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This research proposes an Optimized Current chopping Control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements, comprising minimized torque ripple to reduce vibrations and acoustic noises, maximized output torque to enhance vehicle acceleration, and improved efficiency, which contributes to extending the EV's battery life. Therefore, an optimization problem is formulated and solved offline, incorporating a CCC-based SRM drive model. The control variables for this optimization problem are the switching angles of the SRM. A multi-objective function is chosen to combine three performance indices: torque ripple, average torque, and efficiency. The Harris Hawks Optimization (HHO) method is utilized in this paper to solve the optimization problem and find the optimal switching angles based on the selected objective function. HHO demonstrates a strong search capability that can effectively handle the nonlinear magnetization characteristics of SRMs. Constraints on the switching angles are also included in the optimization problem to control the phase current's RMS value and power consumption. The optimized switching angles are applied to a current chopping control (CCC) strategy and an asymmetric half-bridge converter to implement the proposed HHO-based CCC drive. Moreover, to demonstrate the effectiveness of the proposed HHO-based CCC drive, a comparative analysis based on simulations and experimental measurements is presented against other CCC approaches for SRM drives, including modified particle swarm algorithm (MPSO)-based CCC drives and analytical-based CCC drives. • A finite element analysis (FEA) software is utilized to determine the magnetic characteristics of SRMs accurately. • The obtained magnetic characteristics are then verified using a series of indirect experimental measurements to build an accurate four-phase 8/6 SRM model. • The Harris Hawks optimization (HHO) method is employed in this paper to find the optimal switching angles based on the selected objective function. • This research proposes an optimized current chopping control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements. • The proposed HHO-based CCC drive is validated and compared based on simulations and experimental measurements against other CCC approaches for SRM drives, including modified particle swarm algorithm(MPSO)-based CCC drives and analytical-based CCC drives. [ABSTRACT FROM AUTHOR]

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance. [ABSTRACT FROM AUTHOR]

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method. • A weighted envelope noise-resistant correlation approach coined PMONRC is developed. • An adaptive enveloping-demodulation method is proposed for AM-FM signature enhancing. • Fault information of sub-signals are appraised for more rational weights allocation. • The PMONRC is effective for fault-induced hidden periods detection and extraction. • The PMONRC is applicative for the scenarios of limited system signal availability. [ABSTRACT FROM AUTHOR]

The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters. • Noise is modeled as a GSTM distribution. • Sliding window uses multiple measurements to resist dynamic outliers. • VB method and MHE framework combine for joint inference of unknown parameters. • The superiority of the proposed filter has been confirmed through simulations. [ABSTRACT FROM AUTHOR]

Both constrained state estimation and constrained reference tracking could be formulated as optimization problems, and the symmetry between these two problems could be shown. As variables in the stochastic model, both the mean of process noise and the mean of measurement noise do not have to be zero. The main contributions of this paper are that we take the mean of process noise and the mean of measurement noise, which do not have to be zero, and show the symmetry between constrained state estimation and constrained reference tracking for this case. In other words, we study the symmetry between state estimation and reference tracking with an additional constraint on the measurement noise and the process noise. By this symmetric relationship, the constrained state estimation problem can be solved as a constrained reference tracking problem, and vice versa, despite the presence of more general process and measurement noise. [ABSTRACT FROM AUTHOR]

Compared with the globe valves and other on-off valves, cavitation and noise are more severe in the throttle control valve of the refrigerating system due to the higher saturated vapor pressure of the refrigerant. A short tube throttle valve (STTV) is an important throttle control component and cavitation noise easily occurs because of the pressure drop caused by flow throttling. To suppress the cavitation noise, this paper proposed a numerical investigation verified by experimental data into cavitation and noise characteristics inside the STTV. Three kinds of typical throttle valve structures are proposed in this study. The flow field and cavitation noise of the STTV under different refrigerant flow rates were analyzed numerically. The noise levels of the prototype and three optimized structures are comparatively assessed and analyzed. The findings indicate that the level of cavitation noise rises with an increase in flow rate through the STTV. Specifically, when the flow rate transitions from 0.014 kg/s to 0.024 kg/s, there is a corresponding increase in noise levels, moving from 102.8 dB to 122.5 dB. There is less cavitation noise upstream and the flow is stable, while the noise is mainly concentrated downstream with symmetrical distribution characteristics. The distribution seems to have small noise near the internal wall, while large noise is in the center of the downstream in the STTV, and the maximum noise is observed at the corner of the valve seat of the valve outlet. The optimized valve featuring a slope structure on the valve seat significantly reduces cavitation noise, with a maximum noise reduction of 24.94 dB compared to the prototype valve. [ABSTRACT FROM AUTHOR]

This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state–space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input–output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples. • Present a state estimator to estimate the states of bilinear state space systems. • Apply the hierarchical principle to handle the associated terms among sub-algorithms. • Present a data filtering-based multi-innovation gradient-based iterative algorithm. [ABSTRACT FROM AUTHOR]

Flow-acoustic feedback is one of the main types of noise in a cavity, is caused by the instability of the cavity shear layer and is enhanced through an acousticwave feedback mechanism. The flow characteristics of the cavity boundary/shear layer and the characteristic frequencies of the flow-acoustic feedback in the cavities are studied numerically, with aspect ratios ranging from 1/2 to 4/3. The freestream Mach number is equal to 0.11, corresponding to an Re-based cavity length of 2.1×105. Improved Delayed Detached Eddy Simulations combined with Ffowcs Williams-Hawkings acoustic analogy are used to simulate the flow and noise characteristics of the cavities. Autocorrelation analysis of flow field fluctuations is used to establish a link between the boundary/shear layer pressure fluctuations and flow-acoustic feedback noise. For the low aspect ratio cavities investigated in this paper, convection velocities along the shear layer development direction are obtained using wavenumberfrequency analysis. The deeper the cavity, the lower the shear layer flow velocity. Correspondingly, the characteristic frequencies of the narrowband noise generated by the flow-acoustic feedback shift linearly toward the low frequency band as the cavity depth increases. The results of the predicted noise characteristic frequencies obtained using wavenumber-frequency analysis and Rossiter's empirical formula are in agreement with the calculated results. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic resonance in nonlinear system driven by an additive signal-noise mixture. No exact analytical solution of the stochastic resonance equation has been found. It is usually solved approximately. In this paper, this equation is solved numerically. The noise dispersion under the stochastic resonance effect is determined. This study has convincingly shown that a weak signal could be amplified due to noise power and the output signal-to-noise ratio has a nonlinear dependence on the noise level. The results are applicable to radars in telecommunications and radio engineering. [ABSTRACT FROM AUTHOR]

