Noise Suppression Capability Research Articles

A pulsed lock-in method for DC ensemble nitrogen-vacancy center magnetometry

This article proposes a scheme for DC nitrogen-vacancy (NV) center magnetometry that combines the advantages of the lock-in detection and pulse-type schemes. The proposed pulsed lock-in scheme does not lead to optical power broadening of optically-detected magnetic resonance, resulting in smaller linewidth, higher contrast, and better photon shot-noise-limited sensitivity compared with the conventional continuous-wave lock-in scheme. The optimal conditions, optimal sensitivity, and noise-suppression capability of the proposed method are compared with those of the conventional methods from both theoretical and experimental points of view. Through experimental measurements, an improvement by a factor of four in the sensitivity and an improvement by a factor of 60 in the minimum resolvable magnetic field (MRMF) compared with conventional method were obtained. Using a confocal experiment setup, the proposed scheme achieves an MRMF of 100 pT and a sensitivity of 3 nT·Hz-1/2 corresponding to a volume-normalized sensitivity of 2.5 pT/(Hz·mm−3)1/2, demonstrating the potential of this method in magnetic measurement applications with high sensitivity and high spatial resolution.

Laser-frequency stabilization with differential single-beam saturated absorption spectroscopy of 4He atoms.

Differential single-beam saturated-absorption spectroscopy (DSSAS) is proposed to stabilize lasing frequency and suppress Doppler-broadened background and common-mode optical noise. The spectral first-derivative demodulated signal of metastable He4 atoms is used as an error signal to stabilize a fiber laser around 1083 nm. Experimental results show that, compared with existing non-DSSAS frequency stabilization, DSSAS stabilization produces better stability and lower fluctuations, especially for frequency-noise-corrupted lasers. In DSSAS stabilization, for data acquired over 7000 s, the root mean square frequency fluctuation of the fiber laser is 16.4 kHz, and the frequency stability described by the modified Allan deviation is 4.1 × 10-12 at 100 s. Even for a defective laser with poor frequency stability, the proposed scheme demonstrates experimentally high capability of noise suppression and reduces the frequency fluctuations by two orders of magnitude. Given its simplicity and compact design, frequency stabilization by DSSAS is promising for quantum-sensor applications, such as atomic magnetometers, atomic gyroscopes, and atomic clocks.

Closed loop fractional order drug delivery control scheme for chemotherapy

Chemotherapy is one of the most extensively utilized cancer treatment strategies worldwide. It is intended to eliminate fast-developing cancer cells in a patient’s body. The amount of chemotherapeutic drug that must be administered precisely into a patient’s body determines the efficacy of the treatment and governs the patient survival during chemotherapy. Therefore, controlling the chemotherapeutic drug dose delivered to the patient is essential. This research aims to propose a two-degree-of-freedom fractional order proportional–integral–derivative (2FOPID) controller with a set point filter for implementing an automatic drug delivery control scheme during chemotherapy. The Whale optimization algorithm (WOA) is used to tune the parameters of the 2FOPID controller, resulting in a WOA-tuned 2FOPID controller (W2FOPID). The performance of the proposed W2FOPID is compared with the Integral–Proportional–Derivative (IPD), Internal Model Control (IMC), and Fractional Order IMC (FOIMC) schemes. The experimental results demonstrate that the proposed W2FOPID controller is effective, accurate, and robust for drug concentration control during chemotherapy. W2FOPID outperforms IPD, IMC, and FOIMC schemes in terms of Integral Absolute Error by 79.9%, 25.3%, and 23.36%, respectively. In addition, W2FOPID exhibits excellent set-point tracking, noise suppression and uncertainty handling capabilities.

Supermode noise suppression with polarization-multiplexed dual-loop for active mode-locking optoelectronic oscillator.

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.

Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation

Abstract. The European Space Agency (ESA) Earth Explorer Mission Aeolus was launched in August 2018, carrying the first Doppler wind lidar in space. Its primary payload, the Atmospheric LAser Doppler INstrument (ALADIN), is an ultraviolet (UV) high-spectral-resolution lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being an HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio (the cross-polarized return signal is not measured). This way, the retrieval is independent of a priori lidar ratio information. The optical properties are retrieved using the standard correct algorithm (SCA), which is an algebraic inversion scheme and therefore sensitive to measurement noise. In this work, we reformulate the SCA into a physically constrained maximum-likelihood estimation (MLE) problem and demonstrate a predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information by the MLE in conjunction with the expected physical bounds concerning positivity and the expected range of the lidar ratio. To consolidate and to illustrate the improvements, the new MLE algorithm is evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aeolus data collocated with measurements by a ground-based lidar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The largest improvements were seen in the retrieval precision of the extinction coefficients and lidar ratio ranging up to 1 order of magnitude or more in some cases due to effective noise dampening. In real data cases, the increased precision of MLE with respect to the SCA is demonstrated by increased horizontal homogeneity and better agreement with the ground truth, though proper uncertainty estimation of MLE results is challenged by the constraints, and the accuracy of MLE and SCA retrievals can depend on calibration errors, which have not been considered.

A Reweighted Overlapping Group Shrinkage Method for Bearing Fault Diagnosis

Sparse regularization has received widespread attention in bearing fault diagnosis as it overcomes the limitation of the artificially constructed dictionary. However, the amplitude underestimation caused by different penalty functions reduces the precision of extracted fault features. To handle this difficulty, a Reweighted Overlapping Group Shrinkage (R-OGS) method for bearing fault diagnosis is proposed in this paper. The group property of the fault signal and the amplitude underestimation problem are considered simultaneously. A novel group weighting coefficient based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the group is designed to mitigate the amplitude compression caused by the group penalty. Although a non-convex penalty function is used to improve the performance, the cost function can be convex with appropriate parameter selection. The convexity condition is given and the solution algorithm is derived based on the Majorization-Minimization (MM) algorithm. The setting strategy of the group size and the regularization parameter is given after in-depth analysis. Numerical simulation shows that the R-OGS method has stronger amplitude retention of fault feature signal and noise suppression capability than previous Overlapping Group Shrinkage (OGS). The bearing fault experiment signals verify that the proposed R-OGS method has superior performance in fault feature extraction compared to the Moreau-enhanced Total Variation Denoising (MTVD) and the Fast Kurtogram (FSK).

A High Resolution Synchronous Demodulation Method Based on Gated Integrator for Precision Sensors

This article proposes a synchronous demodulation method based on gated integrator for precision sensors, which shows significant improvement in signal-to-noise ratio (SNR) and resolution. Several traditional demodulation methods are introduced, especially the lock-in amplifier in detail for comparison. Through a comprehensive analysis of the gated integrator, it is proven that the proposed method shows a larger useful signal output with similar noise suppression capability compared with the lock-in amplifier so a better SNR could be obtained. Circuits of the two demodulation methods are manufactured and tested, and the results show that the output characteristic of the proposed method is consistent with the theoretical analysis. A precision eddy current displacement sensor prototype with the two demodulation methods is designed and tested as an example. It is found that the resolution of the sensor is improved with the proposed method by about 0.4 bits. Furthermore, this method is expected to function efficiently for any precision sensor requiring synchronous demodulation.

Amorphous‐Ga2O3 Optoelectronic Synapses with Ultra‐low Energy Consumption

AbstractDeveloping optoelectronic synaptic devices with low energy consumption is of critical importance for neuromorphic computing and visualization systems. In this work, amorphous‐Ga2O3 (a‐Ga2O3) is adopted to realize a low‐power optoelectronic synapse considering its distinguished features of ultra‐wide bandgap, high responsiveness to light stimulation, and strong persistent photoconductivity effect. The basic synaptic functions such as short‐term plasticity (STP) and long‐term plasticity (LTP) have been successfully mimicked. A noise suppression capability is achieved as well, similar to the visual cells. Strikingly, the total energy consumption for triggering an LTP synaptic event is only 136 fJ, approaching the one of a biological synapse. The underlying mechanism for the a‐Ga2O3 synaptic performance is explored with a combined research of X‐ray photoelectron spectroscopy, Kelvin probe force microscopy, electric tests, and Technology Computer‐Aided Design simulations. The consistent results suggest the key role of the synergetic modulation effect of optical and electric fields on the dynamic behaviors of oxygen vacancy (VO) defects. This work indicates the potential applications of a‐Ga2O3 in low‐power optoelectronic synapses.

