Sensor Noise Level Research Articles

Consequences of EEG electrode position error on ultimate beamformer source reconstruction performance.

Inaccuracy of EEG electrode coordinates forms an error term in forward model generation and ultimate source reconstruction performance. This error arises from the combination of both intrinsic measurement noise of the digitization apparatus and manual coregistration error when selecting corresponding points on anatomical MRI volumes. A common assumption is that such an error would lead only to displacement of localized sources. Here, we measured electrode positions on a 3D-printed full-scale replica head, using three different techniques: a fringe projection 3D scanner, a novel “Flying Triangulation” 3D sensor, and a traditional electromagnetic digitizer. Using highly accurate fringe projection data as ground truth, the Flying Triangulation sensor had a mean error of 1.5 mm while the electromagnetic digitizer had a mean error of 6.8 mm. Then, again using the fringe projection as ground truth, individual EEG simulations were generated, with source locations across the brain space and a range of sensor noise levels. The simulated datasets were then processed using a beamformer in conjunction with the electrode coordinates registered with the Flying Triangulation and electromagnetic digitizer methods. The beamformer's output SNR was severely degraded with the digitizer-based positions but less severely with the Flying Triangulation coordinates. Therefore, the seemingly innocuous error in electrode registration may result in substantial degradation of beamformer performance, with output SNR penalties up to several decibels. In the case of low-SNR signals such as deeper brain structures or gamma band sources, this implies that sensor coregistration accuracy could make the difference between successful detection of such activity or complete failure to resolve the source.

On the percentage of lightning flashes that begin with initial breakdown pulses

AbstractThe initial breakdown (IB) stage of lightning flashes typically occurs in the first 20 ms of a flash and includes a series of IB pulses often detected with electric field change sensors. There is disagreement about the percentage of negative cloud‐to‐ground (CG) flashes that begin with IB pulses. This study includes new data on IB pulses in 198 CG flashes in Austria (latitude ~48˚N), Florida, USA (~29˚N) and South Dakota, USA (~44˚N) with, respectively, 100%, 100%, and 95% of the flashes having IB pulses. The data indicate that the amplitude of the largest IB pulse, range normalized to 100 km, is often weak, < 0.5 V m−1, with the lower latitude having a greater percentage (36%) of these weak maximum IB pulses than the higher latitude (11%). Since sensor noise levels are often larger than this value, detection of smaller amplitude IB pulses may be difficult. A similar result is seen in the amplitude ratio of the largest IB pulse to the first return stroke: at the lower latitude, 50% of flashes had a ratio < 0.1 versus 8% of flashes at the higher latitude. However, comparisons of the amplitude ratios from Austria (~48˚) and South Dakota (~44˚) do not support a simple latitude dependence. The data also show that 5–10% of IB pulses occur more than 100 ms before the first return stroke. These findings may explain why some previous studies found percentages <100%. Overall, the results indicate that all negative CG flashes probably begin with IB pulses.

Information content with low- vs. high-Tc SQUID arrays in MEG recordings: The case for high-Tc SQUID-based MEG

BackgroundMagnetoencephalography (MEG) is a method of studying brain activity via recordings of the magnetic field generated by neural activity. Modern MEG systems employ an array of low critical-temperature superconducting quantum interference devices (low-Tc SQUIDs) that surround the head. The geometric distribution of these arrays is optimized by maximizing the information content available to the system in brain activity recordings according to Shannon's theory of noisy channel capacity. New methodHerein, we present a theoretical comparison of the performance of low- and high-Tc SQUID-based multichannel systems in recordings of brain activity. ResultsWe find a high-Tc SQUID magnetometer-based multichannel system is capable of extracting at least 40% more information than an equivalent low-Tc SQUID system. The results suggest more information can be extracted from high-Tc SQUID MEG recordings (despite higher sensor noise levels than their low-Tc counterparts) because of the closer proximity to neural sources in the brain. Comparison with existing methodsWe have duplicated previous results in terms of total information of multichannel low-Tc SQUID arrays for MEG. High-Tc SQUID technology theoretically outperforms its conventional low-Tc counterpart in MEG recordings. ConclusionsA full-head high-Tc SQUID-based MEG system's potential for extraction of more information about neural activity can be used to, e.g., develop better diagnostic and monitoring techniques for brain disease and enhance our understanding of the working human brain.

