Precision measurement and suppression of low-frequency noise in a current source with double-resonance alignment magnetometers

Low-noise high-stability current sources have essential applications such as neutron electric dipole moment measurement and high-stability magnetometers. Previous studies mainly focused on frequency noise above 0.1 Hz while less on the low-frequency noise/drift. We use double resonance alignment magnetometers(DRAMs) to measure and suppress the low-frequency noise of a homemade current source(CS) board. The CS board noise level is suppressed by about 10 times in the range of 0.001-0.1 Hz and is reduced to 100 n A/√Hz at 0.001 Hz. The relative stability of CS board can reach2.2 × 10^(-8). In addition, the DRAM shows a better resolution and accuracy than a commercial 7.5-digit multimeter when measuring our homemade CS board. Further, by combining the DRAM with a double resonance orientation magnetometer,we may realize a low-noise CS in the 0.001-1000 Hz range.

Iterative Phase Noise Suppression for Full-Duplex Communication Systems

Oscillator phase noise is one of the bottlenecks that limits the self-interference(SI)cancellation capability of full-duplex systems.In this paper,we propose a method for the suppression of common phase error(CPE)and intercarrier interference(ICI)induced by the phase noise in full-duplex orthogonal frequency division multiplexing(OFDM)systems.First,we regard the effect of CPE as a portion of the SI channel and perform estimation,reconstruction and elimination in the time domain.Then,the ICI signal is estimated and suppressed in the frequency domain.Additionally,by analysing the performance of proposed algorithm,we further develop an iterative mechanism to reduce the parameter estimation error and improve SI cancellation capability.Simulation results show that the proposed method has a significant SI cancellation capability improvement over the traditional SI cancellation schemes.

SMART STRUCTURES USING PIEZOELEMENTS FOR ACTIVE VIBRATION AND NOISE SUPPRESSION IN AVIATION AND AEROSPACE

Smart material and structure (SMS) is a challenging novel technique for the 21 century especially in fields of aviation and aerospace. Vibration and noise suppression smart structure is an important branch of SMS. There are several typical structures such as the cabin of an airplane, space station, the solar board of satellite and the rotor blade of a helicopter, of which the vibrations and radiation noises have bad influences on precise equipments and aiming systems. In order to suppress vibrations and noises of these structures, several algorithms are applied to the models which simulate the structures. Experiments are performed to suppress vibrations and noises by bonding sensors and actuators to the structures at the optimized locations and using computer based measurement and control systems. For the blade vibration control of a helicopter, a non contact method of signal transmission by magneto electric coupling is discussed. The experimental results demonstrate that the methods used for active control are effective.

Random noise suppression for seismic data using a non-local Bayes algorithm

For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.

Seismic Noise Suppression for Ground-Based Investigation of an Inertial Sensor by Suspending the Electrode Cage

Performance test of a high precise accelerometer or an inertial sensor on the ground is inevitably limited by the seismic noise. A torsion pendulum has been used to investigate the performances of an electrostatic accelerometer, where the test mass is suspended by a fiber to compensate for its weight, and this scheme demonstrates an advantage, compared with the high-voltage levitation scheme, in which the effect of the seismic noise can be suppressed for a few orders of magnitude in low frequencies. In this work, the capacitive electrode cage is proposed to be suspended by another pendulum, and theoretical analysis shows that the effects of the seismic noise can be further suppressed for more than one order by suspending the electrode cage.

Novel Retinex algorithm by interpolation and adaptive noise suppression

In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.

On-Line Suppression of Periodic Noises in Evoked Potentials

Under clinical conditions,the major components of noises in human evoked potentialsare periodic impulses,such as power interferences, system oscillations. Reducing their effects by analogfilter is inevitably associated with the distortions of the signal of interest.In this paper,a new approachto suppress periodic noises is presented using time-domain operators which are applied on the individualdigitized responses before averaging.A new algorithm for real time processing is developed.The effectiveness of the algorithm in dealing with simulated as well as actual evoked responses is demonstrated.The results show that this algorithm has good performance in periodic noise suppression and has zerophase shift, thus it is very suitable to be implemented on biomedical instrumentations used inclinical setups.

