Sensor Alignment Research Articles

Investigating the mechanical properties of composites containing exfoliated graphite/epoxy sensors: Experimental testing and simulation

One approach to structural health monitoring (SHM) involves embedding sensors within a composite material. However, the integration of these sensors can potentially introduce flaws that might affect the composite’s mechanical properties. This research aims to explore the impact of embedding exfoliated graphite (EG)/epoxy sensors on the mechanical characteristics of composite systems through laboratory experiments and numerical simulations. Sensor strips composed of varying volume fractions of EG/epoxy were fabricated. Tensile test specimens were prepared by embedding these sensors in the epoxy matrix oriented both lengthwise and widthwise. Baseline specimens of EG/epoxy without sensors were also created for comparison. Tensile tests were performed on the samples to evaluate the effects of the embedded sensors on the composite’s elastic modulus and tensile strength. The results indicated a slight improvement in both elastic modulus and tensile strength with the introduction of EG. Crucially, the orientation of the sensors within the samples had a significant impact on the composite’s mechanical properties. Samples with widthwise-aligned sensors showed reduced tensile strength due to delamination along the sensor edges. Finite element simulations using a viscoelastic model based on the experimental data were conducted to analyze the effect of sensor alignment on mechanical properties. The findings revealed that a grid pattern alignment of sensors significantly enhanced mechanical performance compared to lengthwise or widthwise alignment, particularly at 0.1% and 0.3% EG volume fractions, highlighting the effectiveness of a grid pattern for embedding sensors in SHM applications.

Flat Magnetic X–Y Alignment Sensor

Flat Magnetic <i>X</i>–<i>Y</i> Alignment Sensor

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Abstract A Large Ion Collider Experiment (ALICE) experiment is one of the four experiments at the Large Hadron Collider (LHC) designed to investigate the status of matter under very high energy densities produced during heavy-ion collisions. The ALICE inner tracking system (ITS) consists of seven concentric cylindrical layers of monolithic silicon pixel sensors known as ALICE pixel detector (ALPIDE). The sensors are used to reconstruct the paths of charged particles generated in the collisions. The sensor alignment of the detector must be adjusted to a high precision standard. The adjustment objective is to obtain a detector that can undertake high-resolution measurements. This paper introduces a method for measuring the reference markers utilized in sensor alignment determination. Markers engraved at the chip corners have been detected using the Hough transform, Canny edge detection, and template matching techniques. The distances between two markers are measured to determine the accuracy of the pixel sensor alignment before and after assembly. The proposed methods exhibit an accuracy exceeding 99% and demonstrate high speed analysis. The average processing times for detecting the circle and cross markers are 105.9 ms/image and 113.8 ms/image, respectively. The sensor alignment of the detector must be adjusted to a high precision standard. However, recent studies have shown deviations of up to 5 μ m above the desired value in the measured sensor position. Such deviations do not represent a major issue, nevertheless it is important to measure them in order to speed-up and make more accurate the recursive track-based alignment procedure used to reconstruct the position of each pixel sensor in the tracking detector. The proposed method offers a promising solution to deliver precise and rapid measurements for a large number of examined objects.

Deep Learning for Inertial Sensor Alignment

Deep Learning for Inertial Sensor Alignment

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Cross-sensor domain adaptation for data-driven correction of pedestrian level wind measurement in urban environments using cup anemometers

Low-cost-sensor correction with strong generalization ability necessitates data-driven techniques to be practical. This study aims to use an ultrasonic anemometer (UA) as a reference sensor to correct multiple cup anemometers (CAs). Paired UA and CA measure the training data, while other CAs collect the test data. Yet, this introduces a distribution discrepancy in the training and test data from different CAs. As wind conditions are below the starting velocity, CA response mainly depends on its mechanical properties. Generally, preparing new training data using the same sensor or bias alignment can alleviate this problem. As an explanatory and efficient data preprocessing, we developed cross-sensor domain adaptation (CSDA) by signal decomposition to reconstruct measurements from two CAs. CSDA contributes to drawing close the distribution of two CA responses by erasing the short-term fluctuations of their measures at low wind speeds. Further, a clustering-based artificial neural network (CANN) is adopted as a transfer-learning model to predict the samples from CA to UA, facing the changing wind conditions. CANN accuracy is assessed at three locations. Comparing the CANN in the single-sensor and cross-sensor usage cases, CSDA-CANN achieved the highest accuracy. Our method can be applied to wind comfort, wind energy, urban sustainability, etc.

