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• Physical origin of bimodal complex permeability spectrum of a noise suppression sheet has been investigated using both sheets and constituent single magnetic flakes. • Complex permeability spectra of single magnetic flakes have been measured with very high sensitivity. • Low and high frequency peaks of bimodal complex permeability spectrum are quantitatively explained by the magnetization dynamics of the flux closure states inside the flake. • Micro-beam X-ray magnetic circular dichroism microscopy revealed that segmented magnetic domains form the local flux closure states inside the flake. In recent years, highly integrated active components inside small mobile communication devices have led to serious electromagnetic interference problems. The countermeasure to this problem is covering a noise source with a noise suppression sheet (NSS) which is composed of ferromagnetic flakes and polymers. The NSS absorbs effectively electromagnetic waves due to a large imaginary part of relative permeability μ r ″ at the specific frequency region. This function is attributed to the bimodal μ r ″ spectrum of NSS, but its physical origin has not been revealed so far. In this study, the bimodal μ r ″ spectrum of NSS has been analyzed and its physical mechanism has been discussed. The μ r ″ spectrum of a constituent single magnetic flake was measured by using a newly developed high-sensitivity permeability measurement system, which showed a bimodal spectrum quite similar to that of NSS. This fact indicates that the physical origin of the bimodal μ r ″ spectrum is attributed to the magnetization dynamics of each single constituent flake. After measuring the μ r ″ spectra of single flakes with various materials, such as Fe, Fe-Al, Fe-Co-V, and Fe-Si-Al alloys, these bimodal μ r ″ spectra were quantitatively explained by the magnetization dynamics of the flux closure states inside the flake. The magnetic domain structure of a single flake was observed by a micro-beam X-ray magnetic circular dichroism microscopy. The result confirmed that the segmented magnetic domains form the local flux closure states inside the flake. [ABSTRACT FROM AUTHOR]

Space gravitational wave detection is one of the most critical techniques that maintain high stability of the laser link to reduce the coupling of ranging noise. We model and simulate the laser pointing jitter noise of the laser link between two satellites and build an experimental system for testing. We adopt an FFT-based method for Monte Carlo generation of a random noise time-series signal with a prescribed power spectral density. This experimental system with noise inputs can be simulated in a way that is more consistent with the actual operation of the satellite in space. The experimental results show that laser pointing jitter noise mainly comes from the jitter of the satellite platform. In the 1 mHz to 1 Hz band, laser pointing jitter noise is an order of magnitude higher in the space environment than in the laboratory environment. However, the pointing system can still suppress the laser pointing jitter noise to the noise floor. [ABSTRACT FROM AUTHOR]

The intended speech must be dealt with in the process of speech communication while under the impact of noise experienced in a variety of situations that degrade speech intelligibility and quality. This work proposes a multiple sub-frames analysis for the elimination of noise variants with compensation of the magnitude and phase spectrum from speech degraded by noise. The clean speech samples are extracted from the ITU-T recommended dataset at a 16 kHz sampling rate and down-sampled to an 8 kHz sampling rate. The noise signal variants are added from the AURORA and NOISEX-92 datasets at diverse input SNR levels (0 dB, 5 dB, 10 dB, 15 dB). The duration of window frames is chosen to be 25 msec in length, together with a shift percentage of 40%, to maintain the continuous nature of frames in speech. The smoothing factor for noise updating in a specific sub-frame is set to 9, and the spectral floor parameter for determining the precise amount of noise elimination in the corrupted spectrum is set to 0.03. The phase spectrum is compensated by incorporating a recompense function that is updated in combination with the sub-frame analysis. The accomplishment of the suggested approach is assessed with regard to objective metrics, including Segmental-Signal-to-Noise-Ratio (SegSNR), Mean-Square-Error (MSE), and Perceptual-Evaluation-of-Speech-Quality (PESQ) scores corresponding to specific sub-frames of speech, respectively. To further analyze the improved quality, simple listening assessment and spectrogram analysis are incorporated, followed by a comparative investigation with prior noise-suppressive algorithms on the corrupted speech corpus. [ABSTRACT FROM AUTHOR]

• EEG-assessed pre-target activity predicts speech-in-noise recognition. • α activity in left temporal areas predicts speech recognition probability. • Enhanced α power increases recognition probability of less heavily masked targets. • Enhanced α power decreases recognition probability of more heavily masked targets. [ABSTRACT FROM AUTHOR]

Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional autoencoder network based on self-supervised learning was developed herein to attenuate seismic random noise. Unlike conventional methods, our approach did not use synthetic clean data or denoising results as a training label to build the training and test sets. We directly used patches of raw noise data to establish the training set. Subsequently, we designed a robust deep convolutional neural network (CNN), which only depended on the input noise dataset to learn hidden features. The mean square error was then evaluated to establish the cost function. Additionally, tied weights were used to reduce the risk of over-fitting and improve the training speed to tune the network parameters. Finally, we denoised the target work area signals using the trained CNN network. The final denoising result was obtained after patch recombination and inverse operation. Results based on synthetic and real data indicated that the proposed method performs better than other novel denoising methods without loss of signal quality loss.

