Methodology For Noise Reduction Research Articles

A Numerical Study on the Flap Side-Edge Noise Reduction Using Passive Blowing Air Concept

The flap side-edge is a vital contributor to airframe noise. In this study, we propose a novel flap side-edge noise reduction method based on the concept of active blowing air. A long slot is opened from the flap’s lower surface to the tip surface to induce a secondary jet flow, which is driven by the local pressure difference between the flap’s lower surface and the tip surface. The unsteady flow field around the flap side-edge was computed by the lattice Boltzmann solver PowerFLOW, and the far-field noise was predicted by the FW-H equation. It is demonstrated that the dominant features of the flap side-edge flow are the double vortex structures, and the new passive blowing air reduction method can achieve about 3.3 dB noise reduction. Moreover, the underlying noise reduction mechanism has been analyzed and revealed. It is shown that the secondary jet flow from the long slot on the flap side-edge would dissipate the flap side-edge vortex and displace the flap side-edge vortical structure away from the flap surface, thus resulting in a decrease in the pressure fluctuations on the flap side-edge surface. As a result, the flap side-edge noise was reduced. In contrast to the current active air blowing technique, the newly proposed blowing air technique is passive and quite simple and does not require an extra air source or control system. This novel flap side-edge noise reduction technology provides a new flow control strategy and noise reduction methodology and can be further optimized.

A Contra-Rotating Open Rotor Noise Reduction Methodology by Using Anhedral Blade Tip

Abstract In this article, a novel contra-rotating open rotor (CROR) noise reduction methodology based on the anhedral blade tip applied to the front blade is developed. Results indicate that anhedral blade tip can provide noise reduction over 60 deg. polar angle range in both upstream and downstream areas at takeoff condition. The noise reduction becomes more significant as the lean angle of anhedral blade tip increases, and the maximum noise reduction is over 4 dB. Further analysis shows that anhedral blade tip decreases the strength and size of blade tip vortex shed from the front blade and reduces its interaction with the rear rotor, which decreases the fluctuation of loading acting on the rear rotor and its loading noise. Furthermore, the anhedral blade tip does not have a significant effect on the aerodynamic performance of CROR at cruise.

Noise Reduction in Electrical Signal Using OMP Algorithm Based on DCT and DSC Dictionaries

The noise is the most common issue present in all signals. Depending on the process or application, the reduction of the noise is mandatory mainly in areas such as signal classification, pattern recognition, and training process, among others. In recent years, diverse methodologies for noise reduction have been proposed, but they have left some issues with no solution. Such are the noise reduction in signals immersed in white noise without changes in their amplitude or phase or the high performance of recovering components of the desire signals distributed among the frequency spectrum. Also, filtering techniques without significant changes in signal phase and amplitude are necessary. This article proposes a methodology for noise reduction based on the orthogonal matching pursuit algorithm with dictionaries constructed from kernel functions of the discrete cosine transform and discrete sine transform. The methodology is proved in synthetic signals and real electrical signals, reaching noise reduction of white noise (~34 dB in electrical signals) and recovering without distortion.

Noise reduction in hybrid CMOS-NEMS oscillators

In this paper, a hybrid oscillator is proposed in which nano switch components are employed to reduce power dissipation and enhance the switching time. The work involves the design and simulation of a nano switch that has been combined with CMOS to achieve a new design of a ring oscillator. In addition, a noise reduction methodology which is based on improving the damping and spring factors has been adopted in the design to enhance the output and approaching the ideal signal.

CIS Band Noise Prediction Methodology Using Co-Simulation of Camera Module

This paper describes a CMOS image sensor (CIS) horizontal band noise reduction methodology considering on-chip and offchip camera module PCB design parameters. The horizontal band noise is a crucial issue for high quality camera of modern smartphone applications. This paper discusses CIS horizontal band noise mechanism and proposes the solution by optimization of design factors in CIS and camera module. Analog ground impedance value and bias voltage condition of pixel array transfer gate have been found to be effective optimization parameters. Through the real experimental data, we proved that proposed solution is instrumental in reducing the horizontal band noise.

