Measurements of Current Noise and Distortion in Resistors
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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. -
Application of Noise Mapping in an Indian Opencast Mine for Effective Noise Management
12th ICBEN Congress on Noise as a Public Health Problem Application of noise mapping in an Indian opencast mine for effective noise management Veena Manwar1, Bibhuti Bhusan Mandal2, Asim Kumar Pal3 1 National Institute of Miners’ Health, Department of Occupational Hygiene, Nagpur, India (corresponding author) 2 National Institute of Miners’ Health, Department of Occupational Hygiene, Nagpur, India 3 Indian Institute of Technology-Indian School of Mines (IIT-ISM), Department of Environmental Science and Engineering, Dhanbad, India Corresponding author's e-mail address: [email protected] ABSTRACT So far as mining industry is concerned, noise pollution is not new. It is generated from operation of equipment and plants for excavation and transport of minerals which affects mine employees as well as population residing in nearby areas. Although in the Recommendations of Tenth Conference on Safety in Mines, noise mapping has been made mandatory in Indian mines still mining industry are not giving proper importance on producing noise maps of mines. Noise mapping is preferred for visualization and its propagation in the form of noise contours so that preventive measures are planned and implemented. The study was conducted in an opencast mine in Central India. Sound sources were identified and noise measurements were carried out according to national and international standards. Considering source locations along with noise levels and other meteorological, geographical factors as inputs, noise maps were generated by Predictor LimA software. Results were evaluated in the light of Central Pollution Control Board norms as to whether noise exposure in the residential and industrial area were within prescribed limits or not. -
Designline PROFILE 42
High Performance Displays FLAT TV SOLUTIONS DesignLine PROFILE 42 Plasma FlatTV 106cm / 42" WWW.CONRAC.DE HIGH PERFORMANCE DISPLAYS FLAT TV SOLUTIONS DesignLine PROFILE 42 (106cm / 42 Zoll Diagonale) Neu: Verarbeitet HD-Signale ! New: HD-Compliant ! Einerseits eine bestechend klare Linienführung. Andererseits Akzente durch die farblich gestalteten Profilleisten in edler Metallic-Lackierung. Das Heimkino-Erlebnis par Excellence. Impressively clear lines teamed with decorative aluminium strips in metallic finish provide coloured highlights. The ultimate home cinema experience. Für höchste Ansprüche: Die FlatTVs der DesignLine kombinieren Hightech mit einzigartiger Optik. Die komplette Elektronik sowie die hochwertigen Breitband-Stereolautsprecher wurden komplett ins Gehäuse integriert. Der im Lieferumfang enthaltene Design-Standfuß aus Glas lässt sich für die Wandmontage einfach und problemlos entfernen, so dass das Display noch platzsparender wie ein Bild an der Wand angebracht werden kann. Die extrem flachen Bildschirme bieten eine unübertroffene Bildbrillanz und -schärfe. Das lüfterlose Konzept basiert auf dem neuesten Stand der Technik: Ohne störende Nebengeräusche hören Sie nur das, was Sie hören möchten. Einfaches Handling per Fernbedienung und mit übersichtlichem On-Screen-Menü. Die Kombination aus Flachdisplay-Technologie, einer High Performance Scaling Engine und einem zukunftsweisenden De-Interlacer* mit speziellen digitalen Algorithmen zur optimalen Darstellung bewegter Bilder bietet Ihnen ein unvergleichliches Fernseherlebnis. Zusätzlich vermittelt die Noise Reduction eine angenehme Bildruhe. For the most decerning tastes: DesignLine flat panel TVs combine advanced technology with outstanding appearance. All the electronics and the high-quality broadband stereo speakers have been fully integrated in the casing. The design glass stand included in the scope of supply can easily be removed for wall assembly, allowing the display to be mounted to the wall like a picture to save even more space. -
Sensory Unpleasantness of High-Frequency Sounds