Elastic-Wave Reverse Time Migration Random Boundary-Noise Suppression Based on CycleGAN

In elastic-wave reverse-time migration(ERTM),the reverse-time reconstruction of source wavefield takes advantage of the computing power of GPU,avoids its disadvantages in disk-access efficiency and reading and writing of temporary files,and realizes the synchronous extrapolation of source and receiver wavefields.Among the existing source wavefield reverse-time reconstruction algorithms,the random boundary algorithm has been widely used in three-dimensional(3D)ERTM because it requires the least storage of temporary files and low-frequency disk access during reverse-time migration.However,the existing random boundary algorithm cannot completely destroy the coherence of the artificial boundary reflected wavefield.This random boundary reflected wavefield with a strong coherence would be enhanced in the cross-correlation image processing of reverse-time migration,resulting in noise and fictitious image in the migration results,which will reduce the signal-to-noise ratio and resolution of the migration section near the boundary.To overcome the above issues,we present an ERTM random boundary-noise suppression method based on generative adversarial networks.First,we use the Resnet network to construct the generator of CycleGAN,and the discriminator is constructed by using the PatchGAN network.Then,we use the gradient descent methods to train the network.We fix some parameters,update the other parameters,and iterate,alternate,and continuously optimize the generator and discriminator to achieve the Nash equilibrium state and obtain the best network structure.Finally,we apply this network to the process of reverse-time migration.The snapshot of noisy wavefield is regarded as a 2D matrix data picture,which is used for training,testing,noise suppression,and imaging.This method can identify the reflected signal in the wavefield,suppress the noise generated by the random boundary,and achieve denoising.Numerical examples show that the proposed method can significantly improve the imaging quality of ERTM.

Speech Signal Recovery Based on Source Separation and Noise Suppression

In this paper, a speech signal recovery algorithm is presented for a personalized voice command automatic recognition system in vehicle and restaurant environments. This novel algorithm is able to separate a mixed speech source from multiple speakers, detect presence/absence of speakers by tracking the higher magnitude portion of speech power spectrum and adaptively suppress noises. An automatic speech recognition (ASR) process to deal with the multi-speaker task is designed and implemented. Evaluation tests have been carried out by using the speech da- tabase NOIZEUS and the experimental results show that the proposed algorithm achieves impressive performance improvements.

Design of Background Noise Suppression Circuit for Mobile Phones

With the purpose of reducing the influence of background noise on the call quality of mobile phones, background noise suppression circuit is designed based on the principle of self-adaptive noise cancellation. Because this method is not involved in the nature of the noise itself, it can be used both for stationary noise cancellation and quasi-stationary noise cancellation. The working principle and circuit design of the system are introduced in detail. Simulated experiment was conducted in the lab, and its experimental results were analyzed. The experimental results show that the circuit works well with low cost, and has a broad prospect of application and popularization.

Shot Noise Suppression in a Quantum Point Contact with Short Channel Length

An experimental study on the current shot noise of a quantum point contact with short channel length is reported. The experimentally measured maximum energy level spacing between the ground and the first excited state of the device reached up to 7.5meV, probably due to the hard wall confinement by using shallow electron gas and sharp point contact geometry. The two-dimensionM non-equilibrium shot noise contour map shows noise suppression characteristics in a wide range of bias voltage. Fano factor analysis indicates spin-polarized transport through a short quantum point contact.

