A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence

Ocular surface squamous neoplasia(OSSN)is a common eye surface tumour,characterized by the growth of abnormal cells on the ocular surface.OSSN includes invasive squamous cell carcinoma(SCC),in which tumour cells penetrate the basement membrane and infiltrate the stroma,as well as non-invasive conjunctival intraepithelial neoplasia,dysplasia,and SCC in-situ thereby presenting a challenge in early detection and diagnosis.Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments,such as topical medicines,whereas advanced invasive lesions may need orbital exenteration,which carries a risk of death.Artificial intelligence(AI)has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management.AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN,aiding ophthalmologists in early detection and diagnosis.AI can also track and monitor lesion progression over time,providing objective measurements to guide treatment decisions.Furthermore,AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response.This manuscript highlights the role of AI in OSSN,specifically focusing on its contributions in early detection and diagnosis,assessment of lesion progression,treatment planning,telemedicine and remote monitoring,and research and data analysis.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

Small Object Detection via Precise Region-Based Fully Convolutional Networks

In the past several years,remarkable achievements have been made in the field of object detection.Although performance is generally improving,the accuracy of small object detection remains low compared with that of large object detection.In addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was oversampled.Results showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

Medical equipments high precise detection technology basing on morphology-harris operator

Medical equipments related to life safety of human, it is important to detect by a high precise method. Image mosaic which based on Harris corner operator is a commonly used method in this area;Harris operator has low calculation burden, it is simple and stable, so it is more effective comparing with other feature point extracted operators. But in this algorithm, corner points can only be detected in a single-scale, there may be losing information of corner points, causing corner point location offset, extracting false corner points because of noise. In order to solve this question, the acquired images should be processed by dilation and erosion operation firstly, then do image mosaic. Results show that image noise can be eliminated effectively after those morphological processes, as well as the false positive noise generated by image glitch. The success rate of image mosaic and detection accuracy can be greatly improved through the Morphology-Harris operator. Measurement of precision instruments which based on this new method will improve the measurement accuracy, and the research in this area will promote the further development of machine vision technology.

Detection Precision of Seedmeter for Large-granule Seeds

A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.

APPROACH OF IMPROVING PRECISION IN ULTRASONIC DOPPLER BLOODSTREAM SPEED MEASUREMENT BY CHAOS-BASED FREQUENCY DETECTING

It is critical for cerebral vascular disease diagnosis through Doppler to detect the maximum and the minimum of the carotid blood flow speed accurately. A kind of Duffing system under an external periodic power with dump is introduced in the letter, numerical analysis is carried out by four-order Runge-Kutta method. An oscillator array is designed according to the frequency of the ultrasonic wave. When the external signals are inputted, computational algorithm is used to scan the array in turn and analyze the result, and the frequency can be determined. Based on the methods above, detecting the carotid blood flow speed accurately is realized. The Signal-to-Noise Ratio (SNR) of-20.23dB is obtained by the result of experiments. In conclusion, the SNR has been improved and the precision of the measured bloodstream speed has been increased, which can be 0.069% to 0.13%.

Soil Nutrient Detection and Recommendation Using IoT and Fuzzy Logic

Precision agriculture is a modern farming practice that involves the usage of Internet of Things(IoT)to provide an intelligent farm management system.One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth.The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers.This can be done by identifying the deficient nutrients with the help of an IoT system.As traditional approach is time consuming,an IoT-enabled system is developed using the colorimetry principle which analyzes the amount of nutrients present in the soil and a fuzzy expert system is designed to recommend the quantity of fertilizers to be added in the soil.A set of 27 IF-THEN rules are framed using the Mamdani inference system by relating the input and output membership functions based on the linguistic variable for fertilizer recommendation.The experiments are conducted using MATLAB for different ranges of Nitrogen(N),Phosphorous(P)and Potassium(K).The NPK data retrieved by the system is sent to the ThingSpeak cloud and displayed on a mobile application that assists the farmers to know the nutrient information of their field.This system delivers the required nutrient information to farmers which results in efficient green farming.

Cell-free DNA liquid biopsy for early detection of gastrointestinal cancers:A systematic review

