Noise Parameters Research Articles

(Invited) In-Depth DC and Low Frequency Noise Characterization of Nanosheet FETs at Room and Cryogenic Temperatures

In this work, an in-depth static and low frequency noise characterization of nanosheet FETs, consisting of two vertically stacked silicon channels per device, is performed. A comparison of the performances from n- versus p-channel FETs operated at 300 K and 78 K, in terms of static and low frequency noise parameters variability, is also discussed. The main electrical parameters are estimated from the I-V characteristics using access resistance robust methodologies. An additional criterion demonstrating that the extracted parameters are not influenced by the second mobility attenuation factor is proposed. The low frequency noise studies prove that the dominant flicker noise mechanism is linked to correlated carrier number and mobility fluctuations, with additional access resistances noise contribution in very strong inversion. The importance of the considered total flicker noise model on the estimation of the Coulomb scattering coefficient is highlighted.

Stochastic bifurcations induced by Lévy noise in a fractional trirhythmic van der Pol system

Trirhythmical nature has aroused considerable interest in describing dynamic behaviors of a fractional self-sustained system. In this paper, the fractional trirhythmic system is considered and a bifurcation analysis in a stochastic fractional trirhythmic self sustained system subjected to Lévy noise perturbation is presented. The fractional electronic circuit has been used to model the system and the oscillations are described by a nonlinear fractional differential equation to show a new bifurcation parameter. Then, based on the minimum mean square error principle, the fractional derivative term is found to be equivalent to the linear combination of the damping force and restoring force, and the original system is further simplified to an equivalent integer order system. Using the predictor–corrector simulation method, it is shown a good agreement between the fractional and the equivalent integer order system. The detailed parameter space study reveals that the multi-rhythmic properties of the oscillation of this system can be efficiently controlled by fractional order parameter and Lévy noise parameters. Finally, the analytical solutions are validated by numerical results of the Monte Carlo simulation of the original fractional modified van der Pol oscillator. The trirhythmic region can be detected in the parameter space (α,β,γ,δ) by choosing an approximate fractional order. The stability index and the noise intensity can in any case govern the dynamics of the self sustained system, but regulating the skewness parameter cannot bring about transitions between unimodal, bimodal and trimodal in this multi-rhythmic system. More bifurcations appear by adjusting the fractional order in this system. These results may be conducive to further exploring bifurcations in the real-world applications. This study sheds new insight lights on the understanding nontrivial effects of fractional derivative on a trirhythmic self sustained system.

Time delay feedbacks enhanced bifurcation in the birhythmic fractional self-sustained system subjected to correlated noise

The response of the birhythmic fractional Van der Pol (VdP) self-sustained oscillator driven both by time delay feedbacks and correlated noise is considered. We mainly focus on the effects of time-delay feedback parameter and fractional damping on stochastic properties of the birhytmic system, such as P-bifurcation. The minimum mean-square error is used to reduce the system to its equivalent integer-order nonlinear stochastic equation. An analytical approximation is adopted to obtain the system behavior and it is compared with numerical simulation. The delay self-control feedbacks are transformed into state variables without delay by means of the harmonic approximation. The maximum extension of the control parameters to have a birhythmic solution can be thus determined by an appropriated choice of the fractional order and delay time parameters. In particular, when the fractional damping is lowered below 2, the birhythmic region disappears. However, the influence of the fractional damping changes as a function of the correlation noise parameter. Instead, the delay feedback and the fractional derivative are not so effective on the transition. The input delay and fractional derivative can be greatly enhanced by the nonlinear interaction. Specifically, the amplitude of the induced oscillations can be much larger than that of the input fractional with the cooperation of an appropriated time delay and nonlinear terms. It is then shown that the time delay feedback parameters and the fractional damping also act as bifurcation parameters.

Evidential value of voice quality acoustics in forensic voice comparison

Voice recordings in forensic voice comparison casework typically involve speech style mismatch and are separated by days or weeks, but studies that aim to empirically validate the evidential value of speech features rarely include systematic comparisons on contemporaneous vs. non-contemporaneous recordings and match vs. mismatch in speech style. This study addresses this gap and focuses on the acoustics of laryngeal voice quality, since voice quality has been reported to be one of the most popular and useful features for forensic voice comparison. 75 male speakers aged 18–45 were selected from a forensically-oriented database of Australian English speakers in Sydney/New South Wales. The evidential strength of a number of spectral tilt and additive noise parameters were tested under the Bayesian likelihood-ratio framework. Results show that system performance using these parameters as input were stable across 50 replications. When speech style is controlled for, VQ parameters yielded promising results and better system validity was achieved when using more VQ parameters. However, they offered limited speaker-discriminatory value when speech style mismatch is involved, and non-contemporaneous recordings only led to a small decline in performance. Overall, forensic practitioners should be cautious when using spectral tilt measures and additive noise measures as speaker discriminants in forensic casework.