Hydraulic profiling with the direct-push permeameter: Assessment of probe configuration and analysis methodology

Summary The characterization of hydraulic conductivity (K) variations in heterogeneous aquifers has proven to be a significant challenge. Recent field and numerical assessments, however, have demonstrated the considerable potential of direct-push profiling for characterization of vertical K variations at the resolution needed for contaminant site investigations. The direct-push permeameter (DPP), in particular, has been found to be an effective characterization tool (0.4-m resolution in current configuration) over the K range expected in aquifers. The potential of this tool is explored further here through numerical simulations to assess the probe configuration and the analysis approach that are most appropriate for profiling in highly permeable heterogeneous systems. A probe configuration with transducers placed between 0.1 and 0.4 m from the injection screen appears to be most suitable for general field applications, as it can yield a reasonable resolution (few decimeters) in the presence of a typical level of sensor noise. DPP data are commonly analyzed using the spherical form of Darcy’s Law. Although this approach will provide reliable K estimates in many situations, it can introduce error in the presence of thin (relative to the distance between injection screen and transducers) layers of vastly differing K. Simultaneous numerical inversion (under steady-shape conditions) of all DPP tests in a profile can yield improved results if information about aquifer structure is available. DPP K estimates have little sensitivity to the zone of compaction created during probe advancement, but estimates are sensitive to channeling along the probe surface. Proper probe design (probe shape and position of transducers) and test procedures (low injection rates) can reduce the potential for and impact of such channeling. These points are demonstrated using K data from an outcrop study in which K varies by orders of magnitude on the scale of a few decimeters.

Comparative Evaluation of the Consistency of Three‐dimensional Spatial Representations used in Autonomous Robot Navigation

An increasing number of robots for outdoor applications rely on complex three‐dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three‐dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross‐validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real‐world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three‐dimensional normal distributions transform (3D‐NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D‐NDT is found to be the highest among the tested models, with a similar performance over varying input data. © 2013 Wiley Periodicals, Inc.

Diffusion tensor smoothing through weighted Karcher means.

Diffusion tensor magnetic resonance imaging (MRI) quantifies the spatial distribution of water Diffusion at each voxel on a regular grid of locations in a biological specimen by Diffusion tensors- 3 × 3 positive definite matrices. Removal of noise from DTI is an important problem due to the high scientific relevance of DTI and relatively low signal to noise ratio it provides. Leading approaches to this problem amount to estimation of weighted Karcher means of Diffusion tensors within spatial neighborhoods, under various metrics imposed on the space of tensors. However, it is unclear how the behavior of these estimators varies with the magnitude of DTI sensor noise (the noise resulting from the thermal e!ects of MRI scanning) as well as the geometric structure of the underlying Diffusion tensor neighborhoods. In this paper, we combine theoretical analysis, empirical analysis of simulated DTI data, and empirical analysis of real DTI scans to compare the noise removal performance of three kernel-based DTI smoothers that are based on Euclidean, log-Euclidean, and affine-invariant metrics. The results suggest, contrary to conventional wisdom, that imposing a simplistic Euclidean metric may in fact provide comparable or superior noise removal, especially in relatively unstructured regions and/or in the presence of moderate to high levels of sensor noise. On the contrary, log-Euclidean and affine-invariant metrics may lead to better noise removal in highly structured anatomical regions, especially when the sensor noise is of low magnitude. These findings emphasize the importance of considering the interplay of sensor noise magnitude and tensor field geometric structure when assessing Diffusion tensor smoothing options. They also point to the necessity for continued development of smoothing methods that perform well across a large range of scenarios.

Active Concentration-Independent Chemical Identification With a Tunable Infrared Sensor

This paper presents an active-sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a nondispersive infrared spectroscope on the basis of a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Therefore, we use nonnegative matrix factorization to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; the active-sensing consistently outperformed the passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show that the method can predict the sample identity irrespective of concentration.

