A dual-range Janus-structure pressure sensor with broaddetection range and high resolution combiningtriboelectricity and piezoelectricity

Enabling pressure sensors with high resolution and a broad detection range isof paramount importance yet challenging due to the limitations of each knownsensing method. Overlying different sensing mechanisms to achieve complementaryfunctions is a promising approach, but it often leads to increaseddevice thickness, crosstalk signals and complex signal channel management.Herein, we present a dual-functional conformable pressure sensor that adoptsa Janus thin film layout, enabling simultaneous piezoelectric and triboelectricsignal detection capabilities between just one electrode pair, showing a mostcompact device configuration. Notably, despite its thin thickness (80 μm for apackaged device), it exhibits a broad-range detection capability with high signalresolution and fast response time, demonstrating a distinct signal-relaycharacteristic corresponding to piezoelectricity and triboelectricity. Despite theslimness and simple structure, it shows an impressive signal resolution of0.93 V·kPa^(-1) in the range of 0.1–140 kPa and 0.05 V·kPa^(-1) in the range of140–380 kPa. Moreover, the device fabrication can be combined with thekirigami method to improve fitting to joint surfaces. This work introduces aninnovative paradigm for designing advanced pressure sensing mechanisms,enabling a single device that can meet diverse application scenarios through itssimplicity, slim layout, conformable, and self-powered characteristics to adaptto multiple scenarios.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Eggshell-inspired membrane—shell strategy for simultaneously improving the sensitivity and detection range of strain sensors

The tradeoff between sensitivity and detection range(maximum and minimum stretchability)is a key limitation in strain sensors;to resolve this,we develop an efficient and novel strategy herein to fabricate a highly sensitive and stretchable strain sensor inspired by the membrane-shell structure of poultry eggs.The developed sensor comprises a soft and stretchable surface-grafting polypyrrole(s-PPy)film(acting as the membrane)and a brittle Au film(acting as the shell),wherein both films complement each other at the electrical and mechanical levels.Au forms cracks under strain contributing to its high sensitivity and low detection limit,and s-PPy can bridge Au cracks and increase stretchability which has not been used in strain sensors before.The surface-grafting strategy not only enhances interface adhesion but also tunes the brittle property of native PPy to render it stretchable.Utilizing the synergetic effect of the membrane-shell complementary structure,the strain sensors achieve ultrahigh sensitivity(>10^(7)),large stretchability(100%),and an ultralow detection limit(0.1%),demonstrating significant progress in the field of strain sensors.The membrane-shell(Au/s-PPy)-structured strain sensor can successfully detect finger motion,wrist rotation,airflow fluctuation,and voice vibration;these movements produce strain in the range of subtle to marked deformations.Results evidence the ultrahigh performance and bright application prospects of the developed strain sensors.

Extraordinary tunable dynamic range of electrochemical aptasensor for accurate detection of ochratoxin A in food samples

We report the design of a sensitive,electrochemical aptasensor for detection of ochratoxin A(OTA)with an extraordinary tunable dynamic sensing range.This electrochemical aptasensor is constructed based on the target induced aptamer-folding detection mechanism and the recognition between OTA and its aptamers results in the conformational change of the aptamer probe and thus signal changes for measurement.The dynamic sensing range of the electrochemical aptasensor is successfully tuned by introduction of free assistant aptamer probes in the sensing system.Our electrochemical aptasensor shows an extraordinary dynamic sensing range of 11-order magnitude of OTA concentration from 10^−8 to 10^2 ng/g.Of great significance,the signal response in all OTA concentration ranges is at the same current scale,demonstrating that our sensing protocol in this research could be applied for accurate detections of OTA in a broad range without using any complicated treatment of signal amplification.Finally,OTA spiked red wine and maize samples in different dynamic sensing ranges are determined with the electrochemical aptasensor under optimized sensing conditions.This tuning strategy of dynamic sensing range may offer a promising platform for electrochemical aptasensor optimizations in practical applications.

Bioinspired anti-freezing 3D-printable conductive hydrogel microfibers for highly-sensitive and wide-range detection of ultralow and high strains

Soft strain sensors that can transduce stretch stimuli into electrical readouts are promising as sustainable wearable electronics.However,most strain sensors cannot achieve highly-sensitive and wide-range detection of ultralow and high strains.Inspired by bamboo structures,anti-freezing microfibers made of conductive poly(vinyl alcohol)hydrogel with poly(3,4-ethylenedioxythiphene)-poly(styrenesulfonate)are developed via continuous microfluidic spinning.The microfibers provide unique bamboo-like structures with enhanced local stress to improve both their length change and resistance change upon stretching for efficient signal conversion.The microfibers allow highlysensitive(detection limit:0.05%strain)and wide-range(0%-400%strain)detection of ultralow and high strains,as well as features of good stretchability(485%strain)and anti-freezing property(freezing temperature:-41.1°C),fast response(200 ms),and good repeatability.The experimental results,together with theoretical foundation analysis and finite element analysis,prove their enhanced length and resistance changes upon stretching for efficient signal conversion.By integrating microfluidic spinning with 3D-printing technique,the textiles of the microfibers can be flexibly constructed.The microfibers and their 3D-printed textiles enable highperformance monitoring of human motions including finger bending and throat vibrating during phonation.This work provides an efficient and general strategy for developing advanced conductive hydrogel microfibers as highperformance wearable strain sensors.