Respiratory rate estimation from multi-channel signals using auto-regulated adaptive extended Kalman filter

Background:Respiration rate (RR) is a major cause for false alarms in intensive care units (ICU) and is primarily impaired by the artifact prone signals from skin-attached electrodes. Catheter-integrated esophageal electrodes are an alternative source for multi-channel physiological signals from multiple organs such as the heart and the diaphragm. Nonlinear estimation and sensor fusion are promising techniques for extracting the respiratory activity from such multi-component signals, however, pathologic breathing patterns with rapid RR changes typically observed in patient populations such as premature infants, pose significant challenges. Methods:We developed an auto-regulated adaptive extended Kalman filter (AA-EKF), which iteratively adapts the system model and the noise parameters based on the respiratory pattern. AA-EKF was tested on neonatal esophageal observations (NEO), and also on simulated multi-components signals created using waveforms in CapnoBase and ETNA databases. Results:AA-EKF derived RR (RRAA-EKF) from NEO had lower median (inter-quartile range) error of 0.1 (10.6) breaths per minute (bpm) compared to contemporary neonatal ICU monitors (RRNICU): −3.8 (15.7) bpm (p < 0.001). RRAA-EKF error of −0.2 (3.2) bpm was achieved for ETNA wave forms and a bias (95% LOA) of 0.1 (−5.6, 5.9) in breath count. Mean absolute error (MAE) of RRAA-EKF with Capnobase waveforms, as median (inter-quartile range), at 0.3 (0.2) bpm was comparable to the literature reported values. Discussion:The auto-regulated approach allows RR estimation on a broad set of clinical data without requiring extensive patient specific adjustments. Causality and fast response times of EKF based algorithms makes the AA-EKF suitable for bedside monitoring in the ICU setting.

Transmission Line Audible Noise Prediction Based on CNN-BiLSTM-Attention Method

High-voltage transmission lines’ audible noise parameters are impacted by a variety of multidimensional elements. in order to better the accuracy of audible noise prediction and effectively utilize the time-series properties in the observed audible noise data. In this paper, we propose a combined model of a convolutional neural network (CNN) and a bidirectional long and short-term memory network (BiLSTM)-based attention mechanism based on feature filtering for transmission line audible noise prediction. Firstly, using the transmission line real-world audible noise data as the dataset, the multidimensional factor time series parameters are optimally filtered, and high correlation feature vectors are extracted by using CNN. Secondly, the extracted feature vectors are fed into the BiLSTM for training and prediction, and the prediction performance is further improved by introducing an attention mechanism at the BiLSTM end so that the model focuses on learning more important data features. Finally, the prediction analysis using actual recorded audible noise data from a 500 kV AC transmission line in Sichuan Province demonstrates that the combined CNN-BiLSTM-Attention model suggested in this paper has a higher prediction accuracy than the BiLSTM, CNN-BiLSTM, and BiLSTM-Attention models.

Water Table and Permeability Estimation From Multi‐Channel Seismoelectric Spectral Ratios

AbstractRecent developments in predicting and interpreting seismoelectric (SE) signals suggest a great potential for studying near‐surface hydrogeological properties, particularly in the vadose zone. Previous studies have revealed that the SE spectral ratios obtained from earthquake‐triggered SE data contain valuable hydrogeological information concerning porous media (e.g., permeability, porosity, fluid viscosity, and salinity). This study introduces Multi‐Channel SeismoElectric Spectral Ratios (MC‐SESRs) by considering an active seismic source acting on the ground surface. The frequency‐ and saturation‐dependent excess charge density is adopted to calculate the cross‐coupling coefficients. Applying a supervised learning task based on a flat neural network, the so‐called “broad learning (BL)” model, to map and extract the features of MC‐SESRs data, we seek to determine the permeability and the water table depth. Our results indicate that (a) MC‐SESRs are sensitive to the water table depth and permeability; (b) using more traces of SESRs data for inversion can increase accuracy; and (c) the changing water table can be rapidly determined by the MC‐SESRs by resorting to the BL inverse model, and it can attain an excellent accuracy while disturbed by data noise and misspecified model parameters (e.g., porosity and permeability) with errors of up to 20%. The proposed MC‐SESRs inversion has potential applications for non‐invasive monitoring in shallow porous media (e.g., frost thawing and geothermal upwelling).

