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Journal articles on the topic 'Electronic noise'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Song, Younggul, and Takhee Lee. "Electronic noise analyses on organic electronic devices." Journal of Materials Chemistry C 5, no. 29 (2017): 7123–41. http://dx.doi.org/10.1039/c7tc01997a.

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2

Takahashi, Minoru. "Electronic noise attenuation system." Journal of the Acoustical Society of America 90, no. 6 (1991): 3388. http://dx.doi.org/10.1121/1.401390.

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3

Hamada, Hareo. "Electronic noise attenuation system." Journal of the Acoustical Society of America 86, no. 4 (1989): 1631–32. http://dx.doi.org/10.1121/1.398661.

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4

Miller, Harry B. "Electronic noise‐reducing system." Journal of the Acoustical Society of America 80, no. 6 (1986): 1870. http://dx.doi.org/10.1121/1.394218.

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5

Brandt, M. S., S. T. B. Goennenwein, and M. Stutzmann. "Spin-dependent electronic noise." Physica E: Low-dimensional Systems and Nanostructures 10, no. 1-3 (2001): 67–70. http://dx.doi.org/10.1016/s1386-9477(01)00055-8.

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6

Li, Yongsong, Zhengzhou Li, Kai Wei, Weiqi Xiong, Jiangpeng Yu, and Bo Qi. "Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation." Sensors 19, no. 2 (2019): 339. http://dx.doi.org/10.3390/s19020339.

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks

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7

Chernyak, Mykola, and Roman Chornomorets. "Experimental studies of electrical noise in the aircraft control system." MECHANICS OF GYROSCOPIC SYSTEMS, no. 39 (May 20, 2020): 31–46. http://dx.doi.org/10.20535/0203-3771392020229073.

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Currently, the problem of reducing noise in electrical equipment is important, because a noise in the system affects its components and can cause unpredictable behavior of the electrical system. This is especially important onboard of unmanned aerial vehicle (UAV), where all components are located close to each other and their noise has a significant cross-effect. Conductors passing through a noisy environment can pick up a noise and direct it to another circuits, where it creates interference. Some examples of such noise problems are: degraded accuracy characteristics of microcontroller modul

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8

S.U., PIATRUSHA, GINZBURG L.V., TIKHONOV E.S., et al. "NOISE INSIGHTS INTO ELECTRONIC TRANSPORT." ПИСЬМА В ЖУРНАЛ ЭКСПЕРИМЕНТАЛЬНОЙ И ТЕОРЕТИЧЕСКОЙ ФИЗИКИ 108, no. 1-2 (2018): 71–72. http://dx.doi.org/10.1134/s0370274x18130131.

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9

Broja, Manfred, and Olof Bryngdahl. "Quantization noise in electronic halftoning." Journal of the Optical Society of America A 10, no. 4 (1993): 554. http://dx.doi.org/10.1364/josaa.10.000554.

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10

Piatrusha, S. U., L. V. Ginzburg, E. S. Tikhonov, et al. "Noise Insights into Electronic Transport." JETP Letters 108, no. 1 (2018): 71–83. http://dx.doi.org/10.1134/s0021364018130039.

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11

Kreß, Dieter, Olaf Ziemann, and Ralf Dietzel. "Electronic simulation of phase noise." European Transactions on Telecommunications 6, no. 6 (1995): 671–74. http://dx.doi.org/10.1002/ett.4460060610.

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12

MYKHALEVSKIY, DMYTRO. "RELIABILITY OF THE CONTROL OF ELECTRONIC DEVICES BY LOW-FREQUENCY NOISE." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (2023): 220–23. http://dx.doi.org/10.31891/2307-5732-2023-319-1-220-223.

