Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In high-density recording systems performance is largely determined by noise. n general different noise sources are present, where each source has direct impact on the overall performance. Therefore the characterization of a noise source provides insightful information about the recording system. In general noise characterization involves the choice of an appropri- ate stochastic model for it and furthermore the estimation of the parameters of the selected stochastic model. The characterization results can be used as diagnostic information to evaluate existing recording systems and they play an important role in state of the art bit-detection techniques. In these techniques the estimated stochastic parameters are used as side information to improve the detection reliability. For this reason an accurate estimation is very important. The estimation algorithm proposed in this paper, achieves a high estimation accuracy for systems where both data- dependent media noise and correlated additive noise are present. This algorithm makes use of the difference in data-dependency beten the two noise sources to jointly estimate the stochastic parameters of the two noise sources. I. INTRODUCTION of the media noise is indispensable.  an algorithm is proposed that accomplishes an accurate estimation in case both data-dependent media noise and colored additive noise are present. The parameters of the assumed stochastic models are estimated based on a-priori information about the data-dependency of the media noise.   will assume that the media noise samples originate from a white Gaussian stochastic process. Because the functionality of the estimation algorithm is based on the data-dependency of the media noise and not on the correlation, the estimation algorithm could be expanded to cover also correlated media noise. Furthermore the estimation algorithm will also estimate the stochastic parameters of the correlated additive noise, i.e. the algorithm performs a joint estimation of the stochastic parameters of the media noise and of the additive noise. The noise estimation scheme has important practical value in existing recording systems: it is an easy add- on to existing receivers. The resulting estimates of the noise parameters provide relevant diagnostic information about the functionality of the system and possible improvements in the recording system. In Section II a system model is presented. Subsequently in Section III the estimation of the additive and media noise parameters is derived. Finally in Section IV a simple adaptive procedure is proposed to estimate these noise parameters in an iterative way.
['Steven van Beneden', 'Jamal Riani', 'Jan W. M. Bergmans', 'Andre H. J. Immink']
I am writing a research paper on a idea Data-Dependent Noise Estimation in Digital Recording Systems.
[{"from": "human", "value": "I am writing a research paper on a idea Data-Dependent Noise Estimation in Digital Recording Systems."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In high-density recording systems performance is largely determined by noise. n general different noise sources are present, where each source has direct impact on the overall performance. Therefore the characterization of a noise source provides insightful information about the recording system. In general noise characterization involves the choice of an appropri- ate stochastic model for it and furthermore the estimation of the parameters of the selected stochastic model. The characterization results can be used as diagnostic information to evaluate existing recording systems and they play an important role in state of the art bit-detection techniques. In these techniques the estimated stochastic parameters are used as side information to improve the detection reliability. For this reason an accurate estimation is very important. The estimation algorithm proposed in this paper, achieves a high estimation accuracy for systems where both data- dependent media noise and correlated additive noise are present. This algorithm makes use of the difference in data-dependency beten the two noise sources to jointly estimate the stochastic parameters of the two noise sources. I. INTRODUCTION of the media noise is indispensable.  an algorithm is proposed that accomplishes an accurate estimation in case both data-dependent media noise and colored additive noise are present. The parameters of the assumed stochastic models are estimated based on a-priori information about the data-dependency of the media noise.   will assume that the media noise samples originate from a white Gaussian stochastic process. Because the functionality of the estimation algorithm is based on the data-dependency of the media noise and not on the correlation, the estimation algorithm could be expanded to cover also correlated media noise. Furthermore the estimation algorithm will also estimate the stochastic parameters of the correlated additive noise, i.e. the algorithm performs a joint estimation of the stochastic parameters of the media noise and of the additive noise. The noise estimation scheme has important practical value in existing recording systems: it is an easy add- on to existing receivers. The resulting estimates of the noise parameters provide relevant diagnostic information about the functionality of the system and possible improvements in the recording system. In Section II a system model is presented. Subsequently in Section III the estimation of the additive and media noise parameters is derived. Finally in Section IV a simple adaptive procedure is proposed to estimate these noise parameters in an iterative way."}]