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Due to the temperature fluctuation, both the self-gravity disturbance level of the gravitational wave detection vehicle and the laser interference measurement system are affected, which brings great noise to the gravitational wave measurement, and the temperature field fluctuation has become one of the most important noise sources of the inertial sensor. In order to ensure the noise level of the inertial sensor to meet the needs of detection, this paper uses the AC bridge to reduce the self-noise of the temperature measurement system, improve the resolution of the temperature measurement system, and realize a high-resolution and low-noise temperature measurement system. The experimental results show that the effective value of self-noise of the temperature measurement system is about 3.65 × 10 − 5 ∘ C , and the long-period noise is less than 1 × 10 − 5 ∘ C. It can be used in the space gravitational wave detection test and control system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We show that fast technical fluctuations of microwave signals can be strongly suppressed by cryogenic resonators. The experiments were carried out with sapphire resonators cooled to approximately 6 K at frequencies around 11 GHz. Each sapphire crystal was shaped like a spindle with the rotational and crystal axes aligned within a degree. Noise suppression factors in excess of 50 dB were measured at 10-kHz offset from the carrier. This was achieved by rotating the sapphire spindle and moving the coupling probes relative to its surface. Microwave signals with reduced fast technical fluctuations allow the high-precision tests of fundamental physics, as well as the development of better radars. [ABSTRACT FROM AUTHOR]

The drag-free control technology is a critical technology to guarantee the ultra-low disturbance satellite platform. Thrust noise is the primary disturbance of the drag-free control system because the thruster acts as the actuator of this system, so it would seriously affect the performance of the control system. To study and suppress thrust noise, a cusped field thruster, which has the feasibility of being applied to the drag-free control tasks, is used in this research. According to the experimental results of thrust noise, it can be seen that the thrust noise spectrum under different working conditions is different slightly, but the trend is similar. The amplitude spectrum densities of thrust in the high-frequency region present white noise characteristics, while the amplitude spectrum densities increase with the decrease of frequency in the low-frequency region. Based on the input-output thrust model and the experimental data of the anode voltage and flow rate, the effects of the anode voltage fluctuation and flow rate fluctuation on the thrust noise are analyzed. The high-frequency part (above 0.05Hz) of thrust noise is mainly caused by the flow rate noise, while the drift of thruster operation states leads to low-frequency (10−3Hz–0.05Hz) fluctuation of thrust. Finally, based on the feedback control theory, a noise suppression method is designed by controlling the anode voltage. Closed-loop simulation results of the thruster verify that the method is effective. • The thrust noise of a cusped field thruster is measured and analyzed in detail. • The causes of thrust noise in the different frequency bands are investigated clearly. • A noise suppression method is designed based on the closed-loop control theory. [ABSTRACT FROM AUTHOR]

In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed.

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified.

Aiming at the problem that the existing noise suppression methods are not suitable for marine electromagnetic environment monitoring, a noise suppression method based on adaptive least mean square (LMS) algorithm is proposed to suppress the noises of ocean wave induced magnetic field in marine electromagnetic environment monitoring. First, a general model of ocean wave induced magnetic field and a model under the condition of infinite water depth are constructed to analyze the ocean wave induced magnetic field. Then, the transmitting and receiving signals in the process of marine controlledsource electromagnetic exploration are further analyzed to identify the noise in the whole process. Finally, the LMS adaptive filtering algorithm is used to estimate and suppress the noise of ocean wave induced magnetic field. In order to verify the effectiveness of the proposed method, an experimental platform is built to test and analyze the method. The results show that the proposed method can filter out most of the noise in ocean wave induced magnetic field and geomagnetic field, and greatly improve the time domain waveform. Compared with other methods, the proposed method has the advantages of simple calculation, strong real-time performance and good noise suppression effect, which can improve the monitoring ability of marine electromagnetic environment. [ABSTRACT FROM AUTHOR]

