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1H multi-quantum (MQ)/single-quantum (SQ) correlation spectroscopy at fast magic angle spinning (MAS) is widely used to probe spatial proximity between protons. However, MQ/SQ spectra are always as...

In this paper, we investigate the quantum force sensing in a single optomechanical system by the use of a coherent quantum noise cancellation scheme. The system consists of a Fabry–Perot cavity simultaneously coupled to a mechanical oscillator and an ensemble of atom. Accordingly, the dynamics of the systems are studied by utilizing the quantum Langevin equations approach. It is found that the backaction noise can be eliminated from coherent quantum noise cancellation. Remarkably, we examine a force sensor that uses coherent noise cancellation to beat the standard quantum limit. We also investigate the effect of coherent transitions to atomic noise suppresses, and these simultaneous processes can significantly enhance the performance of the quantum force sensor. Our scheme can be generalized to other hybrid optomechanical systems, and these results may have spectacular applications for the realization of the quantum force sensor.

Erratic noise often has high amplitudes and a non‐Gaussian distribution. Least‐squares–based approaches therefore are not optimal. This can be handled better with non–least‐squares approaches, for example based on Huber norm which is computationally expensive. An alternative method has been published which involves transforming the data with erratic noise to pseudodata that have Gaussian distributed noise. It can then be attenuated using traditional least‐squares approaches. This alternative method has previously been used in combination with a curvelet transform in an iterative scheme. In this paper, we introduce a median‐filtering step in this iterative scheme. The median filter is applied following the slope direction of the seismic data to maximally preserve the energy of useful signals. The new method can suppress stronger erratic noise compared with the previous iterative method, and can better deal with random noise compared with the single‐step implementation of the median filter. We apply the proposed robust denoising algorithm to a synthetic dataset and two field data examples and demonstrate its advantages over three different noise attenuation algorithms.

In this paper, the near-carrier enhancement of phase-noise (PN) at turnover temperature ( ${T} _{to}$ ) in a quasi-thickness-Lame (QTL) mode thin-film piezoelectric-on-silicon (TPoS) oscillator is reported for the first time. QTL-TPoS resonators fabricated on degenerately-doped n-type silicon offer a ${T} _{to}$ greater than 80°C and are suitable for implementation of highly-stable ovenized oscillators. In this work, a ~123MHz QTL-TPoS resonator is heated up to ${T} _{to}$ (~90°C) by injecting current through the silicon body of the resonator. It is experimentally observed that at turnover temperature, the phase-noise slope close to the carrier frequency ( $\Delta $ f 100Hz ) decreases substantially in contrast to the expected trends. A ~10dB improvement in phase noise at 10Hz offset and a ~25dB improvement at 1Hz offset is recorded when the oscillator is operating at ${T} _{to}$ compared to room temperature. [2020-0206]

Esta investigación explora la utilización de materiales alternativos como el filamento termoplástico ABS y un moderno método de manufactura, como lo es la impresión 3D para reproducir la estructura de un instrumento medidor de revoluciones por minuto RPM de un motor aeronáutico con características de atenuación de campos electromagnéticos y supresión de ruido, similares a los instrumentos originales producidos con aluminio embutido, para lograr este propósito se emplea la ley de Gauss y el efecto de la jaula de Faraday, además mediante la combinación de plástico y fibra de vidrio se logra un sistema de paredes dobles que atenúan la propagación del sonido. Empleando el diseño e ingeniería asistidos por computador se ejecuta la simulación y validación del prototipo empleando el método de análisis por elementos finitos y análisis de compatibilidad electromagnética, finalmente los resultados de las pruebas de laboratorio y de campo permiten cuantificar las nuevas características mecánicas obtenidas. Palabras Clave: impresión 3D, instrumento aeronáutico, jaula de Faraday. Referencias [1]R. Collinson, Introduction to Avionics Systems, Rochester, Kent, U.K.: Springer, 2013. [2]G. M. P., Introducción a los procesos de manufactura, México: Mc Graw Hill, 2014. [3]B. v. d. Berg, 3D Printing, Leiden : Springer, 2016. [4]Airbus, «Airbus Technical Magazine,» FAST Flight Airworthiness Support Technology, vol. único, nº 55, p. 40, 2015. [5]J. H. &. L. Serway, Física para ciencias e Ingeniería, Filadelfia: Mc Graw Hill, 2002. [6]M. Moser, Ingeniería Acústica, teoría y aplicaciones., Berlin: Springer, 2009. [7]R. E. Olcina, «Radiación de energía electromagnética,» de Interferencias electromagnéticas en componentes electrónicos, Madrid, Dialnet, 1992, pp. 389-394. [8]Y. Freedman, Física Universitaria, México: Pearson, 2009. [9]B. P. N. Anderson, The Elecromagnetic Field, York, London: Springer, 1968. [10]U. S. Dixit, Simulations for design and manufacturing, Singapore: Springer, 2018.

