Abstract: Hearing impairment is the number one chronic
disability affecting many people in the world. Background noise is
particularly damaging to speech intelligibility for people with
hearing loss especially for sensorineural loss patients. Several
investigations on speech intelligibility have demonstrated
sensorineural loss patients need 5-15 dB higher SNR than the normal
hearing subjects. This paper describes Discrete Hartley Transform
Power Normalized Least Mean Square algorithm (DHT-LMS) to
improve the SNR and to reduce the convergence rate of the Least
Means Square (LMS) for sensorineural loss patients. The DHT
transforms n real numbers to n real numbers, and has the convenient
property of being its own inverse. It can be effectively used for noise
cancellation with less convergence time. The simulated result shows
the superior characteristics by improving the SNR at least 9 dB for
input SNR with zero dB and faster convergence rate (eigenvalue ratio
12) compare to time domain method and DFT-LMS.
Abstract: Over the past years, the EMCCD has had a profound
influence on photon starved imaging applications relying on its unique
multiplication register based on the impact ionization effect in the
silicon. High signal-to-noise ratio (SNR) means high image quality.
Thus, SNR improvement is important for the EMCCD. This work
analyzes the SNR performance of an EMCCD with gain off and on. In
each mode, simplified SNR models are established for different
integration times. The SNR curves are divided into readout noise (or
CIC) region and shot noise region by integration time. Theoretical
SNR values comparing long frame integration and frame adding in
each region are presented and discussed to figure out which method is
more effective. In order to further improve the SNR performance,
pixel binning is introduced into the EMCCD. The results show that
pixel binning does obviously improve the SNR performance, but at the
expensive of the spatial resolution.