Atmospheric CW S-Lidars with Si/InGaAs Arrays: Potentialities in Real Environment

The article proposes a methodology for analyzing the performance of S-lidars (S comes from Scheimpflug) as a new class of environmental remote sensors operating under conditions of wide variability of optical weather and sky background brightness. The novelty of the problem statement, the methods used and the results obtained are determined by their application to laser sensing systems with unconventional design principles and the consequent need to revise the traditional ways of assessing their potential capabilities. The research method is based on a dimensionless-parametric approach, which allows comparing phenomena and systems of different scales and combining complementary characteristics and parameters. Effects of the dimensionless optical weather factor on lidar potential are shown being investigated under various environmental conditions, from the clear atmosphere through haze and mist to fog when probing in Vis/SWIR spectral bands and using Si/InGaAs detector arrays. It is shown exactly how and to what extent the significant differences in their spectral sensitivity and internal noise parameters are susceptible to the wide spectral and energy variability of the sky background brightness observed at very different angles to the Sun. A detailed analysis of the two most important influencing factors within the system, “S-Lidar instrument + Optical weather + External background source”, taking into account their wide variability, allowed us to describe their joint nonlinear influence and, thus, to anticipate the imposed limitations. The proposed dimensionless-parametric concept for predicting the potential capabilities of S-lidars with Si/InGaAs arrays is aimed at expanding applications of this rapidly developing class of remote sensors in a wide variety of environments.

Evaluation of Information Indicators of Quantization Noise Generated by Speech Signal Analogue-Digital Conversion

The paper presents a technique for estimating the information parameters of the quantization noise generated by the analog-to-digital conversion of the measuring signal . An experiment and algorithm descriptions are presented to confirm the correctness of the method for processing and extracting the information parameters of the signal and quantization noise , in which a connecting cable is used instead of a measuring antenna . The measurement results are shown in the form of graphs of the dependences of the quantization noise extraction time by accumulation on influencing factors and controlled parameters , which demonstrate the limiting values of the extraction of information parameters of the quantization noise of the analog-to-digital conversion of the measuring signal . Confirmation of the hypothesis of estimating the information parameters of the quantization noise is obtained .

Determination of Bayesian Cramér–Rao Bounds for Estimating Uncertainties in the Bio-Optical Properties of the Water Column, the Seabed Depth and Composition in a Coastal Environment

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds.

Noise and linearity analysis of recessed-source/drain junctionless Gate All Around (Re-S/D-JL-GAA) MOSFETs for communication systems

Noise becomes a critical performance for any electronic component when it is being used for communication systems, it makes MOSFETs less effective at performing their functions in a nanoscale regime. This paper proposed two devices namely, Re-S/D-JL-GAA and Re-S/D-GD-JL-GAA MOSFETs, and examines the noise parameters such as MNF, ACF, CCF, and Real Z0. In this study, the noise and linearity performance has been demonstrated for the mentioned devices at 20 nm channel length. All the noise parameters in device cases have been simulated at the frequency of 1 THz. The result determines that this device (Re-S/D-JL-GAA) applicable for the analog, RF low noise performance because the noise parameter is minimum compared to other devices. Here linearity parameters have been analyzed and the results reveal that the characteristics of device Re-S/D-GD-JL-GAA MOSFET are most significant compared to device cases. Hence it is a preferred device for communication applications compared to other devices.

Investigation of exhaust emissions and performance of a diesel engine fueled with preheated raw grape seed oil/propanol blends

The enrichment of alternative fuels with oxygen in diesel engines is one of the influential techniques utilized to improve performance and lower emissions. This work analyzed the influences of grape seed oil-propanol fuel mixture, which can be an alternative to diesel fuel, on diesel engine. Grape seed oil (GSO), which was used raw, was extracted from grape seeds by cold pressing method. In order to improve physical and chemical attributes of fuel mixtures, preheating process was applied. The engine tests were realized in an air cooled single cylinder diesel engine. GSO was stirred with diesel fuel in volumes of 10% and 30% and also 5% propanol was added. In this study, CO, NOX, smoke, HC, BSFC, EGT, BTE, vibration and noise parameters of diesel engine were taken into consideration. Moreover, thermal images of the engine were also recorded. The results indicated that BSFC, CO, smoke and HC diminished but NOX, BTE, EGT, vibration and noise increased in the preheated and propanol added test fuels. Preheating process for grape seed oil ensured a convenient fuel flow and decreased the oil viscosity and propanol addition contributed to the enrichment of the fuel in terms of oxygen.

