Range Of Sensors Research Articles

Spectrometric performance of SiC radiation detectors at high temperature

In this work, we have investigated the performance of a 4H–SiC radiation sensor in the temperature range from 25 °C to 450 °C to explore its compatibility as detector of fast ion losses in plasma diagnostic of future nuclear fusion reactors. To simulate the escape of fusion-born alpha particles in D-T (deuterium-tritium) fusion plasmas, spectroscopic measurements were carried out in a vacuum chamber by irradiating the detector with a 3.5 MeV alpha beam from a Tandem accelerator. The detector was found to have an energy resolution ≤2% over the entire temperature range analyzed. Relevantly, the excellent spectrometric capabilities of the device have allowed us to calculate from experimental data, with unprecedented accuracy, the average energy required to create a single electron-hole pair in 4H–SiC as a function of temperature.

Wide refractive index detection range sensors based on D-shape photonic crystal fiber with a nanoscale gold wire

Optical sensing based on photonic crystal fiber (PCF) is attracting interests. Currently, PCF sensing suffered from the insensitivity with the changes of environmental surroundings with low refractive index. A D-shape PCF supporting plasmonic resonances based on gold nanowires is designed to demonstrated the sensitivity optical sensing in the refractive index range from 1.00 to 1.30. The finite element method is performed to optimize the device parameters, including the diameter of the large and tiny air holes, the size of the gold nanowire, and the space between the air holes. Utilizing plasmon-induced confined electrical around gold nanowires, the predicted device performance in spectral and amplitude sensitivities are up to 19,400 nm/RIU and 5.15 × 10−6 RIU, respectively. It is believed that the designed sensor can promote the development of applications in biological and pharmaceutical.

Viewpoint Planning for Range Sensors Using Feature Cluster Constrained Spaces for Robot Vision Systems.

The efficient computation of viewpoints for solving vision tasks comprising multi-features (regions of interest) represents a common challenge that any robot vision system (RVS) using range sensors faces. The characterization of valid and robust viewpoints is even more complex within real applications that require the consideration of various system constraints and model uncertainties. Hence, to address some of the challenges, our previous work outlined the computation of valid viewpoints as a geometrical problem and proposed feature-based constrained spaces (C-spaces) to tackle this problem efficiently for acquiring one feature. The present paper extends the concept of C-spaces to consider multi-feature problems using feature cluster constrained spaces (GC-spaces). A GC-space represents a closed-form, geometrical solution that provides an infinite set of valid viewpoints for acquiring a cluster of features satisfying diverse viewpoint constraints. Furthermore, the current study outlines a generic viewpoint planning strategy based on GC-spaces for solving vision tasks comprising multi-feature scenarios effectively and efficiently. The applicability of the proposed framework is validated on two different industrial vision systems used for dimensional metrology tasks.

Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring.

The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population. Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ± 4.8 years, M/F = 51/67, duration of diabetes 5.4 ± 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c - glucose management index (GMI). B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI ≥ 0.5. Time below range was similar for both. Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI ≥ 0.5 may be an easy way to identify high-risk patients.

VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments

Having a profound understanding of the surrounding environment is considered one of the crucial tasks for the reliable operation of future self-driving cars. Light Detection and Ranging (LiDAR) sensor plays a critical role in achieving such understanding due to its capability to perceive the world in 3D. Similar to 2D perception tasks, current state-of-the-art methods in 3D perception tasks rely on deep neural networks (DNNs). However, the performance of 3D perception tasks, specially point-wise semantic segmentation, is not on par with their 2D counterparts. One of the main reasons is the lack of publicly available labelled 3D point cloud datasets (PCDs) from 3D LiDAR sensors. In this work, we are introducing the VoxelScape dataset, a large-scale simulated 3D PCD with 100K annotated point cloud scans. The annotations in the VoxelScape dataset include both point-wise semantic labels and 3D bounding box labels. Additionally, we used a number of baseline approaches to validate the transferability of VoxelScape to real 3D PCD for two challenging 3D perception tasks. The promising results have shown that training DNNs on VoxelScape boosted the performance of the 3D perception tasks on the real PCD. Furthermore, we are also releasing the proposed data generation pipeline for the research community to facilitate realistic simulation of 3D LiDAR point cloud data for different scenarios beyond those covered in our VoxelScape dataset. The VoxelScape dataset and the corresponding LiDAR simulation codes are publicly available at https://voxel-scape.github.io/dataset

