Natural and Man-Made Terrestrial Electromagnetic Noise: an Outlook
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Spectrum Management Technical Handbook
NTIA TN 89-2 SPECTRUM MANAGEMENT TECHNICAL HANDBOOK TECHNICAL NOTE SERIES NTIA TECHNICAL NOTE 89-2 SPECTRUM MANAGEMENT TECHNICAL HANDBOOK l U.S. DEPARTMENT OF COMMERCE Robert A. Mosbacher, Secretary Alfred C. Sikes, Assistant Secretary for Communications and Information JULY 1989 ACKNOWLEDGEMENT NT IA acknowledges the efforts of Mark Schellhammer in the completion of Phase I of this handbook (Sections 1-3). Other portions will be added as resources are available. ABSTRACT This handbook is a compilation of the procedures, definitions, relationships and conversions essential to electromagnetic compatibility analysis. It is intended as a reference for NTIA engineers. KEY WORDS CONVERSION FACTORS COUPLING EQUATIONS DEFINITIONS ·TECHNICAL RELATIONSHIPS iii TABLE OF CONTENTS Subsection Page SECTION 1 TECHNICAL TERMS:DEFINITIONS AND NOTATION TERMS AND DEFINITIONS . .. .. 1-1 GLOSSARY OF STANDARD NOTATION . .. 1-13 SECTION 2 TECHNICAL RELATIONSHIPS AND CONVERSION FACTORS INTRODUCTION . .. .. .. 2-1 TRANSMISSION LOSS FOR RADIO LINKS . �. .. .. 2-1 Propagation Loss . .. .. .. 2-1 Transmission Loss ........................................ 2-2 Free-Space Basic Transmission Loss .......................... 2-2 Basic Transmission Loss .................................... 2-2 Loss Relative to Free-Space .................................. 2-2 System Loss . .. 2-3 ; Total Loss . .. .. 2-3 ANTENNA CHARACTERISTICS . .. 2-3 Antenna Directivity ........................................ 2-3 Antenna Gain and Efficiency .................................. 2-3 I -
Waveos User Guide
WAVEOS USER GUIDE DTUS070 rev A.5 – December, 2016 Page 2 / 273 COPYRIGHT (©) ACKSYS 2016-2017 This document contains information protected by Copyright. The present document may not be wholly or partially reproduced, transcribed, stored in any computer or other system whatsoever, or translated into any language or computer language whatsoever without prior written consent from ACKSYS Communications & Systems - ZA Val Joyeux – 10, rue des Entrepreneurs - 78450 VILLEPREUX - FRANCE. REGISTERED TRADEMARKS ® Ø ACKSYS is a registered trademark of ACKSYS. Ø Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. Ø CISCO is a registered trademark of the CISCO company. Ø Windows is a registered trademark of MICROSOFT. Ø WireShark is a registered trademark of the Wireshark Foundation. Ø HP OpenView® is a registered trademark of Hewlett-Packard Development Company, L.P. Ø VideoLAN, VLC, VLC media player are internationally registered trademark of the French non-profit organization VideoLAN. DISCLAIMERS ACKSYS ® gives no guarantee as to the content of the present document and takes no responsibility for the profitability or the suitability of the equipment for the requirements of the user. ACKSYS ® will in no case be held responsible for any errors that may be contained in this document, nor for any damage, no matter how substantial, occasioned by the provision, operation or use of the equipment. ACKSYS ® reserves the right to revise this document periodically or change its contents without notice. DTUS070 rev A.5 – December, 2016 Page 3 / 273 REGULATORY INFORMATION AND DISCLAIMERS Installation and use of this Wireless LAN device must be in strict accordance with local regulation laws and with the instructions included in the user documentation provided with the product. -
A Novel Ship Target Detection Algorithm Based on Error Self-Adjustment Extreme Learning Machine and Cascade Classifier
Cognitive Computation (2019) 11:110–124 https://doi.org/10.1007/s12559-018-9606-5 A Novel Ship Target Detection Algorithm Based on Error Self-adjustment Extreme Learning Machine and Cascade Classifier Wandong Zhang1,2 · Qingzhong Li1 · Q. M. Jonathan Wu2 · Yimin Yang3 · Ming Li1 Received: 30 June 2018 / Accepted: 10 October 2018 / Published online: 24 October 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for ship target detection and tracking because of its wide visual field and large sea area coverage. However, most of the existing ship target detection methods of HFSWR face two main difficulties: (1) the received radar signals are strongly polluted by clutter and noises, and (2) it is difficult to detect ship targets in real-time due to high computational complexity. This paper presents a ship target detection algorithm to overcome the problems above by using a two-stage cascade classification structure. Firstly, to quickly obtain the target candidate regions, a simple gray-scale feature and a linear classifier were applied. Then, a new error self-adjustment extreme learning machine (ES-ELM) with Haar-like input features was adopted to further identify the target precisely in each candidate region. The proposed ES-ELM includes two parts: initialization part and updating part. In the former stage, the L1 regularizer process is adopted to find the sparse solution of output weights, to prune the useless neural nodes and to obtain the optimal number of hidden neurons. Also, to ensure an excellent generalization performance by the network, in the latter stage, the parameters of hidden layer are updated through several iterations using L2 regularizer process with pulled back error matrix. -
Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines
Report of Investigations 8828 Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines By John Durkin UNITED STATES DEPARTMENT OF THE INTERIOR William P. Clark, Secretary BUREAU OF MINES Robert C. Horton, Director Library of Congress Cataloging in Publication Data: Durkin, John Vertical magnetic noise in the voice frequency band within and above coal mines. (Report of investigations ; 8828) Bibliography: p. 21-22. Supt. of Docs. no.: I 28.23:8828. 1. Coal mines and mining-Communication systems. 2. Electromag- netic noise. 3. Voice frequency. I. Title. 11. Series: Report of in- vestigations (United States. Bureau of Mines) ; 8828. TN23.U43 [TN344] 622s [622] 83-600 164 CONTENTS Page Abstract ....................................................................... 1 Introduction ................................................................. 2 EM atmospheric noise ........................................................... 2 Noise source ................................................................. 2 Earth-ionosphere waveguide ................................................... 5 Prior atmospheric noise measurements ......................................... 8 Surface vertical magnetic noise .......................................... 9 Underground vertical magnetic noise ...................................... 11 EM vertical magnetic noise field tests ......................................... 12 Noise measuring instrumentation.............................................. 12 Noise data .................................................................. -
Transmitter T (X) Channel E (X) Receiver R
Analog Communication ( 10EC53 ) Introduction The function of the communication system is to make available at the destination a signal originating at a distant point. This signal is called the desired signal. Unfortunately, during the passage of the signal through the channel and front-end of the receiver, this desired signal gets corrupted by a number of undesired signals. This is referred to as Noise. Noise is any unknown or unwanted signal. Noise is the static you hear in the speaker when you tune any AM or FM receiver to any position between stations. It is also the snow or confetti that is visible on a TV screen. The received signal is modeled as r (t) = s (t) + n (t) Where s (t) is the transmitted/desired signal n (t) is the additive noise/unwanted signal The signal n (t) gets added at the channel. It disturbs the transmission and processing of signal in Communication System. Over which one cannot have a control. In general term it is an unwanted signal that affects a wanted signal. It is a random signal that cannot be represented with a simple equation. But some time can be deterministic components (power supply hum, certain oscillations).Deterministic Components can be eliminated by proper shielding and introduction of notch filters. If their were no noise perfect communication would be possible with minimum transmitted power. At the receiver only amplification of the signal power to the desired level is required. If R (x)=T (x) then e (x)=0 Transmitter Receiver T (x) R (x) If R (x) ≠ T (x) then e (x) ≠ 0.The received signal is corrupted. -
Demodulation, Bit-Error Probability and Diversity Arrangements
RADIO SYSTEMS – ETIN15 Lecture no: 6 Demodulation, bit-error probability and diversity arrangements Anders J Johansson, Department of Electrical and Information Technology [email protected] 5 April 2017 1 Contents • Receiver noise calculations [Covered briefly in Chapter 3 of textbook!] • Optimal receiver and bit error probability – Principle of maximum-likelihood receiver – Error probabilities in non-fading channels – Error probabilities in fading channels • Diversity arrangements – The diversity principle – Types of diversity – Spatial (antenna) diversity performance 5 April 2017 2 RECEIVER NOISE 5 April 2017 3 Receiver noise Noise sources The noise situation in a receiver depends on several noise sources Noise picked up Wanted by the antenna signal Output signal Analog Detector with requirement circuits on quality Thermal noise 5 April 2017 4 Receiver noise Equivalent noise source To simplify the situation, we replace all noise sources with a single equivalent noise source. Wanted How do we determine Noise free signal N from the other sources? N Output signal C Analog Detector with requirement circuits on quality Noise free Same “input quality”, signal-to-noise ratio, C/N in the whole chain. 5 April 2017 5 Receiver noise Examples • Thermal noise is caused by random movements of electrons in circuits. It is assumed to be Gaussian and the power is proportional to the temperature of the material, in Kelvin. • Atmospheric noise is caused by electrical activity in the atmosphere, e.g. lightning. This noise is impulsive in its nature and below 20 MHz it is a dominating. • Cosmic noise is caused by radiation from space and the sun is a major contributor. -
