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The present study was designed to investigate the effects of two noise conditions, continuous and noncontinuous noise, and three intensity levels, upon a serial learning task and the interaction effects of these factors on learning.

A dissertation submitted in fulfllment for the degree of Masters of Science in the Faculty of Science School of Computer Science and Applied Mathematics October 2017. / This work explores a new method of applying partial di erential equations to audio signal processing, particularly that of noise removal. Two methods are explored and compared to the method of noise removal used in the free software Audacity(R). The rst of these methods uses a non-linear variation of the di usion equation in two dimensions, coupled with a non-linear sink/source term, in order to lter the imaginary and real components of an array of overlapping windows of the signal's Fourier transform. The second model is that of a non-linear di usion function applied to the magnitude of the Fourier transform in order to estimate the noise power spectrum to be used in a spectral subtraction noise removal technique. The technique in this work features nite di erence methods to approximate the solutions of each of the models. / LG2018

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Medicine in Emergency Medicine. Johannesburg, 2012 / Introduction: Healthcare professionals working in the Emergency Medicine field are often required to function in difficult environments. Noise is one environmental factor that may adversely affect their performance. Objectives: To firstly determine if there is any difference in cognitive task performance required for clinical decision-making of healthcare professionals in a quiet compared to a noisy environment and secondly, to assess the subjective experience of participants with regards to performance in a noisy environment. Design: Prospective cross-over study. Setting: Three Academic Hospitals in Johannesburg. Participants: Forty one doctors exposed to emergency management of patients. Methods: A 30 minute examination consisting of six matched and pre-validated questions was conducted. Half of the questions were completed with exposure to ambient noise (range 40-45dB(A)) and the other half with exposure to pre-recorded background Emergency Department noise at 80-85dB(A). The questions were completed in alternating quiet and noise. Each question was scored out of 10 and the time taken to complete each question was recorded. Main Results: Overall mean test scores in quiet and noise were 18.7/30 and 19.4/30 (p=0.36) respectively, with overall time for test completion of 836s in quiet and 797s (p=0.005) in noise. While there was no statistically significant difference in task performance, 65% of the doctors found the noise distracting with 88% experiencing varying degrees of stress. Conclusions: This study showed no difference in cognitive performance in a quiet compared to a noisy environment. Deterioration in functionality might be seen with higher levels of noise and/or longer exposure.

Graduation date: 2000

Thesis (Ph. D.)--University of Rhode Island, 2006. / Typescript. Includes bibliographical references (leaves 141-150).

It can be a real challenge to construct the ambient noise modeling among the harbors of very frequent shipping. Therefore, in these harbors, a great amount of resource has to be utilized for precisely control the ambient noise occurring parameters. In this study, the underwater measuring skill is adapted to collect the ambient noise raw data among the first harbor area of Kaohsiung Harbor and the sound intensity of various ships as well. Besides, the signal processing skill is also conducted for ship tracing experiment. The harbor ambient noise mainly comes from ships. Thus, the understanding of ship activity and their different sound intensity can be a great help to construct the ambient noise modeling. The results of this study indicate¡GShips outside the harbor create more low-frequency noise than those inside the harbor.Waves inside the harbor create more middle-frequency noise than those outside the harbor.In the harbor, those ships with onboard electricity create more noise and there is less noise around the wharfs without any ships. In the part of ship noise study, the intensity is usually correlated with ship types, displacement, ship speed, primary engine and auxiliary engine, etc. However, only two ship types, small and large ships, are categorized in this study. Small ships can be operated easily with the most power outcome, which can create the sound intensity of 155 to 165 dB in average. On the contrary, large ships are usually limited within their operating range and can not move with the most power outcome under the security consideration. Therefore, the sound intensity of large ships is measured between 169 to 177 dB. In the ship tracing experiment, operations are the basis for constructing the harbor ambient noise model.

It¡¦s not easy to keep photographs clean in every day. A photograph is liable to be polluted by accumulating defects such as dusts, which can degrade the imaging quality. In the thesis, a method of image restoration is proposed for image polluted by multiplicative transmittance noise. The method is based on estimating the approximate autocorrelation function of the unpolluted image. This autocorrelation function is obtained by analyzing the relationship among the autocorrelation function for polluted image, unpolluted image and noise. Further more, the noisy image is restored by the property of the autocorrelation function. A polluted photograph in imaging system is modeled by a thin random screen against the original image. In this model, defects are Poisson-distribution and may be overlapped. Since transmittance effect of each defect is multiplicative, the transmittance of random screen is computed as a product of Poisson-distribution-centered random function. Then, the statistical autocorrelation function of random screen is accordingly computed. More specifically, we should rearrange image data as periodic signal to avoid insufficient data in computing the process autocorrelation function. The simulated polluted image is restored by the amplitude information from the estimated autocorrelation function of the original image. Simulating results is demonstrated that the RMS of the restored image computed with the polluted image is improved.

Sound waves are highly conductive in the ocean; therefore, they are used in underwater detection and signal transmission. During these applications, we may receive some signals, such as radiated noise, self-noise, reverberation noise, target noise, and ambient noise. These signals are influential in the application of underwater acoustics, so many people study underwater noise and create numerous mathematical and physical models in order to improve the applications. According to the past researches, ambient noise is the most complicated one among all the other underwater noise. Until now, there are still some unknown factors in the ambient noise study, many of which are too intricate to be modulated. Accordingly, it is very important to know more about the ambient noise for the enhancing of the quality of underwater communication and detection, which is the reason why people keep researching on ambient noise. Among all kinds of ambient noise, wind-generated noise is not only loud in volume, but also wide in frequency scale, which makes it the most detectable noise during sound detection. In order to understand wind-generated noise, we have compiled past papers first to look for the correlation between physical mechanisms and models of mathematics. In addition, we have constructed a measuring system for underwater sound in the ocean and another measuring system for wind on land. Data from the two measuring system were analyzed by statistics. One of the correlations between wind and ambient noise is that the stronger wind appears, the louder ambient noise is generated. But wind does not always generate ambient noise; it has to be strong enough to generate ambient noise. However, even when the wind is strong enough, ambient noise is not generated immediately, but with a delay. By analyzing the data, we have not only proved the credibility of the data from the old researches, but also presented a method of statistics for analyzing the wind delay effect, and the minimum velocities of wind which generates ambient noise.