eye and hand use position, velocity, and timing information of moving targets, we conducted a series of three experiments. Subjects performed combined eye-hand catch-up movements toward visual targets that moved with step-ramp-like velocity profiles. Visual feedback of the hand was prevented by blanking the target at the onset of the hand movement. A multiple regression was used to determine the effects of position, velocity, and timing accessed before each movement on the movement amplitudes of eye and hand. The following results were obtained: 1. The predictive strategy of eye movements could be modeled by a linear regression on the basis of the position error and the target velocity. This was not the case for hand movements, for which there was a significant partial correlation between the movement amplitude and the product of target velocity and movement duration. This correlation was not observed for eye movements suggesting that the predictive strategy of hand movements takes movement duration into account, in contrast to the strategy used in eye movements. 2. To determine whether the movement amplitudes of eye and hand depend on a categorical classification between a discrete number of movement types, we compared an experiment in which target position and velocity were distributed continuously with an experiment using only four different combinations of target position and velocity. No systematic differences between these experiments were observed. This shows that the system output is a function of continuous, interval-scaled variables rather than a function of discrete categorical variables. 3. We also analyzed the component of the movement amplitudes not explained by the regression, i.e., the residual error. The residual errors between subsequent trials were correlated more strongly for eye than for hand movements, suggesting that short-term temporal fluctuations of the predictive strategy were stronger for the eye than for the hand.

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