With the advent of agent technology, the need for security of applications becomes a concern that is beyond classical data security capability and considerations. Without proper security protection especially against a malicious host attack, the widespread use of agent technology can be severely impeded. The Random Sequence 3-level obfuscated algorithm has been proposed by the authors to improve agent security: in this paper, an enhancement to the protection level of this algorithm is proposed. The effectiveness of the obfuscation algorithm is enhanced by addition of noise, which surrounds the true value carried by the agent with false values. A malicious host can thus at best guess the true value carried by the agent. [ABSTRACT FROM AUTHOR]

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault detection problem for the rotary steerable drilling tool system under unknown vibrations and limited computational resources. Firstly, the drilling tool system can be modeled by a nonlinear stochastic system with unknown time-varying noise covariances. Then, the dynamic event-triggered mechanism is introduced to save computational resources, and the caused transmission error is completely decoupled by nonuniform sampling. Subsequently, a novel unscented Kalman filter is proposed by combining the expectation maximization method to estimate states when noise covariances are unknown. A residual and an evaluation function are constructed to detect faults. Finally, a numerical simulation and an experiment on a drilling tool prototype validate the superior performance of the proposed fault detection scheme, which has lower missed alarm rates and consumes less time than existing methods. • Fault detection (FD) is studied for drilling tool with unknown noise covariances. • FD filter is designed based on unscented Kalman filter and expectation maximization. • Residual is decoupled from transmission errors caused by the event-triggered scheme. • FD of nonlinear system is achieved by using χ 2 test. [ABSTRACT FROM AUTHOR]

Axial piston pump is complex fluid machinery with mechanical fluid coupling, and it has the characteristics of multiple noise sources. In order to solve the high noise problem of axial piston pump, this paper proposes a noise reduction method by identifying the main noise sources of axial piston pump and laying new damping materials to reduce noise. It breaks through the existing method of optimizing the parameters of piston pump shell and valve plate structure. A multi-parameter trajectory sensitivity identification and noise reduction method for vibration transmission path of axial piston pump is proposed in this paper. First, the lumped parameter method is used to establish the mathematical model of vibration transmission path of axial piston pump. The sensitivity of structural vibration response to transmission path multi-parameters are analyzed based on trajectory sensitivity method. The most sensitive parameters in the main noise transmission path are determined. Then, the experimental study of radiated noise transmission path of axial piston pump is carried out. A new type of viscous damping material is proposed to reduce the noise on the transmission path of the main noise source. The experimental results show that the noise of piston pump is reduced by 2.32 dB. The research results provide a new method and way for the design and manufacture of low noise piston pump. [ABSTRACT FROM AUTHOR]

A firearm generates complex phenomena in muzzle flow and modelling the flow field around the projectile has great importance on high-intensity noise prediction. The negative effects of noise can be reduced using a suppressor which can be internally or externally attached to the barrel of a firearm. The purpose of this paper is to numerically and experimentally investigate the effect of the number and distance of circular ring baffles in the suppressor on the flow field and far field noise levels. Calculations were carried out in two-dimensional, axisymmetric, transient conditions and Ffowcs Williams and Hawkings acoustic analogy (FW-H) equations were solved to predict the far field noise. Nine cases including a gun without a suppressor, a suppressor without baffles, one, three, and five baffles which were placed at 20 mm intervals from the suppressor inlet, and one, three, five, seven, and nine baffles which were placed with equal intervals in the suppressor were simulated and compared; generations of noise during the shooting process were analyzed. The results showed that in the case without a suppressor, the peak sound pressure level was 156.1 dB at a 2.5 m distance, while this value decreased by nearly 7.6% in the case of the suppressor with seven baffles which has an average value of 144.2 dB. The results obtained here may provide a beneficial reference for predicting the muzzle noise and optimizing the number of baffles in the suppressor for small caliber gun systems. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of environmental noise from mining operations, focusing on the specific case of a quarry located in the municipality of Trebejov, Slovakia. The study aims to demonstrate an effective approach to reducing noise pollution through the implementation of mobile noise barriers. The research was motivated by the negative impact of noise from the processing line on the wellbeing of the population residing in the vicinity of the facility. The study involved conducting extensive noise measurements at various locations surrounding the quarry. The collected data were analyzed to assess the existing noise levels and identify the areas of highest noise exposure. Based on the findings, the implementation of mobile noise barriers was proposed as a solution. The results of the study demonstrated that the mobile noise barriers successfully reduced the noise levels below the legal limits, significantly improving the well-being of the population living near the quarry. The case study opens up the possibility of using mobile noise barriers in other areas and operations with high noise exposure. [ABSTRACT FROM AUTHOR]