FBMC-OQAM is a promising multi-carrier technique being considered for future wireless communication systems, owing to the flexibility it provides via the choice of prototype filters, robustness against asynchronous transmissions, and the absence of cyclic-prefix requirement. The performance of FBMC-OQAM systems is however limited by the accuracy of channel estimation due to the presence of so-called intrinsic interference. In this letter, we consider two well-known preamble-based channel estimation approaches for FBMC-OQAM, and propose generic preamble structures, followed by formulation of mathematical optimization problems to improve the resilience of both estimation schemes against this intrinsic interference. The proposed preambles, without incurring any additional run-time complexity at transceiver, lead to improved noise suppression, eventually leading to superior channel estimation performance. The benefit of the proposed preambles is shown not only numerically in terms of the relevant performance metrics, but also via link-level simulations under realistic transmission scenarios.

In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis. Graphical abstract Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm.

The focus of this work is on understanding the effect of water injection from the launch pad on the noise generated during rocket’s lift-off. To simplify the problem, we consider a supersonic jet impinging on a flat plate with water injection from the impingement plate. The Volume of Fluid model is adopted in this work to simulate the two-phase flow. A Hybrid Large Eddy Simulation – Unsteady Reynolds Averaged Simulation approach is employed to model turbulence, wherein Unsteady Reynolds Averaged Simulation is used near the walls, and Large Eddy Simulation is used elsewhere in the computational domain. The numerical issues associated with simulating the noise of two-phase supersonic flow are addressed. The pressure fluctuations on the impingement plate obtained from numerical simulations agree well with the experimental data. Furthermore, the predicted effect of water injection on the far-field broadband noise is consistent with that of the experiment. The possible mechanisms for noise reduction by water injection are discussed.

A novel scheme to suppress the remnant mode conversion noise with a surface mount device (SMD) capacitor on dual back-to-back bent differential lines is proposed. To suppress the remnant mode conversion noise, an additional SMD capacitor is loaded on the differential lines between two bends to equalize even- and odd-mode velocities. The mode conversion noise suppression characteristics of the proposed scheme were analyzed and compared with various schemes. Also, eye diagrams confirmed that the addition of the SMD capacitor suppressed remnant mode conversion noise while preserving differential signal quality in dual back-to-back bent differential lines.

An artificial material made by kneading magnetic metal flakes in plastic sheets has been used widely in PCs or mobile phone handsets. This sheet is called a noise suppression sheet to reduce unwanted coupling or unwanted signals in the device. The sheet has strong frequency-dependent characteristics in the permeability. It is essential to evaluate these characteristics accurately to develop the sheets and to design the device mentioned above. Currently, the sheets have been evaluated by the transmission line methods, but the accuracy was not enough. The evaluation by the resonance cavity perturbation method is proposed in this article. Although the accuracy of the resonance cavity perturbation method is high, it works well at only one resonance frequency. The expansion of the measuring frequency by using a harmonic resonance cavity is proposed in this article. A comparison of the proposed method to the transmission line method is also discussed.

With monitoring of the acoustic emission phenomenon caused by rock deformation and failure, microseismic monitoring has been widely used in the development of unconventional oil and gas fields. Due to the complex environment and diversity types of the noise, the signal energy of surface microseismic monitoring is weak and the signal-to-noise ratio (S/N) of raw data is very low. In the process of data processing, many human resources are needed to discriminate the first-break picking because of the low S/N, and this directly affects the error of microseismic event location. We have adopted the regularization to Stein unbiased risk estimation (R-SURE) algorithm based on the continuous wavelet transform to separate the signal from the noise in different decomposition levels. The regularization factor is the adaptive change of the different geology and fracturing engineering, which is related to shale brittleness, fracturing pressure, and displacement. As a result, the threshold from the R-SURE algorithm is multiresolution in different levels, and the S/N could be improved effectively. In addition, we established the threshold discriminant for picking up the first-break wave of low-S/N data combined with the Akaike information criterion and characteristic function, which compared the maximum absolute value in the time window. The method has good robustness and low computational complexity. The first arrival is automatically and accurately judged, which improves the accuracy of the event location. We successfully applied these methods to the surface microseismic monitoring of shale gas fracturing in several wells in southwest China. The S/N of the raw data has been improved, the effective stimulated reservoir volume and the performance of the gas production are predicted with the results, which provides important technical support for shale gas development in the area.

