Monitoring the Acoustic Performance of Low- Noise Pavements
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Laser Linewidth, Frequency Noise and Measurement
Laser Linewidth, Frequency Noise and Measurement WHITEPAPER | MARCH 2021 OPTICAL SENSING Yihong Chen, Hank Blauvelt EMCORE Corporation, Alhambra, CA, USA LASER LINEWIDTH AND FREQUENCY NOISE Frequency Noise Power Spectrum Density SPECTRUM DENSITY Frequency noise power spectrum density reveals detailed information about phase noise of a laser, which is the root Single Frequency Laser and Frequency (phase) cause of laser spectral broadening. In principle, laser line Noise shape can be constructed from frequency noise power Ideally, a single frequency laser operates at single spectrum density although in most cases it can only be frequency with zero linewidth. In a real world, however, a done numerically. Laser linewidth can be extracted. laser has a finite linewidth because of phase fluctuation, Correlation between laser line shape and which causes instantaneous frequency shifted away from frequency noise power spectrum density (ref the central frequency: δν(t) = (1/2π) dφ/dt. [1]) Linewidth Laser linewidth is an important parameter for characterizing the purity of wavelength (frequency) and coherence of a Graphic (Heading 4-Subhead Black) light source. Typically, laser linewidth is defined as Full Width at Half-Maximum (FWHM), or 3 dB bandwidth (SEE FIGURE 1) Direct optical spectrum measurements using a grating Equation (1) is difficult to calculate, but a based optical spectrum analyzer can only measure the simpler expression gives a good approximation laser line shape with resolution down to ~pm range, which (ref [2]) corresponds to GHz level. Indirect linewidth measurement An effective integrated linewidth ∆_ can be found by can be done through self-heterodyne/homodyne technique solving the equation: or measuring frequency noise using frequency discriminator. -
Best LIFE Environment Projects 2015
RONM VI EN N T E P E R O F I J L E C T T S S E B Best LIFE Environment projects 2015 LIFE Environment Environment LIFE ENVIRONMENT | BEST LIFE ENVIRONMENT PROJECTS 2015 EUROPEAN COMMISSION ENVIRONMENT DIRECTORATE-GENERAL LIFE (“The Financial Instrument for the Environment”) is a programme launched by the European Commission and coordinated by the Environment and Climate Action Directorates-General. The Commission has delegated the im- plementation of many components of the LIFE programme to the Executive Agency for Small and Medium-sized Enterprises (EASME). The contents of the publication “Best LIFE Environment Projects 2015” do not necessarily reflect the opinions of the institutions of the European Union. Authors: Gabriella Camarsa (Environment expert), Justin Toland, Jon Eldridge, Wendy Jones, Joanne Potter, Stephen Jones, Stephen Nottingham, Marianne Geater, Isabelle Rogerson, Derek McGlynn, Carlos de la Paz (NEEMO EEIG), Eva Martínez (NEEMO EEIG, Communications Team Coordinator). Managing Editor: Hervé Martin (European Commission, Environment DG, LIFE D.4). LIFE Focus series coordination: Simon Goss (LIFE Communications Coordinator), Valerie O’Brien (Environment DG, Publications Coordinator). Technical assistance: Chris People, Luule Sinnisov, Cristóbal Gines (NEEMO EEIG). The following people also worked on this issue: Davide Messina, François Delceuillerie (Environment DG, LIFE Unit), Giulia Carboni (EASME, LIFE Unit). Production: Monique Braem (NEEMO EEIG). Graphic design: Daniel Renders, Anita Cortés (NEEMO EEIG). Photos database: Sophie Brynart (NEEMO EEIG). Acknowledgements: Thanks to all LIFE project beneficiaries who contributed comments, photos and other useful material for this report. Photos: Unless otherwise specified; photos are from the respective pro- jects. For reproduction or use of these photos, permission must be sought directly from the copyright holders. -
Problems in Residential Design for Ventilation and Noise
Proceedings of the Institute of Acoustics PROBLEMS IN RESIDENTIAL DESIGN FOR VENTILATION AND NOISE J Harvie-Clark Apex Acoustics Ltd, Gateshead, UK (email: [email protected]) M J Siddall LEAP : Low Energy Architectural Practice, Durham, UK (email: [email protected]) & Northumbria University, Newcastle upon Tyne, UK ([email protected]) 1. ABSTRACT This paper addresses three broad problems in residential design for achieving sufficient ventilation provision with reasonable internal ambient noise levels. The first problem is insufficient qualification of the ventilation conditions that should be achieved while meeting the internal ambient noise level limits. Requirements from different Planning Authorities vary widely; qualification of the ventilation condition is proposed. The second problem concerns the feasibility of natural ventilation with background ventilators; the practical result falls between Building Control and Planning Authority such that appropriate ventilation and internal noise limits may not both be achieved. Greater coordination between planning guidance and Building Regulations is suggested. The third problem concerns noise from mechanical systems that is currently entirely unregulated, yet again can preclude residents from enjoying reasonable indoor air quality and noise levels simultaneously. Surveys from over 1000 dwellings are reviewed, and suitable noise limits for mechanical services are identified. It is suggested that commissioning measurements by third party accredited bodies are required in all cases as the only reliable means to ensure that that the intended conditions are achieved in practice. 2. INTRODUCTION The adverse effects of noise on residents are well known; the general limits for internal ambient noise levels are described in the World Health Organisations Guidelines for Community Noise (GCN)1, Night Noise Guidelines (NNG)3 and BS 82332. -