Single-channel speech enhancement in highly non-stationary noise conditions is a very challenging task, especially when interfering speech is included in the noise. Deep learning-based approaches have notably improved the performance of speech enhancement algorithms under such conditions, but still introduce speech distortions if strong noise suppression shall be achieved. We propose to address this problem by using a two-stage approach, first performing noise suppression and subsequently restoring natural sounding speech, using specifically chosen neural network topologies and loss functions for each task. A mask-based long short-term memory (LSTM) network is employed for noise suppression and speech restoration is performed via spectral mapping with a convolutional encoder-decoder network (CED). The proposed method improves speech quality (PESQ) over state-of-the-art single-stage methods by about 0.1 points for unseen highly non-stationary noise types including interfering speech. Furthermore, it is able to increase intelligibility in low-SNR conditions and consistently outperforms all reference methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate traveltime of first arrivals is of great importance in investigating subsurface velocity information. A significant challenge preventing the picking of the first arrival, however, is that the recorded traces in complex mountain areas are often characterised by weak energy, strong noise and dramatic phase variation. The method of super-virtual refraction interferometry (SVI) is capable of retrieving and enhancing the weak first arrivals from those traces and attenuating the random noise. Unfortunately, the conventional SVI has equal-weighted stacking, and is susceptible to strong local noise. This paper introduces adaptive data-driven weights based on local similarity into SVI to solve this problem. Both near- and far-offset reference traces of high quality are technically selected for better preservation of useful information. Next, we develop some neighboring super-virtual traces in the stacking process for further enhancement of weak signals, which is a further extension and theoretically superior to conventional SVI in increasing the total stacking number. The successful applications of model and field data show the great advantages of our improved method. Compared with conventional SVI, our method has a better local noise suppression effect and stronger enhancement ability, especially at weak refractions. More importantly, it can provide a significant guarantee of higher quality data, thus distinctly achieving a more accurate and reliable traveltime in first arrival picking. [ABSTRACT FROM AUTHOR]

Herein, non‐fullerene acceptor‐based organic photodiodes are compared with fullerene acceptor‐based organic photodiodes. The non‐fullerene acceptor, ethylhexyl‐rhodanine‐benzothiadiazole‐coupled indacenodithiophene (eh‐IDTBR)‐based organic photodiodes show a higher detectivity (1.61 × 1013 cm Hz1/2 W−1) and a faster response time (≈2.7 µs) than the fullerene acceptor, [6,6]‐phenyl C71 butyric acid methyl ester (PC71BM)‐based organic photodiodes (3.25 × 1012 cm Hz1/2 W−1 and ≈6.24 µs, respectively) owing to the excellent dark current suppression of the carrier injection and the low trap density of eh‐IDTBR. Moreover, the eh‐IDTBR‐based photodetector shows better operational device stability than the fullerene counterpart under electrical and thermal stress. This is corroborated by the morphology and crystallography analyses, both of which reveal that the eh‐IDTBR‐containing photo‐sensitive films remain intact after imposing an external stress. This study elucidates important key advantages of the non‐fullerene acceptor, eh‐IDTBR, and sets a milestone as it compares the non‐fullerene acceptors and fullerene acceptors in organic photodiodes for the first time. This work sets a new record for the performance and stability of solution‐processable non‐fullerene acceptor‐based organic photodiodes. [ABSTRACT FROM AUTHOR]

