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.

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.

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)

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.

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.

《激光雷达测量技术与应用》课程教学设计与思考

在工程教育专业认证背景下,本文针对《激光雷达技术与应用》课程在以往的课程教学设计中存在的重理论轻实训、灌输式课堂教学、课程考核评价形式单一等问题进行了课程层面的教学设计模式改革探索,实现了教学目标、教学内容、教学方法、教学评价等环节与OBE教学理念的高度融合。实践证明,本课程教学设计改革成效显著,培养了学生自主创新学习意识和工程意识,提高了学生解决复杂工程问题的能力。本文旨在为地方转型高校基层专业教师,在具体课程教学中,如何准确把握、贯彻工程教育基本内涵和标准提供借鉴。

UAV Laser scanning technology:a potential cost-effective tool for micro-topography detection over wooded areas for archaeological prospection

Airborne laser scanning(ALS)has recently been identified as a potential tool in topographic mapping for archaeological prospection.However,most existing applications in this field refers to manned ALS systems,for which the high operation and maintenance costs limits its application in small-scale archaeological investigation.In this paper,we conducted an exploratory study on the application of the unmanned aerial vehicle(UAV)laser scanning(ULS)system in ancient micro-topography detection over wooded areas.Compared with manned ALS technology,we analyzed the advantages and potentials of ULS technology for archaeological applications.Then we outlined existing mainstream survey-grade UAV-based laser scanners,data processing and visualization approaches.Furthermore,we performed case studies in three cultural heritage sites in Zhejiang Province,China using two representative mainstream survey-grade ULS systems.Results were then verified by an in-site investigation.Finally,the correct selection of ULS devices,the planning of data acquisition missions and the use of appropriate data processing methods specifically for archaeological prospection were discussed.This paper provides a cost-effective and flexible solution for micro-topography detection in wooded areas.ULS technology,as demonstrated here,can be an important supplement to existing archaeological investigation methods,particularly for small-scale areas,and has promising prospects in archaeological applications.

Body-area sensor network featuring micropyramids for sports healthcare

Monitoring physiological signals of the human body can provide extremely important information for sports healthcare,preventing injuries and providing efficient guidance for individual sports.However,the signals related to human healthcare involve both subtle and vigorous signals,making it difficult for a sensor to satisfy the full-scale monitoring at the same time.Here,a novel conductive elastomer featuring homogeneously micropyramid-structured PDMS/CNT composite is used to fabricate highperformance piezoresistive sensors by a drop-casting method.Benefiting from the significant increase in the contact area of microstructure during deformation,the flexible sensor presents a broad detection range(up to 185.5 kPa),fast response/recovery time(44/13 ms),ultrahigh sensitivity(242.4 kPa–1)and excellent durability over 8,000 cycles.As a proof of concept,the as-fabricated pressure sensor can be used for body-area sports healthcare,and enable the detection of full-scale pressure distribution.Considering the fabulous sensing performance,the sensor may potentially become promising in personal sports healthcare and telemedicine monitoring.

Overcoming the limitations of 3D sensors with wide field of view metasurface-enhanced scanning lidar

Lidar, a technology at the heart of autonomous driving and robotic mobility, performs 3D imaging ofa complex scene by measuring the time of flight of returning light pulses. Many technological challenges,including enhancement of the observation field of view (FoV), acceleration of the imaging frame rate,improvement of the ambiguity range, reduction of fabrication cost, and component size, must besimultaneously addressed so that lidar technology reaches the performance needed to strongly impact theglobal market. We propose an innovative solution to address the problem of wide FoV and extendedunambiguous range using an acousto-optic modulator that rapidly scans a large-area metasurface deflector.We further exploit a multiplexing illumination technique traditionally deployed in the context of telecommu-nication theory to extend the ambiguity range and to drastically improve the signal-to-noise ratio of themeasured signal. Compacting our metasurface-scanning lidar system to chip-scale dimension would opennew and exciting perspectives, eventually relevant to the autonomous vehicles and robotic industries.

Surface cleaned Cu_(x)Pd_(y)alloy:Synthesis and their superior performances in hydrogen evolution reaction and biosensor

High cost and restricted activity of electrocatalysis are the major challenges for hydrogen generation and biosensors.In this work,we provided a one-pot synthesis of Cu_(x)Pd_(y)alloy nanoparticles(NPs)with controllable atomic ratio and“clean surface”.Benefiting from the preferable d-band structure,the Cu_(62)Pd_(38)NPs exhibited a lower overpotentials in the hydrogen evolution reaction(HER)over the full pH range.In the acidic media,Cu_(62)Pd_(38)NPs achieved a low overpotential of 28.12 mV for HER,which was 25.73%of Pd NPs.In the neutral solution,the overpotential by Cu_(62)Pd_(38)NPs is only 41.71%for that by uncleaned CuPd NPs.In alkaline media,the overpotential by Cu_(62)Pd_(38)NPs was declined from 38.01 to 20.20 mV after 720 min yielding hydrogen,which was only 53.14%for the initial overpotential.As applied in biosensor,the synergistic effect of Cu and Pd accelerated the kinetics of electrocatalytic process,resulting in an enhanced performance.The glucose sensor constructed by Cu_(67)Pd_(33)exhibited a wider detection range up to 100.0 mM.And the sensitivity is 379.4μA/(mM·cm^(2)),which is ca.4.63 and 14.09 folds for that by pure Cu NPs and Pd NPs,respectively.An optimal atomic percent would be conducive to optimize electrocatalytic activity of Cu_(x)Pd_(y)alloy.The volcano plots for Cu_(x)Pd_(y)would open up a new avenue for designing electrocatalysis with rationalized cost and optimized performance.

一种基于全等三角形的点云自动配准方法

点云配准是点云数据处理中比较关键的步骤,直接影响处理结果。经典的迭代最近点(iterative closest point,ICP)算法需要目标点云与源点云之间有良好的初始姿态,否则会遇到局部最优等问题。因此,提出了一种基于全等三角形的点云自动配准方法。该方法通过边长相等和面积相等来构造全等三角形,找到源点云与目标点云的对应点,建立源点云与目标点云的对应关系和转换参数最优估计,完成粗配准,再结合ICP算法进行精配准,实现点云的自动配准。结果表明,与四点一致集(4-points congruent sets,4PCS)配准算法和ICP算法的结合算法相比,所提算法能有效改善ICP算法对初值依赖的问题,并且配准精度有一定提升。