Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

Manipulator-based autonomous inspections at road checkpoints:Application of faster YOLO for detecting large objects

With the increasing number of vehicles,manual security inspections are becoming more laborious at road checkpoints.To address it,a specialized Road Checkpoints Robot(RCRo)system is proposed,incorporated with enhanced You Only Look Once(YOLO)and a 6-degree-of-freedom(DOF)manipulator,for autonomous identity verification and vehicle inspection.The modified YOLO is characterized by large objects’sensitivity and faster detection speed,named“LF-YOLO”.The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network,for object detection tasks,along with optimized anchor boxes and improved loss function.During the manipulator motion,Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System(ROS).The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design,which has been found to be more effective during actual object detection,in terms of decreased average detection time by 68.25%and 60.60%,and increased average Intersection over Union(Io U)by 20.74%and6.79%compared to YOLOv3 and YOLOv4 through experiments.The comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.

Optimization Model: Resource Distribution for Risk Factors of Type 2 Diabetes Prevention

Type 2 Diabetes, a lifestyle disease, can be prevented/delayed by adopting a healthy lifestyle. Awareness of the same amongst the citizens can be one of the best ways to initiate a decline in the positive census of the disease. We use this paper to illustrate an optimization model where the budget can be distributed based on the census data of the risk factors involved. It uses a non-linear programming model and can easily be modified into a linear one. The alternative options and constraints too, are mentioned in the paper. The results show that the mid-western states need more share of the allocation based on risk factors. The model distributes the percentage of the budget allocated to different states based on a fixed risk factor constraint.

A Review of Researches on Deep Learning in Remote Sensing Application

In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.

Efficient Deep Learning Modalities for Object Detection from Infrared Images

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.For military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime missions.So,there is a need for such a system,which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious event.This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video streams.The accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented solution.In addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

Medium Range Prediction of Summer Monsoon Activities over India vis-a-vis Their Correspondence with the Observational Features

A detailed examination on various aspects of the southwest monsoon over the Indian subcontinent using analyses and forecasts of the NCMRWF global data assimilation and forecast system for the years 1994 and 1996 is carried out in this study. Objective procedures developed by Ramesh et al. (1996) based on the monsoon 1995 data are employed in the present work. It is found that all the deterministic features of the summer monsoon viz., the onset, its advancement, stagnation/revival and the withdrawal, can reasonably by prognosticated after employing the objective methodologies and monitoring daily variations of certain derived quantities from the large scale analyses and forecasts. Further, a good correspondence of the observed large scale/synoptic scale circulation features and the observed rainfall etc. with the deterministic characteristics of the summer monsoon show a good prospect for real time prognosis of the important summer monsoon activities over the Indian subcontinent.

Patterns and Frameworks for E&P Middleware—Introduction to Architecture of PetroCOVERY

This paper presents a domain-specific middleware definition for petroleum exploration and production (E&P) domain on personal view.With detailed introduction of PetroCOVERY architecture,we examine some key features about E&P middleware and evaluate its architecture.E&P middleware should tier different encapsulation layers,which in higher level support interoperability among

Analysis of the Framework of Microsoft DNA

Microsoft DNA(Distributed interNet Application) has p rovided a effective framework of combination PC with the Internet perfectly; In addition, this open framework hase also given the integration of multilingual co mponents a strong managing platform. This article has deeply analysed the main technologys of Microsoft DNA, involve COM basic technology, framework of DNA, Microfost UDA(Universal Data Access),com ponent management with COM+, asynchronous communication with MSMQ(M icrosoft Message Queue). In addition, based on a comparative analysise with the C/S(Client/Server) framework, the author has illustrated the excellency of the Multi-tier framework of Microsoft DNA.

