Real-time Noise Research Articles

Real-Time Processing and High-Quality Imaging of Navigation Strip Data Using SSS Based on AUVs

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

GPS-based noise mapping of Ahmedabad city

ABSTRACT Noise mapping is an effective tool to measure noise. By noise mapping one can represent noise graphically. Noise mapping was carried out for Ahmedabad city. Data was collected at 633 different locations across the city. Latitude, longitude and real-time noise levels were noted at each location. ArcGIS software was used for creating noise maps and colour noise maps. The percentage of the city covered by the respective value of contour of noise was also found using the software, as well as the number of people in the city suffering from the respective levels of noise. Considering all the results, a multi-linear regression model was developed to predict noise, using SPSS statistics software. The developed model was analysed using an R2 test as well as a paired t-test.

Perceiving noise in daily life: How real-time sound characteristics affect personal momentary noise annoyance in various activity microenvironments and times of day.

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments.

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches.

Development of a real-time noise estimation model for construction sites

As construction noise negatively affects the health and quality of life of stakeholders, field managers need to properly monitor and manage noise. Thus, the authors developed a model that estimates real-time noise levels at a construction site and the surroundings to enable preemptive responses to noise-related issues. To accurately estimate noise, necessary field data were collected using an unmanned aerial vehicle (UAV) and noise sensors. The noise estimation model was composed of two sub-models: the noise-customized spatial interpolation model and the noise propagation model. The noise-customized spatial interpolation model was developed to estimate the internal noise of the construction site using a few sensor noise levels. Meanwhile, the noise propagation model was developed to estimate the noise level outside the construction site using internal noise estimation results, obstacles, weather information, and noise sources information. The model was evaluated through field tests at a construction technology demonstration center, environments identical to real construction sites in South Korea. The model showed satisfactory performance, with an accuracy of 96.71% and a root mean square error (RMSE) of 2.62 for the internal construction site noise and an accuracy of 96.03% and an RMSE of 2.70 for outside the construction site. To facilitate the usage of the noise estimation results for field managers, the research team visualized the results using the Unity 3D Engine. The results will enable field managers to assess workers’ long-term noise exposure and respond to potential civil complaints, gearing up to realize environmental, social, and governance (ESG) goals in the construction industry.

Application of Signal Denoising Technology Based on Improved Spectral Subtraction in Arc Fault Detection

In the research of fault arc detection technology, proper signal denoising can greatly improve the recognition ability of the arc fault detection algorithm. Therefore, this study proposes a current signal enhancement algorithm based on improved spectral subtraction from the statistical law of electrical noise in a low-voltage distribution system and the principle of the arc fault detection algorithm. According to the results of the arc fault detection algorithm, the algorithm selects an appropriate reference current as the basis for noise estimation and further assumes that the types and quantities of power loads are relatively fixed and the current noise is relatively stable in a certain power consumption period in the same power consumption place, so as to obtain the current noise in the current power consumption environment, and then uses improved spectral subtraction to realize the real-time noise reduction of current signals required by the arc fault detection algorithm. The experimental results show that this method and the arc fault detection algorithm complement each other, and the processing of current signal is more targeted, which can make the selected arc fault characteristics more different between the normal state and fault state, eliminate the interference of high-frequency clutter in current signal on arc fault characteristics and greatly improve the sensitivity of the original detection algorithm on the premise of unchanged detection reliability.

Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer.

A novel fusion denoising algorithm of spectral subtraction and Wiener filtering for MEMS magnetic signal in PDR positioning

PDR positioning is currently the core issue of autonomous positioning, there is a problem of high noise and interference in dynamic measurement using PDR (Pedestrian Dead Reckoning) MEMS (Micro-Electro-Mechanical System) device, which is often a low precision multi-source integration sensor. Two new models of improved spectral subtraction (ISS) and PDRS (PDR-Spectral subtraction) spectral subtraction are constructed in this paper according to the spectrum features of the periodic noise generated from personnel stepping in PDR positioning. It is proposed a two-stage noise reduction method of spectrum subtraction-Wiener filtering for obtaining the static magnetic sequence of personnel path and reducing the real-time noise in PDR positioning, which main mechanism is to use the step power spectrum as the background noise for primary noise reduction, secondary Wiener filtering noise reduction according to the correlation between the denoised signal and the reference signal. A self-made MEMS-based positioning device HBEQ-1 and a high-precision magnetometer FVM-400 were used to collect dynamic and static magnetic value from the test area, and a spectral subtraction-Wiener two-stage noise reduction test was carried out. The experimental results show that the improved ISS spectral subtraction and PDRS spectral subtraction are effective in denoising the dynamic magnetic measurement, but there is still a certain deviation between the peak value and the amplitude of the denoised signal. After secondary noise reduction by Wiener filtering, the signal morphology similarity of magnetic curve and the reference reached 0.7975, and the matched path values of the dynamic magnetic value reached around 12000–13000. It showed that the denoising effect is good and the waveform of denoised signal and the reference signal is similar, but there is spatial stretching of the overall magnetic sequence. It is shown that the spectral subtraction-Wiener noise reduction method cannot eliminate the inertial delay bias generated by personnel walking, the processing does not distort the magnetic signal, maintains spatial characteristics of the static magnetic sequence of personnel path, which is an excellent model for noise reduction of dynamic magnetic measurements in PDR positioning.