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The article examines the stages of input and output control of electronic equipment products according to the level of their own low-frequency noise, namely, the measurement of an informative parameter and its comparison with predetermined limits. It was established that for each type of control there is a need to have a methodology for assessing the compliance of control results with valid characteristics and its effectiveness. One of the main parameters of the effectiveness of all stages of control is the probability, which requires separate studies and the definition of a universal mechanis

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13

Massaro, Alessandro. "ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems." AI 5, no. 2 (2024): 533–49. http://dx.doi.org/10.3390/ai5020027.

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and

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14

Mohanty, Sumant Sekhar, and Sushreeta Tripathy. "Application of Different Filtering Techniques in Digital Image Processing." Journal of Physics: Conference Series 2062, no. 1 (2021): 012007. http://dx.doi.org/10.1088/1742-6596/2062/1/012007.

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Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without

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15

Zhang, Bing, Congzhen Hu, Youze Xin, et al. "Analysis of Low-Frequency 1/f Noise Characteristics for MoTe2 Ambipolar Field-Effect Transistors." Nanomaterials 12, no. 8 (2022): 1325. http://dx.doi.org/10.3390/nano12081325.

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Low-frequency electronic noise is an important parameter used for the electronic and sensing applications of transistors. Here, we performed a systematic study on the low-frequency noise mechanism for both p-channel and n-channel MoTe2 field-effect transistors (FET) at different temperatures, finding that low-frequency noise for both p-type and n-type conduction in MoTe2 devices come from the variable range hopping (VRH) transport process where carrier number fluctuations (CNF) occur. This process results in the broad distribution of the waiting time of the carriers between successive hops, ca

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16

Vickery, Lindsay. "The Western Edge: some recent electronic music from Western Australia." Organised Sound 6, no. 1 (2001): 69–74. http://dx.doi.org/10.1017/s1355771801001108.

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A survey is presented of developments in recent Western Australian electronic music, focusing on the geographical influence on local composers' work. The article follows specific cases of practitioners in the fields of Sound Art (Alan Lamb and Hannah Clemen), Live Electronics (Cathie Travers and the electronic music quartet Magnetic Pig), Interactive Electronics (Jonathan Mustard and Lindsay Vickery) and Noise/Lo Fi Electronics (Cat Hope and Lux Mammoth).

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17

Ma, Ying, Xiao Hua Zhang, and Bing Lei Xing. "A Speech Enhancement Algorithm Based on the “Music Noise” Analysis." Applied Mechanics and Materials 543-547 (March 2014): 2784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2784.

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Interference is inevitable process of voice communication will be from the surrounding environment and transmission medium noise, communication equipment, electronic noise, and other speakers. These interference makes the voice receiver received for noisy speech signal with noise pollution. According to the traditional spectral subtraction residual musical noise is too strong, the weighted processing is reduced and the power spectrum correction, spectral subtraction method was adopted to improve the traditional. According to the analysis of real speech data collection simulation, improved spec

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18

Bandyopadhyay, Aritra, Kaustuv Deb, Avishek Chakraborty, Atanu Das, and Rajib Bag. "A Neighborhood Impact Driven K-Medoid Clustering and Fuzzy Logic Blended Approach for High Density Impulse Noise Detection and Removal." Traitement du Signal 39, no. 5 (2022): 1737–49. http://dx.doi.org/10.18280/ts.390532.

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In the field of image processing, removing impulse noise has been regarded as one of the most important tasks, primarily because of the noise pattern it presents. Existing filters used the effect of only those non-noisy pixels which were present inside the specified windows ignoring the effect of the non-noisy pixels present in the surrounding windows. So, the least distant non-noisy pixels in the present window as well as in the surrounding windows may have an influence on the present window's noisy pixels. Hence, considering the above factors, in this paper, a two-step technique named KMDCIF

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19

Al-Attabi, Ali, and Ali Al. "Spectral Graph Filtering for Noisy Signals Using the Kalman filter." ECTI Transactions on Electrical Engineering, Electronics, and Communications 21, no. 2 (2023): 249818. http://dx.doi.org/10.37936/ecti-eec.2023212.249818.