Time-Series Prediction of Environmental Noise for Urban Iot Based on Long Short-Term Memory Recurrent Neural Network
applied sciences Article Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network Xueqi Zhang 1,2 , Meng Zhao 1,2 and Rencai Dong 1,* 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; [email protected] (X.Z.); [email protected] (M.Z.) 2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected]; Tel.: +86-010-62849112 Received: 12 January 2020; Accepted: 6 February 2020; Published: 8 February 2020 Abstract: Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). -
Fault Location in Power Distribution Systems Via Deep Graph
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks Kunjin Chen, Jun Hu, Member, IEEE, Yu Zhang, Member, IEEE, Zhanqing Yu, Member, IEEE, and Jinliang He, Fellow, IEEE Abstract—This paper develops a novel graph convolutional solving a set of nonlinear equations. To solve the multiple network (GCN) framework for fault location in power distri- estimation problem, it is proposed to use estimated fault bution networks. The proposed approach integrates multiple currents in all phases including the healthy phase to find the measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated faulty feeder and the location of the fault [2]. It is pointed by the IEEE 123 bus benchmark system. Simulation results show out in [15] that the accuracy of impedance-based methods can that the GCN model significantly outperforms other widely-used be affected by factors including fault type, unbalanced loads, machine learning schemes with very high fault location accuracy. heterogeneity of overhead lines, measurement errors, etc. In addition, the proposed approach is robust to measurement When a fault occurs in a distribution system, voltage drops noise and data loss errors. Data visualization results of two com- peting neural networks are presented to explore the mechanism can occur at all buses. The voltage drop characteristics for of GCNs superior performance. A data augmentation procedure the whole system vary with different fault locations. Thus, the is proposed to increase the robustness of the model under various voltage measurements on certain buses can be used to identify levels of noise and data loss errors. -
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. -
Noise Tutorial Part IV ~ Noise Factor
Noise Tutorial Part IV ~ Noise Factor Whitham D. Reeve Anchorage, Alaska USA See last page for document information Noise Tutorial IV ~ Noise Factor Abstract: With the exception of some solar radio bursts, the extraterrestrial emissions received on Earth’s surface are very weak. Noise places a limit on the minimum detection capabilities of a radio telescope and may mask or corrupt these weak emissions. An understanding of noise and its measurement will help observers minimize its effects. This paper is a tutorial and includes six parts. Table of Contents Page Part I ~ Noise Concepts 1-1 Introduction 1-2 Basic noise sources 1-3 Noise amplitude 1-4 References Part II ~ Additional Noise Concepts 2-1 Noise spectrum 2-2 Noise bandwidth 2-3 Noise temperature 2-4 Noise power 2-5 Combinations of noisy resistors 2-6 References Part III ~ Attenuator and Amplifier Noise 3-1 Attenuation effects on noise temperature 3-2 Amplifier noise 3-3 Cascaded amplifiers 3-4 References Part IV ~ Noise Factor 4-1 Noise factor and noise figure 4-1 4-2 Noise factor of cascaded devices 4-7 4-3 References 4-11 Part V ~ Noise Measurements Concepts 5-1 General considerations for noise factor measurements 5-2 Noise factor measurements with the Y-factor method 5-3 References Part VI ~ Noise Measurements with a Spectrum Analyzer 6-1 Noise factor measurements with a spectrum analyzer 6-2 References See last page for document information Noise Tutorial IV ~ Noise Factor Part IV ~ Noise Factor 4-1. Noise factor and noise figure Noise factor and noise figure indicates the noisiness of a radio frequency device by comparing it to a reference noise source. -
Next Topic: NOISE
ECE145A/ECE218A Performance Limitations of Amplifiers 1. Distortion in Nonlinear Systems The upper limit of useful operation is limited by distortion. All analog systems and components of systems (amplifiers and mixers for example) become nonlinear when driven at large signal levels. The nonlinearity distorts the desired signal. This distortion exhibits itself in several ways: 1. Gain compression or expansion (sometimes called AM – AM distortion) 2. Phase distortion (sometimes called AM – PM distortion) 3. Unwanted frequencies (spurious outputs or spurs) in the output spectrum. For a single input, this appears at harmonic frequencies, creating harmonic distortion or HD. With multiple input signals, in-band distortion is created, called intermodulation distortion or IMD. When these spurs interfere with the desired signal, the S/N ratio or SINAD (Signal to noise plus distortion ratio) is degraded. Gain Compression. The nonlinear transfer characteristic of the component shows up in the grossest sense when the gain is no longer constant with input power. That is, if Pout is no longer linearly related to Pin, then the device is clearly nonlinear and distortion can be expected. Pout Pin P1dB, the input power required to compress the gain by 1 dB, is often used as a simple to measure index of gain compression. An amplifier with 1 dB of gain compression will generate severe distortion. Distortion generation in amplifiers can be understood by modeling the amplifier’s transfer characteristic with a simple power series function: 3 VaVaVout=−13 in in Of course, in a real amplifier, there may be terms of all orders present, but this simple cubic nonlinearity is easy to visualize. -