A Noise Suppression Method for Speech Signal by Jointly Using Bayesian Estimation and Fuzzy Theory

Speech recognition systems have been applied to inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult. In these actual circumstances, some countermeasure methods for surrounding noise are indispensable. In this study, a new method to remove the noise for actual speech signal was proposed by using Bayesian estimation with the aid of bone-conducted speech and fuzzy theory. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal was theoretically derived. In the proposed noise suppression method, bone-conducted speech signal with the reduced high-frequency components was regarded as fuzzy observation data, and a stochastic model for the bone-conducted speech was derived by applying the probability measure of fuzzy events. The proposed method was applied to speech signals measured in real environment with low SNR, and better results were obtained than an algorithm based on observation of only air-conducted speech.

Methods and Devices of Speckle-Noise Suppression (Review)

Speckles in images formed by a laser beam are interference noise arising due to coherent nature of laser radiation. As the number of projection systems using laser light sources increases, new methods and devices appear for reducing the contrast of speckle-noise and thereby suppressing it. Taking into account the long-standing nature of the problem and the continuing relevance of its solution, this review discusses modern practical methods and devices—despecklers, used to reduce the speckle contrast and to achieve high quality of projected images. The review discusses the nature of the speckles appearance, considers their statistical properties, and describes the measurement of the speckle contrast. The requirements for despecklers are considered, the most important of which are speckle suppression efficiency, simplicity of design and compactness, low power consumption and low optical loss. The characteristics of different types of despecklers, including diffusers, devices based on electroactive polymers, optical waveguides, colloidal solutions and liquid crystals, as well as orthogonal phase matrices and diffraction gratings proposed as despeckers, are examined in detail. It is shown that despecklers with decorrelation of the phase front based on the mirror deformation and optical fiber have less light losses. Electro-optical-liquid crystal despecklers do not have mechanically deformable or moving elements that reduce the reliability and durability of operation, and are more compact and simple in design. A comparative table of characteristics of the most effective despecklers in which the speckle contrast is reduced to ten percent or less, with an indication of their advantages and disadvantages, is given.

Stable continuous-wave single-frequency intracavity frequency-doubled laser with intensity noise suppressed in audio frequency region

We demonstrated a continuous wave(cw) single-frequency intracavity frequency-doubled Nd:YVO_4/LBO laser with 532 nm output of 7.5 W and 1.06 μm output of 3.1 W, and low intensity noise in audio frequency region.To suppress the intensity noise of the high power 532 nm laser, a laser frequency locking system and a feedback loop based on a Mach-Zehnder interferometer were designed and used.The influences of the frequency stabilization and the crucial parameters of the MZI, such as the power splitting ratio of the beam splitters and the locking state of the MZI, on the intensity noise of the 532 nm laser were investigated in detail.After the experimental optimizations, the laser intensity noise in the frequency region from 0.4 kHz to 10 kHz was significantly suppressed.

Simultaneously Suppressing Low-Frequency and Relaxation Oscillation Intensity Noise in a DBR Single-Frequency Phosphate Fiber Laser

Effective multiple optoelectronic feedback circuits for simultaneously suppressing low-frequency and relaxation oscillation intensity noise in a single-frequency phosphate fiber laser are demonstrated. The forward transfer function, which relates the laser output intensity to the pump modulations, is measured and analyzed. A custom two-path feedback system operating at different frequency bands is designed to adjust the pump current directly. The relative intensity noise is decreased by 20dB from 0.2 to 5kHz and over lOdB from 5 to lOkHz. The relaxation oscillation peak is suppressed by 22dB. In addition, a long term （24h） laser instability of less than 0.05% is achieved.

Suppressing random noise to broaden the useful bandwidth of seismic reflection data

The signal-to-noise ratio （SNR） of seismic reflection data in many areas is rather poor and conventional two-dimensional filters designed to suppress noise with different moveout from the signal tend to generate artifacts. We have extended a method of multichannel filtering, based on the hypothesis that signals on adjacent channels are similar, for enhancing the SNR on stacked sections. Using only the mid-range frequencies where the SNR is highest, the event trend is found for overlapping windows on the section and the average signal vector is calculated. Then the data from the full bandwidth section are projected onto the spatially varying unit similarity vectors and the results are merged for the overlapping windows. Application of the method to synthetic data containing steeply dipping events and to a stacked section for a marine 2D line has produced good results. The modifications we have introduced carry a small overhead in computing time but they should enable the method to be used effectively even on sections containing steep dips.