Статья посвящена синтезу алгоритма шумоподавления в видеоинформации, поступающей от бортовых съёмочных систем летательных аппаратов, основанному на технологии дискретных вейвлет-преобразований. Показано, что разработанный метод является развитием фильтрации Колмогорова-Винера, использующей для шумоподавления дискретное преобразование Фурье. The article is devoted to the synthesis of the noise reduction algorithm in the video information received from on-Board camera systems of aircrafts, based on the technology of discrete wavelet transformations. It is shown that the developed method is the extension of Kolmogorov-Wiener filtration using discrete Fourier transform for noise reduction.

A nonlinear third-order extended state observer (TESO) is proposed for the rotor position and speed estimation for sensorless interior permanent magnet synchronous motor drives. The disturbance observation bandwidth of the proposed observer is expanded by deploying a gain optimization method. The measurement noise excited in the control loop is suppressed effectively by substituting the previous high gain configuration. Experimentally, both the steady-state and transient performance of the TESO are verified on testing rigs. In comparison with the performance of a conventional linear extended state observer, the TESO has a better capability of disturbance observation and noise suppression in a wide operating range.

Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work, an acoustic metamaterial capsule is proposed to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta-atoms, which have a simple structure that facilitates manufacturing. Two different metamaterial capsules are designed, simulated, manufactured, and experimentally validated that utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling one to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsules of simple c-shape structure.

Seismic data in desert area generally have low signal-to-noise ratio (SNR) due to special surface conditions. Desert noise is characterized as low-frequency, non-Gaussian and non-stationary noise, which makes the noise suppression in desert area more challenging by conventional methods. Conventional methods are effective for the signal with high SNR, but in desert seismic signal, the SNR is low and the signal can easily be obliterated in desert noise. In this paper, we propose an approach that operates in synchrosqueezing transform (SST) domain and use classification techniques obtained from supervised machine learning to identify the coefficients associated with signal and noise. First of all, we transform the real desert seismic data into time–frequency domain by SST. Secondly, we select features by calculating the SST coefficients of signal and noise. And then, we train them in the Adaboost classifier. Finally, when the training is completed, we can obtain the final classifier that can effectively separate the signal from noise. We perform tests on synthetic and field records, and the results show great advantages in suppressing random noise as well as retaining effective signal amplitude.

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve the time direction in the computation of the signal envelopes needed for the iterative sifting process. Instead, we apply the sifting algorithm spatially in the inline-crossline plane. At each time slice, we calculate the upper and lower signal envelopes by means of a filter whose length is adapted dynamically at each sifting iteration according to the spatial distribution of the extrema. The denoising of a 3D volume is achieved by removing the most oscillating modes of each time slice from the noisy data. We determine the performance of the algorithm by using three public-domain poststack field data sets: one 2D line of the well-known Alaska 2D data set, available from the US Geological Survey; a subset of the Penobscot 3D volume acquired offshore by the Nova Scotia Department of Energy, Canada; and a subset of the Stratton 3D land data from South Texas, available from the Bureau of Economic Geology at the University of Texas at Austin. The results indicate that random and coherent noise, such as footprint signatures, can be mitigated satisfactorily, enhancing the reflectors with negligible signal leakage in most cases. Our method, called empirical-mode filtering (EMF), yields improved results compared to other 2D and 3D techniques, such as [Formula: see text] EMD filter, [Formula: see text] deconvolution, and [Formula: see text]-[Formula: see text]-[Formula: see text] adaptive prediction filtering. EMF exploits the flexibility of EMD on seismic data and is presented as an efficient and easy-to-apply alternative for denoising seismic data with mild to moderate structural complexity.

We demonstrate noise suppression and precise phase control of a commercial 2.45GHz magnetron aiming for wireless power transfer application. The impurity of the microwave spectrum was confirmed to be due to the switching frequency of 76kHz in the power supply. With the decoupling capacitor installed to the output of the high-voltage power-supply driving the magnetron, the spectral purity of the magnetron was significantly enhanced. And we achieved extreme precision in phase-control (peak-to-peak 0.3°) for the high-power (1 kW) microwave magnetron by applying the phase locking loop to the noise-suppressed magnetron system.

The rapid iterative development of information technology, computer technology and Internet of things technology promotes the construction of smart city, and the related concepts of smart city gradually move from theory to practice. As an important part of the development of smart city, the intelligent lighting and intelligent control of urban lighting system become increasingly urgent with the development of the latest Internet of things technology and video image processing technology. Based on the current Internet of things data communication and related processing technology as the background, this article constructs the overall control model of smart city lighting system, including GPRS wireless transmission technology and ZigBee technology. In the core algorithm, this article innovatively presents an improved video image processing technology based on Gaussian mixture algorithm, which mainly describes the background of a specific scene in multi-dimensional model, so as to improve the reliability of the whole scene judgment. At the level of noise suppression, the video image processing algorithm proposed in this article designs interference noise suppression technology based on the principle of mean filtering, so as to achieve the accuracy and integrity of urban traffic scene extraction. In order to solve the security problems of the whole video image processing technology in information extraction, storage and analysis, this article adds the electromagnetic information leakage prevention algorithm in the video image processing technology, which mainly processes the video image at the source, so as to reduce the electromagnetic radiation of the overall information. Based on this, this article completed the construction of the software and hardware of the city intelligent lighting system. On this basis, the software and hardware construction of urban intelligent lighting system is completed. At the same time, the experiment was carried out in a small-scale scene of a city. The experimental results show that the intelligent urban lighting system based on video image processing technology is feasible and can achieve the desired effect. At the same time, the lighting energy consumption level can be reduced by about 30%, and the corresponding failure rate can be reduced by about 30%.