A novel measurement method for ash deposition based on coplanar capacitance principle: Theoretical, numerical and experimental studies

Monitoring for the fouling and slagging is critical to ensure the stable operation of gasification system. This paper proposes a novel measurement method for the ash deposition based on coplanar capacitance principle. A theoretical analysis of electrostatic field is conducted to obtain the correlation between the coplanar capacitance value and the deposition thickness. A finite element simulation is carried out to discuss the electric field characteristics of ash depositing on the coplanar capacitor. It shows that the capacitance value initially increases and tends to be constant with the increase of deposition thickness. The lower distance between positive and negative electrodes is favorable to the capacitance value (signal strength) but unfavorable to the maximum measurable thickness. Furthermore, a cold state experiment is conducted to verify the correctnesses of the theoretical and numerical models, and a hot state experiment is carried out to study the capacitance characteristics of sensor in the high temperature range of 700 °C–1200 °C. Results show that the permittivity of the material is lower under the higher test frequency, which leads to the lower capacitance value of sensor. The complex impedance curve of ash is conformed to IBLC dielectric model, this explains that the sensor capacitance increases with the test temperature increasing caused by the increase of permittivity due to the enlargement of ash grain. The change of Si/Al content has little effect on the dielectric properties of ash, however, the higher Fe2O3 content or the lower CaO content results into the lower ash fusion point, larger grain size, and thence higher permittivity and sensor capacitance. The maximum measurable thicknesses of the sensors in the cold state and hot state are 11.2 mm and 10.0 mm, respectively, which are independent with the test frequency, test temperature, and ash component.

A split-luciferase lysate-based approach to identify small-molecule modulators of phosphatase subunit interactions

An emerging strategy for the therapeutic targeting of protein phosphatases involves the use of compounds that interfere with the binding of regulatory subunits or substrates. However, high-throughput screening strategies for such interfering molecules are scarce. Here, we report on the conversion of the NanoBiT split-luciferase system into a robust assay for the quantification of phosphatase subunit and substrate interactions in cell lysates. The assay is suitable to screen small-molecule libraries for interfering compounds. We designed and validated split-luciferase sensors for a broad range of PP1 and PP2A holoenzymes, including sensors that selectively report on weak interaction sites. To facilitate efficient hit triaging in large-scale screening campaigns, deselection procedures were developed to eliminate assay-interfering molecules with high fidelity. As a proof-of-principle, we successfully applied the split-luciferase screening tool to identify small-molecule disruptors of the interaction between the C-terminus of PP1β and the ankyrin-repeat domain of the myosin-phosphatase targeting subunit MYPT1.

Strain experimental modal analysis of an Euler–Bernoulli beam based on the thermoelastic principle

The strain mode shapes can be utilized as an indicator of vibration fatigue-hotspots and are utilized for predicting the distribution of damage intensity. However, the conventional approach for measurement, which involves using strain sensors, does not offer the necessary high spatial density required for accurately identifying critical locations or creating a comprehensive map of damage intensity. Non-contact methods have been employed to indirectly determine the full-field strain shapes, but when measuring kinematic quantities, a relation between kinematics and stress/strain must be known. For Euler–Bernoulli beam, the double spatial derivative is required which introduces a significant uncertainty. In contrast, by leveraging the thermoelastic principle, the full-field stress/strain response of an arbitrary structure can be directly measured using a high-speed infrared (IR) camera. The thermoelastic principle has not been extensively researched for strain experimental modal analysis (EMA). In this study, the hybrid EMA (based on one high-dynamic range sensor) was researched for thermoelastic identification of an Euler–Bernoulli beam. The minimum stress/temperature variation required to achieve accuracy comparable to scanning-laser kinematics-based strain mode shapes was investigated. The findings demonstrate that even when the noise floor is significantly higher than the signal, full-field strain mode shapes can be identified using IR cameras and the hybrid EMA method. By considering the minimum stress/temperature variation determined in this research (for aluminum and steel), the accuracy of thermoelasticity-based strain shapes can be evaluated during the experiment-design stage. While this research is theoretically and experimentally based on Euler–Bernoulli beam, generalization of the thermoelastic principle to arbitrary structure is feasible.