An event-based modification of the classical relay feedback experiment without the inclusion of additional elements (integrator, time delay, ...) for identification of the spectrum of stable processes between zero and the phase cross-over frequency is presented. By inserting an event-based sampler in the control loop, the natural behaviour of a classical relay is simulated and the system is forced to work in two modes. The event-based sampler activates the first mode by sending control actions to the process every time the error signal crosses zero; this mode is to discover the approximated value of the cross-over frequency ω 18 0 ∘ . During the second mode, the event-based sampler sends samples to the process simulating that the error signal crosses zero at ω 18 0 ∘ / N where N is the number of points to identify in the range 0 ≤ ω ≤ ω 18 0 ∘ . One advantage of this procedure is that the logic used in an already existing relay feedback experiment to fit a transfer function model or tune a controller could be maintained just replacing the relay block by the event-based sampler block presented in the paper. Simulations and experiments with different processes and in presence of noise demonstrate the effectivity of the procedure. • Identification of stable processes between zero and the phase cross-over frequency. • Event-based modification of the classical relay feedback experiment. • Overcomes some of the limitations of the relay feedback method. • Can identify the steady gain and low-frequency points of the spectrum. • Simulations and experiments demonstrate effectivity in processes with noise. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is widely used in image segmentation because of its ability to describe the uncertain information presented in images. However, traditional fuzzy clustering ignores the spatial contextual information in the image, which makes it poor in processing images corrupted by high noise. In this paper, we present a novel fuzzy clustering algorithm called PFLWCM-CIM algorithm for noise image segmentation by introducing the image patches, the correntropy induced metric (CIM), and a fuzzy local weighted factor. Firstly, we use image patches as samples in clustering. Compared with individual pixels, image patches can preserve the local geometry of images by considering comprehensive features. Secondly, a new distance measure is developed on the basis of image patches and CIM to describe the relationship between samples and cluster centers. The CIM is more robust to noise than the traditional L 2 -norm. Next, a novel weighting method to characterize the similarity between two pixels named local weights is proposed. The local weights combine the spatial location relationship and the pixel value relationship of two pixels simultaneously, which describes the relationship between pixels from a more reasonable perspective. Furthermore, a new fuzzy local weighted factor is put forward by integrating the new distance measure and the local weights, then the PFLWCM-CIM algorithm is proposed based on the new factor and the idea of image patches. Several commonly used fuzzy clustering algorithms are incorporated into the experiments in segmenting images polluted by various types of noise. Experimental results demonstrate that our algorithm has reached state-of-the-art results in several metrics. [ABSTRACT FROM AUTHOR]

Aiming at the problems that existing methods lose a lot of spatial information and are easily interfered by image content, this paper proposes an end-to-end trainable high-resolution noise artifact tracking network (HRNAT-Net) for image splicing forgery detection. The network adopts HRNet as the network backbone, which can always retain high-resolution features, thereby effectively reducing the loss of important spatial information such as edge features. Besides, the network utilizes dilated convolution, which further preserves rich tampering information. Noise information are used as auxiliary information to build a dual-stream network to exploit visual and noise features simultaneously. Through multiple multi-scale feature fusion, the expressive ability of features is further enhanced. Finally, the visual features and noise features of corresponding resolutions are concatenated, and through multi-scale feature fusion, visual features and noise features are combined to locate the tampered area. The proposed network effectively solves the loss of spatial information, and simultaneously seeks forensic clues from the visual and noise levels. We evaluate the network performance on the CASIA v2.0 dataset and the Columbia dataset using metrics such as pixel-level Precision, Recall, and F1, and the detection accuracy (F1) reaches 0.845 and 0.950, respectively. Experimental results show that the proposed network outperforms state-of-the-art methods and has good robustness to JPEG compression, Gaussian noise, and resizing attacks. [ABSTRACT FROM AUTHOR]

Near infrared-visible (NIR-VIS) face recognition is to match an NIR face image to a VIS image. The main challenges of NIR-VIS face recognition are the gap caused by cross-modality and the lack of sufficient paired NIR-VIS face images to train models. This paper focuses on the generation of paired NIR-VIS face images and proposes a dual variational generator based on ResNeSt ( RS-DVG). RS-DVG can generate a large number of paired NIR-VIS face images from noise, and these generated NIR-VIS face images can be used as the training set together with the real NIR-VIS face images. In addition, a triplet loss function is introduced and a novel triplet selection method is proposed specifically for the training of the current face recognition model, which maximizes the inter-class distance and minimizes the intra-class distance in the input face images. The method proposed in this paper was evaluated on the datasets CASIA NIR-VIS 2. 0 and BUAA-VisNir, and relatively good results were obtained. [ABSTRACT FROM AUTHOR]

With the development of passive sensing technology, WiFi-based identification research has attracted much attention in areas such as human–computer interaction and home security. Although WiFi sensing-based human identification has achieved initial success, it is currently mainly applicable to scenarios where the user's identity category is fixed and not applicable to scenarios where the user's identity category changes frequently. In this paper, we propose an identification system (CIU-L) in a scenario where user's identity categories frequently change, allowing for incremental registration and unregistration of identity categories. To the best of our knowledge, this is the first attempt to register and unregister user identity information under the previous identity category constraints. CIU-L proposes a training and updating strategy in the registration phase of new user to avoid catastrophic forgetting of old user's identity information, and trains a targeted noise for the user to be unregistered in the unregistration phase of old user, achieving precise removal of the user to be unregistered without affecting the retained users. In addition, this paper presents adequate comparative experiments of CIU-L with other systems in the user identity category fixing scenario. The experimental results show that the average difference between CIU-L and other systems in terms of Accuracy, Precision, Recall and F1-Score is within 5% of each other, while running time and storage space are saved by more than 6 times, which is more capable of meeting the needs of identity recognition in real scenarios. [ABSTRACT FROM AUTHOR]

