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Unit 2. Lesson 5. Noise Pollution
ACOUSTICAL OCEANOGRAPHY Unit 2. Lesson 5. Noise Pollution Objectives: Upon completion of this unit, students will understand that noise pollution is more than loud noises. They will also learn what causes hearing damage and that animals, as well as humans, are subject to hearing loss. Vocabulary words: litter, pollution, loudness-related hearing loss, blast trauma What is Noise Pollution? Litter on the side of the road, is thought to be a sound so junk floating in the water, and intense that it could shatter smokes spewing into the glass, or crack plaster in rooms atmosphere from factory or on buildings. That is not so. smokestacks are obvious forms It can come from sources such of pollution. There are other as jet airplanes, constant types of pollution that are not droning of traffic, motorcycles, as obvious. Noise pollution is high-power equipment, or loud one form. What is noise music. pollution? It is defined as sounds, or noises, that are loud, annoying and harmful to the ear. Often, sound pollution How is Noise Pollution Harmful? Sound energy is transferred When sound reaches the through compressions and human ear, it causes structures rarefactions. to vibrate. Intense vibrations (Reference can rupture the eardrum, but lesson 1, if more often, loudness-related necessary.) hearing loss usually develops If the over time. When sound enters intensity is the ear, it is transferred to the very large, it can harm human brain as a nerve impulse. Each and animal ears, and do nerve is composed of tiny nerve damage to physical structures. fibers, surrounded by special fluid within the ear. -
Monitoring the Acoustic Performance of Low- Noise Pavements
Monitoring the acoustic performance of low- noise pavements Carlos Ribeiro Bruitparif, France. Fanny Mietlicki Bruitparif, France. Matthieu Sineau Bruitparif, France. Jérôme Lefebvre City of Paris, France. Kevin Ibtaten City of Paris, France. Summary In 2012, the City of Paris began an experiment on a 200 m section of the Paris ring road to test the use of low-noise pavement surfaces and their acoustic and mechanical durability over time, in a context of heavy road traffic. At the end of the HARMONICA project supported by the European LIFE project, Bruitparif maintained a permanent noise measurement station in order to monitor the acoustic efficiency of the pavement over several years. Similar follow-ups have recently been implemented by Bruitparif in the vicinity of dwellings near major road infrastructures crossing Ile- de-France territory, such as the A4 and A6 motorways. The operation of the permanent measurement stations will allow the acoustic performance of the new pavements to be monitored over time. Bruitparif is a partner in the European LIFE "COOL AND LOW NOISE ASPHALT" project led by the City of Paris. The aim of this project is to test three innovative asphalt pavement formulas to fight against noise pollution and global warming at three sites in Paris that are heavily exposed to road noise. Asphalt mixes combine sound, thermal and mechanical properties, in particular durability. 1. Introduction than 1.2 million vehicles with up to 270,000 vehicles per day in some places): Reducing noise generated by road traffic in urban x the publication by Bruitparif of the results of areas involves a combination of several actions. -
AN279: Estimating Period Jitter from Phase Noise
AN279 ESTIMATING PERIOD JITTER FROM PHASE NOISE 1. Introduction This application note reviews how RMS period jitter may be estimated from phase noise data. This approach is useful for estimating period jitter when sufficiently accurate time domain instruments, such as jitter measuring oscilloscopes or Time Interval Analyzers (TIAs), are unavailable. 2. Terminology In this application note, the following definitions apply: Cycle-to-cycle jitter—The short-term variation in clock period between adjacent clock cycles. This jitter measure, abbreviated here as JCC, may be specified as either an RMS or peak-to-peak quantity. Jitter—Short-term variations of the significant instants of a digital signal from their ideal positions in time (Ref: Telcordia GR-499-CORE). In this application note, the digital signal is a clock source or oscillator. Short- term here means phase noise contributions are restricted to frequencies greater than or equal to 10 Hz (Ref: Telcordia GR-1244-CORE). Period jitter—The short-term variation in clock period over all measured clock cycles, compared to the average clock period. This jitter measure, abbreviated here as JPER, may be specified as either an RMS or peak-to-peak quantity. This application note will concentrate on estimating the RMS value of this jitter parameter. The illustration in Figure 1 suggests how one might measure the RMS period jitter in the time domain. The first edge is the reference edge or trigger edge as if we were using an oscilloscope. Clock Period Distribution J PER(RMS) = T = 0 T = TPER Figure 1. RMS Period Jitter Example Phase jitter—The integrated jitter (area under the curve) of a phase noise plot over a particular jitter bandwidth. -