Sound event detection (SED) recognizes the corresponding sound event of an incoming signal and estimates its temporal boundary. Although SED has been recently developed and used in various fields, achieving noise-robust SED in a real environment is typically challenging owing to the performance degradation due to ambient noise. In this paper, we propose combining a pretrained time-domain speech-separation-based noise suppression network (NS) and a pretrained classification network to improve the SED performance in real noisy environments. We use group communication with a context codec method (GC3)-equipped temporal convolutional network (TCN) for the noise suppression model and a convolutional recurrent neural network for the SED model. The former significantly reduce the model complexity while maintaining the same TCN module and performance as a fully convolutional time-domain audio separation network (Conv-TasNet). We also do not update the weights of some layers (i.e., freeze) in the joint fine-tuning process and add an attention module in the SED model to further improve the performance and prevent overfitting. We evaluate our proposed method using both simulation and real recorded datasets. The experimental results show that our method improves the classification performance in a noisy environment under various signal-to-noise-ratio conditions.

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.

Robust and efficient low signal-to-noise ratio (SNR) single pixel infrared (IR) target detection has been a key technique in IR searching and tracking system. However, there exist two critical difficulties, i.e. noise disturbance and low contrast between target and background, influencing detection accuracy. A novel IR target detection method is proposed to overcome those difficulties, by utilising the point-spread character. Specifically, a point-spread indicator is advocated to protect the potential target with the median filter to suppress the noise. Afterwards, a two-step enhancement model is built to increase the contrast value between target region and surrounding background effectively. In the first step, a point-spread local contrast method is proposed to enhance targets and suppress background. In the second step, a high boost filter is introduced to further enhance the target. In addition, an adaptive segmentation method is exploited to extract targets from the enhanced image. Experimental results on testing sequences demonstrate that this method outperforms the other state-of-the-art methods in single pixel IR target detection with higher detection rate, lower false alarm rate and less time consuming.

The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array.

A novel low-cost and energy-efficient approach for reaching 40 Gb/s signals is proposed for cost-sensitive optical access networks. Our proposed design is constituted of an innovative low-complex high-performance digital signal processing (DSP) architecture for pulse amplitude modulation (PAM-4), reuses existing commercial cost-effective 10-G components and eliminates the need of a power-hungry radio frequency (RF) component in the transmitter. Using a multi-functional 17-tap reconfigurable adaptive Volterra-based nonlinear equalizer with noise suppression, significant improvement in receiver optical power sensitivity is achieved. Results show that over 30 km of single-mode fiber (SMF) a link power budget of 33 dB is feasible at a bit-error-rate (BER) threshold of 10−3.

A capacitive measurement of the biosignals is a very comfortable and unobtrusive way suitable for long-term and wearable monitoring of health conditions. This type of sensing is very susceptible to noise from the surroundings. One of the main noise sources is power-line noise, which acts as a common-mode voltage at the input terminals of the acquisition unit. The origin and methods of noise reduction are described on electric models. Two methods of noise removal are modeled and experimentally verified in the paper. The first method uses a passive capacitive grounding electrode, and the second uses an active capacitive Driven Right Leg (DRL) electrode. The effect of grounding electrode size on noise suppression is experimentally investigated. The increasing electrode area reduces power-line noise: the power of power-line frequency within the measured signal is 70.96 dB, 59.13 dB, and 43.44 dB for a grounding electrode area of 1650 cm2, 3300 cm2, and 4950 cm2, respectively. The capacitive DRL electrode shows better efficiency in common-mode noise rejection than the grounding electrode. When using an electrode area of 1650 cm2, the DRL achieved 46.3 dB better attenuation than the grounding electrode at power-line frequency. In contrast to the grounding electrode, the DRL electrode reduces a capacitive measurement system’s financial costs due to the smaller electrode area made of the costly conductive textile.

As one of the main payloads mounted on the Yutu-2 rover of Chang’E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Kármán crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted.

