Range Of Sensors Research Articles

Ultrasensitive lightweight magnetic probe for non-destructive inspection of high-voltage overhead lines

For power utilities with comprehensive asset management programs, inspection of high-voltage line conductors is critical to ensure network reliability. Current non-destructive testing methods for ferromagnetic materials are still not completely adapted to perform these inspections quickly, safely and accurately. This paper presents the prototype of a novel probe that is specifically designed for the inspection of conductors steel core. It is based on a novel lightweight and symmetrical design intended for automated inspection by drone. The sensitivity of the new probe is achieved by compensating for the conductor’s imperfect centering during inspection and by creating a zero magnetic field point that allows the use of low range and accurate fluxgate sensors. Experimental results verify that the prototype is capable of detecting broken wires, quantifying metal area loss and inspecting steel core splice joints under unsaturated magnetization, which is the key element to achieve a lightweight probe.

Compact fiber sensor for pH measurement based on the composite effect of hydrogel deformation and LC refractive index variation.

A novel, to the best of our knowledge, type of compact pH fiber sensor combined with a hydrogel based on the whispering gallery mode (WGM) is proposed and integrates a liquid crystal (LC) microdroplet in a capillary in a compact structure as small as 180 µm. In the research, the hydrogel performs both as a supporting frame and a responsive material that causes morphological deformation of the LC microdroplet with pH variation. Moreover, a new phenomenon of pH-induced LC refractive index variation is observed and applied in the pH measurement, so that the acid itself can also lead the LC microdroplet structure transition. Thus, the WGM method is applied to detect the composite effect simultaneously to improve the sensing capability. The sensitivity of the sensor in the pH range from 4.55 to 6.86 reaches 3.19 nm/pH. The response time is short, within 60 s. The simple and compact structure of the sensor reduces the cost and enhances the stability, which is of great potential for biomedical pH measurement.

First validation of GEDI canopy heights in African savannas

Savannas have complex, discontinuous woody vegetation structures that vary greatly in vertical and spatial arrangement and change due to climatic, ecological and management impacts. While airborne laser scanning (ALS) data have provided detailed information on vertical vegetation structure and is widely used in ecological studies, it is lacking in availability and repeat frequency. Although the Global Ecosystem Dynamics Investigation (GEDI) waveform Light Detection and Ranging (LiDAR) sensor and algorithms were optimized for measuring dense forests, it was anticipated that GEDI metrics could provide useful characterization of lower stature, sparse savannas structures. This study provided the first baseline validation of Version 2 GEDI (L2A) relative height 98 (RH98) by comparing the on-orbit GEDI-RH98orb to the simulated GEDI-RH98sim derived from ALS data across diverse savanna vegetation. It furthermore determined the influence of various factors on error, e.g. algorithm setting group (SGs), beam type, day vs. night, beam sensitivity, and vegetation phenology. After applying quality flags, 22,813 GEDI footprints were analyzed across 11 sites. SGs 4–6 that are aimed at dense forests had much larger errors than SGs 1–3. The phenological conditions at the time of GEDI data acquisition had a very large influence on the error of RH98orb. During leaf-on conditions for savanna vegetation with RH98sim < 15 m, RH98orb was very accurate with R2 = 0.61, mean bias = −0.55 m, %bias = −11.1%, RMSE = 1.64 m and %RMSE = 29.8%. In leaf-off conditions where RH98sim < 15 m, RH98orb was less accurate with R2 = 0.43, mean bias = −1.47 m, %bias = −26.5%, RMSE = 2.03 m and %RMSE = 40.9%. During leaf-off conditions, the GEDI LiDAR signal at the start of the waveform may be weaker as it interacts with denuded branches and may be truncated as noise, leading to a large negative height bias. Therefore, assessments of deciduous vegetation structures should be conducted during leaf-on periods. In leaf-on conditions, GEDI's RH98orb was very accurate between canopy heights of 3 and 7 m, with a mean bias of −0.79 m (−10%). The bias of RH98orb was not influenced by canopy cover. Due to the GEDI LiDAR pulse width of 15.6 ns, the GEDI-RH98 data product cannot reliably estimate canopy heights of shrubs below 2.34 m and will require more complex deconvolution of the waveform. GEDI's RH98 accurately estimates the canopy height of trees between 3 and 15 m allowing assessment of canopy heights over vast savanna areas.