Low-cost brain computer interface for everyday use

With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user. The device is compact, having a circular shape with a radius of only 25mm, which would allow for comfortable daily use during both daytime and nighttime. Our solution is also very cost effective, approximately $350 for 24 electrodes. The built-in noise suppression capability improves the accuracy of recordings with a peak input noise below 0.35μV. Here, we provide the results of the tests for the developed device. On our GitHub page, we provide detailed specification of the steps involved in building this EEG device which should be helpful to readers designing similar devices for their needs https://github.com/Ildaron/ironbci .

On the aeroacoustic characterization of a robust trailing-edge serration

This paper presents an experimental study on the aeroacoustics of a flat plate rig with a highly instrumented serrated trailing-edge. The role of near-field flow properties, namely, surface pressure fluctuations and spanwise coherence, in the noise suppression capability of serration is not properly understood. The results from this test rig aim to provide additional insight into the effects of the serration on the hydrodynamic field (flow field) and the scattering of the pressure waves along the trailing-edge. Despite its unconventional size, beamforming results showed a significant reduction of far-field noise over a broadband frequency range. The associated flow field is characterized by mean and spectral analyses of static and dynamic surface pressure measurements as well as hot-wire measurements. The mean pressure coefficient results and the boundary layer velocity profiles over the serrated trailing-edge showed minute differences compared to the baseline straight trailing-edge. However, the frequency-dependent energy content of the unsteady surface pressure fluctuations demonstrates an elevated energy region around the serration edges at low frequencies. Although there is an increase in the energy content of the low frequency pressure fluctuations on the serrated trailing-edge, a significant phase difference of the pressure waves is observed, which may be indicative of destructive interference. The temporal studies regarding the unsteady surface pressure fluctuations corroborate the presence of quasi-periodic large scale structures emanating from the serration edges.

Input aperture restriction of the spatial spectral compressive spectral imager and a comprehensive solution for it.

Compressive spectral imaging (CSI) is an attractive spectral imaging technique since it could acquire a spectral image data cube in a single snapshot. One notable CSI scheme is the spatial spectral compressive spectral imager (SSCSI), which has low complexity and high quality of the recovering spectral image. However, the SSCSI suffers from a small input aperture, which reduces the optical efficiency and signal-to-noise ratio of the system. In this paper, the effect of the input aperture size on the SSCSI system is analyzed. It shows that with the increase of input aperture, the incident light from different spectral bands will overlap with each other on the mask, and the encoding pattern of each spectral band will be ambiguous. Thus, the reconstruction quality of the data cube will highly deteriorate. A new scheme is proposed to deal with this problem. First, the observed image is resampled and recombined into new sub-observed images to improve the frequency response of the encoding pattern. Then each sub-observed image is divided into multiple sub-sets to reduce the coherence of the sensing matrix. Compared to the original reconstruction algorithm for the SSCSI system, the peak signal-to-noise ratio (PSNR) is promoted by more than 3dB, and the spectral reconstruction accuracy and noise suppression capability are also improved.

