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Hearing aids based on models of cochlear compression using adaptive compression thresholds

A hearing aid device providing instantaneous gain compression for sound signals and adaptive control of nonlinear waveform distortion, the device comprising: (a) at least one bandpass nonlinearity (BPNL) amplifier comprising a first bandpass filter, a second bandpass filter, and a memoryless nonlinear (MNL) compressive audio amplifier configured to receive a sound signal from the first bandpass filter and provide an MNL compressive audio amplifier output to the second bandpass filter, wherein the MNL compressive audio amplifier is configured to produce the MNL compressive audio amplifier output by providing memoryless gain compression directly on a sound signal that is (1) received from the first bandpass filter and (2) exhibits instantaneous amplitudes greater than a compression threshold, the BPNL amplifier thereby producing a desired gain compression on the received sound signal at an output of the second bandpass filter, and (b) a controller in communication with the BPNL amplifier, the controller being configured to adjust the compression threshold of the MNL compressive audio amplifier. Adjustment of the compression threshold in each BPNL amplifier may be achieved at least partially in response to a user input and / or to sound signal changes. By adaptively controlling the compression threshold, performance of the device can by optimized to match its environment.
Owner:HEARING EMULATIONS

Method for identifying characteristic land categories of ocean remote sensing images of coast on basis of semi-supervised learning

The invention discloses a method for identifying characteristic land categories of ocean remote sensing images of a coast on the basis of semi-supervised learning and belongs to the field of identification of semi-automatic remote sensing images. The method comprises the following steps of: selecting a marking sample for each type of characteristic ground objects; constructing a dividing result facing to the remote sensing images of an object; computing an initial estimation value of probability that pixels of all samples are subordinate to the characteristic land categories and computing theprobability that sample data falls under components of the characteristic land categories; amending a probability image by using a characteristic space rule; judging the characteristic land categories which the remote sensing images belong to, realizing the identification of the characteristic land categories and outputting an identification result drawing. The method is combined with the priori knowledge and the statistical property of the data and can guide the data mining process by the topographical priori knowledge. Practice proves that the algorithm can effectively carry out classification of the remote sensing images to obtain a satisfying result, has the characteristics of high efficiency and high accuracy and can be directly used for maintaining and updating remote sensing thematic information of all levels of fundamental geographic information databases in China.
Owner:NANJING UNIV
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