Ecology of the Cardiovascular System: Part II –A Focus on Non-Air R Related Pollutants
Trends in Cardiovascular Medicine 29 (2019) 274–282 Contents lists available at ScienceDirect Trends in Cardiovascular Medicine journal homepage: www.elsevier.com/locate/tcm Ecology of the cardiovascular system: Part II –A focus on non-air R related pollutants ∗ J.F. Argacha, MD, PhD a, , T. Mizukami a, T. Bourdrel b, M-A Bind c a Cardiology Department, Universitair Ziekenhuis Brussel, VUB, Belgium b Radiology Department, Imaging Medical Center Etoile-Neudorf, Strasbourg, France c Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA a r t i c l e i n f o a b s t r a c t Keywords: An integrated exposomic view of the relation between environment and cardiovascular health should Noise consider the effects of both air and non-air related environmental stressors. Cardiovascular impacts of Bisphenol ambient air temperature, indoor and outdoor air pollution were recently reviewed. We aim, in this second Pesticides part, to address the cardiovascular effects of noise, food pollutants, radiation, and some other emerging Dioxins Radiation environmental factors. Electromagnetic field Road traffic noise exposure is associated with increased risk of premature arteriosclerosis, coronary Endothelium artery disease, and stroke. Numerous studies report an increased prevalence of hypertension in people Oxidative stress exposed to noise, especially while sleeping. Sleep disturbances generated by nocturnal noise are followed Hypertension by a neuroendocrine stress response. Some oxidative and inflammatory endothelial reactions are observed Coronary artery disease during experimental session of noise exposure. Moreover, throughout the alimentation, the cardiovascular Stroke system is exposed to persistent organic pollutants (POPs) as dioxins or pesticides, and plastic associated chemicals (PACs), such as bisphenol A. -
Signal-To-Noise Ratio and Dynamic Range Definitions
Signal-to-noise ratio and dynamic range definitions The Signal-to-Noise Ratio (SNR) and Dynamic Range (DR) are two common parameters used to specify the electrical performance of a spectrometer. This technical note will describe how they are defined and how to measure and calculate them. Figure 1: Definitions of SNR and SR. The signal out of the spectrometer is a digital signal between 0 and 2N-1, where N is the number of bits in the Analogue-to-Digital (A/D) converter on the electronics. Typical numbers for N range from 10 to 16 leading to maximum signal level between 1,023 and 65,535 counts. The Noise is the stochastic variation of the signal around a mean value. In Figure 1 we have shown a spectrum with a single peak in wavelength and time. As indicated on the figure the peak signal level will fluctuate a small amount around the mean value due to the noise of the electronics. Noise is measured by the Root-Mean-Squared (RMS) value of the fluctuations over time. The SNR is defined as the average over time of the peak signal divided by the RMS noise of the peak signal over the same time. In order to get an accurate result for the SNR it is generally required to measure over 25 -50 time samples of the spectrum. It is very important that your input to the spectrometer is constant during SNR measurements. Otherwise, you will be measuring other things like drift of you lamp power or time dependent signal levels from your sample. -
Image Denoising by Autoencoder: Learning Core Representations
Image Denoising by AutoEncoder: Learning Core Representations Zhenyu Zhao College of Engineering and Computer Science, The Australian National University, Australia, [email protected] Abstract. In this paper, we implement an image denoising method which can be generally used in all kinds of noisy images. We achieve denoising process by adding Gaussian noise to raw images and then feed them into AutoEncoder to learn its core representations(raw images itself or high-level representations).We use pre- trained classifier to test the quality of the representations with the classification accuracy. Our result shows that in task-specific classification neuron networks, the performance of the network with noisy input images is far below the preprocessing images that using denoising AutoEncoder. In the meanwhile, our experiments also show that the preprocessed images can achieve compatible result with the noiseless input images. Keywords: Image Denoising, Image Representations, Neuron Networks, Deep Learning, AutoEncoder. 1 Introduction 1.1 Image Denoising Image is the object that stores and reflects visual perception. Images are also important information carriers today. Acquisition channel and artificial editing are the two main ways that corrupt observed images. The goal of image restoration techniques [1] is to restore the original image from a noisy observation of it. Image denoising is common image restoration problems that are useful by to many industrial and scientific applications. Image denoising prob- lems arise when an image is corrupted by additive white Gaussian noise which is common result of many acquisition channels. The white Gaussian noise can be harmful to many image applications. Hence, it is of great importance to remove Gaussian noise from images. -