EHC 238 Front Pages Final.Fm
This report contains the collective views of an international group of experts and does not necessarily represent the decisions or the stated policy of the International Commission of Non- Ionizing Radiation Protection, the International Labour Organization, or the World Health Organization. Environmental Health Criteria 238 EXTREMELY LOW FREQUENCY FIELDS Published under the joint sponsorship of the International Labour Organization, the International Commission on Non-Ionizing Radiation Protection, and the World Health Organization. WHO Library Cataloguing-in-Publication Data Extremely low frequency fields. (Environmental health criteria ; 238) 1.Electromagnetic fields. 2.Radiation effects. 3.Risk assessment. 4.Envi- ronmental exposure. I.World Health Organization. II.Inter-Organization Programme for the Sound Management of Chemicals. III.Series. ISBN 978 92 4 157238 5 (NLM classification: QT 34) ISSN 0250-863X © World Health Organization 2007 All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e- mail: [email protected]). Requests for permission to reproduce or translate WHO publications – whether for sale or for noncommercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: [email protected]). The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
Reliability of Atmospheric Radio Noise Predictions L
JOURNAL OF RESEARCH of the National Bureau of Standards- D. Radio Propagation Vol. 650 , No.6, November- December 1961 Reliability of Atmospheric Radio Noise Predictions l John R. Herman Contribution from AVCO Corporation, Research and Advanced Development Division, Geophysics Section, 201 Lowell Street, Wilmington, Mass. (Received May 15, 1961) Measured radio noise values are compared with the corresponding International Radio Consultative Committee [C.C.I.R., 1957) predicted values at four noise measuring stations. Five freq uencies between 0.013 and 10.0 Mc/s are considered. The stations selected for this ~t ud y include Balboa, Panama, near t wo major radio noise centers, and Byrd Station, Antarctic, remote from. atmosp heric radio noise sources. It is fouod that the predicted and measured noise levels are in good agreement except at some places and times, where large discrepancies occur. :Most of the disagreements are found at places where the predi ctions are based on extrapolations of data measured at other stations. R easons for the disagree men ts are disc u sed. 1. Introduction Becausc the success or failure of communications by radio waves depends upon the signal-to-noise i ratio at the receiver, it has become necessary to obtain some knowledge of the noise levels to be expected. Atmospherically-generated radio noise levels vary with time of day, scason, and geographi cal lo cation. Prediction of tbe variations on a worldwide basis have been published in Radio Propagation Unit (R.P.U.) Technical R eport No.5 [1945 1947] and National Bureau of Standards Circular 462 [1948] in terms of t he minimum field stl' e ngth~ r ~ quir e ~ to maintain int ell i~ibl e. -
Applied Engineering Using Schumann Resonance for Earthquakes Monitoring
applied sciences Article Applied Engineering Using Schumann Resonance for Earthquakes Monitoring Jose A. Gazquez 1,2, Rosa M. Garcia 1,2, Nuria N. Castellano 1,2 ID , Manuel Fernandez-Ros 1,2 ID , Alberto-Jesus Perea-Moreno 3 ID and Francisco Manzano-Agugliaro 1,* ID 1 Department Engineering, University of Almeria, CEIA3, 04120 Almeria, Spain; [email protected] (J.A.G.); [email protected] (R.M.G.); [email protected] (N.N.C.); [email protected] (M.F.-R.) 2 Research Group of Electronic Communications and Telemedicine TIC-019, 04120 Almeria, Spain 3 Department of Applied Physics, University of Cordoba, CEIA3, Campus de Rabanales, 14071 Córdoba, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-950-015396; Fax: +34-950-015491 Received: 14 September 2017; Accepted: 25 October 2017; Published: 27 October 2017 Abstract: For populations that may be affected, the risks of earthquakes and tsunamis are a major concern worldwide. Therefore, early detection of an event of this type in good time is of the highest priority. The observatories that are capable of detecting Extremely Low Frequency (ELF) waves (<300 Hz) today represent a breakthrough in the early detection and study of such phenomena. In this work, all earthquakes with tsunami associated in history and all existing ELF wave observatories currently located worldwide are represented. It was also noticed how the southern hemisphere lacks coverage in this matter. In this work, the most suitable locations are proposed to cover these geographical areas. Also, ELF data processed obtained from the observatory of the University of Almeria in Calar Alto, Spain are shown. -