Control of Devices by Hand Gestures

Gesture control technology is gaining importance because of the rapid increase in automation. Laptop Control Using Hand Gestures is one such technique to detect gestures made by hand or body which act as an input to communicate with the PC or laptop. In this project we have made use of hardware components including Arduino UNO, Ultrasonic Sensors (Range Sensors) and connecting wires along with Arduino code and Python code for implementation of the project. The Ultrasonic sensor detects the hand gestures and this when integrated with Arduino and Python code performs actions accordingly on the laptop screen. This technology is rapidly in the field of VR, AR, 3D Design, medical, etc. It will be quite handy for paralyzed patients. Since they have great difficulty in controlling the mouse and typing on the keyboard, they can easily perform hand gestures which perform actions accordingly on the screen. One of the key areas which requires development is sensing gestures accurately. We feel that if we are successful in meeting our goals then we will consider ourselves as contributors to the future of natural gesture based interfaces. Keywords — Hand gesture, Ultrasonic sensors, Arduino UNO, Python

Stretchable and Highly Sensitive Strain Sensor Based on a 2D MXene and 1D Whisker Carbon Nanotube Binary Composite Film.

We fabricate a 2D MXene and 1D whisker carbon nanotube (WCNT) binary composite, where the MXene layer was sandwiched between two WCNT films, and assemble a flexible resistive-type strain sensor using this composite film. The deformations of the conductive networks trigged by the external mechanical stimuli cause the variations of the number of effective conductive paths, which result in the changes of the electric resistance of composite films. The resistances of the MXene/WCNT composite films that carry the strain information about the external mechanical stimuli are monitored. In addition, we demonstrate the role of the conductive MXene networks and the WCNT networks in responding to the external mechanical stimuli. The MXene networks dominate the variations of the resistance of the strain sensors in the low strain range. In the middle strain range, the deformations of both the MXene networks and the WCNT networks are responsible for the variations of the resistance of the strain sensors. In the high strain range, an "island bridge" like conductive network forms, where MXenes act as islands and WCNTs connect the adjacent MXene islands like bridges. The multiple types of conductive networks lead to the high sensitivity of the MXene/WCNT-based strain sensors over a wide strain range and a wide response window. This stretchable strain sensor exhibits good performances in detecting human muscle motions with a wide strain range and has the potentials of being applicable to wearable electronics.

Two-Dimensional Non-Carbon Materials-Based Electrochemical Printed Sensors: An Updated Review

Recently, there has been increasing interest in electrochemical printed sensors for a wide range of applications such as biomedical, pharmaceutical, food safety, and environmental fields. A major challenge is to obtain selective, sensitive, and reliable sensing platforms that can meet the stringent performance requirements of these application areas. Two-dimensional (2D) nanomaterials advances have accelerated the performance of electrochemical sensors towards more practical approaches. This review discusses the recent development of electrochemical printed sensors, with emphasis on the integration of non-carbon 2D materials as sensing platforms. A brief introduction to printed electrochemical sensors and electrochemical technique analysis are presented in the first section of this review. Subsequently, sensor surface functionalization and modification techniques including drop-casting, electrodeposition, and printing of functional ink are discussed. In the next section, we review recent insights into novel fabrication methodologies, electrochemical techniques, and sensors' performances of the most used transition metal dichalcogenides materials (such as MoS2, MoSe2, and WS2), MXenes, and hexagonal boron-nitride (hBN). Finally, the challenges that are faced by electrochemical printed sensors are highlighted in the conclusion. This review is not only useful to provide insights for researchers that are currently working in the related area, but also instructive to the ones new to this field.