Ensemble empirical mode decomposition (EEMD) is a noise-assisted analysis method, which can overcome the mode mixing problem in empirical mode decomposition (EMD). However, the choice of signal intrinsic mode functions (IMFs) still depends on subjective experience in most cases. In order to solve the problem, a noise suppression method based on EEMD and permutation entropy (PE) is proposed according to the characteristics of ground-penetrating radar (GPR) signal. In the proposed method, first EEMD is used to decompose the GPR signal into a series of IMFs, and the PE of each IMF is calculated; then, the global threshold obtained by the second-order difference of PE of all IMFs is used to distinguish between noise IMFs and target IMFs; finally, the signal is reconstructed with target IMFs to remove the noise. The experimental results for synthetic and practical GPR data show that the proposed method can effectively remove the noise in the GPR signal and improve the resolution of the target. [ABSTRACT FROM AUTHOR]

The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array. [ABSTRACT FROM AUTHOR]

Experimental observations and simulation results have shown that the dominant noise source of excess noise changes from thermal noise to shot noise with scaling of MOSFETs, and shot noise can be acted by Fermi and Coulomb suppression. But previous studies on shot noise suppression in nano-MOSFETs either ignored the suppression or just emphasized the existence of it but giving no more deep research. Based on Monte Carlo simulation, current noise in realistic nano-MOSFETs is simulated with considering Fermi effect and Coulomb interaction. Thus, shot noise suppression factor (Fano) considering Fermi effect and the Fano considering Fermi effect and Coulomb interaction are obtained. The variation of suppression factors with source-drain voltage, gate voltage, temperature and source-drain doping is investigated with theoretical analysis. The results we obtained are consistent with the experiments and the mesoscopic theoretically explain. [ABSTRACT FROM AUTHOR]

Ultrasound microvessel imaging (UMI) based on the combination of singular value decomposition (SVD) clutter filtering and ultrafast plane wave imaging has recently demonstrated significantly improved Doppler sensitivity, especially to small vessels that are invisible to conventional Doppler imaging. Practical implementation of UMI is hindered by the high computational cost associated with SVD and low blood signal-to-noise ratio (SNR) in deep regions of the tissue due to the lack of transmit focusing of plane waves. Concerning the high computational cost, an accelerated SVD clutter filtering method based on randomized SVD (rSVD) and randomized spatial downsampling (rSD) was recently proposed by our group, which showed the feasibility of real-time implementation of UMI. Concerning the low blood flow SNR in deep imaging regions, here we propose a noise suppression method based on noise debiasing that can be easily applied to the accelerated SVD method to bridge the gap between real-time implementation and high imaging quality. The proposed method experimentally measures the noise-induced bias by collecting the noise signal using the identical imaging sequence as regular UMI, but with the ultrasound transmission turned off. The estimated bias can then be subtracted from the original power Doppler (PD) image to obtain effective noise suppression. The feasibility of the proposed method was validated under different ultrasound imaging parameters [including transmitting voltages and time-gain compensation (TGC) settings] with a phantom experiment. The noise-debiased images showed an increase of up to 15.3 and 13.4 dB in SNR as compared to original PD images on the blood flow phantom and an in vivo human kidney data set, respectively. The proposed noise suppression method has negligible computational cost and can be conveniently combined with the previously proposed accelerated SVD clutter filtering technique to achieve high quality, real-time UMI imaging. [ABSTRACT FROM AUTHOR]

Algorithms for improving noise suppression in hydrophones are discussed. An increase is attained by using a priori information about the structure of the surface noise of the medium. [ABSTRACT FROM AUTHOR]

Magnetic resonance sounding (MRS) is a geophysical method that directly detects, evaluates, and monitors groundwater resource. The amplitude of the MRS signal detected by the instrument is on the order of nanovolts, resulting in very sensitive to environmental noise and power line harmonics. The singular value decomposition (SVD) method separates the signal from the noise based on the different component contributions to the singular values between the MRS signal and noise. In this paper, we propose a noise suppression method based on double SVD (DSVD) for the reliably extraction of an MRS signal with high‐level noise. The first SVD process is to extract the harmonics from the noise‐only data, in which the MRS signal is removed by a band‐stop filter. After subtracting the extracted harmonics from the measured data, we use the SVD algorithm a second time to obtain the MRS signal with further suppression of random noise. From the synthetic results with different signal‐to‐noise ratios, we conclude that the DSVD method improves the signal‐to‐noise ratio by more than 28 dB, and the fitting errors of the initial amplitude and relaxation time are ±2% and ±3%, respectively. Moreover, we analyze the selection criterion for the two key parameters of the algorithm, the delay step size, and the decomposition order. The processing of measured field data further validates the effectiveness of the proposed algorithm. Finally, discussions with other denoising algorithms show that the DSVD algorithm has better performance in a variety of noise case. Key Points: DSVD method can effectively suppress harmonics and random noiseDSVD method improves the SNR by more than 25 dB based on synthetic resultsDSVD method has better performance than model‐based and SSA methods [ABSTRACT FROM AUTHOR]