The magnetotelluric (MT) method is a mainstream geophysical exploration method widely used in deep mineral resource exploration and other fields. However, the MT signal is extremely vulnerable to noise since it employs natural electromagnetic fields as a source. In order to improve the signal-to-noise ratio, a novel MT noise suppression method based on impulsive atoms and a niche particle swarm optimization-orthogonal matching pursuit (NPSO-OMP) algorithm is proposed. First, an over-complete dictionary composed of impulsive atoms is well-developed to match different types of cultural noise. Then, the sparse decomposition process of OMP is optimized with NPSO. Finally, the original MT data are de-noised using the well-developed dictionary and the NPSO-OMP algorithm. By processing the synthetic and measured MT data, and comparing the experimental results with those by traditional methods, we found that the proposed method can effectively suppress the high-intensity impulsive noise in the MT signal under the premise of retaining useful information. The continuity of apparent resistivity and the phase curve are significantly improved, and the results are verified by the remote reference method and robust statistic estimation. When the observation data are subjected to persistent, strongly correlated noise pollution, our method can obtain better results than the remote reference method and robust statistic estimation. Thus, when it is difficult to obtain high-quality MT response curves through the remote reference method and robust statistic estimation, our method is a promising alternative. [ABSTRACT FROM AUTHOR]

Multispectral optoacoustic tomography (MSOT) is an emerging imaging modality, which is able to capture data at high spatiotemporal resolution using rapid tuning of the excitation laser wavelength. However, owing to the necessity of imaging one wavelength at a time to the exclusion of others, forming a complete multispectral image requires multiple excitations over time, which may introduce aliasing due to underlying spectral dynamics or noise in the data. In order to mitigate this limitation, we have applied kinematic α and αβ filters to multispectral time series, providing an estimate of the underlying multispectral image at every point in time throughout data acquisition. We demonstrate the efficacy of these methods in suppressing the inter-frame noise present in dynamic multispectral image time courses using a multispectral Shepp-Logan phantom and mice bearing distinct renal cell carcinoma tumors. The gains in signal to noise ratio provided by these filters enable higher-fidelity downstream analysis such as spectral unmixing and improved hypothesis testing in quantifying the onset of signal changes during an oxygen gas challenge.

The Moore–Penrose inverse of a matrix plays a very important role in practical applications. In general, it is not easy to immediately solve the Moore–Penrose inverse of a matrix, especially for solving the Moore–Penrose inverse of a complex-valued matrix in time-varying situations. To solve this problem conveniently, in this paper, a novel Zhang neural network (ZNN) with time-varying parameter that accelerates convergence is proposed, which can solve Moore–Penrose inverse of a matrix over complex field in real time. Analysis results show that the state solutions of the proposed model can achieve super convergence in finite time with weighted sign-bi-power activation function (WSBP) and the upper bound of the convergence time is calculated. A related noise-tolerance model which possesses finite-time convergence property is proved to be more efficient in noise suppression. At last, numerical simulation illustrates the performance of the proposed model as well.

Aiming at the noise suppression of switched reluctance machine (SRM), the two-step commutation control method is improved. Combining this with pulse width modulation (PWM), the optimisation problem of the switching angle is studied and analysed and given a method to determine the turn-on angle, the zero-voltage turn-off angle and the turn-off angle of the negative bus voltage online. The control method is verified by MATLAB/Simulink simulation and experimental prototype platform. The results show that this method is simple and effective. It can achieve variable angle control, and can reduce the operating noise of the motor under different speed and load conditions. It has a high application value in the field of switched reluctance motor-type electric vehicle development.

A new structure for suppressing the relative intensity noise (RIN) from an optical source is presented. It is based on an integrated dual-polarization dual-parallel modulator and a balanced detector. It only requires a single fiber connected between the transmitter and the receiver whereas two fibers are required in the conventional dual-output modulator based RIN suppression structure. Experimental results demonstrate RIN suppression of >10 dB over 1-16 GHz frequency range with and without RF signal modulation.