This paper addresses the robust stochastic finite-time and fixed-time chaos synchronization of two permanent magnet synchronous motors (PMSMs) in noise environment. The novel adaptive finite-time and fixed-time control schemes are implemented, respectively, which can not only ensure that the stochastic chaos synchronization of PMSMs can be achieved in a fast rate, but also determine the control gains successfully(not necessary to set them in advance). The sufficient conditions are derived in the light of the stochastic finite-time and fixed-time stability theories, where the upper bound of synchronization time can be estimated. Furthermore, the stochastic fixed-time synchronization can get rid of the dependence of initial conditions in PMSMs, which overcomes the critical deficiency of stochastic finite-time synchronization of PMSMs. Finally, simulation results demonstrate the validity of proposed theoretical analysis with comparisons. • This paper considers the noise perturbation in PMSMs. • The novel adaptive finite-time and fixed-time control protocols are exploited. • The sufficient conditions are derived based on stochastic stability theory. • The upper bound of synchronization time is estimated. [ABSTRACT FROM AUTHOR]

In avionics and industrial electronic systems, analog circuits are one of the most commonly used components. Intermittent faults (IFs) are a no fault found (NFF) state in analog circuits that are difficult to detect. In addition, the presence of noise may obscure critical information about the state of the circuit. Considering these challenges, this paper proposes an adaptive multiscale and dual subnet convolutional auto-encoder (AMDSCAE) to detect IFs. The proposed method can adaptively assign different attention to each scale and then fuse the multiscale information, which has better noise robustness. Then, the fault reconstruction error is amplified by the dual subnet structure to enhance the IF detection ability and find weaker faults. Considering the difficulty of obtaining fault sample labels, the proposed model requires only fault-free samples in the training process. In three typical analog filter circuit experiments, AMDSCAE has better noise immunity and can detect weaker IFs. • The proposed method has better noise immunity in detecting intermittent faults. • The adaptive multiscale module can reduce the loss of fault information. • The dual subnet structure is beneficial to amplify the fault reconstruction error. [ABSTRACT FROM AUTHOR]

Sloshing in fuel tanks is one of the major sources of noise in hybrid and high-end vehicles. During sloshing, the fluid causes impacts on tank walls resulting in their vibration, which further leads to noise, referred to as "hit noise." Therefore, hit noise generation is a multi-physics phenomenon involving fluid flow, structural response, and acoustic radiation. This paper presents a multi-physics approach to predict hit noise in a rectangular tank. The methodology involves the prediction of fluid loading on tank walls and their structural response using transient fluid and structural analyses which are weakly coupled. Radiated hit noise is predicted using acoustic finite element analysis. Longitudinal periodic excitation is applied to the fluid domain at different frequencies to simulate the sloshing regime which has dominant fluid-structure interactions. Parameters like tank wall pressures, the resulting dynamic acceleration, and radiated sound pressure levels are monitored and validated with the experimental results available in the literature. [ABSTRACT FROM AUTHOR]

Noise, illumination, temperature, and pressure are the primary external factors influencing the working environment and employee productivity. Physical variables were investigated in this paper over three weeks to determine whether the printing laboratory is suitable for students' practical training and employees' productive work. The A-weighted Sound Pressure Levels in decibels of different types of machines were determined. The lowest measured Leq level was 63.90±2.45 dB(A), while the highest was 80.50±3.90 dB(A). Because the mean value of the noise levels obtained for all investigated machines exceeded the acceptable level for laboratories and classrooms, as stated in Serbian guidelines, a frequency analysis at the 1/3 octave band was performed. The frequency spectra of the machines operating daily are comparable to the spectrum of human speech, resulting in impaired communication, primarily between students and professors during laboratory classes. The horizontal illumination on worktables in the first room ranged from 206.5 to 393.75 lx. The values in the second room, where student desks are located, ranged from 141.8 to 297 lx, with a mean value of 201.8±10.1 lx, significantly lower than the range recommended for classrooms and laboratories. The findings of this research indicate that the measured values of the aforementioned parameters significantly vary from what is considered appropriate for educational settings. Although most students and professors do not perceive this ambient atmosphere as disturbing, it stimulates the organic system and negatively affects overall health. [ABSTRACT FROM AUTHOR]

Freight transportation is one of the largest contributors to the transportation sector's greenhouse gases (GHG) and noise pollution. The adverse effects of the industry are not distributed equally due to large spatial disparities, with minority and low-income populations more prone to live near freight-related facilities and truck routes. However, equity analysis focusing on freight transport is limited since empirical analysis requires costly data collection processes, and analysis in large-scale areas is challenging. The principal objective of this study is to conduct an equity analysis of the impact of freight-related trips in the Atlanta-Chattanooga-Knoxville region using a large-scale agent-based simulation framework. The main results show that low-income (less than $52k) and more heavily non-white population neighbourhoods are disadvantaged neighbourhoods in terms of noise and GHG emissions. The results and methods presented in this paper can be used to provide tools to develop transportation equity and social justice analysis in large networks and can be used to develop policies and regulations to reduce the impact of freight transportation on disadvantaged populations. [ABSTRACT FROM AUTHOR]

In this paper, based on the modified fuzzy c-regression model and noise clustering algorithm, a fuzzy identification method is proposed. Firstly, by considering the relations for the real model, the established model, and the outliers, a modified objective function with noise is proposed to alleviate the affection of noise. Additionally, the consequent parameters of the fuzzy model can be obtained by the iterative formula which obtained by the Lagrangian formula. Furthermore, a modified membership function, which is involved the likelihood function, is propounded to get a more suitable multivariate normal distribution for the data points. Lastly, two examples are illustrated to show the validity and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Aircraft noise is generally regarded as the most annoying source of environmental noise and is the most common source of complaints raised by residents living in the vicinity of airports. Many different types of abatement measures can be utilized by airports to reduce the noise impact, but the manner of their implementation can vary between airports. To assess how noise control strategies are implemented across the United States (US), a national survey of noise abatement strategies was conducted. The paper provides a summary of this survey and reports the noise control approaches at 42 different airports across the US. The survey considered general aspects of noise control, as well as specific questions related to noise abatement procedures, noise limits and curfews. Participants were also asked their opinion on the impact of the International Civil Aviation Organization (ICAO) Balanced Approach, which is the ICAO recommended framework to address aircraft noise. Although just a snapshot of activities across the US is presented, results indicate that a wide array of noise abatement procedures is being implemented. Results also suggest that the ICAOBalanced Approach has yet to be fully embraced in the US, with a large proportion of respondents reporting that they are not familiar with this approach. [ABSTRACT FROM AUTHOR]

