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You searched for subject:(Structured Random Sampling). Showing records 1 – 2 of 2 total matches.

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University of South Africa

1. Mahapa, Tebogo Patience. The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa .

Degree: 2016, University of South Africa

Nightclubs and restaurant bars have become major sources of noise pollution particularly in areas close to residential dwellings. The purpose of this study was to investigate the impact of noise emanating from nightclubs and restaurant bars on the community of Melville, Johannesburg. This study followed both qualitative and quantitative research methods. A total of 100 respondents were randomly sampled within the study area. Qualitative data was collected using a structured questionnaire. A calibrated sound level meter was used to measure environmental noise levels at 10 different measuring points. The research finding revealed that about:  87% of noise levels measured with the sound level meter did not comply with officially acceptable levels of 40dB at night.  69% of respondents indicated that the main source of noise is pollution is nightclubs.  78% of respondents described noise as annoying, disturbing and unwanted.  57% of respondents indicated that members of their household have suffered from sleeping disorders due to noise activities at night disrupting their sleep patterns and resulting in irritability and fatigue. The noise measurements were taken on weekends and public holidays during the day from 10h00 to 14h30 and at night from 22h00 to 02h30. The research findings revealed that the residents of Melville experienced high level of noise at night with nightclub as major source of noise and as a result the majority of the sampled population complained about irritability, fatigue and sleeping disorders due to exposure to noise. The outcome of this research indicated the need of health education on the adverse effects of noise pollution and the need of sound insulation at places of entertainment. Implementation of a noise management policy is needed in order to effectively control and manage the noise pollution in its area of jurisdiction and regular noise level monitoring by constantly taking noise measurements by law enforcements officers. Advisors/Committee Members: Siziba, W (advisor), Moja, S.J (advisor).

Subjects/Keywords: Melville; Johannesburg; Noise pollution; Nightclubs; Restaurant bars; Structured questionnaire; Simple random sampling technique; Ambient noise level and noise level meter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mahapa, T. P. (2016). The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa . (Masters Thesis). University of South Africa. Retrieved from http://hdl.handle.net/10500/25085

Chicago Manual of Style (16th Edition):

Mahapa, Tebogo Patience. “The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa .” 2016. Masters Thesis, University of South Africa. Accessed September 20, 2020. http://hdl.handle.net/10500/25085.

MLA Handbook (7th Edition):

Mahapa, Tebogo Patience. “The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa .” 2016. Web. 20 Sep 2020.

Vancouver:

Mahapa TP. The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa . [Internet] [Masters thesis]. University of South Africa; 2016. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10500/25085.

Council of Science Editors:

Mahapa TP. The impact of nightclubs and restaurant bars noise pollution on the population of Melville, Johannesburg, South Africa . [Masters Thesis]. University of South Africa; 2016. Available from: http://hdl.handle.net/10500/25085

2. Yenduri, Praveen Kumar. Sparse Encoding of Signals through Structured Random Sampling.

Degree: PhD, Electrical Engineering-Systems, 2012, University of Michigan

The novel paradigm of compressive sampling/sensing (CS), which aims to achieve simultaneous acquisition and compression of signals, has received significant research interest in recent years. CS has been widely applied in many areas and several novel algorithms have been developed over the past few years. However, practical implementation of CS systems remains somewhat limited. This is due to the limited scope of many algorithms in literature when it comes to the employed measurement architectures. In several CS techniques, a key problem is that physical constraints typically make it infeasible to actually implement many of the random projections described in the algorithms. Also, most methods focus only on discrete measurements of the signal, which is not always practicable. Therefore, innovative and practical sampling systems must be carefully designed to effectively exploit CS theory in practice. This work focuses on developing techniques that randomly sample in time, that are also characterized by the presence of some structure in the sampling pattern. The structure is leveraged to enable a feasible implementation of acquisition hardware, while the randomness ensures recovery of sparse signals via greedy pursuit algorithms. In certain cases, the presence of a predefined structure in the sampling pattern can be further exploited to obtain other advantages such as reducing the run-time of reconstruction algorithms. The main theme in the thesis is to develop algorithms that bridge the gap between theory and practice of structured random sampling. The work is motivated by several application problems where structured random sampling offers attractive solutions. One of the applications involves development of a low-power architecture for analog-to-digital conversion (ADC), that incorporates time-domain processing and random sampling techniques, improving energy efficiency in both ways. Similar techniques in structured random sampling are employed to develop a novel low-rate neuron model which encodes information present in sensory stimuli at a rate that is proportional to the actual amount of information present in the signal rather than its duration. Along with techniques borrowed from theoretical computer science, structured random sampling has been successfully employed in designing a novel, distributive, spectrum sensing scheme for application in wide-band cognitive radios. Advisors/Committee Members: Gilbert, Anna Catherine (committee member), Zhang, Jun (committee member), Scott, Clayton D. (committee member), Flynn, Michael (committee member).

Subjects/Keywords: Structured Random Sampling; Compressive Sensing; Low Power Compressive Sampling Time Based Analog to Digital Converter (ADC); Low Rate Time Encoding Model of an Integrate-and-Fire Neuron; Compressive, Collaborative Spectrum Sensing for Wideband Cognitive Radios; Continuous Resource Efficient Fast Fourier Sampling; Electrical Engineering; Engineering

…96 5.3 (top) Sampling pattern of the proposed structured random sampling scheme… …103 x ABSTRACT Sparse Encoding of Signals through Structured Random Sampling by Praveen… …structured random sampling. The work is motivated by several application problems where structured… …ADCs. Similar techniques in structured random sampling are employed to develop a novel low… …borrowed from theoretical computer science, structured random sampling has been successfully… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yenduri, P. K. (2012). Sparse Encoding of Signals through Structured Random Sampling. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/95970

Chicago Manual of Style (16th Edition):

Yenduri, Praveen Kumar. “Sparse Encoding of Signals through Structured Random Sampling.” 2012. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/95970.

MLA Handbook (7th Edition):

Yenduri, Praveen Kumar. “Sparse Encoding of Signals through Structured Random Sampling.” 2012. Web. 20 Sep 2020.

Vancouver:

Yenduri PK. Sparse Encoding of Signals through Structured Random Sampling. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/95970.

Council of Science Editors:

Yenduri PK. Sparse Encoding of Signals through Structured Random Sampling. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/95970

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