Measurement Noise Research Articles

Aperiodic EEG predicts variability of visual temporal processing.

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded EEG from participants (both sexes) during resting state and a task in which they detected the presence of two flashes separated by variable inter-stimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggest that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time.Significance statement Neural oscillations are fundamental for temporal organization and communication of information in the brain, while irregular activity has been suggested to disrupt these processes.We investigated the impact of aperiodic electrophysiological activity on a visual integration task known to depend on rhythmic brain organization. Crucially, Individuals exhibiting higher 1/f electrophysiological noise showed increased uncertainty or perceptual noise in temporal perception, highlighting how irregular neural activity affects tasks relying on periodic neural organization. These findings significantly advance our understanding of aperiodic EEG and its implications for perception, shedding light on its intricate interplay with various neural processes.

An assessment of controlled source EM for monitoring subsurface CO2 injection at the wyoming carbonSAFE geologic carbon storage site

We evaluate if electromagnetic (EM) geophysical methods for monitoring geologic carbon storage (GCS) efforts at the Wyoming CarbonSAFE project adjacent to the Dry Fork Station power plant near Gillette, Wyoming. This first involved acquiring both electric and magnetic fields at eleven different locations ranging in distance from immediately adjacent to 4 km from the plant. Passive EM measurements were made to provide spectral EM noise measurements generated by electricity production at the plant and to determine if useful magnetotelluric (MT) data can be successfully collected in the region. The processed data indicate that useful MT data can be collected as long as the site is located more than 2km away from the power plant as well as active roads and rail lines. Controlled source EM data were collected using three different source configurations, two of which connected to steel casings used to complete the injection wells. Comparing the EM noise measurements to the CSEM data show measurable electric and magnetic field signals at all sites. Next a series of three-dimensional (3D) numerical models were built that simulate resistivity changes caused by the proposed CO2 injection at depths ranging from 2.4 to 3.0km. These models were used to simulate various EM measurement configurations. The modeling shows that casing-source CSEM monitoring can provide sensitivity to the injected CO2 if source electrodes are connected to the bottom of one or both of the injection wells.

Depth estimation of pre-Kalahari basement in Southern Angola using seismic noise measurements and drill-hole data

The remote Southern region of Angola is covered by siliciclastic Kalahari Cenozoic formations that host underground aquifers of great importance to local populations affected by water scarcity problems. These aquifers are well developed where Kalahari sands reach appropriate thicknesses. On the other hand, at the eastern end of this area, regional aeromagnetic data recently acquired suggested the possibility of the continuity of the geological structures of the Lufilian Arc, sited in the nearby Zambia and Congo, southwestwards into Angola under the Kalahari formations. Once the Lufilian Arc is associated with the presence of the so-called Central African Copperbelt, this possibility increased the interest in determining the depth to Pan-African rocks under the Kalahari basin. To estimate the thickness of Kalahari formations in this area of difficult access and poor logistics, an expedited and non-invasive geophysical method was needed. Seismic noise and the single-station Nakamura technique were chosen, but due to the large distance of the study area from the ocean, one of the major sources of seismic noise, a test survey was acquired in the Cuvelai region to assess the signal quality, where the data was calibrated using available drill-holes. >170 points of seismic ambient noise were later acquired and the horizontal/vertical (HVSR) amplitude versus frequency curves were 1D inverted for the best velocity/density model for each station. The results were compared with 1D inverted legacy vertical electrical soundings reprocessed and validated in this work, showing similar depth-to-basement, while interpreted velocities/densities of geological formations were sampled and confirmed with measurements. A depth-to-basement map was produced using seismic information, mechanical soundings, and geological information. Despite the relatively reduced geographical area covered, the map presents valuable information for hydrogeology and mineral exploration purposes and agrees with a previously available coarser map of Kalahari thickness and with observations from geological surveys simultaneously conducted at the time of the seismic surveys.

An adaptive estimation of ground vehicle state with unknown measurement noise

Accurate information about the vehicle state such as sideslip angle is critical for both advanced assisted driving systems and driverless driving. These vehicle states are used for active safety control and motion planning of the vehicle. Since these state parameters cannot be directly measured by onboard sensors, this paper proposes an adaptive estimation scheme in case of unknown measurement noise. Firstly, an estimation method based on the bicycle model is established using a square-root cubature Kalman filter (SQCKF), and secondly, the expectation maximization (EM) approach is used to dynamically update the statistic parameters of measurement noise and integrate it into SQCKF to form a new expectation maximization square-root cubature Kalman filter (EMSQCKF) algorithm. Simulations and experiments show that EMSQCKF has higher estimation accuracy under different driving conditions compared to the unscented Kalman filter.