Computational imaging based on projected patterns is an alternative technique to conventional imaging and removes the need for arrayed detectors. Over the last decade, compressive sensing (CS) has emerged as a potential way to perform computational imaging and has found applications in various fields. One approach with which to realize CS is the single-pixel camera (SPC), which makes it possible to acquire an object scene using a small number of coded measurements with a low-cost single-pixel sensor. A computational reconstruction algorithm is then used to recover the image from the linear measurements, which are far fewer than the number of pixels being reconstructed. Unfortunately, the noise suppression of an SPC imposes a challenging issue for reconstructing high-quality images. Nonetheless, realizing multispectral imaging with an SPC is attractive. In this paper, we present a fiber signal collection SPC that achieves high-signal-to-noise visible light imaging and multispectral imaging. We use a fiber to collect and transport the light through an ideal transmission path to replace the lens collection scheme. We develop a proof-of-concept prototype using fiber collection and analyze the system performance on object reflectivity, active area of the detector, and system noises. The fiber-based prototype system provides noticeable improvements in image quality (correlation coefficient) and the ability of multispectral (RGB) imaging compared with a conventional SPC. Our simplified approach to multispectral imaging can be readily extended to other wavebands.

State of Charge (SOC) is a key parameter for battery management and vehicle energy management. Recently used SOC estimation methods for lithium-ion battery for vehicles have problems of too simple a base model for the battery and large sampling noise in both the voltage and current signals. To improve the accuracy of SOC estimation and consider that the extended Kalman filter algorithm needs linear approximation of the system equation, the unscented Kalman filter (UKF) algorithm was used to reduce the influence of sampling noise, and an improved algorithm with better filtering effect and SOC estimation accuracy was proposed. Based on the SOC estimation and battery model, the peak power prediction method for the battery is proposed and used in the power distribution strategy for Series HEV. Considering the frequent changes in load current and sampling noise, an experiment was designed to verify the effectiveness and robustness of the algorithm. The experimental results show that the UKF algorithm and the improved UKF algorithm can achieve 6% and 1.5% estimation error. The power distribution strategy based on battery SOC estimation and peak power prediction is tested and validated.

Cognitive radio (CR) is promising for smart grids (SGs) in terms of overcoming the problem of spectrum inefficiency and interference mitigation. With the intensive research into full-duplex communication technologies, the licensed spectrum available for CR-based SGs (CR-SGs) may be more complex and difficult to use, as more primary traffic exists on the licensed spectrum. As a result, CR-SGs may experience more severe interference from primary users (PUs), and the corresponding transmission opportunities may be impaired. In this paper, based on distinctive polarization features of samples from different PUs, the signal polarization of each PU is extracted from the hybrid samples by studying the polarization similarity of the samples. Based on the signal polarization derived for each PU, the transmitting opportunities for CR-SGs can be derived. Both the interference from CR-SGs to PUs and the throughput of CR-SGs are examined. Numerical results show that the spectrum opportunities for CR-SGs can be guaranteed and that the interference inflicted on PUs is at a satisfactory level.

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.

In recent years, a variety of modern signal processing methods have been used to suppress the noise of Magnetotelluric (MT) signals, and have achieved certain effects on the improvement of MT data quality. However, we found that the traditional MT denoising method usually loses some useful low-frequency information in the original signal while suppressing the strong interference. Therefore, we try to apply CEEMDAN-DFA combined with endpoint detection technology to the noise suppression of MT data in order to improve the quality of MT data. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is developed on the basis of empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD). CEEMDAN can well avoid the mode mixing. that often occurs in EMD, and it has the advantages of better decomposition completeness, smaller reconstruction error and lower calculation cost than EEMD. The scaling exponent α in the detrended fluctuation analysis (DFA) is used to select useful IMFs to reconstruct the signal and has a credible evaluation criterion. The results of simulation experiments show that the denoising effects obtained by using this method is obviously better than that of wavelet transform (WT) and EMDDFA. Then, in view of the fact that the whole section of data is processed in the conventional MT denoising methods, while suppressing the strong interference, it will cause signal sections without strong interference to be processed (overprocessing). In this paper, CEEMDAN-DFA is used to filter the noise signal and obtain the profile curve of the noise. Then, the starting and ending points of the interference is determined by using the endpoint detection technology, and only the signal fragments which are determined to contain strong interference in the noisy signal are filtered, and the undisturbed signal fragments are reserved. The results of the measured data after processing show that, compared with traditional methods, the proposed method can not only suppress strong interference better, but also retain more useful information in the original MT signal, and the obtained apparent resistivity-phase curve can more truly reflect the geoelectric information of the underground structure. [ABSTRACT FROM AUTHOR]