Gravity Compensation Techniques for Enhancing Optical Performance in Satellite Multi-band Optical Sensor

This paper discusses a gravity compensation technique designed to reduce wavefront error caused by gravity during the assembly and alignment of satellite multi-band optical sensor. For this study, the wavefront error caused by gravity was analyzed for the opto-mechanical structure of multi-band optical sensor. Wavefront error, an indicator of optical performance, was computed by using the displacements of optics calculated through structural analysis and optical sensitivity calculated through optical analysis. Since the calculated wavefront error caused by gravity exceeded the allocated budget, the gravity compensation technique was required. This compensation technique reduces wavefront error effectively by applying the compensation load to the appropriate position of the housing tube. This method successfully meets the wavefront error budget for all bands. In the future, a gravity compensation equipment applying this technique will be manufactured and used for assembly and alignment of multi-band optical sensor.

Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.

Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture data as a reference and for computing virtual inertial data such that the desired solution was known exactly. We generated the inertial tracking simulations by formulating optimal control problems that tracked virtual acceleration and angular velocity while minimizing effort without requiring a task constraint or an initial state. To evaluate the proposed approach, we reconstructed three trials each of straight running, curved running, and a v-cut of 10 participants. We compared the estimated inertial signals and biomechanical variables of the marker and inertial tracking simulations. The inertial data was tracked closely, resulting in low mean root mean squared deviations for pelvis translation (≤20.2mm), angles (≤1.8 deg), ground reaction forces (≤1.1 BW%), joint moments (≤0.1 BWBH%), and muscle forces (≤5.4 BW%) and high mean coefficients of multiple correlation for all biomechanical variables . Accordingly, our results showed that optimal control simulations tracking 3D inertial data could reconstruct the kinematics and kinetics of individual trials of all running motions. The simulations led to mutually and dynamically consistent kinematics and kinetics, which allows researching causal chains, for example, to analyze anterior cruciate ligament injury prevention. Our work proved the feasibility of the approach using virtual inertial data. When using the approach in the future with measured data, the sensor location and alignment on the segment must be estimated, and soft-tissue artifacts are potential error sources. Nevertheless, we demonstrated that optimal control simulation tracking inertial data is highly promising for estimating 3D kinematics and kinetics for a comprehensive biomechanical analysis.

Development of an on-machine surface measurement system for an ultra-precision five-axis machine

Ever-increasing demand for complex freeform surfaces machined by ultra-precision five-axis machines necessitates efficient on-machine surface measurement (OMSM) systems. While OMSM offers significant advantages in metrology efficiency and machining accuracy, integrating it with these machines presents a challenge due to complex data synchronization and processing. This paper addresses this challenge by proposing a dedicated controller for synchronized acquisition of sensor measurement data and machine coordinate data. Additionally, a straightforward sensor alignment method is presented. The effectiveness of the proposed OMSM system is demonstrated through successful 2D and 3D measurements, significantly streamlining the traditional time-consuming offline coordinate measuring machine (CMM) and optical profilometer measurements.

Disagreement Matters: Exploring Internal Diversification for Redundant Attention in Generic Facial Action Analysis

This paper demonstrates the effectiveness of a diversification mechanism for building a more robust multi-attention system in generic facial action analysis. While previous multi-attention (e.g., visual attention and self-attention) research on facial expression recognition (FER) and Action Unit (AU) detection have been thoroughly studied to focus on ”external attention diversification”, where attention branches localize different facial areas, we delve into the realm of ”internal attention diversification” and explore the impact of diverse attention patterns within the same Region of Interest (RoI). Our experiments reveal that variability in attention patterns significantly impacts model performance, indicating that unconstrained multi-attention plagued by redundancy and over-parameterization, leading to sub-optimal results. To tackle this issue, we propose a compact module that guides the model to achieve self-diversified multi-attention. Our method is applied to both CNN-based and Transformer-based models, benchmarked on popular databases such as BP4D and DISFA for AU detection, as well as CK+, MMI, BU-3DFE, and BP4D+ for facial expression recognition. We also evaluate the mechanism on Self-attention and Channel-wise attention designs for improving their adaptive capabilities in multi-modal feature fusion tasks. The multi-modal evaluation is conducted on BP4D, BP4D+, and our newly developed large-scale comprehensive emotion database BP4D++, which contains well-synchronized and aligned sensor modalities, addressing the scarcity of annotations and identities in human affective computing. We plan to release the new database to the research community, fostering further advancements in this field.