Resolving the BWR Stability Paradox of Space-Dependent Decay Ratios

Noise signals obtained from local power range monitors and average power range monitors are routinely used for extracting stability information for boiling water reactors. The stability parameters of decay ratio (DR) and natural frequency are produced by signal processing algorithms. While theoretically a dynamical system like a reactor core composed of coherently coupled components possesses a unique DR, noise measurements from different detectors have been reported in several published works to produce different DRs, creating the impression that a DR is not unique at a given operating state but rather is space dependent. This paper is an attempt to reconcile theory with measurements and resolve the space-dependent DR paradox that was encountered afresh in the course of designing a new high-fidelity online stability monitor. As such, the issue of space dependence could not be overlooked as attributable to variability within the uncertainty of noise analysis algorithms.

Criteria for selection of point-ahead angle compensation strategy in intersatellite laser ranging interferometry of TianQin based on research of tilt-to-length coupling noise

TianQin is expected to deploy three spacecraft in Earth orbit at the altitude of about 1 × 108 m to form an equilateral triangular constellation with arm lengths of about 1.7 × 108 m. It is aimed at detecting gravitational waves in the frequency range from 0.1 mHz to 1 Hz by means of high sensitive laser ranging interferometry with pathlength measurement noise below 1 pm/Hz1/2. The long-range propagation of beam in the intersatellite laser ranging interferometry between the three spacecraft results in a non-negligible time delay of 0.58 s. Therefore, there is an angular difference between the optimal outgoing and incoming laser beam of laser ranging interferometry on each spacecraft, which is denoted as the point-ahead angle. Due to the relative motion between the three spacecraft, the point-ahead angle of TianQin constellation is approximately 22.96 μrad with a dynamic variation range of ±24.71 nrad. Point-ahead-angle mechanism is a fine steering mirror used for point-ahead angle compensation to diminish the beam pointing error, thus to maintain the intersatellite laser link as well as to diminish the tilt-to-length coupling noise, which are vital to the gravitational wave detection. Both the static and dynamic point-ahead angle compensation strategies can be used in TianQin and the selection of the two is based on minimizing the total point-ahead-angle-related tilt-to-length coupling noise. In this paper, a criteria for selection of the two strategies are proposed by establishing a model of point-ahead-angle-related tilt-to-length coupling noise as well as a corresponding evaluation function for calculation and comparison. The results indicate that the dynamic compensation strategy is the optimal choice only when the first-order tilt-to-length coupling coefficient of point-ahead-angle mechanism is less than 6.4 × 10−9 m/rad, otherwise the static compensation strategy should be used. The proposed criteria, as well as the model and the requirement for point-ahead-angle mechanism, can serve as a common theoretical basis in laser ranging interferometry applications.

AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Viral impacts on microbial populations depend on interaction phenotypes-including viral traits spanning the adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of the latent period via one-step growth curves is systematically biased-generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events, which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations.IMPORTANCEQuantifying viral traits-including the adsorption rate, burst size, and latent period-is critical to characterize viral infection dynamics and develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation-the one-step growth curve-to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically underestimate the mean latent period and, in turn, overestimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series, we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

A multi-channel fusion variational autoencoder-based RUL prediction approach for multi-sensor systems

Abstract Deep learning (DL)-based approaches have demonstrated remarkable performance in predicting the remaining useful life (RUL) of complex systems, which is beneficial for making timely maintenance decisions. However, the majority of these DL methods suffer from a lack of interpretability, and it is difficult to mine the degradation features in the presence of significant measurement noises. To remedy the deficiency, a multi-channel fusion variational autoencoder (MCFVAE)-based approach is proposed. A feature fusion module is designed to capture and fuse the multi-channel features, which facilitates the disclosure of the degradation information from the multi-sensor data. A variational inference module is further introduced to generate the compressive representations and project them into a latent space as an interpretable component, which can display the degradation degree of the multi-sensor systems. A regressor module is finally utilized to establish the relationship between the compressive representations and the RUL. The superior feature fusion and distribution characteristics learning abilities of the MCFVAE contribute to achieving robust and interpretable RUL prediction. The effectiveness and superiority of the proposed method are experimentally validated through a publicly available Commercial modular aero propulsion system simulation dataset and compared with the existing methods.

