Journal articles: 'Electronic noise'

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

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

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

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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 (May 2001): 67–70. http://dx.doi.org/10.1016/s1386-9477(01)00055-8.

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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 (January 16, 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 can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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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 modules (Analog-to-Digital Converters (ADC), Phase-Locked Loops (PLL) and other) due to noise on supply and reference voltages, wrong acquisition of the digital signals and interference with global navigation satellite system (GNSS) or remote control system of UAV. This article is dedicated to the research of the influence of electrical noise, which is formed by the components of the UAV control system (engines, electric motor controllers, microcontroller etc.), on the performance and noise protection of electronic components of the UAV control system. After the research it was concluded that the main sources of elecrtrical noise in the UAV control system are: high currents, consumed by electronic speed controllers (with motors), high-speed toggling of clock signal of SPI / I2C communication, regulation by step-down voltage regulator and internal processes inside the microcontroller due to work of flight control firmware. The waveforms of generated noises, caused by each source was measured with oscilloscope and depicted in the article.

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MYKHALEVSKIY, DMYTRO. "RELIABILITY OF THE CONTROL OF ELECTRONIC DEVICES BY LOW-FREQUENCY NOISE." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (April 27, 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 mechanism for its assessment. Therefore, the task was set to obtain a complete method of probability assessment for the input and output control of electronic equipment products by the level of their own noise. To determine the probabilistic characteristics of the control, the main informative parameters were investigated: the measuring noise voltage, which is random in nature, and the random error. It is established that the measured value has a confidence interval that takes into account measurement errors and determination of control limits. Control limits were obtained, on the basis of which analytical expressions were obtained for the distribution of the informative parameter within the specified limits for reliable and unreliable electronic products. Taking into account the limit of separation of products into suitable and unsuitable, control limits were proposed, which contain the coefficient of possible error cut-off. A generalized analytical expression for evaluating the probability of control is obtained, which takes into account the minimization of the effect of systematic and random factors influencing the result to increase the efficiency of input and output control on the level of low-frequency noise.

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S.U., PIATRUSHA, GINZBURG L.V., TIKHONOV E.S., SHOVKUN D.V., KOBLMÜLLER G., BUBIS A.V., GREBENKO A.K., NASIBULIN A.G., and KHRAPAI V.S. "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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Broja, Manfred, and Olof Bryngdahl. "Quantization noise in electronic halftoning." Journal of the Optical Society of America A 10, no. 4 (April 1, 1993): 554. http://dx.doi.org/10.1364/josaa.10.000554.

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Piatrusha, S. U., L. V. Ginzburg, E. S. Tikhonov, D. V. Shovkun, G. Koblmüller, A. V. Bubis, A. K. Grebenko, A. G. Nasibulin, and V. S. Khrapai. "Noise Insights into Electronic Transport." JETP Letters 108, no. 1 (June 25, 2018): 71–83. http://dx.doi.org/10.1134/s0021364018130039.

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

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Massaro, Alessandro. "ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems." AI 5, no. 2 (April 17, 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 frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

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Vickery, Lindsay. "The Western Edge: some recent electronic music from Western Australia." Organised Sound 6, no. 1 (April 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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Mohanty, Sumant Sekhar, and Sushreeta Tripathy. "Application of Different Filtering Techniques in Digital Image Processing." Journal of Physics: Conference Series 2062, no. 1 (November 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 the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.

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Zhang, Bing, Congzhen Hu, Youze Xin, Yaoxin Li, Yiyun Xie, Qian Xing, Zhuoqi Guo, et al. "Analysis of Low-Frequency 1/f Noise Characteristics for MoTe2 Ambipolar Field-Effect Transistors." Nanomaterials 12, no. 8 (April 12, 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, causing the noise to increase as the temperature decreases. Moreover, we found the noise magnitude for p-type MoTe2 FET hardly changed after exposure to the ambient conditions, whereas for n-FET, the magnitude increased by nearly one order. These noise characteristics may provide useful guidelines for developing high-performance electronics based on the emerging transition metal dichalcogenides.

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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 spectral subtraction can effectively reduce the musical noise, can satisfy the requirement of speech enhancement.

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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 (November 30, 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 KMDCIFF (K-medoid clustering identified fuzzy filter) is proposed for removing impulse noise from digital images. In the proposed KMDCIFF algorithm, the first step is noise detection using K-medoid clustering, followed by a fuzzy logic-acquainted noise reduction strategy that utilizes the least distant local and non-local non-noisy pixels for removal operation. The detection process involves the application of K-medoid clustering on all 5×5 windows produced by centering each pixel of the considered image. In order to remove noise, a 7×7 window is constructed with each detected noisy pixel in the center. Analyzing the impact of the least distant local and non-local pixel on each noisy pixel, the same is replaced by an estimated pixel’s intensity value obtained from the most influential non-noisy pixels. KMDCIFF is evaluated using well-known metrics for diverse types of images. At a high noise density of 80%, KMDCIFF exhibited significant peak-signal-to-noise-ratios (PSNRs) of 26.97 dB and 29.67 dB and structural similarity indexes (SSIMs) of 0.8045 and 0.9288 on random and fixed valued impulse noise impacted Lena image, respectively. Comparing the results of the contemporary study to those of previous studies of a similar kind in this sector, the results are unswervingly astounding.

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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 (June 27, 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 compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.

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Suzuki, Yoshiharu, and Naoki Asakawa. "Stochastic Resonance in Organic Electronic Devices." Polymers 14, no. 4 (February 15, 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 phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.

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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 constructed by the support vector machine (SVM) method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio.

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

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

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

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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 (August 2010): 2133–41. http://dx.doi.org/10.1109/tpel.2010.2046424.