Estimation of the Interaction Forces in a Compliant pHRI Gripper

Physical human–robot interaction (pHRI) is an essential skill for robots expected to work with humans, such as assistive or rescue robots. However, due to hard safety and compliance constraints, pHRI is still underdeveloped in practice. Tactile sensing is vital for pHRI, as constant occlusions while grasping make it hard to rely on vision or range sensors alone. More specifically, measuring interaction forces in the gripper is crucial to avoid injuries, predict user intention and perform successful collaborative movements. This work exploits the inherent compliance of a gripper with four underactuated fingers which was previously designed by the authors and designed to manipulate human limbs. A new analytical model is proposed to calculate the external interaction forces by combining all finger forces, which are estimated by using the gripper proprioceptive sensor readings uniquely. An experimental evaluation of the method and an example application in a control system with active compliance have been included to evaluate performance. The results prove that the proposed finger arrangement offers good performance at measuring the lateral interaction forces and torque around the gripper’s Z-axis, providing a convenient and efficient way of implementing adaptive and compliant grasping for pHRI applications.

Optimising the sensing volume of OPM sensors for MEG source reconstruction

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) has been hailed as the future of electrophysiological recordings from the human brain. In this work, we investigate how the dimensions of the sensing volume (the vapour cell) affect the performance of both a single OPM-MEG sensor and a multi-sensor OPM-MEG system. We consider a realistic noise model that accounts for background brain activity and residual noise. By using source reconstruction metrics such as localization accuracy and time-course reconstruction accuracy, we demonstrate that the best overall sensitivity and reconstruction accuracy are achieved with cells that are significantly longer and wider that those of the majority of current commercial OPM sensors. Our work provides useful tools to optimise the cell dimensions of OPM sensors in a wide range of environments.

Density variation-based background filtering algorithm for low-channel roadside lidar data

Light Detection and Range (LiDAR) sensor is considered will be widely deployed in the roadside infrastructure if massive production in the near future, as it can extract High-Resolution Micro-level Traffic Data (HRMTD) which is a cornerstone in Intelligent Transportation Systems (ITS) applications. In the field application, background filtering is the first and foremost step to accelerate HRMTD extraction efficiency and improve extraction precision. In this paper, we proposed a novel background filtering algorithm based on density variation for low-channel roadside LiDAR. First, we segmented the detected area into small cubes and analyzed the character of LiDAR points in the detected area by calculating the density variation of the point cloud in continuous time. Second, we constructed an index to distinguish the road user passing area and removed outliers through the DBSCAN algorithm. Third, we excluded the LiDAR points that were not in the passing area. In the experiments, object points obtained percentage, background points excluded percentage, and effective points percentage were used to evaluate the accuracy of background filtering methods. Compared to the state-of-the-art methods, our algorithm has higher filtering accuracy and can perform well in complex sites in real-time. Besides, the proposed algorithm has the best stability, reflecting that the accuracy of the proposed methods does not decrease significantly as distance increases.

Zn(Li)O nanorods for direct conversion X-ray sensors in the intra-oral energy range

Zn(Li)O nanorods for direct conversion X-ray sensors in the intra-oral energy range

A new combined penetrometer-dielectric-low frequency acoustic-electrical conductivity sensor for measuring the soil physical characteristics