Improved building facade segmentation through digital twin-enabled RandLA-Net with empirical intensity correction model

The Architectural Engineering and Construction (AEC) industry can benefit from accurate building facade segmentation, which can provide valuable insights into building maintenance, urban planning, and security efforts. Light Detection and Ranging (LiDAR) sensors are effective in recognizing building facade components from (three-dimensional) 3D point clouds by registering 3D spatial coordinates and radiometric information, which reveal the spectral property of a scanned surface. Although the radiometric information (i.e., intensity feature) can be used for segmentation, its accuracy may be reduced by factors such as scanning geometry and external factors that affect the object’s radiometric information. To address this issue, this study proposes a robust and automated method for segmenting building facade components using LiDAR point cloud data and an empirical-based intensity correction model to ensure proper segmentation. The proposed method employs RandLA-Net, a deep learning model capable of effectively processing large-scale point cloud data, to classify building facade components based on their spatial features combined with corrected intensity features. By incorporating the proposed method into a digital twin, it is possible to perform accurate building facade segmentation and generate valuable insights into the building’s physical condition, energy efficiency, and aesthetic value in real-time. The effectiveness of the proposed method was experimentally validated using a school building facade, which demonstrated significant improvements in the recognition of facade components and highlighted the potential of digital twin-enabled building facade segmentation for the AEC industry.

A Semisynthetic Bioluminescence Sensor for Ratiometric Imaging of Metal Ions In Vivo Using DNAzymes Conjugated to An Engineered Nano‐Luciferase

AbstractDNA‐based probes have gained significant attention as versatile tools for biochemical analysis, benefiting from their programmability and biocompatibility. However, most existing DNA‐based probes rely on fluorescence as the signal output, which can be problematic due to issues like autofluorescence and scattering when applied in complex biological materials such as living cells or tissues. Herein, we report the development of bioluminescent nucleic acid (bioLUNA) sensors that offer laser excitation‐independent and ratiometric imaging of the target in vivo. The system is based on computational modelling and mutagenesis investigations of a genetic fusion between circular permutated Nano‐luciferase (NLuc) and HaloTag, enabling the conjugation of the protein with a DNAzyme. In the presence of Zn2+, the DNAzyme sensor releases the fluorophore‐labelled strand, leading to a reduction in bioluminescent resonance energy transfer (BRET) between the luciferase and fluorophore. Consequently, this process induces ratiometric changes in the bioluminescent signal. We demonstrated that this bioLUNA sensor enabled imaging of both exogenous Zn2+ in vivo and endogenous Zn2+ efflux in normal epithelial prostate and prostate tumors. This work expands the DNAzyme sensors to using bioluminescence and thus has enriched the toolbox of nucleic acid sensors for a broad range of biomedical applications.

A Semisynthetic Bioluminescence Sensor for Ratiometric Imaging of Metal Ions In Vivo Using DNAzymes Conjugated to An Engineered Nano-Luciferase.

DNA-based probes have gained significant attention as versatile tools for biochemical analysis, benefiting from their programmability and biocompatibility. However, most existing DNA-based probes rely on fluorescence as the signal output, which can be problematic due to issues like autofluorescence and scattering when applied in complex biological materials such as living cells or tissues. Herein, we report the development of bioluminescent nucleic acid (bioLUNA) sensors that offer laser excitation-independent and ratiometric imaging of the target in vivo. The system is based on computational modelling and mutagenesis investigations of a genetic fusion between circular permutated Nano-luciferase (NLuc) and HaloTag, enabling the conjugation of the protein with a DNAzyme. In the presence of Zn2+ , the DNAzyme sensor releases the fluorophore-labelled strand, leading to a reduction in bioluminescent resonance energy transfer (BRET) between the luciferase and fluorophore. Consequently, this process induces ratiometric changes in the bioluminescent signal. We demonstrated that this bioLUNA sensor enabled imaging of both exogenous Zn2+ in vivo and endogenous Zn2+ efflux in normal epithelial prostate and prostate tumors. This work expands the DNAzyme sensors to using bioluminescence and thus has enriched the toolbox of nucleic acid sensors for a broad range of biomedical applications.