IoT based real time air and noise pollution monitoring and controlling

Air and sound pollution is the biggest problem nowadays. So It is required to monitor air quality and noise levels to keep it control for a better future and happy living for all. In order to monitor the pollution, we propose an air pollution as well as sound pollution monitoring system that allows us to monitor and automatically control the air quality and sound pollution in particular areas through IoT. Our proposed system used air sensors to sense presence of harmful pollutants in the fresh air and simultaneously transmits this data to the microcontroller and also device keeps measuring the sound level and reports it to the cloud storage through Internet. These sensors interfaced with microcontroller which senses this data and transmits it to the internet. Our system also interfaced with GPS module to find the latitude and longitude of different places. This methodology allows authorities and people to monitor the air pollution and noise pollution in different areas and take action against it. If system detects the air quality and noise issues, it alerts authorities and also living people. Once the system exceeds the threshold levels of air and noise pollution, controlling action will automatically take by its own. People are dont need to worry about this. Pollution automatically monitored and controlled by our system.

A Cascade Network for Blind Recognition of LDPC Codes

Coding blind recognition plays a vital role in non-cooperative communication. Most of the algorithm for coding blind recognition of Low Density Parity Check (LDPC) codes is difficult to apply and the problem of high time complexity and high space complexity cannot be solved. Inspired by deep learning, we propose an architecture for coding blind recognition of LDPC codes. This architecture concatenates a Transformer-based network with a convolution neural network (CNN). The CNN is used to suppress the noise in real time, followed by a Transformer-based neural network aimed to identify the rate and length of the LDPC codes. In order to train denoise networks and recognition networks with high performance, we build our own datasets and define loss functions for the denoise networks. Simulation results show that this architecture is able to achieve better performance than the traditional method at a lower signal-noise ratio (SNR). Compared with the existing methods, this approach is more flexible and can therefore be quickly deployed.

Live 4D-OCT denoising with self-supervised deep learning

By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction. In this work, we propose a low complexity neural network for denoising, directly incorporated into the image reconstruction pipeline of a microscope-integrated 4D-OCT prototype with an A-scan rate of 1.2 MHz. For this purpose, we trained a blind-spot network on unpaired OCT images using a self-supervised learning approach. With an optimized U-Net, only a few milliseconds of additional latency were introduced. Simultaneously, these architectural adaptations improved the numerical denoising performance compared to the basic setup, outperforming non-local filtering algorithms. Layers and edges of anatomical structures in B-scans were better preserved than with Gaussian filtering despite comparable processing time. By comparing scenes with and without denoising employed, we show that neural networks can be used to improve visual appearance of volumetric renderings in real time. Enhancing the rendering quality is an important step for the clinical acceptance and translation of 4D-OCT as an intra-surgical guidance tool.

IoT cloud based noise intensity monitoring system

According to a survey published by the United Nations Environment Program (UNEP) last month, Dhaka, Bangladesh’s capital, is the world’s noisiest metropolis. Dhaka is consistently ranked as one of the most polluted cities in the world. The World Health Organization (WHO) has set a noise intensity limit of 55 decibels for a certain region. Dhaka, on the other hand, was determined to be twice as loud, at 110 to 132 decibels. As many as 66 percent of traffic cops work on the road to control traffic, and this high noise intensity noise is causing them hearing and sleeping problems on a daily basis. Furthermore, loud noises can increase blood pressure and pulse rates, produce mental stress, and interfere with amusement and personal interactions. Keeping all of this in mind, we designed a real-time noise pollution monitoring system that will assist in conducting experiments in this type of environment by evaluating noise intensity and automatically sending data to a IoT cloud-based platform.

An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors.