Purpose: Dual‐energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms exhibit suboptimal decomposition performance because they fail to fully depict the mapping relationship between DECT images and basis materials under noisy conditions. Convolutional neural network exhibits great promise in the modeling of data coupling and has recently become an important technique in medical imaging application. Inspired by its impressive potential, we developed a new Butterfly network to perform the image domain dual material decomposition. Methods: The Butterfly network is derived from the model of image domain DECT decomposition by exploring the geometric relationship between the mapping functions of the data model and network components. The network is designed as the double‐entry double‐out crossover architecture based on the decomposition formulation. It enters a pair of dual‐energy images as inputs and defines the ground true decomposed images as each label. The crossover architecture, which plays an important role in material decomposition, is designed to implement the information exchange between the two material generation pathways in the network. The proposed network is further applied on the digital phantom and clinical data to evaluate its performance. Results: The qualitative and quantitative evaluations of the material decomposition of digital phantoms and clinical data indicate that the proposed network outperforms its counterparts. For the digital phantom, the proposed network reduces the standard deviation (SD) of noise in tissue, bone, and mixture regions by an average of 95.75% and 86.58% compared with the direct matrix inversion and the conventional iterative method, respectively. The line profiles and image biases of the decomposition results of digital phantom indicate that the proposed network provides the decomposition results closest to the ground truth. The proposed network reduces the SD of the noise in decomposed images of clinical head data by over 90% and 80% compared with the direct matrix inversion and conventional iterative method, respectively. As the modulation transfer function decreases to 50%, the proposed network increases the spatial resolution by average factors of 1.34 and 1.17 compared with the direct matrix inversion and conventional iterative methods, respectively. The proposed network is further applied to the clinical abdomen data. Among the three methods, the proposed method received the highest score from six radiologists in the visual inspection of noise suppression in the clinical data. Conclusions: We develop a model‐based Butterfly network to perform image domain material decomposition for DECT. The decomposition results of digital phantom validate its capability of decomposing two basis materials from DECT images. The proposed approach also leads to higher decomposition quality in noise suppression on clinical datasets as compared with those using conventional schemes. [ABSTRACT FROM AUTHOR]

High-quality seismic data are the basis for stratigraphic imaging and interpretation, but the existence of random noise can greatly affect the quality of seismic data. At present, most understanding and processing of random noise still stay at the level of Gaussian white noise. With the reduction of resource, the acquired seismic data have lower signal-to-noise ratio and more complex noise natures. In particular, the random noise in the desert area has the characteristics of low frequency, non-Gaussian, nonstationary, high energy, and serious aliasing between effective signal and random noise in the frequency domain, which has brought great difficulties to the recovery of seismic events by conventional denoising methods. To solve this problem, an improved feed-forward denoising convolution neural network (DnCNN) is proposed to suppress random noise in desert seismic data. DnCNN has the characteristics of automatic feature extraction and blind denoising. According to the characteristics of desert noise, we modify the original DnCNN from the aspects of patch size, convolution kernel size, network depth, and training set to make it suitable for low-frequency and non-Gaussian desert noise suppression. Both simulation and practical experiments prove that the improved DnCNN has obvious advantages in terms of desert noise and surface wave suppression as well as effective signal amplitude preservation. In addition, the improved DnCNN, in contrast to existing methods, has considerable potential to benefit from large data sets. Therefore, we believe that it can open a new direction in the area of seismic data processing. [ABSTRACT FROM AUTHOR]

This paper presents a new method for automatic monitoring of noisy power quality, which is based on the Hilbert transform (HT) and the proposed slip-singular value decomposition (SVD)-based noise-suppression algorithm. The proposed method first employs the fast Fourier transform (FFT)-based low-pass filter and HT to obtain the instantaneous fundamental amplitude and the FFT sequence of the signal. Second, the slip-SVD-based noise-suppression algorithm and threshold filtering are used to extract cleaned singular value characteristic waveform of the high-frequency signal. Through judging the instantaneous fundamental amplitude, cleaned singular value characteristic waveform, and the FFT sequence, the presence of disturbances including single and combined disturbances can be easily detected by the proposed method. To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation disturbances and the actual data obtained from the practical power systems of China. The test results show that the proposed method has the advantages such as low false detection rate, good noise tolerance capability, short computational time, fewer parameters, practicability, and compatibility in comparison with the traditional disturbance detection methods. Besides, the proposed method can provide some important features such as amplitude, duration, and frequency for classification. Such advantages make the proposed method to be a good choice for real-time applications. [ABSTRACT FROM AUTHOR]

Abstract Instantaneous frequency (IF) estimation of signals embedded in high noise is considered. A time-frequency (TF) approach based on the Wigner distribution (WD) is proposed, i.e. the WD is processed in order to enable accurate IF estimation of both monocomponent and multicomponent signals in high noise environment. To that end, a two-step procedure is introduced. In the first step, the auto terms are emphasized with respect to noise. In the second step, the influence of inner interferences, cross terms and noise is suppressed, yielding the final TF representation. The accuracy performance of the proposed method, validated on simulated and real data, is on par with the state-of-the-art methods. However, it is characterized by significantly lower calculation complexity. [ABSTRACT FROM AUTHOR]