BACKGROUND Gastrointestinal tumors are among the most common cancer types,and early detection is paramount to improve their management.Cell-free DNA(cfDNA)liquid biopsy raises significant hopes for non-invasive early detection.AIM To describe current applications of this technology for gastrointestinal cancer detection and screening.METHODS A systematic review of the literature was performed across the PubMed database.Articles reporting the use of cfDNA liquid biopsy in the screening or diagnosis of gastrointestinal cancers were included in the analysis.RESULTS A total of 263 articles were screened for eligibility,of which 13 articles were included.Studies investigated colorectal cancer(5 studies),pancreatic cancer(2 studies),hepatocellular carcinoma(3 studies),and multi-cancer detection(3 studies),including gastric,oesophageal,or bile duct cancer,representing a total of 4824 patients.Test sensitivities ranged from 71% to 100%,and specificities ranged from 67.4% to 100%.Pre-cancerous lesions detection was less performant with a sensitivity of 16.9% and a 100% specificity in one study.Another study using a large biobank demonstrated a 94.9% sensitivity in detecting cancer up to 4 years before clinical symptoms,with a 61% accuracy in tissue-of-origin identification.CONCLUSION cfDNA liquid biopsy seems capable of detecting gastrointestinal cancers at an early stage of development in a non-invasive and repeatable manner and screening simultaneously for multiple cancer types in a single blood sample.Further trials in clinically relevant settings are required to determine the exact place of this technology in gastrointestinal cancer screening and diagnosis strategies.

桩端岩溶三维超声成像方法及应用研究

研究目的:岩溶地区大直径桩基础受桩端岩溶的威胁大,探查不清或处理不当时可能会出现桩基承载力不足等严重工程质量问题,现有探测方法难以在桩底复杂泥水环境下实现桩端三维精细化探测。本文基于超声相控阵全聚焦成像原理,提出一种全新的桩端岩溶三维探测方法,以实现对桩端一定范围内地质结构进行三维高精度成像。研究结论:(1)本方法和装备在桩底采集超声数据并成像,可对桩端一定深度和冲切角范围内岩溶三维高精度成像;(2)足尺模型试验表明本装备可探测桩端10 m以内溶洞且探测精度优于0.1 m;(3)工程实践表明本装备可有效查明桩端岩溶、破碎裂隙,探测结果准确可靠;(4)本装备能适应桩底各种恶劣的环境,探测过程简单高效,时效性强,对保障岩溶区桩基施工安全与质量控制具有重要的意义。

科技创新推动茶叶质量安全全程管控能力提升

现阶段我国茶叶质量安全水平高,饮茶安全有保障。茶叶饮用安全得益于茶叶科技创新日益发展,茶园绿色防控技术、清洁化茶叶加工技术、茶叶质量安全精准检测技术与风险评估理论创新,将茶叶质量安全从末端监管调整到从茶园到茶杯的全过程,为茶叶质量安全风险监测、风险控制与风险管理提供强有力的科学支撑。性诱剂化学生态防控技术、杀虫灯与诱虫色板物理诱杀技术、茶尺蠖与茶毛虫病毒生物防治技术、以草抑草绿色除草技术等茶园绿色防控技术从源头提升了茶叶农药残留控制水平;清洁化能源加热替代传统的燃煤燃材加热方式,显著降低了茶叶加工中环境污染物和重金属等有害物质的污染;质谱创新技术将茶叶质量安全检测提升到高通量精准检测与非靶向筛查水平,速测创新技术前移了茶叶农药残留的监测环节。文章介绍了近年来我国茶叶质量安全水平,分析了科技创新对茶叶质量安全全程管控能力提升的推动作用。

自动钻铆末端双目高精定位系统设计

设计了基于自动钻铆末端的双目高精定位系统。首先对视觉定位需求进行分析,设计双目视觉系统结构参数,基于视觉光源去除复合材料背景纹理;其次对轮廓进行提取拟合,预处理标定后结合ESPCN和Canny算子进行亚像素边缘提取,利用改进最小二乘法拟合计算圆心坐标;最后对视觉定位系统进行精度验证。结果表明:平面定位误差小于0.02mm,深度定位误差小于0.04mm,满足钻铆机器人的视觉测量要求。

药物基因检测对难治性精神分裂症患者治疗预后的影响

目的探讨药物基因组学检测结果应用对难治性精神分裂症患者疗效及药物不良反应的影响。方法选取2020年1月-2022年6月江苏省淮安市第三人民医院收治的100例难治性精神分裂症患者。依据基因检测结果指导用药,分别于治疗前、治疗4、8、12、16周使用阳性阴性症状量表(positive and egative symptom scale,PANSS)、临床疗效总评量表(clinical global impression,CGI)评定临床疗效,威斯康星卡片分类测验及个人和社会功能评估量表(personal and social function assessment scales,PSP)分别评定认知及社会功能改善情况,同时使用药物副反应量表(treatment emergent symptom scale,TESS)及做血常规、肝功能、肾功能和心电图等检查,以了解药物不良反应。结果治疗4周PANSS评分为(59.62±6.29)分,治疗8周PANSS评分为(54.83±7.37)分,治疗12周PANSS评分为(49.34±7.93)分,治疗16周PANSS评分(44.68±8.73)分,均低于治疗前的(62.93±5.55)分(P<0.001);治疗4、8、12和16周的CGI、PSP、威斯康星卡片分类测验等评分均优于治疗前(P<0.001)。治疗16周TESS评定与治疗4周比较,差异有统计学意义(P<0.01),但血常规、心电图、脑电图、肝功能和肾功能检查异常与否与治疗前比较,差异无统计学意义(P>0.05)。结论应用基因检测可显著提高难治性精神分裂症患者的临床疗效,且并不增加不良反应,因此基因检测可促进该病的临床合理用药、精准用药和个体化治疗。