Research Of Noise In The Unmanned Aerial Vehicle’s Propeller Using Cfd

The major use and need of the multi-rotor UAV in various fields has increased the importance to study the aerodynamics of multi-rotor Unmanned Aerial Vehicles such as the secondary flow over the blade, reduction of noise due to the propeller of the UAV, and the optimization of the design on the propeller with more blades to increase efficiency of the UAV. This paper mainly deals with the reduction of noise which is induced by the propeller. Since there is a demand for compact multi-rotor quite UAV as it has a low probability of detection using radar and infrared but as it generates high drive-line noise caused by propeller it cannot be implemented for some critical applications. As a result, an idea is launched to design a propeller with low drive-line noise levels. A methodology is developed to design a low noise as well as efficient propellers for multi-rotor UAVs. The important parameters like blade thickness, tip loss and blade loading are considered in this research. Also, the effects of propeller important parameters such as activity factor, advance ratio are considered. After the finalization of design consideration of UAV’s propeller and the furthermore noise reduction methodologies also studied such as leading-edge comb, trailing edge tuft, and upper surface porosity in order to generate a perfect UAV for military applications. In order to minimize the noise produced by the propeller the idea of modifying the leading-edges is finalized. Computer-AidedDesign of base propeller and propeller with leading-edge modifications has been generated with the help of CATIA V5 and the acoustic analysis for the static base and propellers with leading-edge modifications with different velocities has been simulated using ANSYS Workbench Fluent 16.2. Finally, a propeller with the leading-edge modification has been found to induce low noise. Keywords: Noise, CFD, Decibel, Propeller, Quite UAV.

Experimental investigations of Soyabean and Rapeseed SVO and biodiesels on engine noise, vibrations, and engine characteristics

Genset engines are popular for power backup as well as for agricultural applications in rural areas. However these engines are noisy and emit toxic pollutants. It is therefore important to develop noise reduction methodologies for genset engines since noise regulations are becoming increasingly stringent globally. Experimental investigations were carried out on a single cylinder engine to assess combustion, noise and vibration characteristics using Soyabean and Rapeseed biofuels (straight vegetable oil (SVO) and Biodiesel). In addition, performance and emission characteristics of these fuels were also investigated. Through this study, it has been found that there is a strong correlation between the heat release rate (HRR) and combustion noise as well as external noise. Higher was the HRR, higher were the magnitudes of combustion noise and external noise. Similar correlation also existed for vibrations.

Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models

We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a distance-based classifier using eigenstructures for the SSE model. We apply the noise-reduction methodology to estimation of the eigenvalues and eigenvectors in the SSE model. We create a new distance-based classifier by transforming data from the SSE model to the non-SSE model. We give simulation studies and discuss the performance of the new classifier. Finally, we demonstrate the new classifier by using microarray data sets.

Background Noise Reduction Design for Dual Microphone Cellular Phones: Robust Approach

Cellular phones are used almost everywhere for the communication purpose around the world. However, until now, in noisy environments, the speech communication quality of cellular phones is still awful because the noisy environments where people truly talk are quite complicated and there exists no suitable background noise reduction solution for dealing with this problem effectively. Based on these reasons, this research successfully proposes a novel noise reduction methodology to solve the background noise corrupted problem of cellular phones via a systematic design that comprises a dual microphone array hardware and a novel algorithm combined with a background noise whitening process, the system modeling method, and the robust estimation concept software. After practical experiments, a fact that this proposed method has the immunity to the really noisy environment where cellular phones are used can be found. This background noise reduction design possesses the following impressive advantages: an average of a 20 dB enhanced noise reduction performance the averaged value of state-of-the-art methods are about 10-15 dB, and an average similarity of 93% of pure speeches can be obtained for the purified speeches by applying this proposed method. Besides, this proposed method can be easily implemented in any of the one shelf or newly developed cellular phones with a really low cost.