Generalized stochastic resonance in a time-delay fractional oscillator with damping fluctuation and signal-modulated noise

In this study, we propose a time-delayed fractional oscillator (FO) subjected to damping fluctuation and signal-modulated noise, and investigate the generalized stochastic resonance (GSR) behaviors. By using (fractional) Shapiro–Loginov formula and Laplace transform, we obtain the first-order moment of system stationary state response, and the output amplitude gain (OAG). Based on the analytical results, it is observed that the GSR behaviors widely exist in the system, and they can be effectively controlled by the system parameters, including driving frequency, noise parameters, fractional order and time delay. It is also demonstrated that fractional order and time delay are two effective dimensions to regulate the GSR intensity under different parameter conditions, and the diversity of GSR behaviors mainly depends on the fractional order.

Ensemble Classification With Noisy Real-Valued Base Functions

In data-intensive applications, it is advantageous to perform partial processing close to the data, and communicate intermediate results to a central processor, instead of the data itself. When the communication or computation medium is noisy, the resulting degradation in computation quality at the central processor must be mitigated. We study this problem for the setup of binary classification performed by an ensemble of base functions communicating real-valued confidence levels. We propose a noise-mitigation solution that optimizes the transmission gains and aggregation coefficients of the base functions. Toward that, we formulate a post-training gradient-based optimization algorithm that minimizes the error probability given the training dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our approach on real data.

Distinguishing a stochastic gravitational-wave signal from correlated noise with joint parameter estimation: Fisher analysis for ground-based detectors

Search sensitivity to a stochastic gravitational-wave background (SGWB) is enhanced by cross-correlating detector signals. However, one of the most serious concerns is the environmental noise correlated between detectors. The global electromagnetic fields on the Earth, known as Schumann resonances, produce the correlated noise through the instrumental magnetic couplings. In this paper, we study the detectability of a SGWB in the presence of the correlated magnetic noise, using the Fisher analysis based on the analytical model of the correlated magnetic noise. We find that there is no significant degeneracy between the SGWB and noise parameters. Marginalizing over the correlated noise parameters degrades the constraint on each SGWB parameter by a factor of $\ensuremath{\sim}2$ at most in the four-detector case, irrespective of the strength of the magnetic coupling. We also confirm that the forecast results are robust against the variation of correlated noise parameters and can vary up to 40% in the realistic range of the coupling parameters for the second-generation detectors. However, ignoring the correlated noise in parameter estimation generally leads to a biased constraint on the SGWB parameters. If the coupling strength is twice as large as expected, this could result in a serious bias.

General analysis of LC oscillator with active and passive transconductance amplification

SummaryIn this paper, a comprehensive analysis of oscillation frequency, oscillation amplitude, and phase noise is conducted for an LC cross‐coupled oscillator with unequal inductors, active and passive transconductance amplification structure, and very low phase noise. In this study, for the first time, a complete analysis of the phase noise of this oscillator is presented using a novel method that does not require complex simulations to calculate the Impact Sensitivity Function (ISF) and phase noise, and as a result, a closed‐form equation is extracted for the phase noise. The calculated results are compared with the corresponding simulations to evaluate the validity of the extracted equations. Using the closed‐form phase noise equation as well as the oscillation frequency, designers can comprehend the design trade‐offs and design an oscillator for specific phase noise and oscillation frequency. The proposed method of calculating the ISF is also applicable and valid for other second‐order oscillators. In the following, the structure of the proposed oscillator is designed and simulated using the standard 0.18‐μm CMOS technology with a 1.8‐ power supply and 575.2‐MHz oscillation frequency to have the optimal coupling coefficient in terms of the phase noise and oscillation amplitude. The simulation results as well as the analyses performed based on the phase noise and oscillation amplitude parameters show that the structure proposed in this study provides a better performance with a much lower phase noise comparing with the structure of a conventional cross‐coupled oscillator as well as the proposed cross‐coupled oscillator with equal inductors.