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Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is com

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20

Suzuki, Yoshiharu, and Naoki Asakawa. "Stochastic Resonance in Organic Electronic Devices." Polymers 14, no. 4 (2022): 747. http://dx.doi.org/10.3390/polym14040747.

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Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phe

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21

An, Feng-Ping, Da-Chao Lin, Xian-Wei Zhou, and Zhihui Sun. "Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/769478.

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Bidimensional empirical mode decomposition (BEMD) algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model c

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22

Kim, Youngsang, and Hyunwook Song. "Noise spectroscopy of molecular electronic junctions." Applied Physics Reviews 8, no. 1 (2021): 011303. http://dx.doi.org/10.1063/5.0027602.

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23

Kim, Youngsang, and Hyunwook Song. "Noise spectroscopy of molecular electronic junctions." Applied Physics Reviews 8, no. 1 (2021): 011303. http://dx.doi.org/10.1063/5.0027602.

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24

Kasap, Safa, and M. Jamal Deen. "Editorial: Selected topics on electronic noise." IEE Proceedings - Circuits, Devices and Systems 149, no. 1 (2002): 1–2. http://dx.doi.org/10.1049/ip-cds:20020162.

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25

Giezendanner, Florian, Juergen Biela, Johann Walter Kolar, and Stefan Zudrell-Koch. "EMI Noise Prediction for Electronic Ballasts." IEEE Transactions on Power Electronics 25, no. 8 (2010): 2133–41. http://dx.doi.org/10.1109/tpel.2010.2046424.

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26

Kozlov, V. A., and M. V. Safonov. "Self-noise of molecular electronic transducers." Technical Physics 48, no. 12 (2003): 1579–82. http://dx.doi.org/10.1134/1.1634680.

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27

Greer, Charles B., Donald W. Keehan, and John A. Springer. "Electronic earth seismic noise measuring method." Journal of the Acoustical Society of America 79, no. 5 (1986): 1640. http://dx.doi.org/10.1121/1.393271.

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28

Navid, Reza. "Noise-based electronic refrigerators-how practical?" IEEE Potentials 27, no. 5 (2008): 37–39. http://dx.doi.org/10.1109/mpot.2008.928013.

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29

Foulkes, Timothy J. "Electronic equipment noise in video facilities." Journal of the Acoustical Society of America 85, S1 (1989): S16. http://dx.doi.org/10.1121/1.2026842.

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30

Ingvarsson, S., Gang Xiao, R. A. Wanner, et al. "Electronic noise in magnetic tunnel junctions." Journal of Applied Physics 85, no. 8 (1999): 5270–72. http://dx.doi.org/10.1063/1.369851.

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31

Calame, J. P., B. G. Danly, M. Garven, and B. Levush. "Studies of electronic noise in gyroklystrons." Physics of Plasmas 7, no. 5 (2000): 2180–85. http://dx.doi.org/10.1063/1.874038.

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32

Seller, P. "Noise analysis in linear electronic circuits." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 376, no. 2 (1996): 229–41. http://dx.doi.org/10.1016/0168-9002(96)00174-x.

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33

Nishiguchi, Katsuhiko, and Akira Fujiwara. "Noise in Nanometer-scale Electronic Devices." NTT Technical Review 13, no. 8 (2015): 11–15. http://dx.doi.org/10.53829/ntr201508fa3.

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34

J, Jaganpradeep, Rajalakshmi J, Arun Antony V, and Priya T. "EVOLUTIONARY AGENTS WITH QUANTUM BASED NANO-ELECTRONIC CIRCUIT DESIGN FOR ELECTRONIC-PHOTONIC INTEGRATED CIRCUITS." ICTACT Journal on Microelectronics 10, no. 2 (2024): 1769–75. https://doi.org/10.21917/ijme.2024.0306.

