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There are several ways that signalers could improve the efficiency of their acoustic signals to counteract the constraints of background noise.
The background noise may be produced by the sea ambient noise or the self-noise of the sonar platform.
Proceedings ArticleDOI
Vincent Guillet, Guy Lamarque 
28 Mar 2010
16 Citations
This provides a new background noise function verified and useful for low and high data rate technologies and for further noise studies.
Using this technique, aircraft noise can be separated from the background noise providing a reference value of the aircraft sound pressure level for the sound level meter measurement.
One mitigating factor in the presence of background noise is earphone type.
Furthermore, this technique can effectively reduce the noise background.
Open accessProceedings ArticleDOI
Heinrich W. Lollmann, Peter Vary 
19 Apr 2009
33 Citations
This paper proposes a new speech enhancement algorithm for the suppression of background noise and late reverberation without a priori knowledge.
The background noise equation was verified using both observational lidar data and a simulated signal, indicating that it provides a reasonable measure of background noise levels in lidar data.
Hence, the the effect due to background noise can be reduced.
From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise.

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How to improve modeling resiliance with artifical noise in neural networks?
5 answers
To enhance modeling resilience in neural networks using artificial noise, a novel noise injection-based training scheme has been proposed. This method involves estimating gradients for both synaptic weights and noise levels during stochastic gradient descent training, optimizing noise levels alongside synaptic weights. By incorporating noise into the network, the model's robustness against adversarial attacks can be significantly improved, as demonstrated in experiments on MNIST and Fashion-MNIST datasets. Additionally, a method has been introduced to reduce label error rates and improve dataset quality by addressing noise condensity issues through a statistical probability-based label flipping process, enhancing the overall performance of neural network models trained on corrected datasets. These approaches collectively contribute to fortifying neural network models against various forms of noise and improving their overall resilience.
Comparison on levels of occupational noise between authorised service centre and general workshop?
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A comparison of occupational noise levels between an authorized service center and a general workshop reveals significant differences in exposure and potential impacts. The use of digital signal processing in noise measurement instruments has enhanced measurement capabilities, leading to confusion in reporting values under different standards. Studies on threshold hearing levels of workers exposed to varying noise levels show increased hearing losses with higher noise exposure, particularly at 4000 Hz frequency. Research on work stress in electronic companies highlights higher stress levels in clean workshops compared to ordinary ones, especially among males, indicating a potential correlation with occupational noise levels. This emphasizes the importance of understanding and addressing occupational noise exposure variations between different work environments.
Am Radio Kit Reciever?
4 answers
An AM radio receiver kit typically includes essential components like front-end circuits for signal conversion, RF amplifiers, mixers, bandpass filters, and detectors for noise elimination. These kits may incorporate integrated circuits (ICs) with built-in amplifiers and filters to enhance performance and reduce noise interference. The tuning circuit in such receivers consists of tuning coils and variable capacitance diodes, while local oscillator circuits generate oscillation signals for frequency mixing. Double superheterodyne AM radio receivers utilize multiple intermediate frequencies and mixer circuits to detect audio signals effectively, often integrating ICs for efficient signal processing. Overall, an AM radio receiver kit combines various circuits and components to facilitate the reception and demodulation of AM radio signals for audio output.
How ambulatory blood pressure monitor absorb noise compared to OBPM?
5 answers
Ambulatory blood pressure monitors (ABPM) utilize various noise absorption techniques compared to oscillometric blood pressure monitors (OBPM). ABPM devices, such as those described in Context_1 and Context_2, employ advanced signal processing methods to filter out background noise effectively. For instance, ABPM systems incorporate high-pass and low-pass filters to enhance signal quality and reduce noise interference. Additionally, ABPM devices, like the one in Context_4, analyze noise signals based on specific waveform parameters to identify Korotkoff noises, which are crucial for accurate blood pressure calculation. These noise absorption mechanisms in ABPM devices contribute to more reliable and precise blood pressure measurements compared to traditional OBPM devices.
Can non-stationarity be effectively addressed through preprocessing techniques in ECMs?
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Non-stationarity in ECMs can be effectively addressed through preprocessing techniques. Various methods exist to handle non-stationarity, such as the Causal-Origin REPresentation (COREP) algorithm, which focuses on tracing the causal origin of non-stationarity to enhance policy learning. Additionally, in the context of time series prediction, suitable transformation methods for non-stationary time series have shown significant improvements in prediction accuracy, with some methods providing over 30% enhancements in half of the evaluated time series. These preprocessing techniques play a crucial role in mitigating the challenges posed by non-stationarity in ECMs, ensuring more accurate and reliable modeling and prediction outcomes.
What can students do to address noise in terms of Operations of Algebraic Expressions?
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To address noise in terms of Operations of Algebraic Expressions, students can apply noise filtering techniques like the high gain observer (HGO) approach, which is effective in controlling perturbed systems. Additionally, for recognizing mathematical expressions, students can utilize segmentation methods involving pre-processing, segmentation, and post-processing stages to reduce noise effects and improve recognition outcomes. Moreover, in signal analysis problems related to noise, the concept of noise operators can be employed to analyze signal in noise scenarios, especially in radar and sonar applications. By implementing these strategies, students can enhance their understanding and accuracy when working with algebraic expressions in the presence of noise.
What are the effects of refrigerant charge amount on the vibration and noise of refrigeration systems?
