3D Sensor Research Articles

Electromagnetic responses on microstructures of duplex stainless steels based on 3D cellular and electromagnetic sensor finite element models

Electromagnetic responses on microstructures of duplex stainless steels based on 3D cellular and electromagnetic sensor finite element models

BIOMECHANICAL ASSESSMENT OF SCAPULAR PLANE ELEVATION EXERCISE WITH DIFFERENT MODES OF RESISTANCE IN SUBJECTS WITH AND WITHOUT IMPINGEMENT SYNDROME.

Background: Strengthening the rotator cuff and scapular stabilizers is usually prescribed for subjects with shoulder pain and is done either using dumbbells or TheraBand. The resistance offered by elastic Theraband increases with increase in change from initial length and therefore the resistance offered by TheraBand, and dumbbell differs across the range of motion. Objectives: The purpose of the study is to evaluate the scapular kinematics and activity of prime mover (Anterior Deltoid) while using two different modes of resistance (TheraBand v/s dumbbell) in subjects with and without impingement syndrome. Methods: Twenty-two asymptomatic subjects and 12 subjects with impingement syndrome were included. 3D motion sensors were attached over the humerus, scapulae and trunk to capture movements of the shoulders as the subject performed scapular elevation without load, with yellow Theraband or 2 Kg Dumbbell. Simultaneous electromyography (EMG) activity of anterior Deltoid was measured and analyzed as percentage of maximum voluntary contraction. Results: During arm elevation, scapular Upward rotation was significantly greater at 90° with TheraBand® as compared to unloaded. It was also greater at 120° as compared to unloaded and dumbbell conditions (p<0.001) during elevation and lowering phase. Scapula upward rotation was significantly lesser at 30° with TheraBand® as compared to other conditions. Conclusion: This study found significant downward rotation and anterior tilt of scapula with TheraBand during lowering phase of exercise as compared to dumbbell and unloaded. The alterations of kinematics may help therapists make informed decisions while making exercise prescriptions.

Associative graph convolution network for point cloud analysis

Since point cloud is the raw output of most 3D sensors, its effective analysis is in huge demand in the field of autonomous driving and robotic manipulation. However, directly processing point clouds is challenging because point clouds are a kind of disordered and unstructured geometric data. Recently, numerous graph convolution neural networks are proposed for introducing graph structure to point clouds yet far from perfect. Specially, DGCNN tries to learn local geometric of points in semantic space and recomputes the graph using nearest neighbors in the feature space in each layer. However, it discards all the information of the previous graph after each graph update, which neglects the relations between each dynamic update. To this end, we propose an associative graph convolution neural network (AGCN) which mainly consists of associative graph convolution (AGConv) and two kinds of residual connections. AGConv additionally considers the information from the previous graph when computing the edge function on current local neighborhoods in each layer, and it can precisely and continuously capture the local geometric features on point clouds. Residual connections further explore the semantic relations between layers for effective learning on point clouds. Extensive experiments on several benchmark datasets show that our network achieves competitive classification and segmentation results.

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.

Performance of 3D trench silicon pixel sensors irradiated up to [formula omitted]

Tracking particles at extreme fluences requires the accurate measurement of the charged particle timing at the pixel level in order to cope with the increased occupancy of HL-LHC experiments. Maintaining this precision throughout the operational life of the detector is crucial. This work demonstrates that the 55×55μm2 wide 150μm thick 3D trench-type pixels, developed by the TimeSPOT Collaboration, maintain the performance of non-irradiated sensors even after exposure to fluences as high as 1⋅10171MeVneqcm−2. The preliminary results of a beam test characterization using minimum ionizing particles at the SPS North area beam facility reveal that the charge collection and the time resolution of the irradiated sensors are comparable to those of non-irradiated ones, by increasing the operational bias voltage. A minor reduction in detection efficiency, approximately 4%, is observed post-irradiation. Currently, 3D trench-type pixels are among the fastest pixel detectors available for tracking charged particles and hold significant promise for future tracking system upgrades. This preliminary evaluation suggests their suitability for use in even harsher radiation environments than High Luminosity Large Hadron Collider (HL-LHC) such as future experiments at the Future Circular Hadron Collider (FCC-hh).

Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

PointCloud-At: Point Cloud Convolutional Neural Networks with Attention for 3D Data Processing

The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods to process the data. Point clouds are difficult to be used directly as inputs in several deep learning techniques. The difficulty is raised by the unstructured and unordered nature of the point cloud data. So, machine learning models built for images or videos cannot be used directly on point cloud data. Although the research in the field of point clouds has gained high attention and different methods have been developed over the decade, very few research works directly with point cloud data, and most of them convert the point cloud data into 2D images or voxels by performing some pre-processing that causes information loss. Methods that directly work on point clouds are in the early stage and this affects the performance and accuracy of the models. Advanced techniques in classical convolutional neural networks, such as the attention mechanism, need to be transferred to the methods directly working with point clouds. In this research, an attention mechanism is proposed to be added to deep convolutional neural networks that process point clouds directly. The attention module was proposed based on specific pooling operations which are designed to be applied directly to point clouds to extract vital information from the point clouds. Segmentation of the ShapeNet dataset was performed to evaluate the method. The mean intersection over union (mIoU) score of the proposed framework was increased after applying the attention method compared to a base state-of-the-art framework that does not have the attention mechanism.

Vision-guided robot calibration using photogrammetric methods

We propose novel photogrammetry-based robot calibration methods for industrial robots that are guided by cameras or 3D sensors. Compared to state-of-the-art methods, our methods are capable of calibrating the robot kinematics, the hand–eye transformations, and, for camera-guided robots, the interior orientation of the camera simultaneously. Our approach uses a minimal parameterization of the robot kinematics and hand–eye transformations. Furthermore, it uses a camera model that is capable of handling a large range of complex lens distortions that can occur in cameras that are typically used in machine vision applications. To determine the model parameters, geometrically meaningful photogrammetric error measures are used. They are independent of the parameterization of the model and typically result in a higher accuracy. We apply a stochastic model for all parameters (observations and unknowns), which allows us to assess the precision and significance of the calibrated model parameters. To evaluate our methods, we propose novel procedures that are relevant in real-world applications and do not require ground truth values. Experiments on synthetic and real data show that our approach improves the absolute positioning accuracy of industrial robots significantly. By applying our approach to two different uncalibrated UR3e robots, one guided by a camera and one by a 3D sensor, we were able to reduce the RMS evaluation error by approximately 85% for each robot.

3D shape sensor based on discrete-point Rayleigh reflectors inscribed by femtosecond pulses in multicore fibers

Fiber optic sensors which use reflectometry methods to process Rayleigh backscattering signal, are susceptible to any optical losses due to the inherently low level of Rayleigh backscattering in standard telecom optical fibers. In this work, we fabricate and study the shape sensor based on a multicore optical fiber with randomly spaced discrete-point reflectors inscribed in its cores by femtosecond laser pulses. By testing the sensor on different 3D shape samples, we demonstrate the robustness of the shape reconstruction accuracy to introduced optical losses of the signal up to 20 dB with the relative reconstruction error being less than 4 %. In contrast, such level of optical losses dramatically increases the reconstruction error when the unmodified fiber cores are used. A higher signal-to-noise ratio together with low birefringence of the inscribed point reflectors enable accurate shape sensing including the forms with low curvature.

Sensor response and radiation damage effects for 3D pixels in the ATLAS IBL Detector

Pixel sensors in 3D technology equip the outer ends of the staves of the Insertable B Layer (IBL), the innermost layer of the ATLAS Pixel Detector, which was installed before the start of LHC Run 2 in 2015. 3D pixel sensors are expected to exhibit more tolerance to radiation damage and are the technology of choice for the innermost layer in the ATLAS tracker upgrade for the HL-LHC programme. While the LHC has delivered an integrated luminosity of ≃ 235 fb-1 since the start of Run 2, the 3D sensors have received a non-ionising energy deposition corresponding to a fluence of ≃ 8.5 × 1014 1 MeV neutron-equivalent cm-2 averaged over the sensor area. This paper presents results of measurements of the 3D pixel sensors' response during Run 2 and the first two years of Run 3, with predictions of its evolution until the end of Run 3 in 2025. Data are compared with radiation damage simulations, based on detailed maps of the electric field in the Si substrate, at various fluence levels and bias voltage values. These results illustrate the potential of 3D technology for pixel applications in high-radiation environments.