In this paper, a hybrid uniplanar compact electromagnetic bandgap (UC-EBG) design is proposed with a wide-band noise suppression in power planes of printed circuit boards (PCBs). To achieve wide-band noise suppression, the proposed hybrid UC-EBG design employs a combination of various uniplanar EBG unit cells with different sizes which have different frequency ranges of noise suppression corresponding to the size of the cells. In addition, an analytic model of the EBG unit cell composed of cavity resonant model and meander line inductance is proposed and validated by 3-dimensional field simulation and measurement. To demonstrate the proposed design, a heterogeneous group of unit cells with different sizes was selected and the noise transfer function was compared with that of a conventional UC-EBG structure. The proposed hybrid UC-EBG design is experimentally verified by noise transfer function measurement with a significant expansion of the stopband up to 478 % in comparison to the conventional uniplanar EBG designs with the same area occupation.

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this article, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.

Solving dynamic Sylvester matrix equations is a prevalent research topic and many methods have been arisen to solve the dynamic Sylvester equation, but few of them consider the noise effect. To investigate the new approach which can suppress the noise effect, integration feedback is added in the conventional Newton-Raphson iterated (CNRI) algorithm to form the proposed integration-implemented Newton-Raphson iterated (IINRI) algorithm based on the control theorem. Besides, this paper transforms the dynamic Sylvester equation into a linear equation which turns into the zeroing finding problem in further by constructing the error function. According to the theoretical analyses and the simulation results, the IINRI algorithm has higher accuracy and strong robustness under different noises (e.g. the constant noise, the linear noise, and the bounded random noise) while the performance of the CNRI algorithm is seriously degraded by the noises, which reveals that the IINRI algorithm is an efficient and powerful approach to solve dynamic Sylvester equation under noise perturbations.

The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as $f-x$ denconvolution and $f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tradeoff between random noise suppression and seismic signal preservation.

To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST). The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details. An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST. Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients. Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement. Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.

The active mode-locking (AML) technique has been widely used in erbium-doped fiber lasers to generate picosecond pulse trains. Here we propose a novel active mode-locking dual-loop optoelectronic oscillator (AML-DL-OEO), which can generate microwave frequency comb (MFC) signals with adjustable comb spacings. Based on this scheme, the order of harmonic mode-locking is dramatically decreased for a certain AML driving frequency compared with a single-loop AML-OEO. Thus, the supermode noise caused by harmonic mode-locking can be efficiently suppressed. In addition, the sidemodes are well suppressed by the dual-loop architecture. An experiment is performed. MFC signals with different comb spacings are generated under fundamental or harmonic mode-locking states. AML-DL-OEO systems with different length differences between two loops are implemented to evaluate supermode noise suppression capability. The performance of the generated MFC signals is recorded and analyzed.

Today personal audio devices are usually used during telephone connections. Mobility and facility take phone callers into challenging noise environments that challenge speech intelligibility and quality. Noise suppression is an essential sector in speech enhancement during telephone connection. Noise suppression has been a research topic for decades, but traditional noise suppression methods have limits. Traditional noise suppression methods commonly perform well in stationary noise environments where background noise does not change rapidly. Today we want more from noise suppression to work satisfyingly in challenging and rapidly changing environments. Solutions for challenging, non-stationary, and rabidly changing noise environments are searched from deep neural networks. This thesis researches a convolutional neural network as a noise suppression algorithm. The chosen network is a modified version of a network called U-Net. We prestudy U-Net with different loss functions, selecting a good option with the help of objective metrics. After preselection, we chose SI-SDR as our loss function. In this thesis, we arranged a listening test where participants compared blindly traditional noise suppression method to DNN-based noise suppression. Participants of the listening test evaluated the following attributes: speech intelligibility, speech quality, noise transparency, and noise level vs. speech. Nevertheless, of assumptions, U-Net did not outperform in all attributes in non-stationary environments. U-Net increased the speech intelligibility, and noise levels were better compared to speech than with the traditional method. The output of the traditional method was better with speech quality and noise transparency. In conclusion, the DNN-based noise suppression method increases speech intelligibility, but it does not guarantee quality.