Highlights • An improved LSGANs is proposed to detect rail crack signal under noise interference. • MSE is added to the generator loss as a regularization. • LSGANs is modified into a conditional version to obtain samples' latent details. • Proposed method is testified to eliminate statistical noise and mechanical noise. • The mechanical noise is acquired from the real operating environment of railway. Abstract In order to implement rail crack detection with acoustic emission (AE) technology in the actual application, an important problem to be solved is how to overcome the noise interference of wheel-rail mechanical interaction. In this paper, an improved Least-Square Generative Adversarial Networks (LSGANs) with regularization is proposed for AE signal denoizing. To overcome mode collapse problem and preserve more details for the crack signals, labels of the signals are introduced into the network and Mean Squared Error (MSE) is also added to the generator loss as a regularization. The proposed method is testified in two experiments: Gaussian noise elimination and mechanical noise elimination. The mechanical noise is acquired in actual railway environment. Through the training process of enhancing crack signals submerged by noise, an optimal parametrization for the network was first selected. The trained generator could be seen as an AutoEncoder and automatically obtain the filter architecture for certain noises. The eventual results demonstrated that the improved LSGANs could preserve more details of the crack signals than traditional denoizing method after noise elimination and has a significant robustness. The denoizing ability of the proposed method is verified and it could effectively remove the statistical noise and mechanical noise in rail defect detection. [ABSTRACT FROM AUTHOR]

Inductance of an on-chip transmission line with a magnetic film-type noise suppressor is quantitatively evaluated based on magnetic circuit analysis to develop design guidelines. A Co85Zr3Nb12(Co-Zr-Nb) film with uniaxial anisotropy deposited on a coplanar waveguide (CPW) was used as a test bench, and a magnetic circuit model was developed with cross-sectional size and material parameters. The magnetic circuit analysis included the ferromagnetic resonance (FMR) frequency shift caused by an additional demagnetizing field due to the presence of the narrow CPW. The inductance calculated using the reluctance around the signal line was almost the same as measured values. Results of this study indicated that the frequency characteristics of the inductance were mostly affected by the real part of the complex permeability considering the demagnetizing field. [ABSTRACT FROM AUTHOR]

Speech production is affected by noise due to the Lombard effect. The traditional method of investigation is through headphone delivery of noise to allow speech to be recorded in quiet, but this could create an occlusion effect artefact during speech production. It is also not directly applicable when wearing hearing protectors, hearing aids, or other devices due to physical interference by the headphones. In these situations, the Lombard effect needs to be elicited by an external noise field and speech recorded in the presence of noise. This is a more challenging measurement situation, but one that preserves perception of own voice and the surrounding noise in interaction with the hearing device worn. Two methods, direct waveform subtraction and adaptive noise cancellation, were evaluated for suppressing the background noise in the recorded speech..The effects of sound recording configuration on performance was investigated for two microphone types (omnidirectional and directional) at two distances (50 and 25 cm) in different noises and in the presence of real talker's movement. Results show that the amount of noise reduction with both suppression methods is greater for fluctuating than continuous noises. Overall, the best recording configuration for noise reduction was with the omnidirectional microphone at 25 cm. Pitch extraction, energy level, and objective speech intelligibility and quality measures show that both suppression methods provide adequate noise reduction for SNRs as low as − 10 dB, which is suitable to successfully recover Lombard speech produced in an external noise field with open ears and when wearing hearing protectors. [ABSTRACT FROM AUTHOR]

In this study, we designed a mobile free space optic receiver that uses several photodetectors to provide omnidirectional receiving capability. Assuming only one transmitter, it is a receiver which builds a single input multiple output optical channel. The photodetectors are fixed to truncated pyramid walls. Electrical signals from the photodetectors are processed using an equal gain combining technique. This architecture allows simple circuits and enables additive noise suppression. The minimum angle between the pyramid base and the direction of falling rays was calculated to determine the threshold for additive noise suppression. Two areas of interest presented themselves: the processing of very weak electrical signals often drowned in noise, and optimization of the number of photodetectors whose fields of view overlapped strongly. We outline the design of the optical receiver circuitry and provide some practical hints concerning its assembly. The receiver was evaluated using bit error rate measurements and comparing signal-to-noise ratio parameters for various photodetector numbers. The measured data confirm the theoretical assumptions. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We propose an algorithm, LAFTER, that recovers features with more signal than noise from half maps. • LAFTER is shown to recover features over a wide range of FSCs and local signal-to-noise ratios. • We suggest effective local noise suppression be evaluated by comparing the filter-sum xFSC to C ref. Abstract We present LAFTER, an algorithm for de-noising single particle reconstructions from cryo-EM. Single particle analysis entails the reconstruction of high-resolution volumes from tens of thousands of particle images with low individual signal-to-noise. Imperfections in this process result in substantial variations in the local signal-to-noise ratio within the resulting reconstruction, complicating the interpretation of molecular structure. An effective local de-noising filter could therefore improve interpretability and maximise the amount of useful information obtained from cryo-EM maps. LAFTER is a local de-noising algorithm based on a pair of serial real-space filters. It compares independent half-set reconstructions to identify and retain shared features that have power greater than the noise. It is capable of recovering features across a wide range of signal-to-noise ratios, and we demonstrate recovery of the strongest features at Fourier shell correlation (FSC) values as low as 0.144 over a 2563-voxel cube. A fast and computationally efficient implementation of LAFTER is freely available. We also propose a new way to evaluate the effectiveness of real-space filters for noise suppression, based on the correspondence between two FSC curves: 1) the FSC between the filtered and unfiltered volumes, and 2) C ref , the FSC between the unfiltered volume and a hypothetical noiseless volume, which can readily be estimated from the FSC between two half-set reconstructions. [ABSTRACT FROM AUTHOR]