3D Light-Direction Sensor Based on Segmented Concentric Nanorings Combined with Deep Learning.

High-precision, ultra-thin angular detectable imaging upon a single pixel holds significant promise for light-field detection and reconstruction, thereby catalyzing advancements in machine vision and interaction technology. Traditional light-direction angle sensors relying on optical components like gratings and lenses face inherent constraints from diffraction limits in achieving device miniaturization. Recently, angle sensors via coupled double nanowires have demonstrated prowess in attaining high-precision angle perception of incident light at sub-wavelength device scales, which may herald a novel design paradigm for ultra-compact angle sensors. However, the current approach to measuring the three-dimensional (3D) incident light direction is unstable. In this paper, we propose a sensor concept capable of discerning the 3D light-direction based on a segmented concentric nanoring structure that is sensitive to both elevation angle (θ) and azimuth angle (ϕ) at a micrometer device scale and is validated through simulations. Through deep learning (DL) analysis and prediction, our simulations reveal that for angle scanning with a step size of 1°, the device can still achieve a detection range of 0∼360° for ϕ and 45°∼90° for θ, with an average accuracy of 0.19°, and DL can further solve some data aliasing problems to expand the sensing range. Our design broadens the angle sensing dimension based on mutual resonance coupling among nanoring segments, and through waveguide implementation or sensor array arrangements, the detection range can be flexibly adjusted to accommodate diverse application scenarios.

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study.

Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402. PRR1-10.2196/63306.

Self‐Powered and 3D Printable Soft Sensor for Human Health Monitoring, Object Recognition, and Contactless Hand Gesture Recognition

AbstractA new galvanic cell design of a self‐powered and 3D‐printable soft sensor showing health monitoring, object recognition, and contactless hand gesture recognition, is reported. The soft sensor consists of a 3D‐printed poly(acrylic acid) (PAA) hydrogel electrolyte layer, a soft anode layer, and a soft cathode layer. The anode layer is a 3D‐printed and Cu2+ cross‐linked poly(N,N‐dimethylacrylamide‐co‐3‐alanine‐2‐hydroxypropylmethacrylate) (PDA) hydrogel dispersed with Cu metal particles (PDA/Cu2+/Cu hydrogel), while the cathode layer is a bottom thin layer of the PAA hydrogel containing MnO2 (PAA/MnO2). Using graphite films as the soft electrodes, the soft sensor is finally assembled. The soft sensor has high force and temperature sensitivities. It gives different electric current responses under stretching, bending, pressing, and impact loading. The soft sensor is demonstrated to be useful in detecting human motion and physiological activities, e.g., breath. Based on the force and contactless temperature sensitivities, the soft sensor is used to recognize human hand gestures and plastic balls with different diameters. This 3D printable soft sensor with self‐powering, contactless motion capturing, and multi‐pimulus sensing capabilities illustrates a new pathway to make soft sensory devices for healthcare and human‐machine interaction applications.

3D fabric-based piezoresistive pressure sensor: design, preparation, performance and simulation

3D fabric-based piezoresistive pressure sensor: design, preparation, performance and simulation

Alignment of a 360° image with posed color images for locally accurate texturing of 3D mesh

With the popularity of 3D content like virtual tours, the challenges of 3D data registration have become increasingly significant. The registration of heterogeneous data obtained from 2D and 3D sensors is required to create photo-realistic 3D models. However, the alignment of 2D images with 3D models introduces a significant challenge due to their inherent differences. This article introduces a rigorous mathematical approach to align a 360° image with its corresponding 3D model generated from images with known camera poses followed by texture projection on the model. We use Scale-Invariant Feature Transform (SIFT) feature descriptors enhanced with a homography-based metric to establish correspondences between the faces of a cubemap and the posed images. To achieve optimal alignment, we use a non-linear least squares optimization technique with a custom objective function. Subsequently, the outcomes of the alignment process are evaluated through texturing using a customized raytracing algorithm. The resulting projections are compared against the original textures, with a comprehensive assessment of the alignment's fidelity and precision.