In bio-signal denoising, current methods reported in the literature consider purely simulated environments, requiring high computational powers and signal processing algorithms that may introduce signal distortion. To achieve an efficient noise reduction, such methods require previous knowledge of the noise signals or to have certain periodicity and stability, making the noise estimation difficult to predict. In this paper, we solve these challenges through the development of an experimental method applied to bio-signal denoising using a combined approach. This is based on the implementation of unconventional electric field sensors used for creating a noise replica required to obtain the ideal Wiener filter transfer function and achieve further noise reduction. This work aims to investigate the suitability of the proposed approach for real-time noise reduction affecting bio-signal recordings. The experimental evaluation presented here considers two scenarios: (a) human bio-signals trials including electrocardiogram, electromyogram and electrooculogram; and (b) bio-signal recordings from the MIT-MIH arrhythmia database. The performance of the proposed method is evaluated using qualitative criteria (i.e., power spectral density) and quantitative criteria (i.e., signal-to-noise ratio and mean square error) followed by a comparison between the proposed methodology and state of the art denoising methods. The results indicate that the combined approach proposed in this paper can be used for noise reduction in electrocardiogram, electromyogram and electrooculogram signals, achieving noise attenuation levels of 26.4 dB, 21.2 dB and 40.8 dB, respectively.

Spectrally encoded dual-mode interferometry with orthogonal scanning.

Spectrally encoded confocal microscopy (SECM) is a high-speed reflectance confocal microscopy technique. Here, we present a method to integrate optical coherence tomography (OCT) and SECM for complementary imaging by adding orthogonal scanning to the SECM configuration. The co-registration of SECM and OCT is automatic, as all system components are shared in the same order, eliminating the need for additional optical alignment. The proposed multimode imaging system is compact and cost-effective while providing the benefits of imaging aiming and guidance. Furthermore, speckle noise can be suppressed by averaging the speckles generated by shifting the spectral-encoded field in the direction of dispersion. Using a near infrared (NIR) card and a biological sample, we demonstrated the capability of the proposed system by showing SECM imaging at depths of interest guided by the OCT in real time and speckle noise reduction. Interfaced multimodal imaging of SECM and OCT was implemented at a speed of approximately 7 frames/s using fast-switching technology and GPU processing.

Research on interference and noise reduction technology of solar radio observation system

The dynamic spectrum of solar radio burst is a very important tool to study the characteristics of solar radio burst. However, due to the influence of the instruments noise of the solar radio telescope, external interference, the change of ambient temperature, absorption of clouds and so on, the sensitivity of the observation system will be reduced. In particular, the weak solar radio burst signal is easily submerged by various interference signals. In order to improve the sensitivity of solar radio telescope to observe solar radio burst signals and remove narrow-band interference signals, a spectrum data processing algorithm of spectral subtraction method based on entropy and energy threshold values (SSM-EE) is proposed in this article. There are multiple processing steps of SSM-EE. The first step is to improve the signal-to-noise ratio (SNR) through accumulating and averaging the spectrum data. The second step is to eliminate narrowband interference. The spectral kurtosis algorithm is used to calculate the spectral kurtosis of the narrowband interference signal, and then whether to smooth it is determined according to whether it is greater than the threshold. The third step is to establish a noise model. Calculating the power spectrum entropy and energy entropy of each frame of the processed spectrum data, judge whether it is a noise signal by judging the relationship between the power spectrum entropy and energy entropy and the corresponding threshold. The update model for noise floor is used to obtain the real-time noise floor data. The last step is to eliminate the background noise through spectral subtraction. The new spectrum data are obtained by subtraction method between the original spectrum data and the noise floor data. After a large number of data simulation and verification of the actual observation data, it shows that the method proposed in this article has good practical value.

Real-Time Construction Site Noise Mapping System Based on Spatial Interpolation

Because construction noise is one of the major factors affecting the health and safety of workers and nearby residents, it must be managed appropriately. Thus, this study aimed to map real-time noise information generated during construction using a sensor network and spatial interpolation. An optimal sensor was selected in terms of cost and performance by comparing noise measurements with an officially certified sensor in a semianechoic chamber. Field constraints that required or avoided placing the sensors were identified to secure satisfactory noise-estimation performance without interfering with fieldwork. A noise-estimation model was developed based on spatial interpolation, and its performance was evaluated in an experimental environment. The experiment was conducted in two different cases: the first case with only one noise source and the second case by adding another extra noise source. For each case, the optimal sensor combinations were found by considering the noise-estimation performance and the number of sensors installed. As a result, for the first case, the model showed an accuracy of 96.90% and a root-mean square error (RMSE) of 2.19, whereas the second case showed 97.40% and 2.31%, respectively. The noise information estimated by the model was visualized through simulation software for better field applicability. Detailed spatial and numerical noise information using the proposed system will help practitioners intuitively understand the real-time tendency of noise propagation, contributing to the determination of hot spots where reduction measures should be implemented. This information can also be used to more accurately identify workers continuously exposed to noise above dangerous levels and evaluate their actual cumulative noise exposure causing possible hearing damage.