The joint denoising method using multi-scale wavelet based on threshold value judgment and recursive least-squares (RLS) were proposed to realize lithium battery film thickness measurement. Continuous thickness measurement of thin film coating can be done via threshold value judgment. Low-frequency noise component similar to characters of film thickness can be reduced via RLS denoising. Joint denoising can further improve measurement accuracy. Compared with multi-scale wavelet denoising, the method proposed can improve the measurement precision by 6% which is more suitable to the thickness measurement of film coating. [ABSTRACT FROM AUTHOR]

In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of OðM²Þ. We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p'th root of the detected signals and imposes the complexity of DAS [OðMÞ]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p > 2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31, -52, -52, -67, -88, and -109 dB, for DAS, DMAS, NL_2, NL_3, NL_4, and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12. [ABSTRACT FROM AUTHOR]

Purpose: Total variation (TV) regularization is efficient in suppressing noise, but is known to suffer from staircase artifacts. The goal of this work was to develop a regularization method using the infimal convolution of the first‐ and the second‐order derivatives to reduce or even prevent staircase artifacts in the reconstructed images, and to investigate if the advantage in noise suppression by this TV‐type regularization can be translated into dose reduction. Methods: In the present work, we introduce the infimal convolution of the first‐ and the second‐order total variation (ICTV) as the regularization term in penalized maximum likelihood reconstruction. The preconditioned alternating projection algorithm (PAPA), previously developed by the authors of this article, was employed to produce the reconstruction. Using Monte Carlo‐simulated data, we evaluate noise properties and lesion detectability in the reconstructed images and compare the results with conventional total variation (TV) and clinical EM‐based methods with Gaussian post filter (GPF‐EM). We also evaluate the quality of ICTV regularized images obtained for lower photon number data, compared with clinically used photon number, to verify the feasibility of radiation‐dose reduction to patients by use of the ICTV reconstruction method. Results: By comparison with GPF‐EM reconstructed images, we have found that the ICTV‐PAPA method can achieve a lower background variability level while maintaining the same level of contrast. Images reconstructed by the ICTV‐PAPA method with 80,000 counts per view exhibit even higher channelized Hotelling observer (CHO) signal‐to‐noise ratio (SNR), as compared to images reconstructed by the GPF‐EM method with 120,000 counts per view. Conclusions: In contrast to the TV‐PAPA method, the ICTV‐PAPA reconstruction method avoids substantial staircase artifacts, while producing reconstructed images with higher CHO SNR and comparable local spatial resolution. Simulation studies indicate that a 33% dose reduction is feasible by switching to the ICTV‐PAPA method, compared with the GPF‐EM clinical standard. [ABSTRACT FROM AUTHOR]