저가형 MEMS 관성측정장치 기반 INS/GPS 통합 항법 장치에서 방위각 추정 성능 향상

INS/GPS 통합 항법 필터는 주로 칼만 필터를 이용하여 구성되고 있다. 그러나 낮은 성능의 관성 센서를 이용한 INS/GPS 통합 항법 필터의 경우 센서 오차 레벨 등의 문제로 인해 정확한 방위각 정보의 제공이 곤란하며, 이로 인해 통합 필터를 구성하는 칼만 필터의 추정 성능이나 안정성을 보장할 수 없게 된다. 본 논문에서는 칼만 필터와 파티클 필터의 결합된 형태의 국지선형 파티클 필터를 이용하여 초기 방위각 오차를 극복할 수 있는 방법을 제시하였다. Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

The IDC Seismic, Hydroacoustic and Infrasound Global Low and High Noise Models

The International Data Centre (IDC) in Vienna, Austria, is determining, as part of automatic processing, sensor noise levels for all seismic, hydroacoustic, and infrasound (SHI) stations in the International Monitoring System (IMS) operated by the Provisional Technical Secretariat of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Sensor noise is being determined several times per day as a power spectral density (PSD) using the Welch overlapping method. Based on accumulated PSD statistics a probability density function (PDF) is also determined, from which low and high noise curves for each sensor are extracted. Global low and high noise curves as a function of frequency for each of the SHI technologies are determined as the minimum and maximum of the individual station low and high noise curves, respectively, taken over the entire network of contributing stations. An attempt is made to ensure that only correctly calibrated station data contributes to the global noise models by additionally considering various automatic detection statistics. In this paper global low and high noise curves for 2010 are presented for each of the SHI monitoring technologies. Except for a very slight deviation at the microseism peak, the seismic global low noise model returns identically the Peterson (1993) NLNM low noise curve. The global infrasonic low noise model is found to agree with that of Bowman et al. (2005, 2007) but disagrees with the revised results presented in Bowman et al. (2009) by a factor of 2 in the calculation of the PSD. The global hydroacoustic low and high noise curves are found to be in quantitative agreement with Urick’s oceanic ambient noise curves for light to heavy shipping. Whale noise is found to be a feature of the hydroacoustic high noise curves at around 15 and 25 Hz.

Control Strategies for Afterload Reduction With an Artificial Vasculature Device

Ventricular assist devices (VADs) have been used successfully as a bridge to transplant in heart failure patients by unloading ventricular volume and restoring the circulation. An artificial vasculature device (AVD) is being developed that may better facilitate myocardial recovery than VAD by controlling the afterload experienced by the native heart and controlling the pulsatile energy entering into the arterial system from the device, potentially reconditioning the arterial system properties. The AVD is a valveless, 80 ml blood chamber with a servo-controlled pusher plate connected to the ascending aorta by a vascular graft. Control algorithms for the AVD were developed to maintain any user-defined systemic input impedance (IM) including resistance, elastance, and inertial components. Computer simulation and mock circulation models of the cardiovascular system were used to test the efficacy of two control strategies for the AVD: 1) average impedance position control (AIPC)-to maintain an average value of resistance during left ventricular (LV) systole and 2) instantaneous impedance force feedback (IIFF) and position control (IIPC)-to maintain a desired value or profile of resistance and compliance. Computer simulations and mock loop tests were performed to predict resulting cardiovascular pressures, volumes, flows, and the resistance and compliance experienced by the native LV during ejection for simulated normal, failing, and recovering LV. These results indicate that the LV volume and pressure decreased, and the LV stroke volume increased with decreasing IM, resulting in an increased ejection fraction. Although the AIPC algorithm is more stable and can tolerate higher levels of sensor errors and noise, the IIFF and IIPC control algorithms are better suited to maintain any instantaneous IM or an IM profile. The developed AVD impedance control algorithms may be implemented with current VADs to promote myocardial recovery and facilitate weaning.