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 John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is used in high-voltage cable online monitoring, the collected signal contains a variety of noises, usually periodic narrow-band interference and white noise are the most common and the most widely affected. In order to suppress the influence of complex noise, a partial discharge signal noise reduction method based on Variational Mode Decomposition (VMD) is proposed. VMD is used to decompose the noisy partial discharge signal to obtain the intrinsic mode functions (IMF) from low to high frequency, then calculate the kurtosis value of each variational modal component, the pulse characteristic component for reconstruction was select, and adaptive threshold used to reduces the noise of the reconstructed signal again. Compared with the wavelet transform threshold method, the noise reduction results are obtained in different noise environments. The results are quantitatively superior to the wavelet standard soft threshold noise reduction method and the wavelet global hard threshold noise reduction method from the mean square error and the waveform similarity coefficient. Simulation and field experiment results show that this method can effectively remove noise signals, and can retain the original signal waveform more completely, which is convenient for subsequent signal processing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A method for the preliminary processing of MRI images of the heart that allows for the elimination of fluctuation and impulse noise from useful signals is proposed. These types of noise are due to the regular geometric structure of the photoelectric elements of the MRI scanner matrix and the structure of the signal transmission channel. The aim of this work is to develop a comprehensive mathematical model for eliminating noise in the signal of an MRI scanner. In this work, mathematical models of linear and median filtering of impulse noise, fluctuation, and geometric noise are implemented. The mathematical models consist of the combined use of linear and median filters for recording MRI images of the heart. In the experiments, real MRI images of the heart from six patients with different diseases were used after noise was added to them. We were able to eliminate the impulse noise, geometric noise, and fluctuation noise in the MRI images by applying our filtering techniques. The filtering technique not only removed the noise, but also increased the contrast of the cancerous volumetric heterogeneous formations in the heart region. [ABSTRACT FROM AUTHOR]

With the rapid development of the vehicle navigation system (VNS), the functions of VNS are improved, while the design becomes very complicated. The conventional field‐circuit simulation method is not precise enough to evaluate the VNS design. It is difficult to ensure that the electromagnetic compatibility index of VNS meets the GMW 3097 standard, resulting in electromagnetic interference (EMI) testing in an absorber lined shielded enclosure (ALSE). If the VNS does not comply with the GMW 3097 standard, it needs to be debugged repeatedly in the expensive ALSE. Therefore, a more accurate simulation method is highly desired, which can be used for improving the designed system to avoid using the expensive ALSE solution. In this study, an advanced field‐circuit simulation method utilising actual parameters of the working printed circuit board is proposed to improve the simulation accuracy. The system is debugged by the proposed simulation method. Meanwhile, the optimisation method is proposed to enhance the EMI performance. Finally, by suppressing noise on the transmission path, the emission level of the VNS at 1.6 GHz is reduced from 5.9 dBμV to below−4 dBμV, which is ∼8 dBμV lower than the GMW 3097 standard. [ABSTRACT FROM AUTHOR]