Testability Estimation of Framework Based Applications

Quality of software systems is highly demanded in today’s scenario. Highly testable system enhances the reliability also. More than 50% of test effort-time and cost are used to produce a highly testable system. Thus, design-for-testability is needed to reduce the test effort. In order to enhance the quality, productivity and reduced cost of the software organizations are promoting to produce the reuse-oriented products. Incorporating reuse technology in both aspects-software development process and test process may payoff many folds. Keeping this view, our study focus the testability of the object-oriented framework based software systems and identify that flexibility at the variable points of the object-oriented framework, chosen for framework instantiation, greatly affects the testability of object-oriented framework based software at each level of testing. In the current paper, we propose a testability model considering the flexible aspect of the variable point to estimate testability in the early phase, requirement analysis phase, of development process of the framework based software. The proposed model helps to improve the testability of the software before the implementation starts thus reducing the overall development cost.

宅基地退出绩效:评价体系、实证分析与提升路径——基于东中西部典型试点区域的全景式考察

研究目的:明晰宅基地退出绩效目标,构建评价体系,对典型试点区域宅基地退出绩效进行实证分析,探寻宅基地退出绩效提升路径。研究方法:质性研究方法与政策绩效评价工具方法的综合运用。研究结果:宅基地退出绩效目标主要由中央政策目标和地方政府、村集体、退出农户的利益诉求构成;回应绩效目标,基于政府绩效管理理论,引入利益相关者理论,并嵌入乡村韧性理论,构建了从过程到结果、兼顾多维利益诉求、考虑长短期变化的宅基地退出绩效评价体系;通过实证考察和评价分析发现,义乌、余江、泸县的宅基地退出绩效总体表现良好,但主要在经济韧性上存在短板,面临着资金紧张、主体乏力、盘活再利用受限等困境。研究结论:全面提升宅基地退出绩效,关键要在宅基地“三权分置”改革统领下,通过优化退出资金运作模式、形成多主体共治局面、拓宽退出土地盘活路径等,健全宅基地退出长效激励机制与保障机制。

分布式流水车间调度研究综述

智能制造、网络制造等新型制造模式的快速发展为分布式生产的推广带来了新的机遇与挑战。目前,国内外学者已对柔性车间调度、分布式作业车间调度问题进行了总结分析,但仍缺乏对分布式流水车间调度问题的综述。以分布式流水车间为研究对象,从分布式流水车间调度的运作模式分析、优化目标设计、特殊约束刻画、模型求解方法及工程应用等方面对现有研究成果进行了归纳总结,并对分布式流水车间调度问题未来的研究方向进行了展望。

黄河河口流路多目标联合运用方式研究

黄河河口流路运用方式面临着新形势下保障防洪安全、维持生态功能和减缓海岸蚀退等多目标需求。采用实测水文资料和现场调查资料,分析河口河道防洪形势、黄河口国家公园建设需求和备用流路现状情况。清水沟流路在小浪底调水调沙运用后防洪形势明显改善,然而仍面临着大洪水的威胁;目前河道滩地呈现约6‱的倒比降,加剧了大洪水时的防洪压力。黄河口国家公园清水沟片区在清四控导工程以下无整治工程,规划的科研平台和大量基础设施要求洪水尽量不漫滩。刁口河片区规划的生态补水量为0.55亿m^(3),附近海岸因缺乏泥沙补给处于蚀退状态。刁口河主槽淤积萎缩,滩地开发建设侵占严重,亟待长期过水加强保护。新形势下黄河河口流路运用方式为“联合运用、常态过水、平滩分流、双河行洪”。刁口河常年维持一定的过水流量。当清水沟流量达到3500 m^(3)/s时向刁口河分流,大洪水时两条流路作为5000 m^(3)/s流量的泄洪通道。

应用型本科院校思政课实践教学探析--以中国矿业大学徐海学院为例

在思政课教学过程中,实践教学环节既是一个重要的内容,也是一个崭新的课题。为更好地探索应用型本科思政课实践教学环节的实施,该文以中国矿业大学徐海学院为例,结合四门具体思政课程(思想道德与法治、中国近现代史纲要、马克思主义基本原理、毛泽东思想和中国特色社会主义理论体系概论)的普遍性与特殊性,对实践教学目标、实践教学方式、实践教学总结三个方面进行探讨。

