System Noise Research Articles

Early Hearing Outcomes and Audiological Experiences With a Novel Fully Implanted Cochlear Implant.

To review audiological experiences and early hearing outcomes from the early feasibility study of a fully implanted cochlear implant. Prospective cohort. Tertiary academic medical center. Three adults (two-male, one female) with bilateral sensorineural hearing loss. Implantation with a fully implanted cochlear implant as part of an early feasibility study. Postoperative unaided and aided pure-tone audiometry, tympanometry, mapping parameters, speech perception, battery life, and quality-of-life assessment. All patients in the early feasibility study of this fully implanted cochlear implant now use their devices regularly. Preoperative and postoperative audiometric measurements showed that their residual hearing in the implanted ear decreased slightly after surgery but was preserved. All patients had type A tympanograms after their transient middle ear effusion resolved. Electrical stimulation levels were comparable to those routinely used in traditional cochlear implants. Two of the three patients use a hearing aid in the implanted ear for additional gain and show significant improvement in speech perception since implantation. Average battery life before recharging is 4 days. All patients are regular users with two showing improvement in quality-of-life metrics after receiving the fully implanted cochlear implant. The patient experience and hearing outcomes from the early feasibility study of a novel fully implanted cochlear implant are detailed in this study and demonstrate ease of operation and daily use by all participants. All patients obtained hearing, but two of three use a hearing aid with their device to overcome unanticipated implant circuitry noise and achieve improved speech perception scores. Current work is focused on reducing this system noise to allow for the device to be used as intended, without a hearing aid.

Linear prediction coefficients correction method for digital speech processing systems with data compression based on the autoregressive model of a voice signal

The problem of distortion of the autoregressive model of the voice signal under the influence of additive background noise in digital speech processing systems with data compression based on linear prediction is considered. In the frequency domain, these distortions are observed in the weakening of the main formants responsible for the intelligibility of the speaker’s speech. To compensate for formant attenuation, it is proposed to modify the parameters of the autoregressive model (linear prediction coefficients) using the impulse response of a recursive shaping filter. Along with the amplitude amplification of the formants, their frequencies remain unchanged to make the speaker’s voice recognizable. The effectiveness of the method was studied experimentally using specially developed software. Based on the experimental results, conclusions were drawn about a significant increase in the relative level of formants in the power spectrum of the corrected voice signal.

A comparison of stochastic and deterministic dynamics of tuberculosis model

Tuberculosis is a deadly infectious disease leading to a major health concern in Southern India while there are major obstacles to controlling its spread. Here, we present a mathematical model with five compartments. We categorize the infected compartment into two subcategories: latent TB-infected individuals and active TB-infected individuals. By introducing white noise in a deterministic system, we formulate a stochastic system. We investigate the model in deterministic and stochastic framework, followed by data calibration using TB infection data from Kanyakumari District in South India from 2019 to 2023. In the deterministic model, we derive the disease-free and endemic equilibrium points, compute the basic reproduction number, and examine their stability. Moreover, we perform sensitivity analysis to evaluate how variations in model parameters affect TB prevalence. In addition, we study the uniqueness of solutions of stochastic model. After that we derive the conditions for disease extinction and stochastic permanence, and execute extensive simulations to capture the variability and randomness in TB transmission dynamics. This study signifies that stochastic dynamics is richer than deterministic one in the way of mitigation TB transmission.

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed behavior. The origin of this cognitive ability, constitutive of general intelligence, remains elusive. Here, we show that moderate levels of computation noise in artificial neural networks promote zero-shot generalization for decision-making under uncertainty. Unlike networks featuring noise-free computations, but like human participants tested on similar decision problems (ranging from probabilistic reasoning to reversal learning), noisy networks exhibit behavioral hallmarks of optimal inference in uncertain conditions entirely unseen during training. Computation noise enables this cognitive ability jointly through "structural" regularization of network weights during training and "functional" regularization by shaping the stochastic dynamics of network activity after training. Together, these findings indicate that human cognition may ride on neural variability to support adaptive decisions under uncertainty without extensive experience or engineered sophistication.

Machine learning for reducing noise in RF control signals at industrial accelerators

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

Multimodality in systems driven by Ornstein-Uhlenbeck noise.

The presence of noise in nonlinear dynamical systems can significantly change their properties. Here, we study the properties of a noise perturbed motion in a single-well potential of |x|n (n>0) type. We explore under what conditions the action of the Ornstein-Uhlenbeck noise induces bimodality of stationary states in static, single-well, power-law potentials. In particular, we inspect the transition from unimodality (n⩽2) to bimodality (n>2). Results of numerical simulations are compared with estimates obtained from the unified colored-noise approximation. Furthermore, we explore the role of a harmonic addition to the general single-well power-law potentials showing its constructive or destructive role.