Engineered Fe3O4 nanoreactor as a high-performance hydrogen peroxide sensor

Nanozymes with peroxidase-mimicking activities have been extensively explored for hydrogen peroxide (H2O2) detection. However, the conventional nanozyme assay with suspensions suffer the limitations of restricted available surface, loss of short-lived radicals, and slow reaction in non-acidic conditions, thus have a greatly reduced detection sensitivity. We here proposed to overcome these drawbacks by constructing aligned nanoreactor sensor with Fe3O4 nanozymes distributed alongside the nanochannels of anodized aluminum oxide (termed as NR-Fe3O4), which allowed for a spatially confined nanozyme reactions (ca. 5 nm) operated under a filtration reaction mode. We presented the unique merits of NR-Fe3O4 on H2O2 detection compared to Fe3O4 nanozyme suspensions, together with the mechanism discussion about how the radical production and capturing got maximized under the confinement. As a result, our NR-Fe3O4 sensor featured a highly sensitive H2O2 detection with a linear concentration from 10 to 5000 nM (detection limit of 0.9 nM), expanded workable pH to neutral, and enhanced nanozyme stability for at least 8 h continuous run. We further showed the great feasibility of using NR-Fe3O4 sensor in complex water matrices for H2O2 detection, as it allowed for a pore rejection of background organics which in turn protected the internal radicals for a capturing and signal magnification. Our findings highlight the great interest of constructing confinement nanoreactors as an innovative approach of enhancing H2O2 detection sensitivity based on the nanozyme assay.

An Improved In-Flight Calibration Scheme for CSES Magnetic Field Data

The CSES high precision magnetometer (HPM), consisting of two fluxgate magnetometers (FGM) and one coupled dark state magnetometer (CDSM), has worked successfully for more than 5 years providing continuous magnetic field measurements since the launch of the CSES in February 2018. After rechecking almost every year’s data, it has become possible to make an improvement to the in-flight intrinsic calibration (to estimate offsets, scale values and non-orthogonality) and alignment (to estimate three Euler angles for the rotation between the orthogonalized sensor coordinates and the coordinate system of the star tracker) of the FGM. The following efforts have been made to achieve this goal: For the sensor calibration, FGM sensor temperature corrections on offsets and scale values have been taken into account to remove seasonal effects. Based on these results, Euler angles have been estimated along with global geomagnetic field modeling to improve the alignment of the FGM sensor. With this, a latitudinal effect in the east component of the originally calibrated data could be reduced. Furthermore, it has become possible to prolong the updating period of all calibration parameters from daily to 10 days, without the separation of dayside and nightside data. The new algorithms optimize routine HPM data processing efficiency and data quality.

Fabrication and verification of a single aligned nanofibre gas sensor based on non‐contact direct writing method

AbstractIn this letter, the non‐contact direct writing method for producing single aligned fibre utilizing polyvinylpyrrolidone (PVP) polymer has been explored. It has been shown that with the help of this method, nanofibres can be created in a controlled manner with designed structures. This form of nanofibres is being studied with the goal of using it in carbon monoxide (CO) gas sensors. The temperature required for the annealing operation was 500°C, and finally the authors obtained nanofibres with diameter of 200 nm. In addition, a simulation technique built on the Monte Carlo method has also been introduced. It is shown that this simulation method is compatible with the experimental results with reasonable accuracy. The results showed that the authors have a correlation of about 0.934 between experimental results obtained in 30 ppb of CO gas and its simulation data.

Multi-Normal-Inverse Wishart mixture distribution based nonlinear filter with applications

In Kalman filter-based spacecraft autonomous celestial navigation, Inertial/Celestial integrated navigation system and asynchronous multi-sensor data fusion, complex environments and sensor alignment errors are likely to lead to inaccurate statistical priors for the noise covariance matrices and the non-zero measurement noise mean vector (MNMV). To address this issue, this paper firstly proposes a Multi-Normal-Inverse Wishart (MNIW) mixture distribution modelling the joint probability density function (PDF) for one-step prediction and measure likelihood, then the MNIW mixture distribution is decomposed into a Gaussian hierarchy, and finally the posterior estimates of the state and variables are obtained using variational Bayesian technique. In this paper, a Multi-Normal-Inverse Wishart mixture distribution-based variational Bayesian extended Kalman filter (VB-EKF) is proposed, in which a first-order Taylor expansion is used to address the non-linear problem. The proposed filter can be used to address non-linear filtering problem with inaccurate noise covariance matrices and measurement bias. Simulations of spacecraft autonomous celestial navigation validate the effectiveness and superiority of the proposed filter.