Model-based subtraction of spikes from surface nuclear magnetic resonance data

Surface nuclear magnetic resonance (surface NMR) has progressed significantly in recent years due to advances in instrumentation. In particular, the introduction of multichannel surface NMR instruments has been effective in improving the signal-to-noise ratio. The current methodology for noise reduction with multichannel instruments is, however, inadequate in complex noise environments, and there is a need for improved signal processing. We have evaluated a study of impulsive noise (spikes) in surface NMR data acquired with a Numis Poly instrument. We have determined how the spectral content can be used to classify spikes as originating from electric fences or sferics. Measurements of spikes from two electric fences were evaluated. The spikes were highly deterministic and can be modeled as impulsive excitations of the band-pass filter in the surface NMR receiver system. We investigated the feasibility of a model-based approach for subtraction of electric fence spikes. Model-based subtraction was shown to be possible, but it is limited by accidental fitting of the NMR signal in its current embodiment. We evaluated an example of a surface NMR data set in which subtraction of powerline harmonic noise and electric fence spike noise removed all coherence in the multichannel data, and the consequences for further noise reduction using multichannel methods were developed.

Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context

A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such data HDLSS data. In this paper, we study asymptotic properties of the first principal component in the HDLSS context and apply them to equality tests of covariance matrices for high-dimensional data sets. We consider HDLSS asymptotic theories as the dimension grows for both the cases when the sample size is fixed and the sample size goes to infinity. We introduce an eigenvalue estimator by the noise-reduction methodology and provide asymptotic distributions of the largest eigenvalue in the HDLSS context. We construct a confidence interval of the first contribution ratio and give a one-sample test. We give asymptotic properties both for the first PC direction and PC score as well. We apply the findings to equality tests of two covariance matrices in the HDLSS context. We provide numerical results and discussions about the performances both on the estimates of the first PC and the equality tests of two covariance matrices.

Mining Dump Truck Noise Sources and Mitigation Approaches

In order to reduce noise-induced hearing loss from work-related noise exposure, mine workers, union representatives, mine managers, equipment manufacturers must work in partnership to successfully construct and implement new and better noise controls. On new machines, cabin noise levels are not normally a problem. Insulation around engines and fans greatly reduces noise and sound-proofing of the cab can keep operator exposure well below 85 dB(A). In other words, over time, exposure to noise levels at or above 85 decibels can lead to permanent hearing loss. However, the benefits of soundproofing will be lost if windows and doors are not kept closed and low noise output air-conditioning units might be needed. This article is concerned with the main sources of noise in dump trucks used for mining application and also the noise reduction methodologies.

Asymptotic Distribution of the Largest Eigenvalue via Geometric Representations of High-Dimension, Low-Sample-Size Data

A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such a data HDLSS data. In this paper, we study HDLSS asymptotics for Gaussian-type HDLSS data. We find a surprising geometric representation of the HDLSS data in a dual space. We give an estimator of the eigenvalue by using the noise-reduction (NR) methodology. We show that the estimator enjoys consistency properties under mild conditions when the dimension is high. We provide an asymptotic distribution for the largest eigenvalue when the dimension is high while the sample size is fixed. We show that the estimator given by the NR methodology holds the asymptotic distribution under a condition milder than that for the conventional estimator. DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7785

영상에 포함된 특징의 방향성을 적용한 시그마 필터의 잡음제거

In the realization of obtained image by various visual equipments, the addition of noise to the original image is a common phenomenon and the occurrence of the noise is practically impossible to prevent completely. Thus, the noise detection and reduction is an important foundational purpose. In this study, we detect the orientation about feature of images and estimate the level of noise variance based on the measurement of the relative proportion of the noise. Also, we apply the estimated level of noise to the sigma filter on noise reduction algorithm. And using the orientation about feature of images by weighted value, we propose the effective algorithm to eliminate noise. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering and regardless of the estimated level of the noise variance.

PCA consistency for the power spiked model in high-dimensional settings

In this paper, we propose a general spiked model called the power spiked model in high-dimensional settings. We derive relations among the data dimension, the sample size and the high-dimensional noise structure. We first consider asymptotic properties of the conventional estimator of eigenvalues. We show that the estimator is affected by the high-dimensional noise structure directly, so that it becomes inconsistent. In order to overcome such difficulties in a high-dimensional situation, we develop new principal component analysis (PCA) methods called the noise-reduction methodology and the cross-data-matrix methodology under the power spiked model. We show that the new PCA methods can enjoy consistency properties not only for eigenvalues but also for PC directions and PC scores in high-dimensional settings.