Power electronic traction transformer (PETT) is a single-phase ac–dc conversional system, which is made up of a cascaded H-bridge converter and output-parallel dc–dc converters, such as dual active bridge dc–dc (DAB) converters. As the voltage fluctuation of the dc output voltage of DAB converters is very small and the gain cross-over frequency of the open-loop transfer function of DAB converters is generally high, the DAB closed-loop control system is very sensitive to the influence of noises. The typical noises include the equivalent noise due to the conflict of controller interrupts, the noise due to the mismatch of sampling frequency and switching frequency (which is denoted as switching noise), the noise due to the second-order voltage ripple in the dc input side, the noise due to the quantization process of the analog-to-digital converter (which is denoted as quantization noise), and the noise due to the measuring error of transducers and conditioning circuits (which is denoted as measurement noise). The generation mechanism of these noises and their impacts on the DAB converters are analyzed. On the basis, the corresponding noise suppression schemes are presented, especially for the switching noise, quantization noise, and measurement noise. Besides, the mathematical models toward the sampling and the phase-shift modulation under multirate circumstance are derived, and the multirate digital control strategy of the PETT is proposed. Finally, based on a five-cell PETT laboratory prototype with a rated power of 30 kW, further research is carried out and the experiment results all verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract In this study, a subwavelength acoustic notch filter with a tapered neck is proposed. This type of structure is capable of suppressing tonal noise at relatively low frequencies, but with a thickness of 1/16 of the targeting sound wavelength. Unlike the traditional acoustic Helmholtz resonator, which is bulky for low frequency noise control, the proposed structure can be formed as a planar acoustic notch filter, occupying a small volume and making the system compact. It is found that when the proposed units are mounted on a waveguide, the achieved noise reduction performance has a close relationship to the separation distance of the resonators. Near total reflection can occur when the propagation phase lag comes up to 90 degrees if acoustic damping is neglected. The damping influence has also been discussed, indicating damping should be reduced for this application. In the experimental study, since the tapered form can reduce damping, it is demonstrated that such sample can have better sound amplification performance compared to the uniform sample. The tapered samples are then used to suppress tonal noise emitted by a loudspeaker. At the optimal configuration, measured results show sound pressure can be suppressed by 84 percent when the units start to work. The proposed planar acoustic notch filter can be used when ventilation and noise suppression are simultaneously required in a narrow space. [ABSTRACT FROM AUTHOR]

We propose an intelligently structured receiver to achieve high-speed transmissions over optical code-division multiple access (OCDMA) networks. Employing spectral-amplitude coding (SAC) reduces multiuser interference (MUI) in OCDMA, but the network bit-rate is limited by phase-induced intensity noise (PIIN) coming from the incoherency of light sources. To mitigate PIIN, the receiver performs interference estimations and regenerations through consecutive stages. The MUI is removed by subtracting the estimated interference term from the received multiplexed signals. For PIIN variance, it is both dependent on and positively related to user number and bit-rate. Reducing the number of interference users allows the network to support transmissions with a higher speed under a given noise level. The proposed scheme has the advantages of all-optical signal processing and a compact structure. Additionally, the function of noise suppression is rarely studied in the existing MUI elimination schemes, such as serial interference cancellation (SIC) and parallel interference cancellation (PIC). The simulation results show the proposed receiver achieves significant increment in bit-rate than the conventional balanced detector in SAC–OCDMA networks. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a noise suppression circuit is proposed and investigated by using resonance technique at the source. Resonance in the source node of the common-gate structure blocks the noise path while transferring the signal from input to output. Through proper analysis, a common gate structure with an active load is improved. As a result, a complementary common gate structure is introduced. A complementary common-gate structure with resonance in the source node can overcome the trade-off between noise and gain in the first stage. Hence, this structure is optimum in terms of the trade-off between gain and noise as well as power dissipation and linearity. Finally, a very-low-noise amplifier is implemented by this method and the post-layout simulation results are obtained: average power gain: 15.8 dB, minimum noise figure: 1.7 dB, bandwidth: 3.1–4.8 GHz, power dissipation of two stage: 11.28 mW, 1-dB compression point at input power: −4.67 dBm, and IP3 at input power: 8.32 dBm. [ABSTRACT FROM AUTHOR]

In this paper, a robust fractional order fuzzy proportional derivative plus fractional order integrator (FOFPD+FOI) control structure is proposed to effectively control a nonlinear, coupled, multi-input multi-output, electrically driven three-link rigid robotic manipulator (EDRRM) system. The FOFPD+FOI controller is realized by using non-integer order differentiator and integrating operators in the integer order fuzzy proportional derivative plus integer order integrator (IOFPD+IOI) controller. A comparative study is carried out to assessed the performance of FOFPD+FOI controller with IOFPD+IOI controller, fractional order proportional, integral and derivative (FOPID) controller and integer order PID controller for reference trajectory tracking, noise suppression, disturbance rejection and model uncertainty. The gains of the controllers were tuned using a meta-heuristic optimization technique cuckoo search algorithm for objective function which is defined as the weighted sum of integral of absolute error and integral of absolute change in controller output. The simulation studies reveal that proposed FOFPD+FOI controller offers much superior performance over PID, FOPID and IOFPD+IOI controllers. [ABSTRACT FROM AUTHOR]