Effects of measurement noise on modal parameter identification

In the past decade, much research has been conducted on data-driven structural health monitoring (SHM) algorithms with use of sensor measurements. A fundamental step in this SHM application is to identify the dynamic characteristics of structures. Despite the significant efforts devoted to development and enhancement of the modal parameter identification algorithms, there are still substantial uncertainties in the results obtained in real-life deployments. One of the sources of uncertainties in the results is the existence of noise in the measurement data. Depending on the subsequent application of the system identification, the level of uncertainty in the results and, consequently, the level of noise contamination can be very important. As an effort towards understanding the effect of measurement noise on the modal identification, this paper presents parameters that quantify the effects of measurement noise on the modal identification process and determine their influence on the accuracy of results. The performance of these parameters is validated by a numerically simulated example. They are then used to investigate the accuracy of identified modal properties of the Golden Gate Bridge using ambient data collected by wireless sensors. The vibration monitoring tests of the Golden Gate Bridge provided two synchronized data sets collected by two different sensor types. The influence of the sensor noise level on the accuracy of results is investigated throughout this work and it is shown that high quality sensors provide more accurate results as the physical contribution of response in their measured data is significantly higher. Additionally, higher purity and consistency of modal parameters, identified by higher quality sensors, is observed in the results.

Comparing Information Content of Upwelling Far-Infrared and Midinfrared Radiance Spectra for Clear Atmosphere Profiling

Abstract The information content of high-spectral-resolution midinfrared (MIR; 650–2300 cm−1) and far-infrared (FIR; 200–685 cm−1) upwelling radiance spectra is calculated for clear-sky temperature and water vapor profiles. The wavenumber ranges of the two spectral bands overlap at the central absorption line in the CO2 ν2 absorption band, and each contains one side of the full absorption band. Each spectral band also includes a water vapor absorption band; the MIR contains the first vibrational–rotational absorption band, while the FIR contains the rotational absorption band. The upwelling spectral radiances are simulated with the line-by-line radiative transfer model (LBLRTM), and the retrievals and information content analysis are computed using standard optimal estimation techniques. Perturbations in the surface temperature and in the trace gases methane, ozone, and nitrous oxide (CH4, O3, and N2O) are introduced to represent forward-model errors. Each spectrum is observed by a simulated infrared spectrometer, with a spectral resolution of 0.5 cm−1, with realistic spectrally varying sensor noise levels. The modeling and analysis framework is applied identically to each spectral range, allowing a quantitative comparison. The results show that for similar sensor noise levels, the FIR shows an advantage in water vapor profile information content and less sensitivity to forward-model errors. With a higher noise level in the FIR, which is a closer match to current FIR detector technology, the FIR information content drops and shows a disadvantage relative to the MIR.

Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements

An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor observation. The objective here is to mask the effects of sensor noise level variation and magnify the system fault signatures. In this regard, the concepts of feature extraction and pattern classification are used for fault detection in aircraft gas turbine engines. The proposed methodology of data-driven fault detection is tested and validated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA for noisy (i.e., increased variance) sensor signals.

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

AbstractState estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high‐quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time‐varying autocovariance least‐squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter. © 2010 American Institute of Chemical Engineers AIChE J, 2011

On the Benefits of In-Flight System Identificationfor Autonomous Airdrop Systems

A unique feature of airdrop systems is the inherent and large variability in flight dynamic characteristics. The same physical article dropped on two different occasions will exhibit significantly different dynamic response. The problem only becomes worse for different test articles. Control systems for autonomous airdrop systems explicitly or implicitly assume knowledge of the flight dynamic characteristics in some way, shape, or form. A question facing autonomous airdrop designers is whether to use precomputed dynamic characteristics inside the control law, or to compute the needed flight dynamic characteristics in-flight and subsequently employ them in the control law. This paper establishes conditions when in-flight identified characteristics, with a focus on the turn rate dynamics, should be used, and when it is better to use precomputed results. It is shown that with expected levels of system variability, sensor noise, and atmospheric wind, in-flight identification generally produces significantly more accurate dynamic behavior of the lateral dynamics than a precomputed model of the nominal system, even when the in-flight identification is performed with highly inaccurate sensor data. The only exception to this rule observed in this work is the situation where atmospheric winds are high and a direct heading measurement is not available. In this situation, a precomputed estimate of the time constant of the lateral dynamics is more accurate than an in-flight estimate. These conclusions are reached though a comprehensive simulation study using a validated airdrop flight dynamic model applied to both a small and large parafoil.