基于改进Faster R-CNN的热轧带钢表面缺陷检测

热轧带钢是钢铁行业的重要产品,其表面缺陷是影响产品质量的重要因素。针对传统缺陷检测算法存在的过程繁琐、精度不足和效率低下等问题,提出一种基于改进更快速区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)的检测算法,实现对热轧带钢表面缺陷的高效、高精度检测。首先,采用特征相加的方法对底层细节特征和高层语义特征进行融合;然后,采用精准的感兴趣区域池化(precise region of interest pooling,Precise ROI Pooling)获取固定大小的特征向量,避免特征出现位置偏差;最后,利用均值偏移聚类算法对带钢数据集进行聚类,获得适用于热轧带钢表面缺陷检测的先验框尺寸。实验结果表明,所提算法在热轧带钢表面缺陷检测数据集上的平均精度均值达到了85.34%,检测速度为23.5帧/s,且鲁棒性良好,满足实际的工业检测需求。

面向实景三维城市建设的物探方法研究与应用

在新型城镇化和实景三维测绘技术不断深化的背景下,地下空间的精细开发与管理对于城市实现高质量发展尤为关键。本研究探索适合实景三维城市建设的地球物理勘探(物探)方法,旨在提升城市地下空间探测的精确度与工作效率。针对城市复杂条件,本文提出了一种综合物探方法,该方法不仅涵盖了浅层、中层和深层的勘探,而且融合了新型基础测绘技术。在一项针对某老旧办公楼改造的案例实践中,由于现场条件限制无法实施钻探,笔者采用了高分辨率的无损勘察技术,利用多通道多频三维探地雷达系统、地震频率成像法和高密度微动探测技术,集成多源数据,探明了办公楼地下防空洞的分布,并揭示了地层结构、岩土分界面及地下建构筑物的状况,最终构建了一个满足实景三维工程级精度要求的BIM模型,该模型融合了地面楼宇、地下管线、地铁和地质体等信息,对模型可实现剖切分析和岩土力学模拟计算等,为办公楼的安全改造和科学管理提供了重要依据。文章还进一步提出,未来物探技术的发展趋势应聚焦于数字化智能化技术的发展,促进跨学科多专业交融,助力实景三维数字中国建设。

煤矿氧气检测高精度VCSEL驱动及温控电路设计

为解决当前常用煤矿氧气检测仪器易受交叉气体干扰且功耗大的问题,基于GD32F303RCT6微控制器和ADN8834热电冷却控制器,设计了一种软启动开关电路控制的垂直腔面发射激光器(Vertical-cavity Surface-emitting Laser,VCSEL)高精度驱动及温控电路。驱动电路中,高频正弦波信号和低频锯齿波信号叠加的二进制数据由微控制器产生,经信号发生电路、电压电流转换电路转化成VCSEL高精度驱动电流信号;温控电路中,设计基于比例积分微分(Proportional Integral Differential,PID)补偿电路和数模转换控制器(Digital to Analog Converter,DAC)目标温度控制电路实现激光器温度自动调节。测试结果表明:驱动电路的电流输出区间为0.680~1.360 mA;锯齿波频率误差小于0.5%,正弦波频率误差小于0.1%;氧气吸收峰扫描精度高达0.07 pm,对应电流扫描精度为0.12μA;温控电路的温度控制精度为±0.012℃。满足了可调谐半导体激光吸收光谱(Tunable Diode Laser Absorption Spectroscopy,TDLAS)煤矿氧气检测应用需求。

星载望远镜消光材料积分散射特性测试研究(英文)

在散射理论的基础上,介绍了一种星载望远镜消光材料积分散射特性测试装置,实现对星载望远镜消光材料散射特性更为全面的测量。对积分散射理论、系统构造、系统性能进行了阐述。对系统进行建模仿真分析,得到结论:消光材料的散射特性在不同点位和入射角下存在明显差异,系统能够测量多种条件下消光材料的散射特性,并得到消光材料全面的散射特性分布。研究结果为根据消光材料特性进行针对性设计提供了更全面、更准确的散射特性分布,为杂散光的测量与抑制、高性能光学仪器的研制与装调以及计算光学等领域的研究提供了参考。为空间引力波探测星载望远镜系统的材料选型、特性研究、杂散光分析与抑制提供了基础。