Potential of Low-Cost Light Detection and Ranging (LiDAR) Sensors: Case Studies for Enhancing Visitor Experience at a Science Museum

A low-cost light detection and ranging (LiDAR) device has several advantages including being able to perform a wide range of angle measurements, less privacy concerns, and robustness to illumination variance owing to its use of infrared (IR) light. In this study, to enhance the visitor experience at a science museum, three case studies using low-cost LiDAR sensors are presented: (1) an interactive floor projection to learn about the phases of the Moon; (2) an information kiosk with touchless interaction and visitor tracking; and (3) a visitor tracking box with horizontal and vertical scanning. The proposed kiosk system uses a mirror to reflect a portion of the scanning plane of the LiDAR sensor, to allow the capture of touchless interactions, track visitor positions, and count the number of nearby visitors. The visitor tracking box also uses two detection planes reflected by a mirror: the vertical plane is for counting visitors crossing the scanning plane and the horizontal plane is for tracking visitor positions to generate the corresponding heat maps for the visualization of museum hotspots. A series of evaluation experiments were conducted at a science museum, whereby an accuracy of 85% was obtained to estimate the number of visitors, with an accuracy increasing in counting people taller than 140 cm. The interactive floor received a visitor rating of 4.3–4.4 on a scale of 1–5.

Eddy Current Testing Based Non-Destructive Inspection for Metal 3D Additive Manufacturing Objects With HTS-SQUID

In this work, we applied eddy-current-testing-based HTS-SQUID non-destructive inspection (ECT-based HTS-SQUID NDI) system for inspection of metal 3D additive manufacturing (AM) objects, which were made of AlSi10Mg powder with powder bed fusion (PBF) method. The inspection system combines an ECT probe with an HTS-SQUID gradiometer as high sensitive readout magnetic sensor in low frequency range to overcome disadvantage of conventional ECT. We prepared 5 samples with different densities by changing parameters in the PBF process. Densities of the samples #1-#5 were estimated to be 2.65, 2.64, 2.62, 2.58, and 2.51 g/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. The samples were measured by the inspection system with an excitation current of 20 mA at 1.6 kHz, and lift-off of about 1 mm. In inspection results, anomalous magnetic patterns appeared on the sample #3, #4 and #5. The inspection results were verified with X-ray CT scanning. Comparison between the ECT inspection and the X-ray CT results reveals that the positions of the anomalous magnetic patterns show strong correlation with the positions of high porosity existing in the samples.

Sorption-Induced Fiber Optic Plasmonic Gas Sensing via Small Grazing Angle of Incidence.

Sensing technologies based on plasmonic nanomaterials are of interest for various chemical, biological, environmental, and medical applications. In this work, w e report an incorporation strategy of colloidal plasmonic nanoparticles (pNPs) in microporous polymer for realizing distinct sorption-induced plasmonic sensing. This approach is demonstrated by introducing tin-doped indium oxide pNPs into a polymer with intrinsic microporosity (PIM-1). The composite film (pNPs-polymer) provides distinct and tunable optical features on the fiber optic (FO) platform that can be used as a signal transducer for gas sensing (e.g., CO2 ) under atmospheric conditions. The resulting pNPs-polymer composite demonstrates high sensitivity response on FO in the evanescent field configuration, provided by the dramatic response of modes above the total-internal-reflection angle. Furthermore, by varying the pNPs content in the polymer matrix, the optical behavior of the pNPs-polymer composite can be tuned to affect the operational wavelength by over several hundred nanometers and the sensitivity of the sensor in the near-infrared range. W e also show that the pNPs-polymer composite film exhibits remarkable stability, over a period of more than ten months, by mitigating the physical aging issue of the polymer. This article is protected by copyright. All rights reserved.

