Validity of non-local mean filter and novel denoising method

Background Image denoising is an important topic in the digital image processing field.This study theoretically investigates the validity of the classical nonlocal mean filter(NLM)for removing Gaussian noise from a novel statistical perspective.Method By considering the restored image as an estimator of the clear image from a statistical perspective,we gradually analyze the unbiasedness and effectiveness of the restored value obtained by the NLM filter.Subsequently,we propose an improved NLM algorithm called the clustering-based NLM filter that is derived from the conditions obtained through the theoretical analysis.The proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering process.In this study,we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered components.Result The experiment yields improved peak signal-to-noise ratio values and visual results upon the removal of Gaussian noise.Conclusion However,the considerable practical performance of our filter demonstrates that our method is theoretically acceptable as it can effectively estimate ideal images.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.

Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

Sharing quantum nonlocality in the noisy scenario

It was showed in [Phys. Rev. Lett. 125 090401(2020)] that there exist unbounded number of independent Bobs who can share quantum nonlocality with a single Alice by performing sequentially measurements on the Bob's half of the maximally entangled pure two-qubit state. However, from practical perspectives, errors in entanglement generation and noises in quantum measurements will result in the decay of nonlocality in the scenario. In this paper, we analyze the persistency and termination of sharing nonlocality in the noisy scenario. We first obtain the two sufficient conditions under which there exist n independent Bobs who can share nonlocality with a single Alice under noisy measurements and the noisy initial two qubit entangled state. Analyzing the two conditions, we find that the influences on persistency under different kinds of noises can cancel each other out. Furthermore, we describe the change patterns of the maximal nonlocality-sharing number under the influence of different noises. Finally, we extend our investigation to the case of arbitrary finite-dimensional systems.

Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces.Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs.However,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise.It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization.Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation structure.Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird sounds.Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct.By clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

Research on the generation method of seawater sound velocity model based on Perlin noise

In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity model.However,due to static pressure,temperature difference and other factors,random disturbances may often frequently in seawater bodies.The impact of such disturbances on data processing results is a topic of theoretical research.Since seawater sound velocity is a difficult physical quantity to measure,there is a need for a method that can generate models conforming to seawater characteristics.This article will combine the Munk model and Perlin noise to propose a two-dimensional dynamic seawater sound velocity model generation method,a method that can generate a dynamic,continuous,random seawater sound velocity model with some regularity at large scales.Moreover,the paper discusses the influence of the inhomogeneity characteristics of seawater on wave field propagation and imaging.The results show that the seawater sound velocity model with random disturbance will have a significant influence on the wave field simulation and imaging results.

Effect of Bogie Cavity End Wall Inclination on Flow Field and Aerodynamic Noise in the Bogie Region of High-Speed Trains

Combining the detached eddy simulation(DES)method and Ffowcs Williams-Hawkings(FW-H)equation,the effect of bogie cavity end wall inclination on the flow field and aerodynamic noise in the bogie region is numerically studied.First,the simulation is conducted based on a simplified cavity-bogie model,including five cases with different inclination angles of the front and rear walls of the cavity.By comparing and analyzing the flow field and acoustic results of the five cases,the influence of the regularity and mechanism of the bogie cavity end wall inclination on the flow field and the aerodynamic noise of the bogie region are revealed.Then,the noise reduction strategy determined by the results of the simplified cavity-bogie model is applied to a three-car marshaling train model to verify its effectiveness when applied to the real train.The results reveal that the forward inclination of the cavity front wall enlarges the influence area of shear vortex structures formed at the leading edge of the cavity and intensifies the interaction between the vortex structures and the front wheelset,frontmotor,and front gearbox,resulting in the increase of the aerodynamic noise generated by the bogie itself.The backward inclination of the cavity rear wall is conducive to guiding the vortex structures flow out of the cavity and weakening the interaction between the shear vortex structures and the cavity rear wall,leading to the reduction of the aerodynamic noise generated by the bogie cavity.Inclining the rear end wall of the foremost bogie cavity of the head car is a feasible aerodynamic noise reduction measure for high-speed trains.