Suppression of Speckle in SAR Images Using Wavelet-Based HMM

In order to suppress the speckle appearing in synthesis aperture radar (SAR) images, a novel speckle reduction method based on wavelet domain hidden Markov tree (HMT) was proposed. First, the image was logarithmic transformed to change the statistical property of the speckles. Then an HMT was constructed in the correspondent wavelet domain. Based on this model, the image signal was restored by maximum likelihood estimation and speckle noise was suppressed. Simulating SAR images had shown that the performance of the filter is satisfactory for both speckle smoothing and edges presentation, and for generating visually natural images as well.

The potential of self-supervised networks for random noise suppression in seismic data

Noise suppression is an essential step in many seismic processing workflows.A portion of this noise,particularly in land datasets,presents itself as random noise.In recent years,neural networks have been successfully used to denoise seismic data in a supervised fashion.However,supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training.Using blind-spot networks,we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample.Based on the assumption that noise is statistically independent between samples,the network struggles to predict the noise component of the sample due to its randomicity,whilst the signal component is accurately predicted due to its spatio-temporal coherency.Illustrated on synthetic examples,the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal;therefore,providing improvements in both the image domain and down-the-line tasks,such as post-stack inversion.To conclude our study,the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques:FX-deconvolution and sparsity-promoting inversion by Curvelet transform.By demonstrating that blind-spot networks are an efficient suppressor of random noise,we believe this is just the beginning of utilising self-supervised learning in seismic applications.

Noise degradation system using Wiener filter and CORDIC based FFT/IFFT processor

On augmentation of past work, an effective Wiener filter and its application for noise suppression combined with a formed CORDIC based FFT/IFFT processor with improved speed were executed. The pipelined methodology was embraced for expanding the execution of the system. The proposed Wiener filter was planned in such an approach to evacuate the iteration issues in ordinary Wiener filter. The division process was supplanted by a productive inverse and multiplication process in the proposed design. An enhanced design for matrix inverse with reduced computation complexity was executed. The wide-ranging framework processing was focused around IEEE-754 standard single precision floating point numbers. The Wiener filter and the entire system design was integrated and actualized on VIRTEX 5 FPGA stage and re-enacted to approve the results in Xilinx ISE 13.4. The results show that a productive decrease in power and area is developed by adjusting the proposed technique for speech signal noise degradation with latency of n/2 clock cycles and substantial throughput result per every 12 clock cycles for n-bit precision. The execution of proposed design is exposed to be 31.35% more effective than that of prevailing strategies.

SPEECH ENHANCEMENT BASED ON DYNAMIC NOISE ESTIMATION WITHIN AUTO-CORRELATION DOMAIN

Most noise suppression algorithms of single channel use the mean of noisy segments to estimate the characteristics of noise spectrum, ignoring the estimation of noise in speech segments. Therefore, when the energy level of noise varies with the time, the performance of removing noise will be degraded. To solve this problem, a speech enhancement approach based on dynamic noise estimation within correlation domain was proposed. This method exploits the characteristics that noise energy mainly concentrates on 0 th order correlation coefficients, signal is auto correlated but signal and noise, noise and noise are uncorrelated, then estimates and decomposes the noise, thus helps to solve the above mentioned problem. The results of recognition experiments on speech signals of 15 Chinese cities’ names corrupted by noise of exhibition hall shows, this approach is better than SS (Spectral Subtraction) method, adapts better to the variances of energy levels of speech signal corrupted by noise, has some practicability to improve the robustness of recognition systems under noisy environment.