Study on model-based pump noise suppression method of mud pulse signal

Mud pulse telemetry system plays a critical role in modern oil drilling engineering. However, the valid signal is complicated to be obtained because of the main disturbing of pump noises, especially in ultra-deep drilling operations. Therefore, four model-based noise suppression plans for the mud pulse signal pump are mainly proposed in this paper. First, three kinds of pump noise state-space models (linear time-invariant model, linear time-varying model, and nonlinear model) are constructed based on the detailed analysis of the pump noise characteristics. Then, the standard Kalman filter based on the linear system and the extended Kalman filter and unscented Kalman filter algorithm adopting the nonlinear system are respectively used to reconstruct and filter the pump noise. Moreover, the wavelet threshold denoising method based on a new power threshold function is specifically adopted to filter residual random noise after the removal of pump noise. Simultaneously, several noise-containing mud pulse signals with different SNRs (Signal-to-Noise Ratios) in the stable/unstable pump noise states were simulated. Besides, the noise suppression capability experiments of the four plans are deeply conducted. The results indicate that all the four plans can suppress and even remove the stable or unstable pump noise from the original signals: plan 1 and plan 2 have similar denoising effect, which can improve the SNR of signals with stable and unstable pump noise of different intensities by approximately 5–25 dB and 5–14 dB; while the plan 3 can increase the SNR of signals by about 4–20 dB and 2–8 dB; and plan 4 can increase that by about 4–20 dB and 1–8 dB, respectively. Under the comprehensive consideration of denoising ability, computation intensity and convergence speed, it is suggested that plan 1 integrating the time-invariant linear model and the standard Kalman filtering reveals superior performance. Furthermore, using the processing method consisting of plan 1 and wavelet threshold denoising method, the SNRs of the denoised signals can reach 6–7 dB even if the original SNR is as low as −30 dB. The proposed method is applied to the actual deep well mud pulse signal processing. The effective downhole pulse transmission waveform is successfully obtained, providing a highly reliable input signal for subsequent data restoration and contributing to an essential and practical significance for improving mud pulse transmission quality.

Research on the readout noise suppression method for digital correlated double sampling

As the areas of CCD detectors and CCD mosaics have become larger and larger, the number of readout channels in astronomical cameras has increased accordingly to keep the image readout time within an acceptable range. For the large area cameras or the mosaic cameras, the analog Correlated Double Sampling (aCDS) circuit used in traditional astronomical cameras for suppressing readout noise is difficult to integrate into the camera controllers within the constraints of the space and energy consumption. Recently, digital CDS (dCDS) technology has been developed to solve this problem, which also offers novel analysis and noise suppression methods. In this study, a mathematical model is presented to conveniently analyze the frequency characteristic of a dCDS circuit, which is then simulated by a numerical method for investigating the noise suppression capability with different sampling weights. Importantly, using this model, the extreme point with lowest readout noise can be predicted for a certain dCDS model; and for a specific CCD readout frequency, readout noise can be suppressed by selecting the proper dCDS model. A testing system is then constructed for validating the efficiency of the proposed method.

Research on Heart Sound Signal Denoising Algorithm Based on Variational Mode Decomposition and Wavelet Threshold

Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denoising effects in turn affect the subsequent diagnosis results. So an improved algorithm based on variational mode decomposition (VMD) and wavelet threshold method is proposed. First, the number of decomposition modes K of the VMD is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into K modes by the VMD algorithm. Then, the modes that need to be retained are decided by the energy curve of each mode. Finally, wavelet threshold denoising method is performed on the retained modes. Experiment simulation results show that under different signal-to-noise ratio conditions, the proposed method can improve heart sounds’ ratio of signal to noise and reduce the root mean square error. Compared with traditional algorithms, it has good noise suppression capabilities under different noise levels.

A New Repetitive Motion Planning Scheme With Noise Suppression Capability for Redundant Robot Manipulators

Repetitive motion planning (RMP) is a crucial issue encountered in studies on redundant robot manipulators. Numerous RMP schemes have been established in previous studies wherein simulations are assumed to be free of noise. However, noise is ubiquitous and can severely affect RMP schemes to the point of causing failure. This paper attempts address the limitations imposed by noise by providing the first RMP scheme with inherent noise-suppression capability. The new RMP scheme for redundant robot manipulators in a noisy environment is proposed on the basis of an equality criterion that is robust against additive noise. The equality criterion is established by incorporating the proportional and integral information of the desired end-effector path. The proposed scheme is reformulated as a quadratic program and is calculated by using a recurrent neural network. Comparative simulation results obtained with PA10 and four-link robot manipulators illustrate the effectiveness and superiority of the proposed RMP scheme over the traditional RMP scheme.