Air and Noise Pollution Abatement Services: an Examination of U.S
U.S. International Trade Commission COMMISSIONERS Stephen Koplan, Chairman Deanna Tanner Okun, Vice Chairman Marcia E. Miller Jennifer A. Hillman Charlotte R. Lane Daniel R. Pearson Robert A. Rogowsky Director of Operations Karen Laney-Cummings Director of Industries Address all communications to Secretary to the Commission United States International Trade Commission Washington, DC 20436 U.S. International Trade Commission Washington, DC 20436 www.usitc.gov Air and Noise Pollution Abatement Services: An Examination of U.S. and Foreign Markets Investigation No. 332-461 Publication 3761 April 2005 This report was principally prepared by the Office of Industries Project Team Jennifer Baumert, Project Leader [email protected] Eric Forden, Deputy Project Leader [email protected] Judith Dean, Economist Staff assigned: William Chadwick, Lisa Ferens, David Ingersoll, Dennis Luther, Christopher Mapes, Erick Oh, Robert Randall, and Ben Randol Office of Operations Peg MacKnight With special assistance from: Lynette Gabourel and Cynthia Payne Primary Reviewers Alan Fox and Mark Paulson under the direction of Richard Brown, Chief Services and Investment Division ABSTRACT As requested by the United States Trade Representative (USTR), this report examines global markets for air and noise pollution abatement services and trade in these services markets for the purpose of providing information that would be useful in conducting trade negotiations and environmental reviews. The report indicates that demand for air and noise pollution abatement services is driven largely by government regulation and enforcement efforts, and to a lesser extent, by international treaty obligations, public sentiment, and private-sector financial resources. The majority of air pollution abatement services are reportedly delivered in conjunction with air pollution control equipment, with European, Japanese, and U.S. -
Active Noise Control Over Space: a Subspace Method for Performance Analysis
applied sciences Article Active Noise Control over Space: A Subspace Method for Performance Analysis Jihui Zhang 1,* , Thushara D. Abhayapala 1 , Wen Zhang 1,2 and Prasanga N. Samarasinghe 1 1 Audio & Acoustic Signal Processing Group, College of Engineering and Computer Science, Australian National University, Canberra 2601, Australia; [email protected] (T.D.A.); [email protected] (W.Z.); [email protected] (P.N.S.) 2 Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University, Xi0an 710072, China * Correspondence: [email protected] Received: 28 February 2019; Accepted: 20 March 2019; Published: 25 March 2019 Abstract: In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions. -
Marine Pollution Ocean 409, University of Washington
MARINE POLLUTION OCEAN 409, UNIVERSITY OF WASHINGTON Syllabus for Spring 2022 Time, Location, Zoom, and Course Website M-W-F 11:30-12:20pm PST The course materials will be posted through UW Canvas. Course Overview Marine Pollution explores anthropogenic impacts on the oceans and marine organisms. Marine pollution occurs when harmful effects result from the entry into the ocean of chemicals, particles, industrial or residential waste, noise, light, or the spread of invasive organisms. In this course, students examine how scientific understanding of marine pollution informs environmental management, thereby linking science and society. Students will develop a detailed understanding of six maJor categories of anthropogenic impacts on marine systems. Each theme will be explored from a variety of angles including pollution source(s), mechanisms of action, delirious effects and mitigation thereof, management of the issue, and some likely futures for the issue/theme. Learning Goals Students successfully completing this course will be able to • Understand a set of core facts about anthropogenic change to the oceans, including key aspects of ocean acidification, deoxygenation, chemical pollution, and deleterious impacts on fish and plankton. • Be able to communicate the effects of marine pollution on global climate. • Develop or enhance skills in team work, inductive reasoning, interpretation of complex data, and the sharing of complex scientific data and interpretations with non-technical audiences. Instructor Contact Information and Office Hours Rick Keil Professor of Chemical Oceanography Ocean Sciences Building, Room 517 [email protected] Randie Bundy Professor of Chemical Oceanography Ocean Sciences Building, Room 411 [email protected] Randie and Rick’s office hours are for one hour immediately following class periods. -