Continuous learning from streaming data is one of the contemporary most challenging topics. Learning algorithms not only need to handle fast-moving big data but should also be able to adapt to future evolving changes. The evolving structure of data streams is the phenomenon referred to as concept drift. Two types of concept drift handling based on ensemble approaches are online ensemble and chunk-based ensemble (window-based ensemble). The main disadvantage of the chunk-based ensembles is the difficulty of tuning the block size to provide a trade-off between fast reactions to drifts. In this paper, we propose Accuracy Updated Ensemble-2 (AUE2) based on the adaptive windowing approach (denoted as A-AUE2) by using Brier Skill Score as sudden and gradual changes detector. Moreover, K-Nearest Neighbors (KNN) based noise filtering method is applied to eliminate the noisy samples from each of the adaptive windows to improve the effectiveness of the ensemble learning approach. This paper's proposed approach has been evaluated on six artificial data sets and four real benchmark data sets by using two base learners. Specially, we illustrate that the proposed approach has outperformed the other state-of-the-art drift detection and handling approaches by using performance metrics and statistical analysis. [ABSTRACT FROM AUTHOR]

Mechanical manufacturing processes and their production machinery are noisy. The noise always stems from transient forces that excite the structure to vibrations, emitting sound waves into the environment due to vibrations on the surface. Noise emission is today considered as a quality defect of machinery and thus methods and means are sought to mitigate the noise emission to a level compatible with human operators. On the other hand as the noise origin is the process itself, the emitted noise has a signal content that directly indicates the status of the process and sometimes also the health state of the machine and its elements. Noise reduction today is still fairly experience based, which requires generating a good understanding of noise generation, noise transmission and noise radiation and is discussed in this paper. Measurement of noise and its analysis are indispensable for the traceback to the origin to focus the countermeasures but also to reveal the signal content out of the sound waves. Noise prediction is still at the threshold of industrial application, but will in future play a major role for including noise topics into the design from the beginning and not only as corrective measures after the machine is built. Available simulation tools are presented in this paper. The greatest progress in this respect stems from room acoustics and the automotive industry, where the requirements are much stricter than in the manufacturing environment. Ways and achievements in the utilization of noise signals for process and machine health monitoring will also be presented. The industry seeks here for broad band sensors, able to carry large amounts of easy to retrieve signals to reduce the number of additional sensors. Typical standards today collect countermeasures being applied for spreading the knowledge generated for intuitive approaches. The paper also shows future trends and developments and the research required in that field. [ABSTRACT FROM AUTHOR]

Introduction: Prevalence of tinnitus range from 7.1% to 14.6% (National Center for Health Statistics, 2016), but the mechanisms responsible for the development of this abnormal sensory state remain poorly understood. Objectives: To determine the evidence for different etiologies and pathophysiology of tinnitus identified by clinical diagnostic tests in the adult population. Study Design: Systematic literature review. Methods: Review of data base using PRISMA guidelines: Google Scholar, Medline, Springer Link, Pubmed. In addition, manual reference search of identified papers. Randomized controlled trials, case control study, prospective cohort studies, and retrospective reviews of consecutive patients in which clear data were reported with respect to etiology and pathophysiology of tinnitus. Results: Sixty seven articles met the inclusion criteria. The papers searched recent studies from 2004 to 2018 for different etiologies such as noise exposure, age, ototoxic drugs, hearing loss among patients with tinnitus. Multiple pathophysiology were identified, including inner ear pathology, auditory nerve synchronisation, central nervous system anomalies and limbic and autonomous nervous system problems. The group of papers evaluated tinnitus patients with specific diagnostic tests such as pure tone audiometry, Immitance audiometry, otoacoustic emission, Auditory brainstem response and diagnostic imaging of fMRI, MRI and PET study. Conclusions: The results indicate a high level of heterogeneity between the studies for all the assessed areas. These results support the need for greater stratification of the tinnitus population and the importance of a standardized Puretone audiometry with extended high frequency, OAE, ABR and diagnostic imaging (fMRI, MRI & PET) method to make comparisons between studies possible. Diagnostic imaging is an important useful method for identification of intracranial pathology that can present with tinnitus as a primary symptom. Establishment of a direct causal link between tinnitus and these etiologies and pathophysiology remains elusive [ABSTRACT FROM AUTHOR]

A method is proposed in this paper to correct the position error in radar cross section (RCS) calibration. In radar RCS measurement system, in order to get the accurate information, the amplitude and phase calibration are very important to remove the error influence. The measurement results are influenced by multipath effect, laboratory background, target position, environmental noise and so on. In this paper, the position error correction is under consideration. Firstly, the method of getting target position is derived. Following this, the phase error caused by it can be got rid of to get more accurate data. Finally, we show the extensive simulation results and experiment results of typical cylinder, which all demonstrate the validity of the method. [ABSTRACT FROM AUTHOR]

Impulse noise is one of the common noise types that affect images. Median filtering denoising method has been widely used for impulse noise. However, as the noise density increases beyond 50%, the noise reduction quality of median filter reduces sharply. In this paper, a very simple nearest neighbor filtering method 1 1 The MATLAB code for the proposed NNFM algorithm is given at: https://github.com/rafimiet/NNFM.git is proposed for impulse noise reduction. The proposed method exploits the inter-pixel correlation in images. More spatially closer the pixels within an image, more the correlation amongst them. The filter window is partitioned into multiple clusters based on their distance from the pixel that needs to be corrected. The closest non-noisy pixel is searched and is used to replace the corrupted pixel. The proposed method has been simulated for grayscale as well as color images. The simulation results show that the proposed method outperforms the state-of-the-art methods. Also, the computational complexity of the proposed methods is very small as compared to the state-of-the-art denoising methods. The only method that outperforms the proposed method is median filtering method. [ABSTRACT FROM AUTHOR]

