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.

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.

High Permeability in Broadband of Co-sputtered [Fe-Fe_(20)Ni_(80)/Cr]_(n) Multilayer Films

To achieve high microwave permeability in wide-band for the micron-thick magnetic films,[Fe-Fe_(20)Ni_(80)/Cr]_(n) multilayer structure was proposed by co-sputtering Fe and FeNi to form the magnetic layers and Cr to form the interlayers.The multilayer structure contributes to the high permeability by reducing the coercivity and diminishing out-of-plane magnetization.The maximum imaginary permeability of[Fe-Fe_(20)Ni_(80)/Cr]_(n) multilayer film reaches a large value of 800 at 0.52 GHz even though its overall thickness exceeds 1μm.Besides,the magnetic resonance frequency of the multilayer film can be modulated from 0.52 to 1.35 GHz by adjusting the sputtering power of Fe from 0 to 86 W,and its bandwidth for μ’’>200(Δf) is as large as 2.0 GHz.The desirable broad Δf of magnetic permeability,which can be well fitted by the Landau-Lifshitz-Gilbert equations,is due to dual magnetic resonances originated from double magnetic phases of Fe and FeNi that are of different saturation magnetization.The micron-thick multilayer films with high permeability in extended waveband are promising candidate for electromagnetic noise suppression application.

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.

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.

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.

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.

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.

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.

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.

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.

Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech output.In recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication systems.Most of the Speech Enhancement research work has been carried out for English,Chinese,and other European languages.Only a few research works involve speech enhancement in Indian regional Languages.In this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the speaker.In thefirst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech restoration.The proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian noise.The proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for training.The performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).

Seismic data denoising based on mixed time-frequency methods

Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of 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.

DISCRETE BIDIRECTIONAL ASSOCIATIVE MEMORY WITH LEARNING FUNCTION

In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.

Characteristics analysis on high density spatial sampling seismic data

China＇s continental deposition basins are characterized by complex geological structures and various reservoir lithologies. Therefore, high precision exploration methods are needed. High density spatial sampling is a new technology to increase the accuracy of seismic exploration. We briefly discuss point source and receiver technology, analyze the high density spatial sampling in situ method, introduce the symmetric sampling principles presented by Gijs J. O. Vermeer, and discuss high density spatial sampling technology from the point of view of wave field continuity. We emphasize the analysis of the high density spatial sampling characteristics, including the high density first break advantages for investigation of near surface structure, improving static correction precision, the use of dense receiver spacing at short offsets to increase the effective coverage at shallow depth, and the accuracy of reflection imaging. Coherent noise is not aliased and the noise analysis precision and suppression increases as a result. High density spatial sampling enhances wave field continuity and the accuracy of various mathematical transforms, which benefits wave field separation. Finally, we point out that the difficult part of high density spatial sampling technology is the data processing. More research needs to be done on the methods of analyzing and processing huge amounts of seismic data.

NON-STATIONARY SIGNAL DENOISING USING TIME-FREQUENCY CURVE SURFACE FITTING

Based on the theory of adaptive time-frequency decomposition and Time-Frequency Dis- tribution Series (TFDS), this paper presents a novel denoising method for non-stationary signal. Ac- cording to the input signal features, an appropriate kind of elementary functions with great concen- tration in the Time-Frequency (TF) plane is selected. Then the input signal is decomposed into a linear combination of these functions. The elementary function parameters are determined by using ele- mentary function TF curve surface to fit the input signal’s TFDS. The process of curved surface fitting corresponds to the signal structure matching process. The input signal’s dominating component whose structure has the resemblance with elementary function is fitted out firstly. Repeating the fitting process, the residue can be regarded as noises, which are greatly different from the function. Selecting the functions fitted out initially for reconstruction, the denoised signal is obtained. The performance of the proposed method is assessed by means of several tests on an emulated signal and a gearbox vi- brating signal.

Abdominal Fat Distribution and the Clustering Risk Factor for Cardiovascular

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.