Retrofit Measures for Aircraft Noise Reduction: Simulation Benchmark and Impact Assessment

DLR, German Aerospace Center, has developed a low-noise retrofit to reduce noise generation of a conventional midrange transportation aircraft, i.e., measures to the airframe and engine exhaust nozzle. Selected measures have been installed onboard of DLR’s flying testbed Advanced Technology Research Aircraft and been subject to a flyover noise campaign. The aircraft in its original configuration and the modified aircraft have been tested and measured in flyover campaigns in 2016 and 2019, respectively. Initial simulation models have been derived from the results and previous component simulations and wind tunnel tests to account for the new reduction measures within DLR’s system noise prediction tool Parametric Aircraft Noise Analysis Module (PANAM). This paper presents the developed measures as selected for the flight test campaigns. An overview of the conducted flight tests is provided. The overall noise simulation process, including aircraft and engine design, flight simulation, and noise prediction, is presented. Simulation results obtained from this process are then compared to the measured levels from the flight campaigns. Estimated simulation uncertainties from PANAM can now be directly compared to differences between measured and simulated levels. Finally, a parameter study is conducted in order to assess the reduction potential of the novel retrofit measures along different simulated flight procedures. Spatial noise distribution in sound exposure level contours is assessed.

Os noise mitigations for benchmarking web browser execution environment performance

Web browsers have almost since birth sought to host and run feature-rich, compute-intensive and complex applications, within their execution environments (EEs), thereby achieving performance akin to native desktop applications. Studies that have proposed performance improvements to these Web browser EEs together with their respective benchmarks have endeavoured to use proven methodologies with which they confirmed the scientific rigour of those benchmarks, but have often overlooked operating system (OS) noise and the negative impact that it has on benchmarks. This paper identifies the various types of OS noise that exist within a typical Intel-based computer system that utilises a Linux-based OS. While also reviewing what related studies have done to mitigate against them. A series of mitigations were then proposed for the Linux-based system that was then benchmarked using the PolyBench/C benchmarking suite. Whilst not all OS noise was mitigated against, our results confirm that the OS noise mitigations that were provided, significantly reduced OS noise. Further significance of this study is that a more controlled benchmarking environment is made available, due to focusing the CPU processing almost entirely on the benchmarks only, while also enhancing the precision and accuracy of the benchmarks. In this study, we lay down a foundation that will make future Web browser EE as well as any other type of computer software benchmarking more precise and accurate, thereby improving the scientific rigour of those benchmarks.

Frequency noise suppression of the high-power pumping laser in the SERF atomic magnetometer

Abstract A high-sensitivity SERF atomic magnetometer based on a noise-suppressed master oscillator power amplifier (MOPA) system is demonstrated. The spectrum and frequency noise of the laser are measured and it is proved that the frequency noise in the MOPA system is affected by the amplified spontaneous emission (ASE) stimulated from the power amplifier. The frequency noise of the MOPA system is decreased by filtering out the ASE background in the spectrum through a bandpass filter. The sensitivity of the SERF atomic magnetometer is improved by using the frequency noise-suppressed MOPA system as a pumping laser. This method can also be applied to other precision measurement fields based on the MOPA systems that have high requirement for frequency noise of laser.

Open-loop narrowband magnetic particle imaging based on mixed-frequency harmonic magnetization response.