3D printed oxygen gas sensor with a Pt-nanoparticles decorated N-doped carbon catalyst and an amine-polymer composited ionic liquid gel electrolyte

A 3D-printed electrochemical oxygen gas sensor was developed using a Pt nanoparticle-decorated N-doped carbon (PtNC) catalyst and an ionic liquid gel electrolyte incorporated with polyethyleneimine. At first, N-doped carbon particles were synthesized hydrothermally, followed by the decoration of a tiny amount of Pt nanoparticles using a microwave reactor. The working electrode was printed with a composite of high-impact polystyrene (HIPs), graphite powder (Gr), and a PtNC catalyst (HIPs/Gr/PtNC), then counter and reference electrodes were separately printed without the PtNC catalyst. The sensing materials were characterized using various analytical methods, including SEM, XPS, Raman, ICP-AES, CV, and EIS. Prior to detecting oxygen gas, the 3D sensor surface was pretreated with potential cycling in an acidic solution. Additionally, a composite gel electrolyte was prepared by incorporating polyethyleneimine into an ionic liquid, which was then coated onto the sensor surface to enhance sensitivity and ensure long-term stability. The analytical parameters using the final sensor were optimized in terms of Pt contents, 3D-printing polymer type, and ionic liquid gel concentration. The amperometric measurements demonstrated a wide dynamic range from 0.1 % to 100 % with the detection limit of 0.13 ± 0.05 % at a 90 % response time of 3.6 ± 0.4 s. To validate the reliability of the proposed sensor, the measurement of O2 content was performed for room air and standard oxygen gas, revealing reliable and stable results for six months.

Multi-receptor skin with highly sensitive tele-perception somatosensory.

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a ΔV/Δd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

ScarfingTool: An Advanced tool to repair Carbon Fiber Reinforced plastic

Carbon Fiber Reinforced Plastic (CFRP) is going to assume more and more importance in mechanical and aerospace engineering in the near future. This is due to its intrinsic high lightness and resistance, if compared to traditional metallic materials. CFRP is characterized by a proper production and repair technology. The tool showed in this paper is conceived to repair and verify the restoration of such kind of material. Its application is limited to the first part of the repair process: in jargon the scarfing operation, otherwise called “defect removal”. This operation is similar to a high-speed grinding.For this reason, the designed tool can be easily applied to other contexts, such as sanding, polishing or deburring.Basically, the ScarfingTool is composed by a high-speed electro-spindle and a 3D smart snapshot sensor. This tool is conceived to be as light as possible to be installed on a light collaborative robot. Moreover, its specific design assures great compliance that, as needed, can be regulated. A high efficiency suction inlet completes the tool and guarantees a clean environment during operation. The scarfing process has already been dealt with in [1] to which the interested reader is addressed.This work is licensed under CC BY-SA 4.0. (https://creativecommons.org/licenses/by-sa/4.0)

3D hollow mesoporous α-Fe2O3 sensor detecting liquified petroleum gas and humidity for diagnosing sleep hypopnea-apnea syndrome

Sensors utilizing mesoporous materials have garnered significant interest; however, they are primarily designed for detecting a single analyte. In this study, we successfully synthesized a three-dimensional (3D) hollow mesoporous α-Fe2O3 using a nanocasting process for the purpose of sensing LPG and humidity. The α-Fe2O3 obtained in this study maintained the cubic framework structure of MCM-48, demonstrating a mesoporous and hollow architecture. The Liquified Petroleum Gas (LPG) sensing characteristics of mesoporous α-Fe2O3 were examined at different operating temperatures (170–210 °C). The gas sensing response was optimized 99.3 % at 190 °C and the response/recovery time comes out to be 11.79 s & 0.84 s respectively. The humidity sensor's traits were evaluated at room temperature, with relative humidity (RH%) ranging from 11 % to 98 %, using mesoporous α- Fe2O3. The response/recovery time of mesoporous α-Fe2O3 as a humidity sensor comes out to be 6.3 s & 7.2 s respectively. The highly sensitive nature of the sensor along with negligible hysteresis, excellent repeatability and considerable stability for 30 days makes it a promisable candidate for real-time subtle respiration monitoring. Meanwhile, mesoporous α-Fe2O3 humidity sensor is also utilized for evaluation of sleep hypopnea-apnea syndrome.