工程认证驱动下应用型高校新工科建设探索--以浙江万里学院电子信息工程专业为例

应用型高校为产业发展建立,它是新工科建设的主力军。工程认证是工科专业建设和教学质量保障的有效途径。针对应用型高校人才培养特点以及工科教学现状,对电子信息工程专业的培养目标、课程体系、实践教学、教学模式和质量保障开展“四方协同”“四元融合”“四阶递进”“四维联动”“四层连环”“四化发展”,六个“四”的新工科建设。实践表明,工程认证驱动的新工科建设培养目标、课程系统更符合新产业、新业态需求,教学模式更适合当代青年学生,教学质量得到有效保障,育人成效更显著。

股东抽逃出资补充责任诉讼程序研究

股东抽逃出资补充责任诉讼程序呈现审判与执行叠加下的“双轨制”。双轨制的实践运作呈现诉讼标的识别不明、股东地位认定存疑、异议之诉延缓债权实现等问题,程序聚合实际呈现程序竞合。对此,可从诉讼标的识别、先诉抗辩权的有无以及裁定—追加的定位三方面进行分析。应合理做到请求权及责任履行顺序性的程序转化。可保留审判导向型程序,以普通共同诉讼应对共同起诉问题。对审判导向型程序的不能清偿予以明确,借助“终本裁定”明确“公司不能清偿”的证明。由于执行裁定难以就“抽逃出资”问题予以准确判断,可废止执行导向型,回归程序单轨制。

布鲁姆教育目标分类理论在本科生《循证医学》教学中的应用效果

目的探讨布鲁姆教育目标分类理论在《循证医学》教学中的应用效果。方法以武汉大学第二临床学院2019级临床医学(5、5+3)、基础医学专业的本科生为研究对象,实施布鲁姆教育目标教学法,在课程前后采用自制问卷对其相关知识内容掌握程度、态度转变及课程满意度进行调查。结果75位学生参与研究,课程后学生对循证医学、Meta分析、试验设计等相关知识认识的正确率为54.67%~100.00%,均较课程前显著提高,差异有统计学意义(P<0.05)。课程后大多数学生认可循证医学对改善临床医疗质量及患者结局(84.00%)、对其批判性思维能力(98.66%)和自主学习能力(96.00%)提升的积极作用,更加愿意通过多途径学习循证医学相关知识和技能(93.33%),与课程前差异均具有统计学意义(P<0.05)。学生对课程的教学内容(93.33%)、讲授方法(85.33%)、教学资源(86.67%)、评价方式(89.33%)的满意度均较高。结论布鲁姆教育目标教学法可有效改善学生对循证医学相关知识的辩证性认识,提高其批判性思维能力和自主学习能力,教学满意度较高,具有一定的推广应用价值。

融合超绿通道的低辨识度草坪杂草识别算法

为提高草坪维护自动化程度,实现除草机器人自动识别精准施药,针对自然环境中草坪杂草难以识别的问题,以YOLOv7为基础目标检测网络,提出一种增强图片颜色信息的草坪杂草检测模型。对RGB图片进行超绿处理,抑制背景干扰信息,突出杂草颜色信息。扩充网络输入通道为四通道,将超绿灰度图与RGB图片融合得到新的四通道图片,提高网络对草坪环境的抗干扰能力。引入三维注意力模块SimAM,提高网络针对低辨识度杂草识别精度,增强网络对草坪杂草目标的识别效果。改进后YOLOv7-4ch网络对草坪杂草识别效果较好,自然环境下识别草坪杂草的mAP@0.5为91.7%,帧速率100 f·s^(-1),模型大小为67.9 Mb,可满足除草机器人识别需求。改进网络与基础YOLOv7网络相比,mAP@0.5提高2.3%,与YOLO系列其他算法相比有较大提升。该算法可有效识别自然环境中的草坪杂草,为其精准化施药提供技术支持。

基于OLE接口的自动化Microsoft Project系统

为提高工作效率和监督水平,在研究Microsoft Project的任务属性和资源属性基础上,提出了利用Mi-crosoft的OLE(Object Linking and Embedding)Automation接口对Microsoft Project进行集成二次开发的方法。最终实现了一种基于OLE接口的自动化Microsoft Project系统。该系统能更好地完成与应用程序之间的数据交换,实现无缝连接,为Microsoft Project的二次开发提供了通用解决方案。