Showing papers by "Uwe Meyer-Baese published in 2000"
30 Mar 2000
TL;DR: A method is proposed, which reconstructs the nerve impulses into acoustic signals and will be used on the impulse-output of a widely used cochlear implant, in order to get an impression of what patients actually perceive with such a device.
Abstract: In the last decades biologists have gained much knowledge about neural firing in the auditory system. It is a challenging problem to use this knowledge for the improvement of hearing aids and cochlear implants. This study first present the model of a human cochlea, which transforms acoustic signals into auditory nerve impulses. Then a method is proposed, which reconstructs the nerve impulses into acoustic signals. This method will then be used on the impulse-output of a widely used cochlear implant, in order to get an impression of what patients actually perceive with such a device. Suggestions for the improvement of coding strategies will be made, based on the findings of this study.
2 citations
30 Mar 2000
TL;DR: A new non linear principal component analyzer is described and applied in connection with a new compression scheme to lossy compression of digitized mammograms and the nonlinear principal component method shows the best compression reslut in combination with the 'neural-gas' network.
Abstract: In this paper we describe a new nonlinear principal component analyzer and apply it in connection with a new compression scheme to lossy compression of digitized mammograms. We use a 'neural-gas' network for codebook design and several linear and nonlinear principal component method as a preprocessing technique. First, we analyze mathematically the nonlinear, single-layer neural network and show that the equilibrium points of this system are global asymptotically stale. Both a regular Hebbian rule and an anti-Hebbian rule are used for the adaptation of the connection weights between the constituent units. The, we investigate the performance of the compression scheme depending on the blocksize, codebook and number of chosen principal components. The nonlinear principal component method shows the best compression reslut in combination with the 'neural-gas' network.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.