Impulse noise is one of the common noise types that affect images. Median filtering denoising method has been widely used for impulse noise. However, as the noise density increases beyond 50%, the noise reduction quality of median filter reduces sharply. In this paper, a very simple nearest neighbor filtering method 1 1 The MATLAB code for the proposed NNFM algorithm is given at: https://github.com/rafimiet/NNFM.git is proposed for impulse noise reduction. The proposed method exploits the inter-pixel correlation in images. More spatially closer the pixels within an image, more the correlation amongst them. The filter window is partitioned into multiple clusters based on their distance from the pixel that needs to be corrected. The closest non-noisy pixel is searched and is used to replace the corrupted pixel. The proposed method has been simulated for grayscale as well as color images. The simulation results show that the proposed method outperforms the state-of-the-art methods. Also, the computational complexity of the proposed methods is very small as compared to the state-of-the-art denoising methods. The only method that outperforms the proposed method is median filtering method. [ABSTRACT FROM AUTHOR]

A novel parameter identification method for locally linear radial basis function-based autoregressive models in presence of colored noises is proposed in this paper. Taking advantage of the global nonlinear and local linear structural characteristics of the models, two dynamical criterion functions are constructed based on the separated parameters to realize the dynamical acquisition and utilization of the entire process data. Two recursive gradient sub-algorithms are derived for estimating the separated parameters by using the nonlinear gradient optimization. To coordinate the associated variables existing in the sub-algorithms and to estimate the unmeasurable noise terms, we combine the sub-algorithms and propose a two-stage extended recursive gradient (2S-ERG) algorithm. In addition, an extended recursive gradient algorithm is given as a comparison. The feasibility of the 2S-ERG algorithm is validated by numerical simulations. • Two dynamical criterion functions are defined to allow the observations gradually participate. • To separately estimate the parameters, two recursive gradient (RG) sub-algorithms are derived. • A method to deal with the associated terms and unmeasurable noise terms is presented. • Coordinating the two RG sub-algorithms gets a two-stage extended recursive gradient algorithm. [ABSTRACT FROM AUTHOR]

Digital cameras are widely used in artificial intelligent systems: machine vision, pattern recognition, automatic defect detection, etc. The quality of these systems is defined in significant degree by the registered images signal-to-noise ratio. Digital camera noise, conditions of shooting, low-light illumination decrease image quality. But information about camera noise allows to significantly improve it. However, noise characteristics are often absent in the instructions even for scientific digital cameras. There are various methods of photosensor noise measurement. They have different accuracy, speed of implementation and processing time. Therefore, the choice of the optimal method for measuring noise is of great interest. The paper presents the virtual camera-based analysis of methods of photosensor characterization. This technique determines the maximum and possible accuracy of the methods. Three methods of photosensor noise characterization are considered: the EMVA (European machine vision association) standard 1288, automatic segmentation of a non-uniform target (ASNT), and automatic segmentation of a striped target (ASST). The method's accuracy and processing time were estimated. [ABSTRACT FROM AUTHOR]

A novel parameter identification method for locally linear radial basis function-based autoregressive models in presence of colored noises is proposed in this paper. Taking advantage of the global nonlinear and local linear structural characteristics of the models, two dynamical criterion functions are constructed based on the separated parameters to realize the dynamical acquisition and utilization of the entire process data. Two recursive gradient sub-algorithms are derived for estimating the separated parameters by using the nonlinear gradient optimization. To coordinate the associated variables existing in the sub-algorithms and to estimate the unmeasurable noise terms, we combine the sub-algorithms and propose a two-stage extended recursive gradient (2S-ERG) algorithm. In addition, an extended recursive gradient algorithm is given as a comparison. The feasibility of the 2S-ERG algorithm is validated by numerical simulations. • Two dynamical criterion functions are defined to allow the observations gradually participate. • To separately estimate the parameters, two recursive gradient (RG) sub-algorithms are derived. • A method to deal with the associated terms and unmeasurable noise terms is presented. • Coordinating the two RG sub-algorithms gets a two-stage extended recursive gradient algorithm. [ABSTRACT FROM AUTHOR]

Mechanical system usually operates in harsh environments, and the monitored vibration signal faces substantial noise interference, which brings great challenges to the robust fault diagnosis. This paper proposes a novel attention-guided joint learning convolutional neural network (JL-CNN) for mechanical equipment condition monitoring. Fault diagnosis task (FD-Task) and signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, achieving good noise robustness through dual-task joint learning. JL-CNN mainly includes a joint feature encoding network and two attention-based encoder networks. This architecture allows FD-Task and SD-Task can achieve deep cooperation and mutual learning. The JL-CNN is evaluated on the wheelset bearing dataset and motor bearing dataset, which shows that JL-CNN has excellent fault diagnosis ability and signal denoising ability, and it has good performance under strong noise and unknown noise. • The feasibility of vibration signal denoising based on deep learning has been studied. • An excellent end-to-end CNN-based vibration signal denoising method is proposed. • A new solution to improve the noise robustness of the fault diagnosis model is proposed. • A new network framework based on joint learning is designed, which performs well under unknown noise. [ABSTRACT FROM AUTHOR]

Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning. The imbalanced class distribution misleads classifiers from correctly classifying the minor class. This paper introduces Reduced Noise-SMOTE (RN-SMOTE) for pre-processing imbalanced data. RN-SMOTE firstly, oversamples the training data using SMOTE which introduces noisy oversampled synthetic instances in the minority class. Then, applying DBSCAN to detect and remove noise. Next, the clean artificial instances are combined with the original data. Finally, RN-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. RN-SMOTE is evaluated using 9 different classifiers and 9 different imbalanced datasets with different imbalance ratios and five of them are used for outlier detection. The results proved that the performance of the classifiers has been improved with RN-SMOTE and outperformed the performance with original data and SMOTE with percentage based on the classifier, dataset and evaluation metric. Also, performance of RN-SMOTE has been compared to the performance of the current state of art and resulted in an increase up to 37.41%, 23.28%, 13.95% and 9.07% in terms of Recall, F1, Precision and Accuracy for RN-SMOTE. [ABSTRACT FROM AUTHOR]

