Noise Control
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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. -
A Basic Design Approach to Clean Room
www.PDHcenter.com PDH Course M143 www.PDHonline.org A Basic Design Guide for Clean Room Applications Course Content PART – I OVERVIEW Clean rooms are defined as specially constructed, environmentally controlled enclosed spaces with respect to airborne particulates, temperature, humidity, air pressure, airflow patterns, air motion, vibration, noise, viable (living) organisms, and lighting. Particulate control includes: !" Particulate and microbial contamination !" Particulate concentration and dispersion “Federal Standard 209E” defines a clean room as a room in which the concentration of airborne particles is controlled to specified limits. “British Standard 5295” defines a clean room as a room with control of particulate contamination, constructed and used in such a way as to minimize the introduction, generation and retention of particles inside the room and in which the temperature, humidity, airflow patterns, air motion and pressure are controlled. Today, many manufacturing processes require that spaces be designed to control particulate and microbial contamination while maintaining reasonable installation and operating costs. Clean rooms are typically used in manufacturing, packaging, and research facilities associated with these industries: 1. Semiconductor: This industry drives the state of the art clean room design, and this industry accounts for a significant number of all operating clean rooms. 2. Pharmaceutical: Clean rooms control living particles that would produce undesirable bacterial growth in the preparation of biological, pharmaceutical, and other medical products as well as in genetic engineering research. Page 1 of 61 www.PDHcenter.com PDH Course M143 www.PDHonline.org 3. Aerospace: The manufacturing and assembling of aerospace electronics, missiles and satellites were the first application of clean rooms. -
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. -
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. -
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 ........................................................................................................... -
Theory of Noise Measurement
Model MAURY MICROWAVE MT993A CORPORATION 06 Jul 1999 THEORY OF NOISE MEASUREMENT Introduction Maury Microwave's noise characterization software (MT993A) is designed to find the complete set of Noise is a natural phenomenon that affects most Γ noise parameters consisting of Fmin, opt, and Rn. Since microwave and RF systems. Because noise masks Γ opt is complex, this makes a total of four scalar desired signals, it is important to understand and Γ parameters. opt is the source reflection coefficient minimize its effects on the performance of micro- which corresponds to Fmin. Rn is a scale factor which wave and RF devices. Although there are several Γ shows how fast F changes with s. (The software types of noise, thermal noise is an important actually displays rn, which is Rn normalized to consideration for microwave designers because it 50 ohms.) greatly affects the linear microwave and RF systems they work with. Thermal noise is the focus of this Γ To find Fmin, opt, and Rn, the simple noise figure application note. must be measured at a variety of source imped- ances. In principle, since four scalar variables are Noise figure (a measure of the noise generated to be found, only four measurements are required. by two-port devices) can easily be determined by In practice, however, it is much more effective using commercial noise figure meters, but the to measure more points, and use a "least means measurements they provide are limited to SIMPLE square’s" mathematical technique to extract the NOISE FIGURE. Simple noise figure is the noise parameters. -
Why Use Signal-To-Noise As a Measure of MS Performance When It Is Often Meaningless?
Why use Signal-To-Noise as a Measure of MS Performance When it is Often Meaningless? Technical Overview Authors Abstract Greg Wells, Harry Prest, and The signal-to-noise of a chromatographic peak determined from a single measurement Charles William Russ IV, has served as a convenient figure of merit used to compare the performance of two Agilent Technologies, Inc. different MS systems. The evolution in the design of mass spectrometry instrumenta- 2850 Centerville Road tion has resulted in very low noise systems that have made the comparison of perfor- Wilmington, DE 19809-1610 mance based upon signal-to-noise increasingly difficult, and in some modes of opera- USA tion impossible. This is especially true when using ultra-low noise modes such as high resolution mass spectrometry or tandem MS; where there are often no ions in the background and the noise is essentially zero. This occurs when analyzing clean stan- dards used to establish the instrument specifications. Statistical methodology com- monly used to establish method detection limits for trace analysis in complex matri- ces is a means of characterizing instrument performance that is rigorously valid for both high and low background noise conditions. Instrument manufacturers should start to provide customers an alternative performance metric in the form of instrument detection limits based on relative the standard deviation of replicate injections to allow analysts a practical means of evaluating an MS system. Introduction of polysiloxane at m/z 281 increased baseline noise for HCB at m/z 282). Signal-to-noise ratio (SNR) has been a primary standard for Improvement in instrument design and changes to test com- comparing the performance of chromatography systems includ- pounds were accompanied by a number of different ing GC/MS and LC/MS. -