Did Noise Pollution Really Improve During COVID-19? Evidence from Taiwan
sustainability Communication Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan Rezzy Eko Caraka 1,2,3,* , Yusra Yusra 4, Toni Toharudin 5, Rung-Ching Chen 2,* , Mohammad Basyuni 6,* , Vilzati Juned 4, Prana Ugiana Gio 7 and Bens Pardamean 3,8 1 Faculty of Economics and Business, Campus UI Depok, Universitas Indonesia, Depok, West Java 16426, Indonesia 2 Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung 41349, Taiwan 3 Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia; [email protected] 4 Sekolah Tinggi Ilmu Ekonomi (STIE), Sabang, Banda Aceh 24415, Indonesia; [email protected] (Y.Y.); [email protected] (V.J.) 5 Department of Statistics, Padjadjaran University, West Java, Bandung 45363, Indonesia; [email protected] 6 Department of Forestry, Faculty of Forestry, Universitas Sumatera Utara, Medan 20155, Indonesia 7 Department of Mathematics, Universitas Sumatera Utara, Medan 20155, Indonesia; [email protected] 8 Computer Science Department, Bina Nusantara University, Jakarta 11480, Indonesia * Correspondence: [email protected] (R.E.C.); [email protected] (R.-C.C.); [email protected] (M.B.) Abstract: Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic Citation: Caraka, R.E.; Yusra, Y.; conditions deteriorated due to disruption in industry business activities and most people stayed Toharudin, T.; Chen, R.-C.; Basyuni, at home and worked from home, which probably reduced the noise pollution. -
Noise Reduction Through Active Noise Control Using Stereophonic Sound for Increasing Quite Zone
Noise reduction through active noise control using stereophonic sound for increasing quite zone Dongki Min1; Junjong Kim2; Sangwon Nam3; Junhong Park4 1,2,3,4 Hanyang University, Korea ABSTRACT The low frequency impact noise generated during machine operation is difficult to control by conventional active noise control. The Active Noise Control (ANC) is a destructive interference technic of noise source and control sound by generating anti-phase control sound. Its efficiency is limited to small space near the error microphones. At different locations, the noise level may increase due to control sounds. In this study, the ANC method using stereophonic sound was investigated to reduce interior low frequency noise and increase the quite zone. The Distance Based Amplitude Panning (DBAP) algorithm based on the distance between the virtual sound source and the speaker was used to create a virtual sound source by adjusting the volume proportions of multi speakers respectively. The 3-Dimensional sound ANC system was able to change the position of virtual control source using DBAP algorithm. The quiet zone was formed using fixed multi speaker system for various locations of noise sources. Keywords: Active noise control, stereophonic sound I-INCE Classification of Subjects Number(s): 38.2 1. INTRODUCTION The noise reduction is a main issue according to improvement of living condition. There are passive and active control methods for noise reduction. The passive noise control uses sound absorption material which has efficiency about high frequency. The Active Noise Control (ANC) which is the method for generating anti phase control sound is able to reduce low frequency. -
AN-839 RMS Phase Jitter
RMS Phase Jitter AN-839 APPLICATION NOTE Introduction In order to discuss RMS Phase Jitter, some basics phase noise theory must be understood. Phase noise is often considered an important measurement of spectral purity which is the inherent stability of a timing signal. Phase noise is the frequency domain representation of random fluctuations in the phase of a waveform caused by time domain instabilities called jitter. An ideal sinusoidal oscillator, with perfect spectral purity, has a single line in the frequency spectrum. Such perfect spectral purity is not achievable in a practical oscillator where there are phase and frequency fluctuations. Phase noise is a way of describing the phase and frequency fluctuation or jitter of a timing signal in the frequency domain as compared to a perfect reference signal. Generating Phase Noise and Frequency Spectrum Plots Phase noise data is typically generated from a frequency spectrum plot which can represent a time domain signal in the frequency domain. A frequency spectrum plot is generated via a Fourier transform of the signal, and the resulting values are plotted with power versus frequency. This is normally done using a spectrum analyzer. A frequency spectrum plot is used to define the spectral purity of a signal. The noise power in a band at a specific offset (FO) from the carrier frequency (FC) compared to the power of the carrier frequency is called the dBc Phase Noise. Power Level of a 1Hz band at an offset (F ) dBc Phase Noise = O Power Level of the carrier Frequency (FC) A Phase Noise plot is generated using data from the frequency spectrum plot.