Dynamic data reconciliation based on elman neural network and particle filter

In the process of modern industries, complex nonlinear dynamic systems present high requirements for measured data. In the actual industrial process system, the measurement data obtained by sensors will inevitably be subject to noise disturbances from the equipment itself or from the outside environment. These noise disturbances will deteriorate the dynamic performance of the system to a certain extent and affect the industrial production. Particle filter (PF) can be used to infer the accurate outputs of nonlinear dynamic system from the contaminated measurement data, but PF is limited to the pre-known state space model. In the actual industrial process, it is difficult to summarize the internal behavior of the system and obtain the pre-known state space model. Therefore, it is impossible to directly use PF in the nonlinear dynamic system with unknown model. In order to solve the above problems, this paper proposes a dynamic data reconciliation method called ENN-PF, which combines Elman neural network (ENN) data-driven modeling with PF. In this method, ENN is used for data-driven modeling, that is, the system model is dynamically identified by using the input and output data of the system, and then the dynamic data reconciliation is carried out by using PF according to the model identified by ENN. Finally, the proposed ENN-PF method is validated by simulations and practical experiments to effectively reduce the interference of measurement noise and improve the dynamic performance of the system.

Perbandingan Tingkat Kebisingan Berbagai Bahan Peredam pada Knalpot Sepeda Motor Racing

The use of racing exhausts on motorised vehicles can cause uncomfortable and dangerous noise pollution if it exceeds the threshold set by the Minister of Environment. Based on the Decree of the Minister of Environment No 07/2009, the noise limit for motorbikes with engine capacity below 80 cc is 77 dB, 80-175 cc is 80 dB, and above 175 cc is 83 dB. This study aims to observe the comparison of noise levels of various silencer materials on racing motorbike exhausts in order to find the most effective material in reducing noise in accordance with these regulations. The noise measurement method follows the ISO 5130 standard, carried out when the engine is running idle and at engine speed range from 0 to 8000 RPM with a variety of different damping materials. The results show that at idle conditions, yellow Glasswool is the best damping material with the lowest noise level of 90.31 dBA, while Stainless wool shows the worst performance with an average noise level of 100.81 dBA. In contrast, in the 0-8000 RPM rotation condition, Stainless wool became the most superior silencer material with a noise level of 105.4 dBA, while grandma's hair Glasswool (fibreglass) became the worst with the highest noise level of 106.4 dBA. The conclusion of this study is that the optimal choice of silencer material can differ depending on the operating conditions of the racing exhaust, with yellow Glasswool being more effective at idle conditions and Stainless wool being more effective at maximum revs.

Unscented Kalman filter application for state estimation of a Qball-X4 quadrotor

In the process of controlling the quadrotor, accurate state estimation plays a crucial role. For positioning purposes, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are employed to determine the position of moving subjects. The Qball-X4 quadrotor is a highly nonlinear object, and when combined with Gauss interference, it can compromise the accuracy of the EKF. To address these challenges, this study focuses on assessing the suitability of the UKF nonlinear filtering method for estimating the state of the Qball-X4 quadrotor. This estimation is based on measurements from the gyroscope and Global Positioning System (GPS). To simulate real-life conditions, measurement noise has been deliberately introduced into the sensors. Rigorous testing under various conditions has emphasized the superior performance of the UKF filter in estimating the state of the quadrotor. This paper presents a valuable method to enhance the accuracy and reliability of the navigation system for the Qball-X4 quadrotor.