Ambient day and night noise levels prediction problems have traditionally been addressed using various statistical and machine learning methods. This paper presents the time-series predictions and forecasting of ambient noise levels using support vector machine (SVM) and deep learning method such as convolutional neural network (CNN) approach. This approach has been rarely reported for modeling ambient noise levels so far, although it has been widely used in air and water pollution predictions and forecasting. The study presents the applications of these techniques in time-series modeling of ambient day and night equivalent noise levels. A case study of ambient noise levels of one site each lying in commercial, residential, industrial and silence zone is presented. Ten-fold cross-validation is used in SVM model to train the model effectively and determine the optimized value of hyper-parameter (g, «, C). Also, CNN with a convolutional and pooling layer architecture framework is designed with optimum value of batch size, activation function, and filter size, among others. The validation and suitability of developed SVM and CNN models are ascertained by various statistical tests. Convolutional neural network approach is observed to outperform SVM model and thus can be a reliable approach for time-series modeling of ambient noise levels with a prediction error of 2.1 dB(A). The forecasting root mean squared error obtained for all the four zones using CNN model is observed to be less than 2.1 dB(A) for day equivalent noise levels and 1.9 dB(A) for night equivalent noise levels. [ABSTRACT FROM AUTHOR]

This paper describes the research, development, and implementation of a frame synchronization method for data transmission systems with short packets, specifically those that use nonseparable factorial coding. The key concept of the method is using a permutation as a syncword. This permutation must satisfy the following condition: the minimum Hamming distance to all its circular shifts must be the maximum. The frame synchronization method employs correlation processing and majority processing of data fragments transmitted through the communication channel, where the fragment length is equal to the syncword length. As a result, the proposed frame synchronization method can realize frame synchronization in data transmission systems with adverse noise conditions. Theoretical and practical assessment of the number of accumulated fragments required to establish frame synchronization indicates that implementing the proposed method reduces the amount of received data, which subsequently reduces the time required to establish a connection between the transmitter and receiver. In addition, interleaving accumulated fragments further reduces the required number of received fragments. [ABSTRACT FROM AUTHOR]

Noise when added to the speech signal deteriorates its quality and makes the speech signal meaningless for the listeners. The active noise cancellation technique can be used for cancelling this noise, recovering the original signal, and making it meaningful. But due to fast varying characteristics of speech signals along with high sensitivity of speech for surrounding noise the use of active noise cancellation on speech signals is quite challenging. In this paper, this challenge of estimation and cancellation of speech signal noise is analysed. The proposed noise cancellation model is implemented using human's speech, birds chirp, and airplane sound as surrounding noise signals. The active noise cancellation is applied using least mean square algorithm (LMS), normalized least mean square algorithm (NLMS) and recursive least mean square algorithm (RLS) for different types of noisy surroundings. The prediction accuracy and SNR of all the three algorithms for real-time human speech are also compared and discussed. The results are supported by root mean square EVM power analysis. [ABSTRACT FROM AUTHOR]

Wind turbines (WT) can produce large amounts of energy and thus help economies make an important step towards a sustainable energy future. However, residents living near wind farms ascribe to wind turbine noise (WTN) a multitude of adverse health impacts and use them as an argument against wind farms. This paper reviews the relevant literature on the effects of wind turbine noise in order to help address the controversy that surrounds the safety of wind turbines and, in this way, to enhance community acceptance of wind farms. The objectives are to compare the findings of quantitative, experimental and longitudinal research on these effects and to detect whether findings are consistent. The analysis showed that while quantitative research recorded adverse health effects, experimental and longitudinal research provided no supporting evidence. However, findings from these different research types were consistent in indicating a risk for sleep posed by WTN. In addition, quantitative and experimental research converges that annoyance to WTN is affected by the visual impact of wind turbines on the landscape as well as attitudes to wind energy in general. Moreover, experimental work has shown that exposure to information on the Internet that accentuates negative health effects due to WTN can account for residents' complaints about adverse health effects. Finally, it is necessary to perform more longitudinal research on the health of residents in relation to WTN as well as more quantitative research on quality of life of individuals residing near wind farms. • Convergent quantitative, longitudinal and experimental findings show risk for sleep. • Wind energy attitudes and the visual impact of wind turbines affect noise annoyance. • Objective information can prevent the ascription of health effects to noise. • More longitudinal research on the health effects of wind turbine noise is needed. • Quality of life of residents exposed to wind turbine noise is understudied. [ABSTRACT FROM AUTHOR]

For state estimation of discrete nonlinear fractional stochastic systems, this study presents two innovative modified fractional central difference Kalman filters. We consider a complicated scenario where the process noise or measurement noise in the system become colored noise. Firstly, the nonlinear function is linearized by utilizing the Stirling polynomial interpolation formula. Thus there is no need to calculate the Jacobi matrix for both algorithms, which means very few application limitations. Then, based on the augmented-state method, we develop an augmented state fractional central difference Kalman filter under the scenario of colored process noise. Afterwards, a state estimation algorithm for handling stochastic systems containing colored measurement noise is put forward by using the measurement expansion method. Finally, to perform the superiority of the developed algorithms, several simulations are carried out. As well, the algorithms derived in this paper are contrasted with the original fractional central difference Kalman filter and three other algorithms. Notably, a simulation with engineering significance for the state-of-charge estimation for lithium-ion batteries is also introduced, Aside from the commonly used numerical simulation. The results verify the superiority of the developed algorithms in sense of estimation accuracy and real-time performance. • Two modified fractional CDKF for fractional-order system are proposed. • Both colored process noise and colored measurement noise can be handled effectively. • The presented algorithms show superiority in terms of real-time performance and estimation accuracy. • The proposed algorithms have fewer limitations and wider application prospects. [ABSTRACT FROM AUTHOR]