Magnetic particle imaging (MPI), a radiation-free, dynamic, and targeted imaging technique, has gained significant traction in both research and clinical settings worldwide. Signal-to-noise ratio (SNR) is a crucial factor influencing MPI image quality and detection sensitivity, and it is affected by ambient noise, system thermal noise, and the magnetization response of superparamagnetic nanoparticles. Therefore to address the high amplitude system and inherent thermal noise present in conventional MPI systems is essential to improve detection sensitivity and imaging resolution. This study introduces a novel open-loop, narrow-band MPI signal acquisition system based on mixed-frequency harmonic magnetization response. Allowing superparamagnetic nanoparticles to be excited by low frequency, high amplitude magnetic fields and high frequency, low amplitude magnetic fields, the excitation coil generates a mixed excitation magnetic field at a mixed frequency of 8.664 kHz (f H + 2f L ), and the tracer of superparamagnetic nanoparticles can generate a locatable superparamagnetic magnetization signal with rich harmonic components in the mixed excitation magnetic field and positioning magnetic field. The third harmonic signal is detected by a Gradiometer coil with high signal-to-noise ratio, and the voltage cloud image is formed. The experimental results show that the external noise caused by the excitation coil can be effectively reduced from 12 to about 1.5 μV in the imaging area of 30 mm × 30 mm, which improves the stability of the detection signal of the Gradiometer coil, realizes the detection of high SNR, and makes the detection sensitivity reach 10 μg Fe. By mixing excitation, the total intensity of the excitation field is reduced, resulting in a slight improvement of the resolution under the same gradient field, and the spatial resolution of the image reconstruction is increased from 2 mm under the single frequency excitation (20.7 kHz) in the previous experiment to 1.5 mm under the mixed excitation (8.664 kHz). These experimental results highlight the effectiveness of the proposed open-loop narrowband MPI technique in improving signal detection sensitivity, achieving high signal-to-noise ratio detection and improving the quality of reconstructed images by changing the excitation magnetic field frequency of the excitation coil, providing novel design ideas and technical pathways for future MPI systems.

Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation

redict the sound quality of dual-phase Hy-Vo chain transmission system noise using a small sample size. Noise acquisition tests are conducted under various working conditions, followed by subjective evaluations using the equal interval direct one-dimensional method. Objective evaluations are performed using the Mel-frequency cepstral coefficient (MFCC). To understand the impact of the MFCC order and the frame number on prediction accuracy, MFCC feature maps of different specifications are analyzed. The dataset is expanded threefold using fuzzy generation with an appropriate membership degree. The convolutional neural network (CNN) is developed, utilizing MFCC feature maps as inputs and evaluation scores as outputs. Results indicate a positive correlation between the frame number and prediction accuracy, whereas higher MFCC orders introduce redundancy, reducing accuracy. The proposed CNN method outperforms three traditional machine learning approaches, demonstrating superior accuracy and resistance to overfitting.

Research on high dynamic pixel structure based on adaptive integral capacitor

Infrared image sensing technology has attracted wide attention due to its advantages such as being unaffected by the environment, good target recognition, and strong anti-interference ability. However, with the improvement of the integration of infrared focal planes, the constraints between the dynamic range, noise, and full-shade of the optoelectronic system are particularly prominent. Therefore, in order to solve the contradiction between noise in weak light and full-shade capacity in strong light, in a 5 T infrared pixel circuit, the relationship between the capacitance value and voltage of the inverted MOS capacitor in a specific voltage range is used to make the integral capacitance of the infrared image sensor automatically change from 6.5 fF to 37.5 fF. A high dynamic pixel structure based on adaptive integral capacitance is proposed. Based on 55 nm CMOS process technology, 12288 × 12288its performance parameters are studied in a pixel-scale infrared sensor. The research results show that 5.5m × 5.5mthe small-size pixel has a large full-shade capacity of 1.31 Me- and a variable conversion gain, a noise of less than 0.43 e, and a dynamic range of more than 130 dB.

Loaded tooth contact analysis for helical gears with surface waviness error

Meshing characteristics are crucial for the transmission performance of vibration noise and strength of gear systems. Gear transmission error has been acknowledged as a prominent excitation in vibration systems, but the surface waviness deviations that directly affect it in contact analysis are generally ignored. This study proposes a comprehensive model for quasi-static analysis of tooth surfaces with waviness error. The model reconstructs the actual tooth surface in reverse based on the gear Fourier measurement principle. Instead of solving the meshing equations to pre-assume contact position, the problem of normal contact between surfaces with deviations is solved by an innovative multiscale grid and a local iterative numerical algorithm. Combining the finite element numerical method and surface integral of the Boussinesq solution determines gear deformation and develops a loaded contact model. The validity of the model is verified through numerical examples. Furthermore, the proposed model is applied to discuss the tooth surfaces with different waviness amplitudes, frequencies, and distribution angles for the first time. It explains the interaction between local microscale and global scale introduced by waviness error on tooth surfaces and reveals the mechanism of the waviness error on meshing performance.