Impact of laser launch location on the performance of laser tomography and multiconjugate adaptive optics for extremely large telescopes

Laser tomography and multiconjugate adaptive optics are under development for ground-based extremely large telescopes. Continuous wave sodium guide star lasers are planned for these systems, but their use raises some difficulties due to the extended nature of the beacons generated in the mesosphere and their spatiotemporal variability. We describe a performance analysis on the impact of laser launch telescope (LLT) location for the Thirty Meter Telescope (TMT) multiconjugate adaptive optics system. A semianalytical first-order noise propagation calculation is presented, supplemented by end-to-end Monte Carlo physical optics simulations. The principal conclusion of the study is that modestly superior performance is achieved with multiple LLT locations around the primary mirror, compared to a single central LLT behind the secondary mirror, but the largest value of any of these improvements is of the order of 20 nm rms for the expected wavefront sensor noise levels, suggesting that the final choice of geometry should depend primarily on the cost and complexity of implementation trade-off. This conclusion is also fully supported by the fact that, for the TMT 70 arcsec laser guide star (LGS) asterism, the fratricide effect reduces the performance of the central launch geometry by only a small amount. The reduction ranges from only a few nm rms at zenith to a few tens of nm at a 45° zenith angle in the worst case that the effect cannot be calibrated.

Evaluation of the performance of high-speed PIV compared to standard PIV in a turbulent jet

In this paper, a comparison between two particle image velocimetry (PIV) systems, one based on a standard cross-correlation charge coupled device (CCD) camera with pulsed laser and another using high-speed complementary metal oxide semiconductor (CMOS) camera with continuous laser is performed. The objective of the paper is to point out advantages and disadvantages of the two systems when computing large and small flow scale statistics. The comparison is performed on velocity measurements in the near and far fields of a circular water jet: on this flow several experimental data and empirical self-similarity laws are available for comparisons. The results show that both systems are suitable for measurements with a preference for the standard one when investigating small-scale statistics. This result depends on the lower number of effectively independent samples acquired by a high-speed system and on the higher noise levels of CMOS sensors in comparison to CCDs.

Sightline Jitter Minimization and Shaping Using Nonlinear Friction Compensation

Friction-induced sightline jitter significantly degrades the image resolution and detection range of a stabilised optical system. Therefore, any controller that can reduce jitter levels in the stabilisation sub-system will have a significant impact on overall electro-optic system performance. This article presents the results of an investigation into several friction compensation models applied to the validated model of an in-service electro-optic turret. The turret test harness, system identification software and friction measurement techniques used in the validation procedure–experimental transfer function analysis–are presented. A worst-case approach was used in setting the sensor noise and base motion acceleration levels. This test data was then used to validate a mathematical model of the turret elevation axis for use in off-line design and tuning of six friction compensation controllers. Three types of friction compensator model were investigated; a linear Kalman filter, an extended Kalman filter with a static friction model and an extended Kalman filter using a dynamic friction model. Additionally, two controller architectures were used. All six controllers were shown to significantly reduce jitter levels overall, but a new controller architecture was shown to also further reduce image degradation due to smearing.

Use of Langmuir-Blodgett films as modifiers for piezoresonance sensors

Films based on arachic acid, alkylated calixresorcinarenes, and β-cyclodextrin were prepared on the surfaces of piesoresonance sensors using the Langmuir-Blodgett method. A linear relationship between the frequency shift of the piesoresonance sensor and the number of monolayers was found in all of the test diphilic compounds. The Langmuir-Blodgett films were used as surface modifiers in piesoresonance sensors for the determination of nitroalkanes. The reversibility of sensor signals, that is, the complete regeneration of the films after blowing with air for 1–3 min, was an important property of the sensors. The sensing coating proposed was characterized by a long lifetime: it withstood 80–100 sorption-desorption cycles. The sensor noise level was ±20 Hz.

Magnetoresistive sensors

Magnetoresistive sensors using spin valves and magnetic tunnel junctions are reviewed,considering applications as readers in hard disk drives, as well as applications where theultimate field detection limits are required (from nT down to pT). The sensor noise level inquasi-DC or high-frequency applications is described, leading to sensor designconsiderations concerning biomedical and read head applications. Magnetic tunnel junctionbased sensors using MgO barriers appear as the best candidates for ultra-low field(pT) detection, either in the high-frequency regime, or for quasi-DC applications.