Research on the flow stability and noise reduction characteristics of quasi-periodic elastic support skin

To enhance flow stability and reduce hydrodynamic noise caused by fluctuating pressure,a quasiperiodic elastic support skin composed of flexible walls and elastic support elements is proposed for fluid noise reduction.The arrangement of the elastic support element is determined by the equivalent periodic distance and quasi-periodic coefficient.In this paper,a dynamic model of skin in a fluid environment is established.The influence of equivalent periodic distance and quasi-periodic coefficient on flow stability is investigated.The results suggest that arranging the elastic support elements in accordance with the quasi-periodic law can effectively enhance flow stability.Meanwhile,the hydrodynamic noise calculation results demonstrate that the skin exhibits excellent noise reduction performance,with reductions of 10 dB in the streamwise direction,11 dB in the spanwise direction,and 10 dB in the normal direction.The results also demonstrate that the stability analysis method can serve as a diagnostic tool for flow fields and guide the design of noise reduction structures.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Research on the Generation Mechanism and Suppression Method of Aerodynamic Noise in Expansion Cavity Based on Hybrid Method

The expansion chamber serves as the primary silencing structure within the exhaust pipeline.However,it can also act as a sound-emitting structure when subjected to airflow.This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow.A fluid simulation model is established,utilizing the Large Eddy Simulation(LES)method to calculate the unsteady flow within the expansion chamber.The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity,exhibiting a high level of consistency with experimental observations.To calculate the aerodynamic noise sources within the cavity,the flow field results are integrated using the method of integral interpolation and inserted into the acoustic grid.The acoustic analogy method is then employed to determine the aerodynamic noise sources.An acoustic simulation model is established,and the flow noise source is imported into the sound field grid to calculate the sound pressure at the far-field response point.The calculated sound pressure levels and resonance frequencies show good agreement with the experimental results.To address the issue of airflow regeneration noise within the cavity,perforated tubes are selected as a means of noise suppression.An experimental platformfor airflow regeneration noise is constructed,and experimental samples are processed to analyze and verify the noise suppression effect of perforated tube expansion cavities under different airflow velocities.The research findings indicate that the perforated tube expansion cavity can effectively suppress low-frequency aerodynamic noise within the cavity by impeding the formation of strong shear layers.Moreover,the semi-perforated tube expansion cavity demonstrates the most effective suppression of aerodynamic noise.

Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.

SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.

Numerical modeling techniques for noise emission of free railway wheels

In this article,we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions.We focus on the fluid-structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources.These are a discretized baffled piston using the discrete calculation method(DCM),a closed cylindrical volume using the boundary element method(BEM)and radiating elastic disks in a cubic enclosure solved with the finite element method(FEM).We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models.Selected space-resolved waveforms are compared with experimental results as well as with a fluid-structure interaction finite element model.The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted,and the baffled pistons limitations caused by a lack of edge radiation effects are investigated.

Symmetric Brownian motor subjected to Lévy noise

In the past few years,attention has mainly been focused on the symmetric Brownian motor(BM)with Gaussian noises,whose current and energy conversion efficiency are very low.Here,we investigate the operating performance of the symmetric BM subjected to Lévy noise.Through numerical simulations,it is found that the operating performance of the motor can be greatly improved in asymmetric Lévy noise.Without any load,the Lévy noises with smaller stable indexes can let the motor give rise to a much greater current.With a load,the energy conversion efficiency of the motor can be enhanced by adjusting the stable indexes of the Lévy noises with symmetry breaking.The results of this research are of great significance for opening up BM’s intrinsic physical mechanism and promoting the development of nanotechnology.

An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Majorana noise model and its influence on the power spectrum

Majorana quantum computation offers a potential approach to securely manipulating and storing quantum data in a topological manner that may effectively resist the decoherence induced by local noise. However, actual Majorana qubit setups are susceptible to noise. In this study, from a quantum dynamics perspective, we develop a noise model for Majorana qubits that accounts for quasi-particle poisoning and Majorana overlapping with fluctuation. Furthermore, we focus on Majorana parity readout methodologies, specifically those leveraging an ancillary quantum dot, and carry out an indepth exploration of continuous measurement techniques founded on the quantum jump model of a quantum point contact.Utilizing these methodologies, we proceed to analyze the influence of noise on the afore-mentioned noise model, employing numerical computation to evaluate the power spectrum and frequency curve. In the culmination of our study, we put forward a strategy to benchmark the presence and detailed properties of noise in Majorana qubits.

Development of a Novel Noise Reduction Algorithm for Smart Checkout RFID System in Retail Stores

This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’checkout counters.This is achieved by integrating a novel AI algorithm,called Improved Laser Simulator Logic(ILSL)into the RFID system.The enhanced RFID system was able to improve the accuracy of item identification,reduce noise interference,and streamline the overall checkout process.The potential of the systemfor noise detection and elimination was initially investigated through a simulation study usingMATLAB and ILSL algorithm.Subsequently,it was deployed in a small-scale environment to validate its real-world performance.Results show that RFID with the proposed new algorithm ILSL and AI basket is capable of accurately detecting the related itemswhile eliminating noise originating fromunrelated objects,achieving an accuracy rate of 88%.