According to Fresnel's formula and the energy conservation law, a model combining the infrared reflected effect and emitted effect is developed to calculate the polarisation degree. With this model, the polarisation degree difference between the sea surface and ship target in long‐wave infrared is simulated. To solve the problem of dim targets detection in a sea background, based on the polarisation difference of the sea surface and ship targets, a method of the non‐subsampled shearlets transformation is proposed to fuse the intensity image and polarisation image. The algorithm of distribution coefficients is applied to improve the contrast ratio between targets to background in low‐frequency subbands. The denoise scheme of the adaptive threshold is adopted to suppress noise and the conceptions of local direction contrast and region gradient are used as a choosing scheme to the preserve features and edges of images in high‐frequency subbands. Image evaluation indices of target contrast with the background and local signal‐to‐noise ratio are used to evaluate the enhancement effect of fused images. Results show that the evaluation indices of fused images with polarisation features are significantly improved, and comparisons with existing methods demonstrate the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, by using integral-type error function and twice zeroing neural-dynamics (or termed, Zhang neural-dynamics, ZND) formula, continuous-time advanced zeroing neural-dynamics (CT-AZND) model is proposed for solving the continuous time-variant nonlinear optimization problem. Furthermore, a discrete-time advanced zeroing neural-dynamics (DT-AZND) model is first proposed, analyzed, and investigated for solving the discrete time-variant nonlinear optimization (DTVNO) problem. Theoretical analyses show that the proposed DT-AZND model is convergent, and its steady-state residual error has an O(g3) pattern with g denoting the sampling gap. In addition, in the presence of various kinds of noises, the proposed DT-AZND model possesses advantaged performance. In detail, the proposed DT-AZND model converges toward the time-variant theoretical solution of the DTVNO problem with O(g3) residual error in the presence of an arbitrary constant noise and has excellent ability to suppress linear-form time-variant noise and bounded random noise. Illustrative numerical experiments further substantiate the efficacy and advantage of the proposed DT-AZND model for solving the DTVNO problem. [ABSTRACT FROM AUTHOR]

Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets. [ABSTRACT FROM AUTHOR]

Dual-comb spectroscopy has been an infusive spectroscopic tool for gas detection due to its high resolution, high sensitivity, and fast acquisition speed over a broad spectral range without any mechanical scanning components. However, the complexity and cost of high-performance dual-comb spectroscopy are still high for field-deployed applications. To solve this problem, we propose a simple frequency domain post-processing method by extracting the accurate position of a specific absorption line frame by frame. After aligning real-time spectra and averaging for one second, the absorbance spectrum of H13C14N gas in the near-infrared is obtained over 1.1 THz spectral range. By using this method, the standard deviation of residual error is only ~0.002, showing great agreement with the conventional correction method. In addition, the spectral resolution is improved from 13.4 GHz to 4.3 GHz compared to direct spectrum averaging. Our method does not require a specially designed common-mode suppression comb, rigorous frequency control system, or complicated computational algorithm, providing a cost-effective scheme for field-deployed Doppler-limited spectroscopy applications.

In this paper, we propose a multi-channel cross-tower with attention mechanisms in latent domain network (Multi-TALK) that suppresses both the acoustic echo and background noise. The proposed approach consists of the cross-tower network, a parallel encoder with an auxiliary encoder, and a decoder. For the multi-channel processing, a parallel encoder is used to extract latent features of each microphone, and the latent features including the spatial information are compressed by a 1D convolution operation. In addition, the latent features of the far-end are extracted by the auxiliary encoder, and they are effectively provided to the cross-tower network by using the attention mechanism. The cross tower network iteratively estimates the latent features of acoustic echo and background noise in each tower. To improve the performance at each iteration, the outputs of each tower are transmitted as the input for the next iteration of the neighboring tower. Before passing through the decoder, to estimate the near-end speech, attention mechanisms are further applied to remove the estimated acoustic echo and background noise from the compressed mixture to prevent speech distortion by over-suppression. Compared to the conventional algorithms, the proposed algorithm effectively suppresses the acoustic echo and background noise and significantly lowers the speech distortion.

To suppress the noise of the fixed-point wavelength modulation spectroscopy system, a composite algorithm based on two-frequency modulation technology and empirical mode decomposition is proposed. The influence drift of the centre wavenumber of the second harmonic is systematically evaluated, the two-frequency modulation model is constructed, and the optimal value of jitter current in the two-frequency modulation is studied. Experiments show that Savitzky–Golay​ filtering, Kalman filtering, variational mode decomposition and empirical mode decomposition algorithms alone cannot effectively denoising of the fixed-point wavelength modulation spectroscopy system. Two-frequency modulation can effectively suppress the interference fringes, and the new composite method can significantly improve the denoising effect of two-frequency modulation. Compared with the results before noise reduction, the standard deviation of methane concentration obtained by the new method is reduced to 3.39%, and the peak-to-peak disturbance is reduced to approximately 4.28%, and the predicted detection limit using Allan variance is 0.472 ppb@0.15 s. • A TFM-EMD composite algorithm is proposed to suppress the interference fringes of the fixed-point WMS system. • The selection mechanism and influence of the optimal jitter current amplitude of two-frequency modulation are clarified. • Low-frequency fluctuations of fixed-point WMS system caused by the fringes cannot be eliminated by filtering algorithm alone. • The standard deviation of the methane concentration obtained by the new method is reduced to 3.39% before denoising. [ABSTRACT FROM AUTHOR]