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. -
Noise Figure Measurements Application Note
Application Note Noise Figure Measurements VectorStar Making successful, confident NF measurements on Amplifiers The Anritsu VectorStar’s Noise Figure – Option 041 enables the capability to measure noise figure (NF), which is the degradation of the signal-to-noise ratio caused by components in a signal chain. The NF measurement is based on a cold source technique for improved accuracy. Various levels of match and fixture correction are available for additional enhancement. VectorStar is the only VNA platform offering a Noise Figure option enabling NF measurements from 70 kHz to 125 GHz. It is also the only VNA platform available with an optimized noise receiver for measurements from 30 to 125 GHz. 1. NF Overview 1.1. NF Definition 1.2. Importance of Accurate NF Measurements 2. NF Measurement Methods 2.1. Y-Factor / Hot-Cold Noise Figure Measurement Method 2.2. Cold-Source Noise Figure Measurement Method 3. NF Measurement Process 3.1. Test Preparation / Setup 3.2. Instrument Setup 3.3. Measurement Procedure 4. Measurement Example 5. Uncertainties 5.1. Uncertainty Calculator 6. Calculation Example 7. Measurement Tips and Considerations 8. References / Additional Resources 1. NF Overview 1.1 NF Definition Noise figure is a figure-of-merit that describes the degradation of signal-to-noise ratio (SNR) due to added output noise power (No) of a device or system when presented with thermal noise at the input at standard noise temperature (T0, typically 290 K). For amplifiers, the concept can be readily seen. Ideally, when a noiseless input signal is applied to an amplifier, the output signal is simply the input multiplied by the amplifier gain. -
Noise Assessment Activities
Noise assessment activities Interesting stories in Europe ETC/ACM Technical Paper 2015/6 April 2016 Gabriela Sousa Santos, Núria Blanes, Peter de Smet, Cristina Guerreiro, Colin Nugent The European Topic Centre on Air Pollution and Climate Change Mitigation (ETC/ACM) is a consortium of European institutes under contract of the European Environment Agency RIVM Aether CHMI CSIC EMISIA INERIS NILU ÖKO-Institut ÖKO-Recherche PBL UAB UBA-V VITO 4Sfera Front page picture: Composite that includes: photo of a street in Berlin redesigned with markings on the asphalt (from SSU, 2014); view of a noise barrier in Alverna (The Netherlands)(from http://www.eea.europa.eu/highlights/cutting-noise-with-quiet-asphalt), a page of the website http://rumeur.bruitparif.fr for informing the public about environmental noise in the region of Paris. Author affiliation: Gabriela Sousa Santos, Cristina Guerreiro, Norwegian Institute for Air Research, NILU, NO Núria Blanes, Universitat Autònoma de Barcelona, UAB, ES Peter de Smet, National Institute for Public Health and the Environment, RIVM, NL Colin Nugent, European Environment Agency, EEA, DK DISCLAIMER This ETC/ACM Technical Paper has not been subjected to European Environment Agency (EEA) member country review. It does not represent the formal views of the EEA. © ETC/ACM, 2016. ETC/ACM Technical Paper 2015/6 European Topic Centre on Air Pollution and Climate Change Mitigation PO Box 1 3720 BA Bilthoven The Netherlands Phone +31 30 2748562 Fax +31 30 2744433 Email [email protected] Website http://acm.eionet.europa.eu/ 2 ETC/ACM Technical Paper 2015/6 Contents 1 Introduction ...................................................................................................... 5 2 Noise Action Plans ......................................................................................... -
AN10062 Phase Noise Measurement Guide for Oscillators
Phase Noise Measurement Guide for Oscillators Contents 1 Introduction ............................................................................................................................................. 1 2 What is phase noise ................................................................................................................................. 2 3 Methods of phase noise measurement ................................................................................................... 3 4 Connecting the signal to a phase noise analyzer ..................................................................................... 4 4.1 Signal level and thermal noise ......................................................................................................... 4 4.2 Active amplifiers and probes ........................................................................................................... 4 4.3 Oscillator output signal types .......................................................................................................... 5 4.3.1 Single ended LVCMOS ........................................................................................................... 5 4.3.2 Single ended Clipped Sine ..................................................................................................... 5 4.3.3 Differential outputs ............................................................................................................... 6 5 Setting up a phase noise analyzer ...........................................................................................................