Low Frequency Electronic Noise Characterization of Au-W/β-Ga2O3 Schottky Diodes

Ultra-wide bandgap (UWBG) semiconductors have attracted tremendous amount of interest for applications in high-power electronics and solar-blind UV photodetectors. In particular, β-Ga2O3 has attracted significant research attention in recent years due to its promising electrical and optical characteristics including a large bandgap, high breakdown electrical field, and large Baliga’s figure of merit compared to well-established wide-bandgap technologies such as GaN and SiC [1].Low frequency electronic noise, which is present in all electronic devices, can be used as a diagnostic tool for semiconductor material and device characterization. By studying the low frequency noise in semiconductor materials and devices, the physics associated with the noise properties of bulk and interface defects and traps can be investigated [2]. Measurement of low frequency noise is also crucial for evaluating material quality and device reliability.In this work, we investigate the forward-bias diode parameters and low-frequency electronic noise in Au-W/β-Ga2O3 Schottky barrier diodes. The Schottky contact is formed by depositing 20 nm of Tungsten (W) using dc sputtering followed by 340 nm of Gold (Au) using e-beam evaporation, and subsequent annealing [3].We first measure the I-V characteristics and extract important diode parameters, such as the Schottky barrier height, ideality factor, and series resistance from room temperature forward-bias current-voltage characteristics. The extracted Schottky barrier height for these devices is in the range 0.7-0.8 eV and the ideality factor is approximately 1.2.We next measure the low-frequency electronic noise in Au-W/β-Ga2O3 Schottky diodes at different forward bias voltages at room temperature. We find that the current noise spectral density exhibits 1/f-type behavior for all forward bias voltages at low frequency with an exponent ranging from 0.75 to 1.15, indicating that the low frequency noise of these devices is dominated by excess (flicker) noise. No Lorentzian-shaped generation-recombination (G-R) noise is observed. At low frequencies, the shot noise contribution is found to be negligible for the values of the current in our devices, and the total noise is 1/f-limited.We also investigate the dependence of the current noise spectral density on forward bias current, revealing different relationships in different current regimes. At low currents, the noise spectral density is found to scale approximately as the square of current, whereas at high currents, the noise spectral density begins to saturate. Such noise behavior can be attributed to different noise sources dominating in different current regimes. Furthermore, using the noise figure of merit adapted for ultra-wide bandgap semiconductor devices [4], we find that the Au-W/β-Ga2O3 devices have lower noise levels compared to other emerging UWBG materials and similar noise levels as well-established technologies such as GaN.These results provide important insights into the electronic noise properties of Au-W/β-Ga2O3 Schottky diodes and show that low frequency noise characterization is an important diagnostic tool for assessing the quality and reliability of gallium oxide based wide-bandgap materials and device technologies.

Prediction of Lithium Deposition By Electrochemical Noise Method in Li-Ion Batteries

Electrochemical noise measurement methods have established a significant presence in corrosion literatures and through these measurements, it becomes feasible to discern the corrosion mode and make a clear distinction between localized and uniform corrosion types, particularly when examined alongside post-mortem studies.In recent years, the increase in the use of lithium-ion batteries demands that the tests to be performed on the batteries are faster, easier, cheaper and, if possible, non-destructive and non-perturbing. While some electrochemical noise studies on batteries have commenced, the existing literature on this subject is limited and questionable. Electrochemical noise measurement, in Lithium based batteries has the potential to serve as a non-invasive diagnostic tool for assessing battery health. One of our previous studies has demonstrated an increased voltage noise in non-rechargeable batteries with Li/MnO2 chemistry upon exposure to a short circuit, indicating detectable morphological changes in metallic lithium due to non-homogenous depletion of lithium anode through electrochemical noise measurement.In Lithium-Ion batteries, lithium deposition refers to the undesired formation of metallic lithium, commonly in the form of dendrites, on the battery’s anode during charge and discharge cycles. This phenomenon is a consequence of uneven lithium-ion plating and stripping, leading to localized overplating. This growth of lithium dendrites poses significant risk, including internal short circuits, compromised battery integrity, and the potential for thermal runaway. These issues can result in reduced battery performance, safety concerns, and shortened cycle life. Effectively addressing lithium deposition is essential for enhancing the safety and reliability of lithium-ion batteries in various applications.Similarly to our previous publication, the electrochemical noise method shows promise as a non-invasive approach to investigate the understand deposition of metallic lithium by monitoring the voltage noise during charge and discharge cycling of batteries. For this purpose, NMC/Graphite pouch and coin cells are employed. In this presentation, we will delve into the details of electrochemical noise in lithium-based batteries, discussing the method’s reliability in assessing lithium deposition in NMC/Graphite lithium-ion batteries and presenting preliminary results. Figure 1

Underwater multizonotope terrain‐aided navigation method with coarse map based on set‐membership filter

AbstractTerrain‐aided navigation (TAN) is a viable method to achieve long‐term underwater navigation for long‐range autonomous underwater vehicles (AUVs). However, the high‐accuracy positioning results of most TAN systems rely on precise a priori seabed terrain maps, which restricts their applicability to a few areas with accurate bathymetric measurements of the seabed terrain. This article introduces a TAN system based on the General Bathymetric Chart of the Oceans (GEBCO) data set for global marine applications. Specifically, to address the low accuracy and poor robustness of the TAN system with imprecise bathymetric measurement and low‐resolution data from the GEBCO data set, this article proposes a multizonotope TAN method based on set‐membership filter (SMF) theory. The SMF theory is employed to handle the unknown distribution of the measurement noise from the GEBCO data set, introducing a multizonotope measurement update model to achieve more precise navigational results while addressing the perceptual ambiguity caused by self‐similar terrain. The smoothness of the terrain is incorporated as a parameter in the generation ranges of multizonotope, enabling adaptive adjustment based on terrain smoothness to reduce costs and enhance navigational performance. The accuracy and robustness of the proposed method are verified through all shipboard experiments, publicly available data sets, and AUV experiments. Compared with state‐of‐the‐art TAN methods, the average and maximum positioning errors have decreased by 64.83% and 48.84%, respectively. Finally, based on the experimental results, a preliminary distribution of suitable areas in the oceans is provided.