Image enhancement is an important pre-processing step for many computer vision applications especially regarding the scenes in poor visibility conditions. In this work, we develop a unified two-pathway model inspired by the biological vision, especially the early visual mechanisms, which contributes to image enhancement tasks including low dynamic range (LDR) image enhancement and high dynamic range (HDR) image tone mapping. Firstly, the input image is separated and sent into two visual pathways: structure-pathway and detail-pathway, corresponding to the M- and P-pathway in the early visual system, which code the low- and high-frequency visual information, respectively. In the structure-pathway, an extended biological normalization model is used to integrate the global and local luminance adaptation, which can handle the visual scenes with varying illuminations. On the other hand, the detail enhancement and local noise suppression are achieved in the detail-pathway based on local energy weighting. Finally, the outputs of structure-and detail-pathway are integrated to achieve the low-light image enhancement. In addition, the proposed model can also be used for tone mapping of HDR images with some fine-tuning steps. Extensive experiments on three datasets (two LDR image datasets and one HDR scene dataset) show that the proposed model can handle the visual enhancement tasks mentioned above efficiently and outperform the related state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Featured Application: This method can adaptively denoise various microseismic signals collected in the geotechnical engineering and improve the signal quality. Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cavity noise caused by the high-speed airflow has been paid much attention in the field of aerospace, and the study of cavity noise suppression has an important significance on improving the safety of aircraft. Effects of the leading-edge plate on the flow and the noise of the cavity model (the ratio of length to depth is 6) at a Mach number (Ma) of 0.6 are investigated with high-speed wind tunnel experiment. The acoustic and the flow field information in the cavity are obtained with the dynamic/static pressure measurement and oil flow experiment. The result shows that the leading-edge plate can greatly lift the shear layer, raise the impact position on the back-wall area, and reduce the flux rate and intensity in the cavity. With the controlling of leading-edge plate, the static pressure and backflow area are effectively suppressed and the SPL and peak noise also fall down significantly. The leading-edge plate provides a new method for cavity noise suppression in subsonic flow condition, which can be effectively applied to flow/noise controlling of cavity structure on aircraft. [ABSTRACT FROM AUTHOR]

Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre‐processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre‐processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super‐resolution (MSR) as a pre‐processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high‐resolution image. This technique signals the possibility of using inexpensive low‐resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods. [ABSTRACT FROM AUTHOR]

SUMMARY: Repetitive motion planning and control (RMPC) is a significant issue in the research of redundant robot manipulators. Moreover, noise from rounding error, truncation error, and robot uncertainty is an important factor that greatly affects RMPC schemes. In this study, the RMPC of redundant robot manipulators in a noisy environment is investigated. By incorporating the proportional and integral information of the desired path, a new RMPC scheme with pseudoinverse-type (P-type) formulation is proposed. Such a P-type RMPC scheme possesses the suppression of constant and bounded time-varying noises. Comparative simulation results based on a five-link robot manipulator and a PUMA560 robot manipulator are presented to further validate the effectiveness and superiority of the proposed P-type RMPC scheme over the previous one. [ABSTRACT FROM AUTHOR]

Seismic exploration is an important means of oil and gas detection, but affected by complex surface and near-surface conditions, and the seismic records are polluted by noise seriously. Particularly in the desert areas, due to the influence of wind and human activities, the complex desert noise with low-frequency, nonstationary and non-Gaussian characteristics is produced. It is difficult to extract effective signals from strong noise using existing denoising methods. To address this issue, the paper proposes a new denoising method, called multimodal residual convolutional neural network (MRCNN). MRCNN combines convolutional neural network (CNN) with variational modal decomposition (VMD) and adopts residual learning method to suppress desert noise. Since CNN-based denoisers can extract data features based on massive training set, the impact of noise types and intensity on the denoised results can be ignored. In addition, VMD algorithm can sparsely decompose signal, which will facilitate the feature extraction of CNN. Therefore, using VMD algorithm to optimize the input data will conducive to the performance of the network denoising. Moreover, MRCNN adopts reversible downsampling operator to improve running speed, achieving a good trade-off between denoising results and efficiency. Extensive experiments on synthetic and real noisy records are conducted to evaluate MRCNN in comparison with existing denoisers. The extensive experiments demonstrate that the MRCNN can exhibit good effectiveness in seismic denoising tasks. [ABSTRACT FROM AUTHOR]