Acoust. Sci. & Tech. 34, 1 (2013) #2013 The Acoustical Society of Japan PAPER Sensory unpleasantness of high-frequency sounds Kenji Kurakata1;Ã, Tazu Mizunami1 and Kazuma Matsushita2 1National Institute of Advanced Industrial Science and Technology (AIST), AIST Central 6, 1–1–1 Higashi, Tsukuba, 305–8566 Japan 2National Institute of Technology and Evaluation (NITE), 2–49–10, Nishihara, Shibuya-ku, Tokyo, 151–0066 Japan ( Received 5 March 2012, Accepted for publication 2 August 2012 ) Abstract: The sensory unpleasantness of high-frequency sounds of 1 kHz and higher was investigated in psychoacoustic experiments in which young listeners with normal hearing participated. Sensory unpleasantness was defined as a perceptual impression of sounds and was differentiated from annoyance, which implies a subjective relation to the sound source. Listeners evaluated the degree of unpleasantness of high-frequency pure tones and narrow-band noise (NBN) by the magnitude estimation method. Estimates were analyzed in terms of the relationship with sharpness and loudness. Results of analyses revealed that the sensory unpleasantness of pure tones was a different auditory impression from sharpness; the unpleasantness was more level dependent but less frequency dependent than sharpness. Furthermore, the unpleasantness increased at a higher rate than loudness did as the sound pressure level (SPL) became higher. Equal-unpleasantness-level contours, which define the combinations of SPL and frequency of tone having the same degree of unpleasantness, were drawn to display the frequency dependence of unpleasantness more clearly. Unpleasantness of NBN was weaker than that of pure tones, although those sounds were expected to have the same loudness as pure tones. -
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). -
All You Need to Know About SINAD Measurements Using the 2023
applicationapplication notenote All you need to know about SINAD and its measurement using 2023 signal generators The 2023A, 2023B and 2025 can be supplied with an optional SINAD measuring capability. This article explains what SINAD measurements are, when they are used and how the SINAD option on 2023A, 2023B and 2025 performs this important task. www.ifrsys.com SINAD and its measurements using the 2023 What is SINAD? C-MESSAGE filter used in North America SINAD is a parameter which provides a quantitative Psophometric filter specified in ITU-T Recommendation measurement of the quality of an audio signal from a O.41, more commonly known from its original description as a communication device. For the purpose of this article the CCITT filter (also often referred to as a P53 filter) device is a radio receiver. The definition of SINAD is very simple A third type of filter is also sometimes used which is - its the ratio of the total signal power level (wanted Signal + unweighted (i.e. flat) over a broader bandwidth. Noise + Distortion or SND) to unwanted signal power (Noise + The telephony filter responses are tabulated in Figure 2. The Distortion or ND). It follows that the higher the figure the better differences in frequency response result in different SINAD the quality of the audio signal. The ratio is expressed as a values for the same signal. The C-MES signal uses a reference logarithmic value (in dB) from the formulae 10Log (SND/ND). frequency of 1 kHz while the CCITT filter uses a reference of Remember that this a power ratio, not a voltage ratio, so a 800 Hz, which results in the filter having "gain" at 1 kHz. -
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. -
47 CFR Ch. I (10–1–06 Edition) § 15.117
§ 15.117 47 CFR Ch. I (10–1–06 Edition) § 15.117 TV broadcast receivers. comprising five pushbuttons and a separate manual tuning knob is considered to provide (a) All TV broadcast receivers repeated access to six channels at discrete shipped in interstate commerce or im- tuning positions. A one-knob (VHF/UHF) ported into the United States, for sale tuning system providing repeated access to or resale to the public, shall comply 11 or more discrete tuning positions is also with the provisions of this section, ex- acceptable, provided each of the tuning posi- cept that paragraphs (f) and (g) of this tions is readily adjustable, without the use section shall not apply to the features of tools, to receive any UHF channel. of such sets that provide for reception (2) Tuning controls and channel read- of digital television signals. The ref- out. UHF tuning controls and channel erence in this section to TV broadcast readout on a given receiver shall be receivers also includes devices, such as comparable in size, location, accessi- TV interface devices and set-top de- bility and legibility to VHF controls vices that are intended to provide and readout on that receiver. audio-video signals to a video monitor, that incorporate the tuner portion of a NOTE: Differences between UHF and VHF TV broadcast receiver and that are channel readout that follow directly from the larger number of UHF television chan- equipped with an antenna or antenna nels available are acceptable if it is clear terminals that can be used for off-the- that a good faith effort to comply with the air reception of TV broadcast signals, provisions of this section has been made. -
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. -
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.