The Basics of Noise Figure Measurements for ATE Joe Kelly and Frank Goh, Verigy
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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. -
Autoencoding Neural Networks As Musical Audio Synthesizers
Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18), Aveiro, Portugal, September 4–8, 2018 AUTOENCODING NEURAL NETWORKS AS MUSICAL AUDIO SYNTHESIZERS Joseph Colonel Christopher Curro Sam Keene The Cooper Union for the Advancement of The Cooper Union for the Advancement of The Cooper Union for the Advancement of Science and Art Science and Art Science and Art NYC, New York, USA NYC, New York, USA NYC, New York, USA [email protected] [email protected] [email protected] ABSTRACT This training corpus consists of five-octave C Major scales on var- ious synthesizer patches. Once training is complete, we bypass A method for musical audio synthesis using autoencoding neural the encoder and directly activate the smallest hidden layer of the networks is proposed. The autoencoder is trained to compress and autoencoder. This activation produces a magnitude STFT frame reconstruct magnitude short-time Fourier transform frames. The at the output. Once several frames are produced, phase gradient autoencoder produces a spectrogram by activating its smallest hid- integration is used to construct a phase response for the magnitude den layer, and a phase response is calculated using real-time phase STFT. Finally, an inverse STFT is performed to synthesize audio. gradient heap integration. Taking an inverse short-time Fourier This model is easy to train when compared to other state-of-the-art transform produces the audio signal. Our algorithm is light-weight methods, allowing for musicians to have full control over the tool. when compared to current state-of-the-art audio-producing ma- This paper presents improvements over the methods outlined chine learning algorithms. -
Noise Tutorial Part V ~ Noise Factor Measurements
Noise Tutorial Part V ~ Noise Factor Measurements Whitham D. Reeve Anchorage, Alaska USA See last page for document information Noise Tutorial V ~ Noise Factor Measurements 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 4-2 Noise factor of cascaded devices 4-3 References Part V ~ Noise Measurements Concepts 5-1 General considerations 5-1 5-2 Noise factor measurements with the Y-factor method 5-6 5-3 References 5-8 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 V ~ Noise Factor Measurements Part V ~ Noise Factor Measurements 5-1. General considerations Noise factor is an important measurement for amplifiers used in low noise applications such as radio telescopes and radar and other radio receivers designed to detect very low signal levels. -
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). -
Application Note Template
correction. of TOI measurements with noise performance the improved show examples Measurement presented. is correction a noise of means by improvements range Dynamic explained. are analyzer spectrum of a factors the limiting correction. The basic requirements and noise with measurements spectral about This application note provides information | | | Products: Note Application Correction Noise with Range Dynamic Improved R&S R&S R&S FSQ FSU FSG Application Note Kay-Uwe Sander Nov. 2010-1EF76_0E Table of Contents Table of Contents 1 Overview ................................................................................. 3 2 Dynamic Range Limitations .................................................. 3 3 Signal Processing - Noise Correction .................................. 4 3.1 Evaluation of the noise level.......................................................................4 3.2 Details of the noise correction....................................................................5 4 Measurement Examples ........................................................ 7 4.1 Extended dynamic range with noise correction .......................................7 4.2 Low level measurements on noise-like signals ........................................8 4.3 Measurements at the theoretical limits....................................................10 5 Literature............................................................................... 11 6 Ordering Information ........................................................... 11 1EF76_0E Rohde & Schwarz -
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. -
Measurements 1: Signal Receiving Techniques
MeasurementsMeasurements 1:1: SignalSignal receivingreceiving techniquestechniques Fritz Caspers CAS, Aarhus, June 2010 Contents • The radio frequency (RF) diode • Superheterodyne concept • Spectrum analyzer • Oscilloscope • Vector spectrum and FFT analyzer • Decibel • Noise basics • Noise‐figure measurement with the spectrum analyzer CAS, Aarhus, June 2010 2 The RF diode (1) • We are not discussing the generation of RF signals here, just the detection • Basic tool: fast RF* diode (= Schottky diode) • In general, Schottky diodes are fast but still have a voltage dependent junction capacity (metal –semi‐ A typical RF detector diode conductor junction) Try to guess from the type of the connector which side is the RF input and which is the output • Equivalent circuit: Video output *Please note, that in this lecture we will use RF for both the RF and micro wave (MW) range, since the borderline between RF and MW is not defined unambiguously CAS, Aarhus, June 2010 3 The RF diode (2) • Characteristics of a diode: • The current as a function of the voltage for a barrier diode can be described by the Richardson equation: The RF diode is NOT an ideal commutator for small signals! We cannot apply big signals otherwise burnout CAS, Aarhus, June 2010 4 The RF diode (3) • In a highly simplified manner, one can approximate this expression as: VJ … junction voltage • and show as sketched in the following, that the RF rectification is linked to the second derivation (curvature) of the diode characteristics: CAS, Aarhus, June 2010 5 The RF diode (4) • This diagram depicts the so called square‐law region where the output voltage (VVideo) is proportional to the input power • Since the input power is proportional to the square of the input voltage (V 2) and the RF output signal is proportional to the input power, this region is called square‐ Linear Region law region. -
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. -
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. -
Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth
11/08/2016 Receiver Sensitivity and Equivalent Noise Bandwidth Receiver Sensitivity and Equivalent Noise Bandwidth Parent Category: 2014 HFE By Dennis Layne Introduction Receivers often contain narrow bandpass hardware filters as well as narrow lowpass filters implemented in digital signal processing (DSP). The equivalent noise bandwidth (ENBW) is a way to understand the noise floor that is present in these filters. To predict the sensitivity of a receiver design it is critical to understand noise including ENBW. This paper will cover each of the building block characteristics used to calculate receiver sensitivity and then put them together to make the calculation. Receiver Sensitivity Receiver sensitivity is a measure of the ability of a receiver to demodulate and get information from a weak signal. We quantify sensitivity as the lowest signal power level from which we can get useful information. In an Analog FM system the standard figure of merit for usable information is SINAD, a ratio of demodulated audio signal to noise. In digital systems receive signal quality is measured by calculating the ratio of bits received that are wrong to the total number of bits received. This is called Bit Error Rate (BER). Most Land Mobile radio systems use one of these figures of merit to quantify sensitivity. To measure sensitivity, we apply a desired signal and reduce the signal power until the quality threshold is met. SINAD SINAD is a term used for the Signal to Noise and Distortion ratio and is a type of audio signal to noise ratio. In an analog FM system, demodulated audio signal to noise ratio is an indication of RF signal quality. -
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 .........................................................................................