Drone Detection using Ultrasonic Sensors for Passive Forward Scattering Radar System

Passive Forward Scattering Radar (PFSR) is a great system that can be used to identify ground moving targets, especially in unsafe areas for reason such as protection and security. Although PFSR can detect any moving target, there are some limitations as PFSR cannot measure height and detect small target such as a drone flying in the sky. A drone is a good device based on the latest technologies, therefore it can also be very dangerous as it can fly in the sky entering prohibited areas that can, for example, breach a border protection system. By developing a system using ultrasonic range sensor interfacing with Raspberry Pi (RPi) and also implementing Internet of Things (IoT) using mobile phone application, which is Telegram, as a security alarm system, this system can integrate with PFSR to be a better system. This project aims to improve the PFSR system with an ultrasonic range sensor remote measuring device. When set up next to the PFSR system, the ultrasonic range sensor has large distance measurement capabilities and use low power to operate. Since ultrasonic range sensors cannot work by themselves, the RPi was used as an operating host that helped to operate and collect data. Then, the data collected was saved into Comma Separated Values (CSV) files to be sent to Telegram mobile application later to the user as notification. The data collected from both systems were compared in order to relate that the ultrasonic range sensor system can indeed support the PFSR system. Since the result was obtained and analysis was done successfully, it can be concluded that the system can support the PFSR system, whereby as the drone height (cm) increased, the signal captured in time (sec) decreased. In addition, this system can contribute in many areas such as borders or streets, to detect speed of vehicles or as a traffic surveillance system and for some other reasons such as protection, security and research.

Development of Obstacle Detection System Based On the Integration of Different Based Sensor for Small-Sized UAV

Due to the physical size and weight limits of small unmanned aerial vehicles (UAVs), developing a reliable obstacle detection a system that can provide an effective and safe avoidance path is extremely difficult. Prior work has tended to use a vision-based sensor as the primary detecting sensor however, this has resulted in a high reliance on texture appearance and a lack of distance sensing capabilities. Furthermore, due to the inability to detect the free region, vision-based sensor detection systems have difficulty developing a trusted safe avoidance path. However, most wide spectrum range sensors are bulky and expensive, making them unsuitable for small UAVs. This projectaims to construct an obstacles detection system with the integration of various based sensors for a small UAV. The potential obstacles are identified by categorizing feature points identified in image frame. The suggested approach was tested in a real-world setting for both of the observed scenarios, which included various obstacles configurations. Two types of scenarios are experimented in this project consists of single frontal obstacles and presence of side obstacles alongside the frontal obstacles. On top of that, the position of the side obstacle is aligned to the frontal obstacle and then will be positioned in the increment of 20cm further from the frontal obstacle in order to analyze the outcome of the proposed algorithm. The proposed detection system had a possibility to be a trustworthy system even after utilizing the depth perception technique, however this does not imply that the proposed system is faultless. The results show that the suggested algorithm system detects and distinguishes between the potential obstacles and free region for a single frontal obstacle perfectly. However, there were improvements that should be implemented with the proposed system's ability of detection for multiple obstacles.

Analysis and Precision of Light Detection and Ranging Sensors Integrated in Mobile Phones as a Framework for Registration of Ground Control Points for Unmanned Aerial Vehicles in the Scanning Technique for Building Information Modelling in Archaeological Sites

The protection of heritage sites is one of the keys that our civilisation presents. That is why great efforts have been invested in order to protect and preserve movable and immovable property with a certain historical value, as is the case of archaeological sites scattered throughout the territory of southern Iberia (Spain) in the form of dolmens and negative structures dug into the ground, constituting a good sample of the megalithic culture in southern Spain. To study, manage and preserve these archaeological monuments, considered a set of cultural assets, various techniques and methodologies are required to facilitate the acquisition of three-dimensional geometric information. The Scan-to-BIM approach has become one of the most up-to-date work exponents to carry out these objectives. The appearance of LiDAR techniques, and recently their incorporation into smartphones through integrated sensors, is revolutionising the world of 3D scanning. However, the precision of these techniques is an issue that has yet to be addressed in the scientific community. That is why this research proposes a framework, through experimental measurement, comparison and knowledge of the limitations of this technology, to know the precision of the use of these smartphones, specifically the iPhone 13 Pro, as a measurement element to establish points of control with the aid of photogrammetry by unmanned aerial vehicles (UAVs) in archaeological sites. The results demonstrate a residual uncertainty of ±5 mm in the capture of GCPs from the mobile phone’s LiDAR light detection and ranging sensor, and there was a deviation of the measurements in a range between 0 and 28 m of distance between the GCPs of (0.021, 0.069) m.