Experimental Study for Revising Visual Noise Measurement of ISO 15739
https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-219 © 2021, Society for Imaging Science and Technology Experimental Study for Revising Visual Noise Measurement of ISO 15739 Akira Matsui1, Naoya Katoh1, Dietmar Wueller2 1Sony Imaging Products & Solutions Inc., Tokyo, Japan, 2Image Engineering GmbH & Co. KG, Kerpen, Germany Abstract values is a negative value, which is unrepresentative of the HVS Visual noise is a metric for measuring the amount of noise property in general cases. perception in images taking into account the properties of the human visual system (HVS). A visual noise measurement method is Revision concept specified in Annex B of ISO 15739, which has been used as a Several issues raised were as follows: it is possible that useful measurement metric over the years since its introduction. As negative XYZ values, introduced during the calculation several issues have been questioned recently, it is now being procedure by the large CSF peak in the achromatic channel, may investigated for revision involving changes to the CSF, color space influence the visual noise calculation accuracy for dark and used for rms noise value measurements, and visual noise formula noisy patches; involving a division calculation in the u*v* combining rms values in three color channels. To derive visual derivation potentially makes the visual noise calculation noise formula involving color noise weighting coefficients unstable in darker cases; using a linear combination of rms representing HVS sensitivity to noise, subjective experiments using values for visual noise formula is different from a statistically simulated luminance and color noise images were done. The general way of combining rms values as sum of variances. -
USP Signal-To-Noise in Empower 2
TECN10123052 Rev. 01 Page 1 of 8 USP Signal-to-Noise in Empower 2 This Technical Note explains Waters’ interpretation of the new USP signal-to-noise (S/N) calculation and presents a choice of implementations using custom field calculations. It contains the following topics: • Discussion of USP Signal-to-Noise Definition • Differences between USP Signal-to-Noise and European Pharmacopeia Signal-to-Noise • Using a Custom Field to determine USP Signal-to-Noise by Making a Blank Injection (the preferred approach) • Using a Custom Field to determine USP Signal-to-Noise Within one Chromatogram • Generic Signal-to-Noise Determination in Empower 2 Discussion of USP Signal-to-Noise Definition Until recently, the USP had not formalized their definition of Signal-to-Noise (S/N). Effective December 2009, the USP 32 standard defines it as follows: S/N = 2H/h where: H = Height of the peak corresponding to the component concerned. h = Difference between the largest and smallest noise values observed over a distance equal to at least five times the width at the half-height of the peak and, if possible, situated equally around the peak of interest. Effective in 2011 with the USP 34 standard, USP added a diagram (Figure 1) to their S/N definition. Figure 1 –USP Signal-to-Noise Determination TECN10123052 Rev. 01 Page 2 of 8 The USP definition states that noise is “the difference between the largest and smallest noise values…” While this measurement as described is simple, it does not compensate for local systematic drift. It is assumed that the noise measurement should account for drift. -
Sound Attenuators for Use in Duct and Return Air Applications
University of Houston Master Construction Specifications Insert Project Name SECTION 23 33 19 - SOUND ISOLATION PART 1 - GENERAL 1.1 RELATED DOCUMENTS: A. The Conditions of the Contract and applicable requirements of Division 1, "General Requirements", and Section 23 01 00, "Mechanical General Provisions", govern this Section. 1.2 DESCRIPTION OF WORK: A. Work Included: Provide sound isolation work as specified, scheduled, and shown on the Drawings. B. Types: The types of sound isolation required for the project include, but are not limited to, duct sound attenuators for use in duct and return air applications. 1.3 QUALITY ASSURANCE: A. Manufacturers: Provide sound isolation units complying with these specifications and produced by one of the following: 1. Industrial Acoustics Company. 2. Koppers Company, Inc. 3. Peabody Noise Control. 4. Rink Corporation. 1.4 SUBMITTALS: A. Shop Drawing submittals shall include, but are not limited to, the following: 1. Cut sheets clearly indicating the size, type, ratings, insertion loss, regenerated noise, materials, construction, connection types, performance and other pertinent data for each sound attenuator to be provided on the project. 2. Independent test laboratory reports on performance of each silencer type being provided. 3. Additional information as required in Section 23 01 00. 1.5 PRODUCT, DELIVERY, STORAGE AND HANDLING: A. Deliver sound attenuators in factory-fabricated water-resistant wrapping. B. Handle sound attenuators carefully to avoid damage to material components, enclosure and finish. C. Store sound attenuators in a clean, dry space and protect from the weather. PART 2 - PRODUCTS 2.1 DUCT SOUND ATTENUATORS: A. General: Provide duct sound attenuators of the size and capacity scheduled or shown on the Drawings and having dynamic insertion losses and static pressure losses equivalent to those scheduled.