Signal to Noise Ratio (SNR Or S/N) SNR Is the Ratio of the Signal Power to Noise Power
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA KAKINADA – 533 001 , ANDHRA PRADESH GATE Coaching Classes as per the Direction of Ministry of Education GOVERNMENT OF ANDHRA PRADESH Analog Communication 26-05-2020 to 06-07-2020 Prof. Ch. Srinivasa Rao Dept. of ECE, JNTUK-UCE Vizianagaram Analog Communication-Day 7, 01-06-2020 Presentation Outline Noise: • Noise and its types • Thermal Noise • Parameters of Noise • Noise in Baseband Communication systems • Gaussian process and NB Noise • Problems 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 2 Learning Outcomes • At the end of this Session, Student will be able to: • LO 1 : Fundamental aspects of Noise • LO 2 : Different Noise types and its Paraneters • LO 3 : Gaussian Process and ND Noise 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 3 Noise Definitions of Noise: • Noise is unwanted signal that affects wanted signal. • Noise can broadly be defined as any unknown signal that affects the recovery of the desired signal. • Noise is an unwanted signal, which interferes with the original message signal and corrupts the parameters of the message signal. This alteration in the communication process, leads to the message getting altered. It most likely enters at the channel or the receiver. Effect of noise • Degrades system performance (Analog and digital) • Receiver cannot distinguish signal from noise • Efficiency of communication system reduces Most common examples of noise are: • Hiss sound in radio receivers • Buzz sound amidst of telephone conversations • Flicker in television receivers, Noise Sources External Internal Noise Noise Atmospheric Industrial Extraterrestrial Solar Noise Cosmic Noise External Noise : • It is due to Man- made and natural resources • Sources over which we have no control such as thunders, snow fall, lightning etc. -
Rolling Bearings Fault Diagnosis and Classi Cation Based on Optimal Wavelet Subband Selection and Deep Neural Network
Rolling bearings fault diagnosis and classication based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha ( [email protected] ) Rajiv Gandhi Proudyogiki Vishwavidyalaya https://orcid.org/0000-0002-6568-8083 Preety D Swami Rajiv Gandhi Proudyogiki Vishwavidyalaya Research Article Keywords: Autoencoder, fault diagnosis, Shannon entropy, softmax classier, stationary wavelet transform. Posted Date: March 23rd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-342393/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha1, a *, Preety D Swami1,b 1 Department of Electronics & Communication Engineering, RGPV, Bhopal- 462033, India *a email-address: [email protected] b email-address: [email protected] Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output. -
High Frequency (HF)
Calhoun: The NPS Institutional Archive Theses and Dissertations Thesis Collection 1990-06 High Frequency (HF) radio signal amplitude characteristics, HF receiver site performance criteria, and expanding the dynamic range of HF digital new energy receivers by strong signal elimination Lott, Gus K., Jr. Monterey, California: Naval Postgraduate School http://hdl.handle.net/10945/34806 NPS62-90-006 NAVAL POSTGRADUATE SCHOOL Monterey, ,California DISSERTATION HIGH FREQUENCY (HF) RADIO SIGNAL AMPLITUDE CHARACTERISTICS, HF RECEIVER SITE PERFORMANCE CRITERIA, and EXPANDING THE DYNAMIC RANGE OF HF DIGITAL NEW ENERGY RECEIVERS BY STRONG SIGNAL ELIMINATION by Gus K. lott, Jr. June 1990 Dissertation Supervisor: Stephen Jauregui !)1!tmlmtmOlt tlMm!rJ to tJ.s. eave"ilIE'il Jlcg6iielw olil, 10 piolecl ailicallecl",olog't dU'ie 18S8. Btl,s, refttteste fer litis dOCdiii6i,1 i'lust be ,ele"ed to Sapeihil6iiddiil, 80de «Me, "aial Postg;aduulG Sclleel, MOli'CIG" S,e, 98918 &988 SF 8o'iUiid'ids" PM::; 'zt6lI44,Spawd"d t4aoal \\'&u 'al a a,Sloi,1S eai"i,al'~. 'Nsslal.;gtePl. Be 29S&B &198 .isthe 9aleMBe leclu,sicaf ,.,FO'iciaKe" 6alite., ea,.idiO'. Statio", AlexB •• d.is, VA. !!!eN 8'4!. ,;M.41148 'fl'is dUcO,.Mill W'ilai.,s aliilical data wlrose expo,l is idst,icted by tli6 Arlil! Eurse" SSPItial "at FRIis ee, 1:I.9.e. gec. ii'S1 sl. seq.) 01 tlls Exr;01l ftle!lIi"isllatioli Act 0' 19i'9, as 1tI'I'I0"e!ee!, "Filill ell, W.S.€'I ,0,,,,, 1i!4Q1, III: IIlIiI. 'o'iolatioils of ltrese expo,lla;;s ale subject to 960616 an.iudl pSiiaities.