Heat-driven self-cleaning glass based on fast thermal response for automotive sensors

High-performance transparent heaters, which can prevent a camera lens from frosting/icing and evaporate droplets on the surface of the lens, are one of the promising self-cleaning devices for automotive optical sensors such as an automotive camera and Light Detection and Ranging (LiDAR) sensor. However, many previous studies on transparent heaters have mainly focused on advanced materials and manufacturing technologies. For the commercialization of transparent heaters, practical methods to improve thermal response and evaluate the cleaning performance of contaminants must be investigated. Hence, we propose a heat-driven self-cleaning glass based on fast thermal response using overdrive voltage for automotive sensors. The proposed device was designed as a mesh-type patterned electrode for high transmittance and fabricated using the Micro-electro-mechanical-systems (MEMS) process. The proposed device generated heat when voltage was applied and reached 85 °C in approximately 4 sec when driven using an overdrive driving method. In addition, performing a test to remove droplets of various volumes generated on the surface of the proposed device, it was confirmed that droplets of various volumes could be removed within 30 sec. For a demonstration of the proposed concept, the heat-driven self-cleaning glass was applied to an automotive camera, and the image distorted by rainwater on the camera lens became clear when the glass was operated. We believe these experimental results are useful to commercialize transparent heaters for the next-generation automotive optical sensors.

Technical Note: Validation of Recon-3D, iPhone LiDAR for bullet trajectory documentation

Bullet trajectory documentation is an important part of shooting reconstructions. Manual methods are quite common, using a protractor and angle gauge, but more advanced methods using laser scanners and photogrammetry are also used for documentation. Past studies have shown that using terrestrial laser scanners (TLS) can reduce the variability in trajectory documentation. In 2020, Apple Inc. released the iPad Pro & iPhone 12 Pro with a Light Detection and Ranging (LiDAR) sensor, which effectively placed a low-cost laser scanner in mobile devices. In May of 2022, an iOS application focused on forensics called Recon-3D was released. Recon-3D uses Apple’s LiDAR sensor and fuses it with photogrammetry to produce 3D point cloud data in the e57 format. The purpose of this research was to compare the accuracy, repeatability, and quality of 3D data, using Recon-3D to the Faro Focus S350 laser scanner in bullet trajectory documentation. As a first test, twelve trajectory rods were installed on a wooden panel in a controlled, indoor environment, in order to optimize the best settings for scanning trajectory rods. Subsequently, a more practical exercise was run outdoors with a vehicle containing five trajectory rods. The mean horizontal angle error for the twelve trajectory rods installed on the wooden panel was 0.14° with a standard deviation of 0.30°. The mean error for the vertical angle was − 0.05° with a standard deviation of 0.25°. For the outdoor vehicle test, the mean error for the horizontal angle was 0.03° with a standard deviation of 0.28°. The mean error for the vertical angle was 0.22° with a standard deviation of 0.36°. These errors are similar to other studies which utilize the terrestrial laser scanner with mean errors well below 1°. Although further work is required to make Recon-3D a more robust application, preliminary results are promising and in line with previous studies where the terrestrial laser scanner has been used. Thus, Recon-3D appears suitable to document trajectory rods when used in shooting reconstructions.

Ultra-Broad Linear Range and Sensitive Flexible Piezoresistive Sensor Using Reversed Lattice Structure for Wearable Electronics.

Flexible pressure sensors have attracted significant attention owing to their broad applicability in wearable electronics and human-machine interfaces. However, it is still challenging to simultaneously achieve a broad sensing range and high linearity. Here, we present a reversed lattice structure (RLS) piezoresistive sensor obtained through a layer-level engineered additive infill structure via conventional fused deposition modeling three-dimensional (3D) printing. The optimized RLS piezoresistive sensor attained a pressure sensing range (0.03-1630 kPa) with high linearity (coefficient of determination, R2 = 0.998) and sensitivity (1.26 kPa-1) due to the structurally enhanced compressibility and spontaneous transition of dominant sensing mechanism of the sensor. It also exhibited great mechanical/electrical durability and a rapid response/recovery time (170/70 ms). This remarkable performance enables the detection of various human motions over a broad spectrum, from pulse detection to human walking. Finally, a wearable electronic glove was developed to analyze the pressure distribution in various situations, thereby demonstrating its applicability in multipurpose wearable electronics.