Pitch and noise normalized acoustic feature for children's ASR

In this work, we have analyzed the pitch robustness of the recently reported power normalized cepstral coefficient (PNCC) feature for noise robust children's speech recognition. The PNCC feature is intended to suppress various types of common additive noise present in speech data. The PNCC feature is noted to be susceptible to pitch variations. In order to normalize the pitch effect on PNCC, a pitch base spectral normalization step is incorporated in the PNCC feature extraction process. By the inclusion of pitch normalization stage, the pitch-robustness of PNCC is enhanced without loss of noise suppression capability. The efficacy of the proposed pitch normalized PNCC (PN-PNCC) feature is investigated using acoustic modeling based on recurrent structures, such as gated recurrent units (GRU), and light gated recurrent units (LiGRU). The PN-PNCC feature is noted to yield performance improvement over PNCC for adults' and children's speech recognition under clean as well as the noisy test condition.

An improved spin readout for nitrogen vacancy center ensemble based on a maximum likelihood estimation method

In this work, a spin readout method using fluorescence dynamics is proposed based on maximum likelihood estimation (MLE) for improving the pulsed spin readout accuracy of nitrogen vacancy (NV) center. This estimation method can saturate the Cramér–Rao bound and enhance the photon-shot-noise-limited spin readout accuracy by 20% compared with conventional photon summation method. By employing a rate equation model of the NV center, the exact solution for the fluorescence dynamics is obtained. Additionally, according to the rate equation model’s relationship with fluorescence dynamics, spin, and pumping light power, a practical MLE readout scheme is furnished, which is able to distinguish spin readout information and classical noise. Compared with the conventional method, the classical noise suppression capability can be improved by 300 times. It is anticipated that the MLE method will effectively benefit sensing capability based on the NV centers even other scalable defect complexes in solid systems.

Passive optical phase noise cancellation.

We report on the realization of an optical phase noise cancellation technique by passively embedding the optical phase noise information into a radio frequency signal and creating a copy of the optical frequency signal, which is pre-corrected by the amount of phase noise introduced by optical phase perturbations. Neither phase discrimination nor an active servo controller is required due to the open-loop design, mitigating some technical problems, such as the limited compensation speed and finite phase/timing jitter, in conventional phase noise cancellation. We experimentally demonstrate that this technique maintains the same delay-limited bandwidth and phase noise suppression capability as in conventional techniques, but significantly shortens the response speed and phase recovery time. Passive decoupling optical phase perturbation represents a powerful technique in the domains of optical frequency standard comparisons and tools for future optical atomic clocks, which are now under investigation for a potential redefinition of the International Time Scale.

A Real‐Time Data Processing Method Based on Orthogonal Phase‐Locked Amplification and Its Application in Frequency Domain Semi‐Airborne Electromagnetic Detection

In the frequency domain semi‐airborne electromagnetic detection method, the receiving signal from the coil sensor contains a quantity of noise. In order to improve signal‐to‐noise ratio (SNR), a novel data processing method based on orthogonal phase‐locked amplification is proposed in this paper. By using GPS second‐pulse technology, the proposed method has good real‐time performance and high SNR. The amplitude and phase of the receiving signals can be easily resolved by simple arithmetical operation and stored in real time, which avoids the large error in the short‐term window signal. The proposed method can provide higher resolution in subsurface anomaly and better noise suppression capability. The measured results show that the proposed method amplitude error is less than 3%, and the phase error is less than 0.6° under the condition of double white noise. Noise suppression capability is 10 times of the existing system and resolution is up to 25 times. The smaller anomaly can be reflected more accurately. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.