Building a City with Low Noise Pollution: Exploring the Mental Health Effect Thresholds of Spatiotemporal Environmental Noise Exposure and Urban Planning Solution.

Urban noise pollution and health hazards have become serious social problems and challenges. Noise prevention and control is the most cost-effective health strategy. However, in urban planning and noise control, reliable evidence is still lacking on individual spatiotemporal environmental noise exposure and its mental health effects. This study used real-time noise exposure data and GPS trackers from 142 volunteers aged 18 to 60 years in Guangzhou, and further analyzed the differences in environmental noise exposure and its mental health impact thresholds under individual spatiotemporal behavior. The results showed that the noise exposure of residents under daily activities has obvious differences in time, space and place. Regarding the threshold relationship between noise exposure and mental health, noise exposure at night, work, personal affairs, travel and sleep activities, as well as at home and work had a threshold effect on residents' mental health. Noise thresholds were 60 dB, 60 dB, and about 34 dB at night, during work or at a workplace, and while sleeping, respectively. The optimal sound environment for personal affairs, traveling, and at home was around 50 dB, 55-70 dB, and 45 dB, respectively. The environmental noise exposure assessment and mental health impact threshold analysis based on the spatial and temporal activities of individuals will provide important reference for government management departments in planning and policy formulation.

The new aircraft noise monitoring system introduced at the three airports in the Osaka area

There are three airports in the Osaka area and each of them has been analyzing and evaluating aircraft noise and publishing its results. However, since these three airports are adjacent to each other, the airspace is complicated, and the monitoring range is wide, which caused so much manual work for data review before obtaining an accurate noise impact assessment. This paper introduces a new aircraft noise monitoring system that has been constructed to reduce the human workload and accurately and integrally monitor aircraft noise at the three airports with flight-track measurement. The new system reduces the time for noise analysis and evaluation using artificial intelligence, and it has become possible to publish accurate noise conditions more quickly and at a lower cost. In addition, since it is difficult for an airport operator to obtain air traffic control data, we constructed a system that can monitor the position coordinates of aircraft as recent as 15 seconds ago. By operating this system integrally with the noise system, we can now visually confirm the noise level at the measurement point and the position of aircraft on a map, and publish the real-time noise data online for residents living near the airports.

A feedforward and feedback composite active noise reduction headset based on inverse filter frequency equalization and its DSP system implementation

The principle of active noise reduction headset is to reduce the original noise by generating inverted noise through the loudspeaker. The loudspeaker system will introduce spectrum distortion inevitably. This distortion will increase the complexity of the secondary channel model, which adversely affect identification of the primary channel in the real-time active noise reduction system. To improve the inherent frequency spectrum distortion of the speaker system, this paper proposes a new frequency equalization method based on inverse filtering. This method is applied to a feedforward and feedback compound active noise reduction earphone system, and a new active noise reduction headphone system is obtained. In order to improve the noise reduction effect, the pollution caused by the secondary sound source to the reference signal is compensated in the active noise reduction control scheme of this paper. Simulation and real-time noise reduction experiments based on DSP solution show that this new active noise reduction control system can achieve better noise reduction effects.

A Residual CNN-Based Denoiser for Reliable Recovery of Bit Stream With Applications to Soft Channel Decoding

The noise existent in practical systems degrades reliability performances, especially when delivering data over fast fading channels with the correlated noise. To address this issue, we propose a residual convolutional neural network aided denoiser (ResCNND) to suppress the noise for more reliable information recovery. The intelligent ResCNND is composed by convolutional layers, batch normalization layers and nonlinear activation functions. The ResCNND aided denoiser is then integrated with different types of channel decoders, and we propose a data preprocessing module to embed the statistical characteristics of channel conditions in terms of channel state information (CSI) as inputs to the neural network. Additionally, we propose the loss function by combining the mean square error loss and the normality test loss to simultaneously reduce the noise power and preserve the Gaussian distribution of residual noises. Then the well trained ResCNND can be used to suppress the noise in real time. In simulations, three types of channel codes, including LDPC, Polar and Turbo codes, are considered. The results demonstrate that the proposed ResCNND-aided receiver can effectively improve the signal-to-noise ratio and bit error rate performances with no requirement of perfect CSI, thereby achieving better reliability and robustness performances than benchmark schemes.