Wood-based aligned carbon microtubule array flexible strain sensor with high sensitivity

Wood-based aligned carbon microtubule array flexible strain sensor with high sensitivity

From two sensors to a single sensor: Better understanding of oxygen–sulfide interfaces

AbstractIntense biogeochemical transformations in sediments and biofilms may occur over sub‐mm distances. Our current understanding of those transformations in such narrowly stratified environments has been facilitated by the introduction of microsensors. Until now most studies have been conducted using individual sensors for the various chemical species, and careful vertical alignment of the sensor tips is then essential for the meaningful interpretation of the resulting data. For instance, the determination of total dissolved sulfide (TDS) at high resolution requires perfect alignment of sensors for H2S and pH, as the pKa for H2S is close to ambient pH. In this study, we show how a recently developed TDS sensor and a new combined H2S/O2microsensor can improve the analysis of sulfidic environments including the oxygen–sulfide interface. The TDS sensor does not require pH correction unlike the conventional H2S sensor, and it thus eliminates the need for a simultaneous pH measurement. The combined sensor allows for perfect alignment of H2S and O2micro profiles and makes it possible to not only more accurately estimate fluxes, but also to determine overlapping zones of oxygen and dissolved sulfide at very high resolution.

SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, these methods are inapplicable for Multivariate Time-Series (MTS) data. MTS data are collected from multiple sensors, each of which follows various distributions. However, most UDA methods solely focus on aligning global features but cannot consider the distinct distributions of each sensor. To cope with such concerns, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA to reduce the domain discrepancy at both the local and global sensor levels. At the local sensor level, we design the endo-feature alignment to align sensor features and their correlations across domains, whose information represents the features of each sensor and the interactions between sensors. Further, to reduce domain discrepancy at the global sensor level, we design the exo-feature alignment to enforce restrictions on the global sensor features. Meanwhile, MTS also incorporates the essential spatial-temporal dependencies information between sensors, which cannot be transferred by existing UDA methods. Therefore, we model the spatial-temporal information of MTS with a multi-branch self-attention mechanism for simple and effective transfer across domains. Empirical results demonstrate the state-of-the-art performance of our proposed SEA on two public MTS datasets for MTS-UDA. The code is available at https://github.com/Frank-Wang-oss/SEA

A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection.

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7% and a training mean intersection over union of 99.5%. Also, the testing mean of the IoU was 94.4%, and the testing pixel accuracy was 96.2%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.

Construction of precision sMDT detector for the ATLAS Muon Spectrometer upgrade

This paper describes the small-diameter monitored drift-tube detector construction at the University of Michigan as a contribution to the ATLAS Muon Spectrometer upgrade for the high-luminosity Large Hadron Collider at CERN. Measurements of the first 30 chambers built at Michigan show that the drift tube wire position accuracy meets the specification of 20 μm. The positions of the platforms for alignment and magnetic field sensors are all installed well within the required precision. The cosmic ray test measurements show single wire tracking resolution of 100 ± 7 μm with an average detection efficiency above 99%. The infrastructure, tooling, techniques, and procedures for chamber production are described in detail. The results from the chamber quality control tests of the first 30 constructed chambers are reported.

Fiber-sensor alignment based on surface microstructures.

Conventional methods have relied on specialized imaging equipment and advanced fabrication process to solve the problem of accurately aligning a microsensor to an optical fiber which is critical for its detection efficiency. To dramatically lower the barrier to high-precision alignment, we present a technique much easier to implement and much lower in cost. By fabricating replicable alignment and proximity structures on the surface of the sensor chip, we can achieve accurate alignment and position the fiber tip very close to the sensor without damaging it. We introduce an easy setup to examine the alignment result and demonstrate accurate alignment of dummy sensors as small as 5μm×5μm. We use our alignment method to realize efficient input coupling for a superconducting transition-edge sensor as an example of fruitful adoption in many possible applications.