Metalenses phase characterization by multi-distance phase retrieval

Metalens, characterized by their unique functions and distinctive physical properties, have gained significant attention for their potential applications. To further optimize the performance of metalens, it is necessary to characterize the phase modulation of the metalens. In this study, we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and robustness. Our findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently long. This system enables the analysis of focal length and aberration by utilizing the computed phase distribution. We extend our investigation to measure the phase distribution of the metalens operating in the near-infrared (NIR) spectrum and identify the impact of defects in the sample on the phase. Additionally, we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working mediums. Our system provides a straightforward method for the phase characterization of metalens, aiding in optimizing the metalens design and functionality.

Noise Pollution in Urban Areas : Measurement and Analysis of Traffic Noise Impacts on Health and Education Services in Bandung City

Noise Pollution in Urban Areas : Measurement and Analysis of Traffic Noise Impacts on Health and Education Services in Bandung City

On robustification of digital event-based controllers for control-affine nonlinear systems

In this paper, the robust digital stabilization problem of nonlinear systems is investigated. In particular, a methodology for the design of robust quantized sampled-data stabilizers updated via an event-triggered mechanism is provided for time-varying control-affine nonlinear systems affected by actuation disturbances and measurement noises. The notion of time-varying steepest descent feedback (TSDF), continuous or not, and the Input-to-State Stability (ISS) redesign methodology are used for the development of the proposed robust event-based digital controller. Under the assumption that the actuation disturbances and measurement noises are bounded with a-priori known bounds and that the amplitude of the measurement noises satisfies a certain condition related to the new added robustification term, the following result is proved: there exist a suitably fast sampling and an accurate quantization of the input/output channels such that the digital implementation of robustified TSDF controllers, updated through a proposed event-triggered mechanism, ensures semi-global practical stability of the related closed-loop system regardless of the above uncertainties. In the methodology here proposed, time-varying sampling periods and the non-uniform quantization of the input/output channels are allowed. Moreover, the theory here developed includes the analysis of the intersampling system behaviour. Possible discontinuities in the function describing the TSDF at hand are also managed. The provided results are validated through a numerical example.

A Pseudo-Spectral Method for Wall Shear Stress Estimation from Doppler Ultrasound Imaging in Coronary Arteries.

The Wall Shear Stress (WSS) is the component tangential to the boundary of the normal stress tensor in an incompressible fluid, and it has been recognized as a quantity of primary importance in predicting possible adverse events in cardiovascular diseases, in general, and in coronary diseases, in particular. The quantification of the WSS in patient-specific settings can be achieved by performing a Computational Fluid Dynamics (CFD) analysis based on patient geometry, or it can be retrieved by a numerical approximation based on blood flow velocity data, e.g., ultrasound (US) Doppler measurements. This paper presents a novel method for WSS quantification from 2D vector Doppler measurements. Images were obtained through unfocused plane waves and transverse oscillation to acquire both in-plane velocity components. These velocity components were processed using pseudo-spectral differentiation techniques based on Fourier approximations of the derivatives to compute the WSS. Our Pseudo-Spectral Method (PSM) is tested in two vessel phantoms, straight and stenotic, where a steady flow of 15mL/min is applied. The method is successfully validated against CFD simulations and compared against current techniques based on the assumption of a parabolic velocity profile. The PSM accurately detected Wall Shear Stress (WSS) variations in geometries differing from straight cylinders, and is less sensitive to measurement noise. In particular, when using synthetic data (noise free, e.g., generated by CFD) on cylindrical geometries, the Poiseuille-based methods and PSM have comparable accuracy; on the contrary, when using the data retrieved from US measures, the average error of the WSS obtained with the PSM turned out to be 3 to 9 times smaller than that obtained by state-of-the-art methods. The pseudo-spectral approach allows controlling the approximation errors in the presence of noisy data. This gives a more accurate alternative to the present standard and a less computationally expensive choice compared to CFD, which also requires high-quality data to reconstruct the vessel geometry.