A Psychoacoustic Noise Reduction Method by Auditory Inhibition

An auditory-motivated noise reduction methodology is proposed based on the inhibitory property of the human auditory system. The proposed psychoacoustic spectral subtraction method utilizes non-linearly and spatially distributed inhibitory weights which are determined based on the critical bands of the auditory system. Moreover, the weights are adjusted based on the segmental a posteriori SNR, such that higher inhibition is applied to the more noisy segments. Spurious noise perturbation in the vicinity of the dominant peaks are suppressed by the inhibitory spectral subtraction which improves speech degraded by additive and musical noise. The performance of the proposed psychoacoustic noise suppression method was evaluated by two perceptual test measures, PESQ and PEMO-Q using TIMIT utterances in clean and noise conditions, and shown to achieve better PESQ scores over the spectral subtraction method. It is further shown that by combining the proposed inhibitory weighting technique with the spectral subtraction method, higher performance benchmark in both PESQ and PEMO- Q may be obtained.

A general noise-reduction framework for fault localization of Java programs

ContextExisting fault-localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. ObjectiveThis paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. MethodWe develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. ResultsThe experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. ConclusionWe conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques.

Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations

In this article, we propose a new estimation methodology to deal with PCA for high-dimension, low-sample-size (HDLSS) data. We first show that HDLSS datasets have different geometric representations depending on whether a ρ -mixing-type dependency appears in variables or not. When the ρ -mixing-type dependency appears in variables, the HDLSS data converge to an n -dimensional surface of unit sphere with increasing dimension. We pay special attention to this phenomenon. We propose a method called the noise-reduction methodology to estimate eigenvalues of a HDLSS dataset. We show that the eigenvalue estimator holds consistency properties along with its limiting distribution in HDLSS context. We consider consistency properties of PC directions. We apply the noise-reduction methodology to estimating PC scores. We also give an application in the discriminant analysis for HDLSS datasets by using the inverse covariance matrix estimator induced by the noise-reduction methodology.

Implementation of Noise Reduction Methodology to Modal Distribution Method

Vibration-based Structural Health Monitoring (SHM) systems use field measurements of operational signals, which are distorted by noise from many sources. Reducing this noise allows a more accurate assessment of the original "clean" signal and improves analysis results. The implementation of a noise reduction methodology for the Modal Distribution Method (MDM) is reported here. The spectral subtraction method is a popular broadband noise reduction technique used in speech signal processing. Its basic principle is to subtract the magnitude of the noise from the total noisy signal in the frequency domain. The underlying assumption of the method is that noise is additive and uncorrelated with the signal. In speech signal processing, noise can be measured when there is no signal. In the MDM, however, the magnitude of the noise profile can be estimated only from the magnitude of the Power Spectral Density (PSD) at higher frequencies than the frequency range of the true signal associated with structural vibrations under the additional assumption of white noise. The implementation of the spectral subtraction method to MDM may decrease the energy of the individual mode. In this work, a modification of the spectral subtraction method is introduced that enables the conservation of the energies of individual modes. The main difference is that any (negative) bars with a height below zero after subtraction are set to the absolute value of their height. Both noise reduction methods are implemented in the MDM, and an application example is presented that demonstrates its effectiveness when used with a signal corrupted by noise.

Noise Reduction in Rhythmic and Multitrial Biosignals With Applications to Event-Related Potentials

A new noise reduction algorithm is presented for signals displaying repeated patterns or multiple trials. Each pattern is stored in a matrix, forming a set of events, which is termed multievent signal. Each event is considered as an affine transform of a basic template signal that allows for time scaling and shifting. Wavelet transforms, decimated and undecimated, are applied to each event. Noise reduction on the set of coefficients of the transformed events is applied using either wavelet denoising or principal component analysis (PCA) noise reduction methodologies. The method does not require any manual selection of coefficients. Nonstationary multievent synthetic signals are employed to demonstrate the performance of the method using normalized mean square error against classical wavelet and PCA based algorithms. The new method shows a significant improvement in low SNRs (typically 0 dB). On the experimental side, evoked potentials in a visual oddball paradigm are used. The reduced-noise visual oddball event-related potentials reveal gradual changes in morphology from trial to trial (especially for N1-P2 and N2-P3 waves at Fz), which can be hypothetically linked to attention or decision processes. The new noise reduction method is, thus, shown to be particularly suited for recovering single-event features in nonstationary low SNR multievent contexts.