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Integrating quantum technology with CMOS offers advancements in manufacturing, assembly, and performance of quantum photonic devices. Traditional quantum detectors with macroscopic interconnects suffer from limited bandwidth and performance due to capacitance constraints and discrete component integration. We developed a quantum noise–limited monolithic electronic-photonic evolutionary agent with a quantum circuit detector, fabricated using a 250-nanometer bipolar CMOS process. The device’s footprint is 80 µm by 220 µm, and it integrates photonics and electronics on a single chip. The detector

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35

Duan, Xinhui, Jia Wang, Shuai Leng, et al. "Electronic Noise in CT Detectors: Impact on Image Noise and Artifacts." American Journal of Roentgenology 201, no. 4 (2013): W626—W632. http://dx.doi.org/10.2214/ajr.12.10234.

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36

Kleinpenning, T. G. M. "On noise and random telegraph noise in very small electronic devices." Physica B: Condensed Matter 164, no. 3 (1990): 331–34. http://dx.doi.org/10.1016/0921-4526(90)90820-k.

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37

Boyanov, Petar. "PRIMARY PROCESSING OF SIGNALS IN AN OPTO-ELECTRONIC DEVICES." Journal Scientific and Applied Research 8, no. 1 (2015): 10–15. http://dx.doi.org/10.46687/jsar.v8i1.172.

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The energy efficiency of systems for primary processing of signals in opto-electronic devices is analyzed for the case of identification and study of remote objects against a bright background and under low-contrast conditions. A criterion is determined for evaluating the energy efficiency of the major unit of the system for primary signal processing - the optic system, and some expressions are derived, relating the value of the signal-to-noise ratio at the device's input with these criteria (amplification factor) and other "ideal" or "real" optic systems' parameters. The specific thing here i

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38

Kumar, Dileep, Dezhan Tu, Naifu Zhu, Dibo Hou, and Hongjian Zhang. "In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network." Journal of Sensors 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/5789510.

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Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest

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39

Guo, Fei, Mei Zhao, Xiu Ying Fan, Jin He Bao, and Wen Bang Sun. "Filtering of Electronic Speckle Correlation Fringes." Applied Mechanics and Materials 198-199 (September 2012): 1202–7. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1202.

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Speckle correlation fringes include a large quantity of speckle noise. It is first to consider how to reduce speckle noise before using phase formula to calculate. Filter is usually used to reduce speckle noise. From two aspects of frequency domain and space domain, we adopted mean filter, medium filter, Butterworth low-pass filter and homomorphism filter to process the speckle correlation fringes obtained by experiment. It is shown that the speckle correlation fringes are clearer and more easily interpreted and automatic processed after being filtered.

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40

Zeng, Yanqiu, Baocan Zhang, Wei Zhao, et al. "Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts." Computational and Mathematical Methods in Medicine 2020 (April 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/1405647.

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Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all

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41

BECIR, A., F. A. A. EL-ORANY, and M. R. B. WAHIDDIN. "CONTINUOUS-VARIABLE QUANTUM KEY DISTRIBUTION PROTOCOLS WITH EIGHT-STATE DISCRETE MODULATION." International Journal of Quantum Information 10, no. 01 (2012): 1250004. http://dx.doi.org/10.1142/s0219749912500049.

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We propose a continuous variable quantum key distribution protocol based on discrete modulation of eight-state coherent states. We present a rigorous security proof against the collective attacks by taking into consideration the realistic lossy and noisy quantum channel, the imperfect detector efficiency, and the detector electronic noise. This protocol shows high tolerance against excess noise and promises to achieve over 100 km distance of optical fiber.

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42

S, Siva Priyanka, and Kishore Kumar T. "Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement." Traitement du Signal 38, no. 3 (2021): 785–95. http://dx.doi.org/10.18280/ts.380325.

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In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction perform

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43

Josephine, S., and S. Murugan. "Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1818–25. http://dx.doi.org/10.1166/jctn.2020.8446.

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In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those i

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44

Edwards, Paul J. "Sub-Poissonian Electronic and Photonic Noise Generation in Semiconductor Junctions." Australian Journal of Physics 53, no. 1 (2000): 179. http://dx.doi.org/10.1071/ph99074.