5 answers
The refrigerant charge amount significantly impacts the vibration and noise levels in refrigeration systems. Studies have shown that different refrigerants lead to varying vibration and noise performances in sealed reciprocating compressors, with values closely related to the type of refrigerant used. Additionally, the cooling performance of a transcritical cycle is greatly affected by the refrigerant charge amount, with undercharging conditions leading to increased expansion losses and higher noise levels. Furthermore, optimizing the refrigerant charge amount is crucial for heat pump systems, as it influences the condensing pressure, subcooling, and ultimately the coefficient of performance (COP) of the system, highlighting the importance of charge optimization for system efficiency and noise reduction.
How do changes in the concentration of signaling molecules affect the stochastic behavior of cellular processes?
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Changes in the concentration of signaling molecules can significantly impact the stochastic behavior of cellular processes. The presence of molecular noise in signaling networks can lead to deviant effects, altering the average behavior of biological systems. Additionally, fluctuations in the concentration of active signaling molecules due to deactivation processes during signal transduction can introduce spatio-temporal variations, especially in asymmetric cellular architectures. Stochastic fluctuations in cellular reactions, particularly in small systems like cells, can generate fluctuations in the number of molecules, affecting signal amplification and accuracy in cellular processes such as chemotaxis. Furthermore, the stochastic nature of biochemical processes in whole-cell biochips can result in a low signal-to-noise ratio, impacting the detectability of signals and leading to relatively large noise disturbances during information extraction from cells.
Why use gaussian noise in cGAN model?
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Gaussian noise is utilized in conditional Generative Adversarial Network (cGAN) models for various reasons. Firstly, the application of additive Gaussian noise in GANs can enhance convergence speed. Secondly, Gaussian noise is crucial for simulating real-world noise scenarios accurately, especially in the context of image denoising using deep learning models. Additionally, Gaussian noise models play a significant role in evaluating the impact of factors like inter-channel stimulated Raman scattering on optical Kerr nonlinearity in transmission systems, providing a more accurate representation of signal power profiles along the link. Lastly, Gaussian noise can also be leveraged to address issues related to posterior approximation accuracy in generative models like Variational Autoencoders, offering a principled approach to handling uncertainty in posterior approximation.
Overview of Hearing Loss in Teenagers?
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Hearing loss in teenagers is a growing concern due to various factors such as exposure to high volumes from headphones and environmental noise like concerts and discos. Studies show that hearing impairment in school-aged children and adolescents can have significant impacts on academic success and quality of life, with prevalence ranging from 0.88% to 46.70%. Adolescents with deafness face unique challenges, including limited access to information and biased attitudes, affecting their self-esteem and identity development. Research on 13-year-old adolescents in the Netherlands revealed a prevalence of sensorineural hearing loss (SNHL) at 6.4% and noise-induced hearing loss (NIHL) at 12.4%, with SNHL decreasing but NIHL increasing between ages 9 and 13. Early detection, prevention strategies, and support systems are crucial in addressing hearing loss issues in teenagers.
What is the mathematical model used to calculate the expected gain for CRPA in GNSS applications?
10 answers
The mathematical modeling for calculating the expected gain of Controlled Reception Pattern Antennas (CRPA) in Global Navigation Satellite Systems (GNSS) applications involves several key considerations, as highlighted across various research contexts. Firstly, the design and optimization of CRPA arrays, as discussed by Byun et al., involve the use of genetic algorithms in conjunction with electromagnetic (EM) simulation tools like FEKO to optimize antenna parameters for dual-band GPS applications, which directly impacts the expected gain by ensuring optimal antenna element configuration and placement on a circular ground platform. This approach is complemented by the work of Maloney et al., who leverage fragmented aperture antenna design and manufacturing, utilizing Finite-Difference Time-Domain (FDTD) simulations combined with genetic optimization to develop miniaturized GPS antennas suitable for CRPA arrays, indicating a methodological framework for gain calculation through design optimization. Lee et al. propose enhancing radiation gain at low-elevation angles through a slot-loaded CRPA array with ground slot radiators, suggesting that the insertion of rectangular slots at the outer perimeter of the platform can improve low-elevation gain, which is a critical factor in the mathematical model for expected gain calculation. Bartone and Stansell explore the performance of a multi-circular ring CRPA configuration, comparing the antenna array factor of a 127-element CRPA to a 7-element CRPA, indicating that directivity, beamwidth, and sidelobe suppression metrics are integral to modeling expected gain. Furthermore, Givhan and Martin's investigation into direction of arrival (DOA) estimation methods using CRPA on post-correlated GNSS signals introduces the application of algorithms like MUSIC, ESPRIT, and carrier phase differences, which, while primarily focused on DOA estimation, indirectly inform the modeling of expected gain by highlighting the importance of signal processing techniques in enhancing signal-to-noise ratio (SNR) and thereby the effective gain of CRPA systems. The structure of CRPA array antennas, as described by Seo Seung Mo, utilizing a least mean square (LMS) algorithm based on space-time adaptive processing (STAP) for arranging antennas in a limited space, underscores the significance of array configuration and adaptive processing in calculating expected gain. Lastly, Gupta et al. present a novel reduced size CRPA with excellent gain characteristics and right-hand circular polarization (RCP) performance, emphasizing the role of antenna element design, specifically tightly wound triangular spirals, in achieving desired gain levels across all GNSS frequency bands. In summary, the mathematical model for calculating the expected gain of CRPA in GNSS applications is multifaceted, incorporating elements of antenna design optimization, signal processing algorithms, array configuration, and adaptive interference suppression techniques. These components collectively contribute to a comprehensive framework for gain calculation, ensuring CRPA systems meet the stringent requirements of GNSS applications.