A review of ToF-based LiDAR

In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry.This technology has emerged as a focal point of research within the realm of 3D imaging.Owing to its relatively straightforward principles and exceptional performance,ToF technology finds extensive applications across various domains including human−computer interaction,autonomous driving,industrial inspection,medical and healthcare,augmented reality,smart homes,and 3D reconstruction,among others.Notably,the increasing maturity of ToF-based LiDAR systems is evident in current developments.This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems,alongside recent research advancements.It elucidates the innovative aspects and technical challenges encountered in both transmitter(TX)and receiver(RX),providing detailed discussions on corresponding solutions.Furthermore,the paper explores prospective avenues for future research,offering valuable insights for subsequent investigations.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Three-channel CMOS transimpedance amplifier for LiDAR sensor receiver

For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).

Volumetric assessment of hepatic grafts using a light detection and ranging system for 3D scanning:Preliminary data

BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.

Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Characteristics of Surface Plasmon Resonance Imaging lnterferometer

In recent life science research,identifying and characterizing entire biomolecular interaction networks is prerequisite to understanding cellular processes on a molecular and biophysical level.A surface plasmon resonance (SPR) imaging interferometer based on spatial phase modulation detection is presented to meet this demand.In this method,the SPR sensing chip surface is illuminated with collimated parallel light beam,and a Wollaston prism is introduced into the reflected light path to produce the interference of polarized light reflected from SPR sensing chip.Information of biomolecular interactions can be obtained by extracting the phase change from the SPR interference patterns.Using our interferometer,we made experiments on a series concentration of NaCl solutions to investigate its detection range,linearity,sensitivity and resolution.The results indicate that the SPR imaging interferometer is mainly sufficient for biomolecular interaction detection.

Mapping tree canopies in urban environments using airborne laser scanning (ALS):a Vancouver case study

Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.Methods: This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.Results: Prediction models based on Airborne Laser Scanning(ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses(berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97%of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.Conclusions: Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m~2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002) Keywords World Trade Center (WTC) - terrorism - emergency response - emergency management - ground zero - remote sensing - emergency operations - disasters - geographic information systems (GIS) - satellite imagery - synthetic aperture radar (SAR) - light detection and ranging imagery (LIDAR)

Laser diode drive method with narrow-width and high-peak current for multi-line LIDAR

Light detection and ranging (LIDAR) based on time of flight (TOF) method is widely used in many fields related to distance measurement. LIDAR generally uses laser diode (LD) to emit the pulsed laser with high peak power and short duration to ensure a large distance measurement range and eye safety. To achieve this goal, we propose a pulsed LD drive method producing the drive current with high peak and narrow pulse width. We analyze the key issues and related theories of the drive current generation based on this method and design an LD driver. A model of drive current generation is established and the influence of operating frequency on drive current is discussed. The LD driver is simulated by software and verified by experiments. The working frequency of the driver changes from 20 kHz to 100 kHz and the charging voltage is set at 130 V. The current produced by this driver has a duration of 8.8 ns and a peak of about 35 A, and the peak output optical power of the LD exceeds 75 W.

On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes

Background： Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species. Methods： We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning （ALS） data and used Most Similar Neighbor （MSN） and Seemingly Unrelated Regression （SUR） as examples of non-parametric and parametric prediction methods, respectively Results： The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species. Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4 % and 12.6-28.9 % based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes. Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR. Conclusions： According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes. Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.

Next Generation Scanning LIDAR Systems for Optimizing Wake Turbulence Separation Minima

Numerous studies have been performed to better understand the behavior of wake vortices with regards to aircraft characteristics and weather conditionsover the pastten years. These studies have led to the development of the aircraft RECATegorization(RECAT) programs in Europe and in USA. Its phase one focused on redefining distance separation matrix with six static aircraft wake turbulence categories instead of three with the current International Civil Aviation Organization(ICAO) regulations. In Europe, the RECAT-EU regulation is now entering under operational implementation atseveral key airports. As proven by several research projects in the past, LIght Detection And Ranging(LIDAR) sensors are considered as the ground truth wake vortex measurements for assessing the safety impact of a new wake turbulence regulation at an airport in quantifying the risks given the local specificities. LIDAR's can also be used to perform risk monitoring after the implementation. In this paper, the principle to measure wake vortices with scanning coherent Doppler LIDARs is described as well as its dedicated post-processing. Finally the use of WINDCUBELIDAR based solution for supporting the implementation of new wake turbulenceregulation is described along with satisfyingresults that have permitted the monitoring of the wake vortex encounter risk after the implementation of a new wake turbulence regulation.

Effects of laser beam divergence angle on airborne LIDAR positioning errors

The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.

Segments-based progressive TIN densification filter for DTM generation from airborne LIDAR data

Airborne light detection and ranging( LIDAR) has revolutionized conventional methods for digital terrain models( DTMs) acquisition. Ground filtering for airborne LIDAR is one of the core steps taken to obtain a high quality DTM. This paper presents a segments-based progressive TIN( triangulated irregular network) densification( SPTD) filter that can automatically separate ground points from non-ground points. The SPTD method is composed of two key steps: point cloud segmentation and clustering by iterative judgement. The clustering method uses the dual distance to obtain a set of seed points as a coarse spatial clustering process. Then the rest of the valid point clouds are classified iteratively. Finally,the datasets provided by ISPRS are utilized to test the filtering performance.In comparison with the commercial software Terra Solid,the experimental results show that the SPTD method in this paper can avoid single threshold restrictions. The expected accuracy of ground point determination is capable of producing reliable DTMs in the discontinuous areas.