Development of simple and economical sensors to measure the soil physical properties is of great importance in precision agriculture. Building on a previous study with a fusion of penetrometer, dielectric and high-frequency acoustic sensors, the present work was aimed to develop a combined penetrometer-dielectric-microphone-apparent electrical conductivity (ECa) sensor to present a cost-effective instrument for measuring the soil physical properties. The multi-sensor probe consists of a cone connected to a load cell, an integrated dielectric-ECa sensor and a microphone inside the cone to record the passive sound produced at the soil-cone interface. To evaluate the multi-sensor probe, soils with different textural compositions (clay content from 142 to 537 g kg-1) were remoulded at three levels of water content and three levels of bulk density. The probe was tested in vertical penetration tests using a hydraulic loading frame. The results of acoustic signal processing using principal component analysis showed satisfactory differentiation between coarse and fine-grained texture classes. The output of the dielectric sensor was related to soil volumetric water content with R2 = 0.81 with some texture dependency. With fusion of penetrometer, dielectric and microphone sensors, R2 increased to 0.9 with RMSE= 0.031 m3 m-3. The soil relative density was predicted from data fusion of penetrometer, dielectric and microphone sensors with a regression model with R2 = 0.86 and RMSE= 3.11%. The ECa sensor showed rational responses to the concentration of NaCl solution as well as decreased the RMSE of soil water content and relative density prediction when fused with the other sensors. The multi-sensor probe developed is an economical instrument for simultaneously measuring the soil physical properties. Field evaluations are needed to further evaluate the sensor in wider range of soil texture and soil characteristics as well as in on-the-go measurement to be applied in precision agriculture.

FEC: Fast Euclidean Clustering for Point Cloud Segmentation

Segmentation from point cloud data is essential in many applications, such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. A fast solution for point cloud instance segmentation with small computational demands is lacking. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a point-wise scheme over the cluster-wise scheme used in existing works. The proposed method avoids traversing every point constantly in each nested loop, which is time and memory-consuming. Our approach is conceptually simple, easy to implement (40 lines in C++), and achieves two orders of magnitudes faster against the classical segmentation methods while producing high-quality results.

Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage.

LIMU-BERT

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for a wide range of sensing applications. Most existing works require substantial amounts of wellcurated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. This article presents a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. With the representations learned via our model, task-specific models trained with limited labeled samples can achieve superior performances in typical IMU sensing applications, such as Human Activity Recognition (HAR).

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition.

Increasingly, the task of detecting and recognizing the actions of a human has been delegated to some form of neural network processing camera or wearable sensor data. Due to the degree to which the camera can be affected by lighting and wearable sensors scantiness, neither one modality can capture the required data to perform the task confidently. That being the case, range sensors, like light detection and ranging (LiDAR), can complement the process to perceive the environment more robustly. Most recently, researchers have been exploring ways to apply convolutional neural networks to 3-D data. These methods typically rely on a single modality and cannot draw on information from complementing sensor streams to improve accuracy. This article proposes a framework to tackle human activity recognition by leveraging the benefits of sensor fusion and multimodal machine learning. Given both RGB and point cloud data, our method describes the activities being performed by subjects using regions with a convolutional neural network (R-CNN) and a 3-D modified Fisher vector network. Evaluated on a custom captured multimodal dataset demonstrates that the model outputs remarkably accurate human activity classification (90%). Furthermore, this framework can be used for sports analytics, understanding social behavior, surveillance, and perhaps most notably by autonomous vehicles (AVs) to data-driven decision-making policies in urban areas and indoor environments.

Map-Free Lidar Odometry (MFLO) Using a Range Flow Constraint and Point Patch Covariances

We present a lightweight real-time method to extract 3D ego-motion using a range flow constraint equation, point patch covariance, and a least squares solution. Our method exploits the structured data provided by range sensors, like rotating LiDARs, to attain 6 DOF odometry without building a map or scan-matching. To evaluate the performance of MFLO, a quadrotor was flown in various environments, and results indicate that MFLO matches and sometimes exceeds the performance of other LiDAR-based odometry methods while using fewer computational resources. In underground environments, MFLO captured 95.7% of the total vertical displacement for a 17.5 m translation upwards through a missile silo, the most of any other LiDAR algorithm evaluated in this study, and captured 92.8% of the total translation for a 42 m translation through an underground mine. In a motion capture lab, MFLO performed with only a 0.89–2.87% displacement percent error and 1.03–2.97% in final position error comparing to ground truth, making it the most consistent LiDAR odometry algorithm without mapping.

Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation.

Depth sensor-based motion analysis systems are of interest to researchers with low cost, fast analysis capabilities, and portability; thus, their reliability is a matter of interest. Our study examined the agreement and reliability in estimating the basic shoulder movements of Azure Kinect, Microsoft's state-of-the-art depth sensor, and its predecessor, Kinect v2, by comparing them with the gold standard marker-based motion analysis system. In our study, the shoulder joint ranges of motion of 20 healthy individuals were analyzed during dominant-side flexion, abduction, and rotation movements. The reliability and agreement between methods were evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman method. Compared to the gold standard method, the old- and new-generation Kinect showed similar performance in terms of reliability in the estimation of flexion (ICC=0.86 vs. 0.82) and abduction (ICC=0.78 vs. 0.79) movements, respectively. In contrast, the new-generation sensor showed higher reliability than its predecessor in internal (ICC=0.49 vs. 0.75) and external rotation (ICC=0.38 vs. 0.67) movement. Compared to its predecessor, Kinect Azure has higher reliability in analyzing movements in a lower range and variability, thanks to its state-of-the-art hardware. However, the sensor should also be tested on multiaxial movements, such as combing hair, drinking water, and reaching back, which are the tasks that simulate extremity movements in daily life.

Online Distance Field Priors for Gaussian Process Implicit Surfaces

Gaussian process (GP) implicit surface models provide environment and object representations which elegantly address noise and uncertainty while remaining sufficiently flexible to capture complex geometry. However, GP models quickly become intractable as the size of the observation set grows—a trait which is difficult to reconcile with the rate at which modern range sensors produce data. Furthermore, naïve applications of GPs to implicit surface models allocate model resources uniformly, thus using precious resources to capture simple geometry. In contrast to prior work addressing these challenges though model sparsification, spatial partitioning, or ad-hoc filtering, we propose introducing model bias online through the GP’s mean function. We achieve more accurate distance fields using smaller models by creating a distance field prior from features which are easy to extract and have analytic distance fields. In particular, we demonstrate this approach using linear features. We show the proposed distance field halves model size in a 2D mapping task using data from a SICK S300 sensor. When applied to a single 3D scene from the TUM RGB-D SLAM dataset, we achieve a fivefold reduction in model size. Our proposed prior results in more accurate GP implicit surfaces, while allowing existing models to function in larger environments or with larger spatial partitions due to reduced model size.

THYROID ELASTOGRAPHY IN CHILDREN

The aim of the research was to study the stiffness of an unchanged thyroid gland using shear wave elastography with elastometry in children in different gender and age groups. 175 children with normal hormonal status aged from 3 to 17 were examined, 80 of them were girls and 95 boys. All children underwent ultrasound examination of the thyroid gland in the "supine" position of the examinees, supplemented by two-dimensional shear wave elastography on Aixplorer apparatus (Supersonic Imagine, France) using a linear sensor in the frequency range of 5-18 MHz. Five measurements of Young's modulus value of the right and left lobes of the thyroid gland were carried out. Using Multi Q-box software, the following values of Young's modulus (E) for each fraction were automatically determined: the mean (Emean), the maximum (Emax), the minimum (Emin), the standard deviation (SD). The study established a statistically significant increase in thyroid stiffness values in children with increasing age, statistically significant differences in the stiffness of thyroid right and left lobes (p = 0.003), the absence of gender differences in thyroid stiffness (median Emean in boys – 7.40 kPa, in girls – 6.80 kPa; p = 0.170). The technique of shear wave elastography that is offered by the authors can become an additional diagnostic tool in assessing the condition of the thyroid gland in children. The established values of thyroid tissue stiffness in children of different age groups can be recommended to be used for the purpose of timely diagnosis of thyroid pathology.

Simulation and sensitivity analysis of a plasmonic FET based sensor in visible spectral range under different design conditions

Simulation and sensitivity analysis of a plasmonic FET based sensor in visible spectral range under different design conditions