We have recently proposed approximate median filters (APMFs). They are based on the sorting network and achieve acceptable image quality under low-cost hardware. In this brief, we develop a specific comparator to improve the capabilities of those filters in noise elimination. The architecture of our inexact median filters (IMFs) is regular and modular. Also, we introduce the histogram-based error dispersion plot as a new error evaluation method to have a better assessment of IMF performance. Simulation results show that the proposed filter is effectively low cost in power, area, and speed. Despite the tradeoff between the filtering accuracy and circuit characteristics, the output quality of the filter is largely similar to that of the precise one. Also, the degradation is almost not noticeable to the human eye. [ABSTRACT FROM AUTHOR]

Hysteresis thresholding is a popular technique for automatic edge detection. However, calculating reasonably high and low thresholds using an unsupervized method remains an issue. Conventional low and high threshold-linking methods sometimes produce noisy edges and fail to detect some obvious edges. Here, a novel edge detection algorithm is proposed that provides efficient calculation of thresholds, and links edge maps for extraction of the final edge map at low complexity. The proposed method suppresses unwanted noisy edges while efficiently preserving obvious edges. The simulation results show that the proposed method provides better results in terms of performance and computation time. Thus, it can be applied to any feature image analysed by an edge detector. [ABSTRACT FROM AUTHOR]

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

A reference‐less ultra‐low‐power wake‐up receiver (WuRx) is proposed for the highly integrated Internet of things applications. The WuRx utilises a direct radio‐frequency envelope detection technique with input synchronised switching to suppress 1/f noise and enhance the sensitivity. Moreover, injection‐locked clock and data recovery circuitry is used to perform on‐chip clock generation. A prototype 2.4 GHz WuRx is designed and implemented with 90 nm complementary metal–oxide–semiconductor technology. The chip area is 1.0 × 0.95 mm2. The measurement results show that the WuRx consumes 25 μW and achieves a sensitivity of −51.5 dBm for 200 kbps data rate. [ABSTRACT FROM AUTHOR]

Objective: Dual-energy CT (DECT) strengthens the material characterization and quantification due to its capability of material discrimination. The image-domain multi-material decomposition (MMD) via matrix inversion suffers from serious degradation of the signal-to-noise ratios (SNRs) of the decomposed images, and thus the clinical application of DECT is limited. In this paper, we propose a noise suppression algorithm based on the noise propagation for image-domain MMD. Methods: The noise in the decomposed images only distributes in two perpendicular directions and is suppressed by estimating the center of mass of the same-material pixel group vertically along the principal axis where the noise disturbance is minimal. The proposed method is evaluated using the line-pair and contrast-rod slices of the Catphan©600 phantom and one patient data set. We compared the proposed method with the direct inversion and the block-matching and three-dimensional (BM3D) filtration methods. Results: The results of Catphan©600 phantom and the patient show that the proposed method successfully suppresses the noise of the basis material images by one order of magnitude and preserves the spatial resolution of the decomposed images. Compared with the BM3D filtration method, the proposed method maintains the texture distribution of the decomposed images at the same SNR and the accuracy of the electron density measurement. Conclusion: The algorithm achieves effective noise suppression compared with the BM3D filtration while maintaining the spatial distribution of the decomposed material images. It is, thus, attractive for advanced clinical applications using DECT. [ABSTRACT FROM AUTHOR]