Reduction in Optical Feedback Noise of Atomic Force Microscopy by High-Frequency Current Modulation

Introduction: With the advancement of technology, nanotechnology has emerged as a prominent research field. The Atomic Force Microscope (AFM) serves as a vital tool in nanoscience and technology, playing an indispensable role across various domains. Method: However, in the AFM photoelectric sensing system, the optical feedback noise from the beam deflection method can lead to inaccuracies in signal identification and analysis, impacting the accuracy and reliability of AFM measurements. To mitigate this interference, this study proposes a highfrequency current superposition system aimed at reducing optical feedback noise. By superimposing high-frequency currents, the semiconductor laser transitions from a single-mode to a multi-mode operational state, thereby altering its mode of operation and consequently reducing optical feedback noise during sensing. Initially, mathematical modeling and simulation analysis were conducted on the high-frequency current superposition noise reduction system to examine the impact of high-frequency current on the intensity noise of the semiconductor laser. Subsequently, the design of the high-frequency current superposition noise reduction system was outlined, encompassing the development of a constant current drive circuit, a voltage-controlled oscillator circuit, and a biasing circuit. Finally, the high-frequency current superposition noise reduction system underwent testing. Results: During the high-frequency current superposition noise reduction test, the system's Signal-to-Noise Ratio (SNR) increased from 15.57 dB to 17.81 dB, and the system's noise peak-to-peak value decreased from 8 mV to 6 mV. Analysis of the superposition frequency and noise reduction effect determined the optimal superposition frequency of the AFM photoelectric sensor system to be 400 MHz. Characterization experiments of the high-frequency superposition noise reduction system compared the clarity of Escherichia coli images before and after noise reduction. Conclusion: The aforementioned experimental results demonstrated that high-frequency current superposition is an effective noise reduction method capable of mitigating optical noise in the AFM photoelectric sensing system.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

Effect of tooth surface pitting on dynamic characteristics under mixed lubrication

The variation of tooth meshing stiffness and frictional characteristics caused by tooth surface pitting increases vibration and noise in the gear transmission system. Assuming a smooth tooth surface and a homogeneous material in studying the gear contact and dynamics is insufficient to reveal the evolution of dynamic contact performance caused by pitting faults. In the present study, we established a coupling analysis model for pitting faults to assess the effect of the tooth surface micro-morphology. The mesh stiffness and static transmission error of gears with randomly distributed pittings were calculated, and the vibration response of gears with different pitting degrees was resolved. Finally, the effectiveness of the applied dynamic response theory analysis was experimentally verified using the constructed pitting fault simulation test bench. The results indicate that pitting failure decreases the effective load-bearing capacity of gear teeth. The mesh stiffness gradually but significantly decreased with the pitting degree, also exhibiting fluctuating values. Pitting faults induced significant periodic features in the vibration response of gear transmission systems, showing complex sidebands around the meshing frequency and its harmonics.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

Virtual source total focusing method for surface defects using leaky Rayleigh waves

Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves.

Indiscernible noise determination of a military system equipped with an internal combustion engine

The main objective of this paper is to explore the non-detectability criteria of military systems and to determine the indiscernible noise of military systems equipped with internal combustion engines. Non-detectability is one of the critical requirements evaluated for military defense systems and it is defined strictly in MIL-STD-1474E standard. Furthermore, this standard specifies the limits of sound pressure levels conservatively for 1/3 octave bands. Though, the standard does not define indiscernible or inaudible noise levels for events in the presence of background noise. It is also not explicitly well-defined in the existing literature. This study presents a comparison of non-detectability distance defined in MIL-STD-1474E standard and indiscernible noise of a military system extracted from the measured octave band sound levels, which are processed based on sound propagation theory. In this study, several parameters such as atmospheric absorption, geometric spreading, ground effect, terrain type, obstacle existence are included to determine the indiscernible noise level of the military system of interest. The results are compared with the non-detectability distance limits given in the MIL-STD-1474E standard, and it is observed that the proposed method of indiscernible noise, which is defined as extracted sound level should be lower than the background noise, is well-correlated.

An efficient predicting approach for blade frequency line-spectrum noise of marine centrifugal pumps

The paper introduces an innovative and efficient calculation method for the spectral flow noise of the pump system based on the spatial distribution dipole, which predicts the blade frequency radiation noise at the pipe flange of the pump system under different operating conditions. First, the unsteady flow simulation of the centrifugal pump is calculated using computational fluid dynamics (CFD). Then, the pressure on each segmented blade surface is decomposed into lift and drag forces. After that, each segment is decomposed into several dipole point sources based on the blade noise theory. Finally, the simplified dipole point sources are considered sound sources, and the blade frequency line spectrum noise at the pipe flange of the pump system is predicted using acoustic-structure coupling. The results indicate that the simplified method provides high prediction accuracy while significantly improving the computational efficiency of blade frequency line spectrum noise calculation for centrifugal pumps.