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This paper addresses sub-Poissonian electronic and photonic noise generation in semiconductor junctions. Recent theoretical and technical advances in the understanding and generation of quantum noise-suppressed (‘quiet’) light have emphasised the links between photonic and electronic shot noise. Shot-noise suppression and single electron–photon control through the operation of the collective and single-electron Coulomb blockade mechanisms are described.

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45

Sozański, Krzysztof. "Overview of Signal Processing Problems in Power Electronic Control Circuits." Energies 16, no. 12 (2023): 4774. http://dx.doi.org/10.3390/en16124774.

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This paper examines various aspects related to digital signal processing in digital control circuits used in power electronics. The discussion focuses on several common issues, including the sampling rate of signals (including phenomena such as aliasing and synchronization), coherent sampling, jitter of sampling pulses, sequential versus simultaneous sampling in multichannel systems, signal resolution (including signal-to-noise ratio, noise-shaping circuits, and changes in sampling speed), interpolation and decimation, and the conversion of analog circuits into digital form. One of the key con

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46

Adomavičius, Paulius. "INVESTIGATION OF NOISE IN ELECTRONIC ULTRASONIC SYSTEMS." Mokslas - Lietuvos ateitis 2, no. 1 (2010): 40–44. http://dx.doi.org/10.3846/mla.2010.009.

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Noise models in ultrasonic control system have been investigated. Ultrasonic system channel consist of exciting generator, ultrasonic transducer, amplitude limiter, amplifier, low band filter and A/D converter. The ultrasonic transducers have been described as Von Hippel model, Van Dyke model or improved Van Dyke model. Advantages and disadvantages of these models are discussed in this paper. Noise models of amplitude limiter and linear operational amplifier are presented. The summary results of calculated noise spectral density of ultrasonic system channel have been presented.

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47

Shang Xin, Li Fan, Ma Zheng-Lei, et al. "Low noise photodetector in 0.1 mHz-1 Hz band." Acta Physica Sinica 74, no. 5 (2025): 0. https://doi.org/10.7498/aps.74.20241635.

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Laser intensity noise suppression in the millihertz frequency band is essential for space-based gravitational wave detection to ensure the sensitivity of the interferometer. Photonic feedback technology is one of the most effective methods for suppressing laser intensity noise. As the first-stage component in the feedback loop, the noise of the photodetector directly couples into the feedback loop, significantly impacting the laser intensity noise. Starting from the requirement to suppress laser intensity noise in the 0.1 mHz-1 Hz frequency band for space-based gravitational wave detection, th

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48

Jia, Xiaofei, Wenhao Chen, Bing Ding, and Liang He. "Noise test method for dual-gate MOSFET device." Modern Physics Letters B 33, no. 31 (2019): 1950387. http://dx.doi.org/10.1142/s0217984919503871.

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In recent years, with the development of mesoscopic physics and nanoelectronics, the research on noise and testing technology of electronic components has been developed. It is well known that noise can characterize the transmission characteristics of carriers in nanoscale electronic components. With the continuous shrinking of the device size, the carrier transport of nanoscale MOSFET devices has been gradually transformed from the traditional drift-diffusion to become the quasi-ballistic or ballistic transport, and its current noise contains granular and thermal noise. The paper by Jeon et a

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49

Naveed, Khuram, Shoaib Ehsan, Klaus D. McDonald-Maier, and Naveed Ur Rehman. "A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors." Sensors 19, no. 1 (2019): 206. http://dx.doi.org/10.3390/s19010206.

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Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subse

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50

Cho, Dae-Hoon, and Ki-Sik Lee. "Noise Improvement Countermeasure by Noise Pattern Analysis for Electrical and Electronic Equipment." Transactions of The Korean Institute of Electrical Engineers 66, no. 1 (2017): 194–202. http://dx.doi.org/10.5370/kiee.2017.66.1.194.

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