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

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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 (May 1986): 1640. http://dx.doi.org/10.1121/1.393271.

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

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Ingvarsson, S., Gang Xiao, R. A. Wanner, P. Trouilloud, Yu Lu, W. J. Gallagher, A. Marley, K. P. Roche, and S. S. P. Parkin. "Electronic noise in magnetic tunnel junctions." Journal of Applied Physics 85, no. 8 (April 15, 1999): 5270–72. http://dx.doi.org/10.1063/1.369851.

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Calame, J. P., B. G. Danly, M. Garven, and B. Levush. "Studies of electronic noise in gyroklystrons." Physics of Plasmas 7, no. 5 (May 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 (July 1996): 229–41. http://dx.doi.org/10.1016/0168-9002(96)00174-x.

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

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Boyanov, Petar. "PRIMARY PROCESSING OF SIGNALS IN AN OPTO-ELECTRONIC DEVICES." Journal Scientific and Applied Research 8, no. 1 (November 14, 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 is the operation of the system for primary processing of signals when the value of recorded contrast equals 1 percent or less. As an evaluation criterion for the energy efficiency of this system, the signal-to-noise ratio is used. Comparative evaluation of various systems for primary processing of signals operating under low-contrast conditions and specific values of the signal-to-noise ratio is performed. The operation analysis for the system for primary processing of information (signals) under low-contrast conditions is performed accounting for the impact of the optic system. The evaluation criterion for the energy efficiency of the major unit of the system for primary processing of information (the optic system) is the amplification factor, which determines the limit value for the signal-to-noise ratio at the output of the optic-electronic device. The assumption is made that the flow, which determines the circle's area, is uniformly distributed, which does not cause significant errors in evaluating the energy efficiency of the optic-electronic system.

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Duan, Xinhui, Jia Wang, Shuai Leng, Bernhard Schmidt, Thomas Allmendinger, Katharine Grant, Thomas Flohr, and Cynthia H. McCollough. "Electronic Noise in CT Detectors: Impact on Image Noise and Artifacts." American Journal of Roentgenology 201, no. 4 (October 2013): W626—W632. http://dx.doi.org/10.2214/ajr.12.10234.

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

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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 complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features.

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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 (June 30, 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 performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

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Josephine, S., and S. Murugan. "Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 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 images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

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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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Zeng, Yanqiu, Baocan Zhang, Wei Zhao, Shixiao Xiao, Guokai Zhang, Haiping Ren, Wenbing 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 the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

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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 (February 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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Adomavičius, Paulius. "INVESTIGATION OF NOISE IN ELECTRONIC ULTRASONIC SYSTEMS." Mokslas - Lietuvos ateitis 2, no. 1 (February 28, 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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Sozański, Krzysztof. "Overview of Signal Processing Problems in Power Electronic Control Circuits." Energies 16, no. 12 (June 17, 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 contributions of this paper is the introduction of a new formula for calculating the resultant signal-to-noise ratio for three-stage digital control circuits. By carefully considering and correcting for sources of error, it is possible to increase the signal-to-noise ratio and minimize distortion components. This, in turn, leads to improved output/input current and voltage parameters, which can have a positive impact on the overall quality of energy processing in power electronic circuits.

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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 (January 1, 2017): 194–202. http://dx.doi.org/10.5370/kiee.2017.66.1.194.

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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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Li, Zu Lin, Yuan Yao, and Jun Hong. "Flicker Noise in Opto-Electronic Oscillator and Photonic-Delay Homodyne Phase Noise Measurement System." Applied Mechanics and Materials 719-720 (January 2015): 869–74. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.869.

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In this paper, the effect of device flicker noise on the characteristics of both opto-electronic oscillator and Photonic-delay homodyne phase noise measurement system is analyzed. An analytic theory model of opto-electronic oscillator (OEO) is derived and verified by experiments in this paper, where the flicker and white noise are both considered. The sensitivity of Photonic-delay homodyne phase noise measurement system is improved by using high-linear photodetector and low-phase noise amplifier resulting from decreasing systematic phase noise.

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Kirmizitas, Hikmet, and Nurettin Besli. "Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames." Elektronika ir Elektrotechnika 28, no. 6 (December 21, 2022): 42–47. http://dx.doi.org/10.5755/j02.eie.30586.

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In this study, a novel multiple frame based image and texture independent Convolutional Neural Network (CNN) noise estimator is introduced. Noise estimation is a crucial step for denoising algorithms, especially for ones that are called “non-blind”. The estimator works for additive Gaussian noise for varying noise levels. The noise levels studied in this work have a standard deviation equal to 5 to 25 increasing 5 by 5. Since there is no database for noisy multiple images to train and validate the network, two frames of synthetic noisy images with a variety of noise levels are created by adding Additive White Gaussian Noise (AWGN) to each clean image. The proposed method is applied on the most popular gray level images besides the color image databases such as Kodak, McMaster, BSDS500 in order to compare the results with the other works. Image databases comprise indoor and outdoor scenes that have fine details and richer texture. The estimator has an accuracy rate of 99 % for the classification and favourable results for the regression. The proposed method outperforms traditional methods in most cases. And the regression output can be used with any non-blind denoising method.

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Weiss, Julien, and Geneviève Comte-Bellot. "Electronic noise in a constant voltage anemometer." Review of Scientific Instruments 75, no. 5 (May 2004): 1290–96. http://dx.doi.org/10.1063/1.1711147.

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Browne, M. A. "Signal Recovery from Noise in Electronic Instrumentation." Electronics and Power 31, no. 11-12 (1985): 845. http://dx.doi.org/10.1049/ep.1985.0494.

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Journal articles on the topic 'Electronic noise'

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