Digital noise is an image defect that is approximately close to the pixel size and differs in brightness or color from the original image. Noise reduction plays an important role in the transmission, processing and compression of video footage and images. There are a large number of methods for removing noise from images, and they can be used not only by special processing programs, but also in some photo and video cameras. Despite this, there is still no universal filtering algorithm, because when processing an image, there is always a need to choose between preserving small details with properties such as size and noise to eliminate unwanted effects. In this paper, a management and reduction of digital noise in video image processing was discussed in the basis of transmission based noise elimination. In addition, that the proposed scheme easily overcomes the various types of noise. It will identify the spoil the image with another type of noise. Hence the noise affected part will eliminated and reduce the effects of noise. [ABSTRACT FROM AUTHOR]

Sources of noise may come from urban transport, areas or objects which are large gatherings of people or industrial areas. Rate setting for the urbane noise is to protect the community from excessive noise action. The main source of noise for inhabited areas near the careers of Oltenia are the bandconveyors of the type 1000 -- 1400 - 1600 -- 1800 -- 2000 -2250 mm. One way to combat noise produced by the bandconveyors is the use of curved screens from soundproofing materials. Choice of the dimensions for the curved screens depends on the noise level and the system characteristics that determine noise. In this paper we determined the attenuation produced of the curved screens of polycarbonate which can be used for attenuation of noise from the bandconveyors. [ABSTRACT FROM AUTHOR]

Noise analysis from heating, ventilation and air conditioning (HVAC) elements in ship cabins is needed to satisfy the strict noise regulation for the ship cabin, and it is very important for their effects to be evaluated in the early stage of design. In this paper, a direct acoustic energy flow analysis (DAEFA) model is presented for the noise analysis of the ship cabin to reflect direct fields of the sources from an HVAC element. Total acoustic fields are considered by deriving the fundamental solution of the energy flow model for direct and reverberant fields in a closed space. Sound propagation in the direct field is implemented using the geometrical acoustics concept, and noise sources introduced into the reverberant sound fields are considered by using the relationship between direct fields and the closed space boundaries. For cabin noise analysis using DAEFA, interior boundaries of the cabin are discretized, and boundary integral formulations for each field are integrated. Noise analysis is performed for various cases with the DAEFA model to minimize cabin noise. The optimal design derived through the analysis was applied to an actual ship, and cabin noise measurement was conducted onboard the applied ship. Comparison of the analysis and measurement results confirms the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

Exhaust noise produced by internal combustion engines is one of the main problems of vehicle noise, and exhaust mufflers can significantly improve this problem. In fact, the sound waves inside the exhaust muffler will cause the muffler shell to vibrate, causing themuffler to produce shell radiation noise. Therefore, it is necessary to carry out the acoustic-structure coupling analysis of the muffler. This paper improved the design of a prototype straight-through perforated pipe-resistant muf- fler (design A) and designed a straight-through perforated pipe-resistantmuffler with a corrugated lining (design B). Structural modal analysis, acoustic modal analysis, and transmission loss analysis of the two mufflers were carried out. The coupling of the acoustic cavity and structure was considered, and the radiation noise of two kinds of resistant mufflers was comparatively analyzed by the directboundary- element method. The acoustic radiation of two kinds of resistant muf- flers was studied. The pressure loss of the two kinds of mufflers was studied by aerodynamic analysis. The results suggested that the design B muffler effectively improved the noise reduction performance of the prototype muffler, reduced the vibration displacement amplitude of the shell, and reduced the sound power level of the design A muffler. [ABSTRACT FROM AUTHOR]

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The Fifth Generation (5G) wireless communications systems are expected to satisfy higher data rates, network scalability, increased number of connections and higher traffic densities in a cost-effective manner. The key essence of 5G technology resides in exploring the frequency bands at millimeter-Wave (mmWave) frequencies. As is well known, the presence of Impulsive Noise (IN) corrupts signals and leads to increased bit error rate and decreased spectral efficiency. In this paper, the performance of mmWave systems in multi-path fading channel and IN is studied and a new thresholding mechanism for the clipping and blanking filters to suppress the impulsive components of noise is suggested. The paper also presents the mathematical expressions to determine the optimum threshold selection for the filters. Simulation results show that use of clipping and blanking filters with the optimal threshold values reduces the adverse effect of IN and improves system performance significantly. [ABSTRACT FROM AUTHOR]

This paper investigates the rate of transformation induced plasticity in TRIP steel (TRansformation-Induced Plasticity) after plastic straining by the use of Barkhausen noise emission. The samples were subjected to a variable degree of plastic straining and analysed by the use of conventional techniques such SEM, XRD, as well as microhardness in order to investigate residual stress and microstructural alterations initiated by the uniaxial tensile test. Barkhausen noise emission is analysed as a function of plastic straining as well as in the direction of the exerted load and interpreted with respect to the aforementioned microstructure and stress alterations. It was found that Barkhausen noise markedly decreases along with increasing plastic straining, up to 20%, followed by a strain region in which the evolution of Barkhausen noise reaches saturation. Samples after the tensile test exhibited marked magnetic anisotropy since the Barkhausen noise emission in the direction perpendicular to the tensile stress remained less affected. Apart from the effective value of Barkhausen noise, the Barkhausen noise envelopes were also analysed. • Barkhausen noise in TRIP steel exhibits steep decrease at low plastic strains. • Barkhausen noise evolution is sensitive to the rate of austenite decomposition. • Higher plastic strains increase magnetic hardness of TRIP steel. [ABSTRACT FROM AUTHOR]