VLF (VeryLow Frequency) spectrograms registered at Kannuslehto ground station, after cleaning them from strong sferics, reveal VLF noise suppression by whistlers and whistler echo trains, which consists in significant reduction in the noise spectral power after a strong whistler event. We have found similar effect in the VLF data from Van Allen Probe B taken in the equatorial region on L‐shell ∼3. Detailed analysis of the data shows that the whistler echo train and the VLF noise have small wave normal angles. Based on this observation, we limit our analysis to parallel (ducted) whistler wave propagation. The persistence of whistler echo train, as well as the VLF noise, suggests that in the events under discussion, plasma is unstable in the frequency range corresponding to the observed VLF noise band. In an attempt to explain the effect of VLF noise suppression, we follow up the long‐standing idea that relates this effect to the reduction of free energy in the unstable plasma distribution by whistler echo train. To develop this idea into qualitative model, we have studied the motion of energetic electrons, responsible for the noise generation, in the field of ducted whistler echo train. We show that energetic electrons that make the main contribution to the growth rate of VLF noise, during their bounce oscillations in the magnetosphere, are subject to multiple resonant impacts from the whistler echo train. These lead to energetic electron diffusion in the phase space and the corresponding reduction in free energy of the unstable distribution. Key Points: The first investigation of VLF noise suppression by multihop whistlers is based on wave and energetic particle measurements of Van Allen ProbesNoise suppression by multihop whistler train is due to whistler‐related modification of electron distribution in the noise generation regionElectron phase space diffusion is investigated with the account of frequency and wave vector variations in the causative whistler train [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A compact and cost effective multislot type electromagnetic bandgap (EBG) structure for high noise isolation in ultra‐wideband applications is proposed. The proposed multislot type structure is a hybrid EBG structure with multislot in an alternating impedance EBG unit cell to achieve both the excellent noise isolation and the bandwidth improvement of the noise suppression. This type of the hybrid structure shows a high noise suppression ability of above −40 dB in an ultra‐wideband stop frequency range from 3 to 20 GHz. The performance of the proposed multislot type EBG structure is evaluated and verified by comparing the simulated and measured results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In Raman distributed temperature system, the key factor for performance improvement is noise suppression, which seriously affects the sensing distance and temperature accuracy. Therefore, we propose and experimentally demonstrate dynamic noise difference algorithm and wavelet transform modulus maximum (WTMM) to de-noising Raman anti-Stokes signal. Experimental results show that the sensing distance can increase from 3 km to 11.5 km and the temperature accuracy increases to 1.58 °C at the sensing distance of 10.4 km.

The seismic data usually contains strong random noise, which impedes the effective usage of the seismic signals for imaging and inversion. We propose an effective seismic denoising method based on a least-squares decomposition model. We assume that each trace in the multi-channel seismic data can be decomposed into several smoothly variable components. Since the decomposition is basically an inverse problem, we apply the temporal smoothness to constrain the inversion and control the stability. Considering the spatial coherency in a multi-channel seismic data, we also apply the spatial smoothness constraint to the decomposition. The space constraint is applied along the structural direction of the seismic events to preserve the dipping energy. The structural constraint is equivalent to applying a structure-oriented smoothing that requires the estimation of the local slope from the input noisy data. We validate the effectiveness of the proposed algorithm via several synthetic and real seismic data. The proposed method outperforms the state-of-the-art single-channel and multi-channel algorithms even in the case of strong random noise. [ABSTRACT FROM AUTHOR]

The conventional method for image impulse noise suppression is standard median filter utilization, which is satisfying for low noise densities, but not for medium to high noise densities. Adding a noise detection step, as proposed in the literature, makes this algorithm suitable for higher noises, but may degrade the performance at low noise densities. An adaptive switching median-based (ASM) algorithm has been used in this paper for noise suppression. First, the algorithm is modified to achieve a higher PSNR, especially for low noise densities. Then, the structure of the modified algorithm is improved to obtain higher operating speed in hardware implementation, for real-time applications. The implemented algorithm works in two steps, detection and filtering. The noise detection method is enhanced, by merging the amount of memory used for the algorithm implementation. As a result, less hardware resources are required, while the chance of false noise detection is reduced, due to the improvement made in the algorithm. In the filtering step, an adaptive window size is used, based on the measured noise density. This improved algorithm is adopted for more efficient hardware implementation. In addition, high parallelism is utilized to boost the operating frequency, and meanwhile, clock gating is used to lower power consumption. This architecture, then, has been implemented physically on an FPGA, and an operating frequency of 93 MHz is achieved. The hardware requirement is approximately 10,000 4-input LUTs, and the processing time for a 512 × 512 pixels image is measured at 12 ms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• 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]