A Comprehensive Review of Noise Measurement, Standards, Assessment, Geospatial Mapping and Public Health

Noise pollution is an emerging issue in cities around the world. Noise is a pernicious pollutant in urban landscapes mainly due to the increasing number of city inhabitants, road and aviation traffic, industrial and construction activities, and appliances or machinery used in daily life. The objective of this paper is to present a comprehensive review of noise measurement, guidelines/standards, and parameters used in noise monitoring; noise assessment associated with the area characteristics and the violations of guidelines/standards; noise geospatial mapping in urban planning; and physiological and psychological effects of noise exposure on public health. The review results revealed that standard methodology was lacking in many of the reviewed studies for noise measurement in various land-use patterns, especially the duration of noise monitoring; noise levels exceeded the prescribed noise standards in almost every noise assessment study across the globe irrespective of land-use or designated zone, and are exhibiting rising trends particularly due to traffic-induced noise; the majority of the urban population has been exposed to the noisy environment and affected with significant physiological and psychological health impacts; noise geospatial mapping has demonstrated high potential in noise abatement and management; and marked drop in noise levels in an urban environment during COVID-19 lockdown period. Based on the review results, the present study has suggested some future research perspectives of noise abatement and management that include a focus on – methodical noise assessment with prescribed guidelines of noise measurement and standards; geospatial noise mapping of urban areas, and real-time information system; universal database management software (DBMS) such as SQL and Improvado to compile data in single storage that will allow multiple users to access data despite different geographical locations; awareness programs using multi-media approaches in urban areas; and strict implementation of noise laws and regulations, that leads to the development of advanced technologies, integrated strategies, and sustainable environmental planning to mitigate the menace of noise pollution.

Convergence rate of general entropic optimal transport costs

We investigate the convergence rate of the optimal entropic cost $$v_\varepsilon $$ to the optimal transport cost as the noise parameter $$\varepsilon \downarrow 0$$ . We show that for a large class of cost functions c on $${\mathbb {R}}^d\times {\mathbb {R}}^d$$ (for which optimal plans are not necessarily unique or induced by a transport map) and compactly supported and $$L^{\infty }$$ marginals, one has $$v_\varepsilon -v_0= \frac{d}{2} \varepsilon \log (1/\varepsilon )+ O(\varepsilon )$$ . Upper bounds are obtained by a block approximation strategy and an integral variant of Alexandrov’s theorem. Under an infinitesimal twist condition on c, i.e. invertibility of $$\nabla _{xy}^2 c(x,y)$$ , we get the lower bound by establishing a quadratic detachment of the duality gap in d dimensions thanks to Minty’s trick.

Monitoring of traffic noise pollution in urban Patna, Bihar, India

Road traffic noise is a major environmental issue that has been faced by many people around the world, including in Patna city. There is a need to investigate noise regularly because of the rapidly increasing traffic volume. This study focuses on the estimation of the urban traffic noise level in Patna, Bihar, India. Based on a preliminary survey of traffic volume, 12 stations were selected. A sound meter was used to measure the “A”-weighted noise level. Noise parameters like L10, L50, and L90 were evaluated from the collected noise data. And these noise parameters were used for the calculation of noise pollution indices like Noise Climate (NC), Equivalent Continuous Noise Level (Leq), Noise Pollution Level (NPL), and Traffic Noise Index (TNI). These noise indices compare with the standard limit of the Central Pollution Control Board (CPCB). The equivalent continuous noise level (Leq) measured at different locations in the commercial cum residential zones varies from 77.7 dBA to 92.5 dBA, which is higher than the prescribed noise level limit of the CPCB. Other noise indices, such as the NPL and TNI, are found to range from 88.2 dBA to 111.5 dBA and 77.9 dBA to 125.9 dBA, respectively, at various sampling locations and are also higher than the prescribed limit of the CPCB. The noise level of all stations was found to exceed the prescribed limit of the CPCB. There is a strong correlation exists between NPL with TNI and NC (r &gt; 0.5). Also, there is a strong correlation exists between Leq with NPL (r &gt; 0.5). Using NPL as a dependent variable and TNI, NC, and Leq as independent variables, the regression model was completely validated.

Dark light enhancement for dark scene urban object recognition

AbstractThis paper presents a low‐light image enhancement method to improve the performance of autonomous piloting tasks based on deep learning methods. In the low light environment, camera sensors cannot capture enough effective photon signals causing poor performance in vision‐based autonomous piloting. Moreover, the lack of training data makes single‐frame low‐light enhancement and denoising algorithms hard to generalize in real‐world scenarios. By analyzing the noise patterns of real‐world cameras under low‐light environments, a noise generation method is proposed to mimic real‐world dark‐light noise and generate noisy‐clean data pairs for training. A method that can calibrate the camera shot noise parameters is then designed to learn low‐light enhancement from the synthesized noisy data pairs. The neural network trained on a synthesized dataset can effectively enhance the dark light image quality. Consequently, the network improves the downstream auto‐piloting applications such as object detection and semantic segmentation. Qualitative and quantitative experiments have been conducted to demonstrate that the method outperforms previous methods in low‐light enhancement.