Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Research on black-and-white image processing method of smart car camera

In view of the images collection and processing problems of the smart car camera, the paper introduces a method which deals with field and line synchronization signal separation and binarization processing of the video signal collected from track fields, and which is capable to extract and position black border trajectory images effectively. According to the experiment results of the method, the camera images can be collected and processed effectively, and the accurate image information can be provided for the smart cars to travel along the track. The method has the advantages of being easy to use, strong adaptability, ideal performance and high practical value. On the basis of advantages the method is of high practical value in smart car races.

3D smart mA调控技术对不同BMI患者图像采集时间质量及辐射剂量的影响

目的:分析3D智能管电流(3D smart mA)调控技术对不同体质量指数(BMI)患者图像采集时间、质量及辐射剂量的影响。方法:择取的180例行胸部CT扫描患者选自西安交通大学第一附属医院2021年6月至2022年12月期间所收治,按照BMI将患者分为三组,A组(18.5 kg/m^(2)≤BMI≤23.9kg/m^(2),n=75)、B组(23.9kg/m^(2)0.05);两位医师对肺部不同层面图像质量(IQS)评分进行评价,Kappa一致性非常好(Kappa值=0.768、0.812、0.861);三组肺部不同层面IQS评分对比,差异无统计学意义(P>0.05);三组肺部不同层面CT对比,差异有统计学意义,且随着BMI增加而下降(P<0.05),三组肺部不同层面图像标准差(SD)值对比,差异无统计学意义(P>0.05);三组容积CT剂量指数(CTDIvol)对比,差异无统计学意义(P>0.05);A组DLP、ED均低于B、C组,B组DLP、ED低于C组(P<0.05)。结论:不同BMI患者应用3D smart mA调控技术,在保证图像质量的前提下,可有效降低辐射剂量。

Painting image browser applying an associate-rule-aware multidimensional data visualization technique

Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

基于深度学习的鹰嘴桃病虫害监测技术研究

针对传统病害鉴定方法效率不高、操作复杂等的问题,提出了一种基于深度学习的鹰嘴桃病虫害识别算法。该算法基于ResNet50网络架构,运用图像识别技术与深度学习相结合的方式,构建了一套高效的病虫害监测系统,能够迅速且准确地识别各类病虫害。首先针对河源连平地区鹰嘴桃的14种常见病虫害,建立了专门的数据集;其次利用深度残差网络模型进行了训练;最后利用PyQt5开发了图形化用户界面,实现了病虫害的自动诊断。这一自动诊断系统不仅高效、省力,而且环保,符合智慧农业的发展趋势,为用户提供了精准的病虫害防治手段。

基于葡萄种植图像识别技术的智慧农业应用

在智慧农业的众多技术中,图像识别技术的重要性日益凸显。该文介绍图像识别技术在葡萄种植过程中对病虫害进行识别,采用轮廓边缘检测算法和U-Net算法在葡萄不同生长期病害特征标定识别中的应用,阐述如何利用简单的图像数理模型进行数据分析,并最终实现各花期病变的自动甄别,提升葡萄种植的管理效率。希望读者通过该文能够理解图像识别技术如何与传统农业相结合,为推动智慧农业的发展提供新思路。

Augmented reality: The use of the PicoLinker smart glasses improves wire insertion under fluoroscopy

AIM To demonstrate the feasibility of the wearable smart glasses, Pico Linker, in guide wire insertion under fluoroscopic guidance. METHODS Under a fluoroscope, a surgeon inserted 3 mm guide wires into plastic femurs from the lateral cortex to the femoral head center while the surgeon did or did not wear Pico Linker, which are wearable smart glasses where the fluoroscopic video was displayed(10 guide wires each). RESULTS The tip apex distance, radiation exposure time and total insertion time were significantly shorter while wearing the Pico Linker smart glasses. CONCLUSION This study indicated that the Pico Linker smart glasses can improve accuracy, reduce radiation exposure time, and reduce total insertion time. This is due to the fact that the Pico Linker smart glasses enable surgeons to keep their eyes on the operation field.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

Field Sensing Characteristic Research of Carbon Fiber Smart Material

In order to research the field sensing characteristic of the carbon fiber smart material, the Tikhonov regularization principle and the modified Newton-Raphson(MNR) algorithm were adopted to solve the inverse problem of the electrical resistance tomography(ERT). An ERT system of carbon fiber smart material was developed. Field sensing characteristic was researched with the experiment. The experimental results show that the specific resistance distribution of carbon fiber smart material is highly consistent with the distribution of structural strain. High resistance zone responds to high strain area, and the specific resistance distribution of carbon fiber smart material reflects the distribution of sample strain in covering area. Monitoring by carbon fiber smart material on complicated strain status in sample field domain is realized through theoretical and experimental study.

Key Issues for Implementing Smart Polishing in Semiconductor Failure Analysis

“Industry 4.0” has become the future direction of manufacturing industry. To prepare for this upgrade, it is important to study the automation of semiconductor failure analysis. In this paper, the sample polishing activity was studied for upgrading to a smart polishing process. Two major issues were identified in implementing the smart polishing process: the optimization of current polishing recipes and the capability of making decisions based on live feedback. With the help of Solver add-in, the current polishing recipes were optimized. To make decisions based on live images captured during polishing, strategies were explored based on finger polishing process study. Our investigation showed that a grey scale line profile analysis on images can be used to build the vision capability of our smart polishing system, on which a decision- making capability can be developed.

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obtained from the data can assist municipal authorities handle assets and services effectually.At the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic.Besides,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability.Few of the commonly available wastes are paper,paper boxes,food,glass,etc.In order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash.Due to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of wastes.In this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart cities.The goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL techniques.The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object classification.In addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a classifier.Moreover,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.In order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques with maximal accuracy of 0.993.

智慧教育时代的教师生长逻辑

智慧教育时代教师如何发挥智慧和机智,继而促进学生智慧的生成成为铸造“大国良师”的时代命题。从体验性、敏感性与生成性的视角来看,智慧教育时代的教师以创变智慧教学的先行者、智慧教育实践的反思者、数字文化基因的培植者的新形象承担着立德树人的新使命。智慧教育时代教师的价值超越表现为:工具理性上实现数字素养和数字思维双超越,价值理性上重现师生主体的天然“灵晕”,意义理性上唤醒教师“类生命”自觉。智慧教育时代的教师生长需要通过物理场域、价值场域以及心理场域搭建人机协同实践磁场,形成教师存在意义的内在观照,并通过培养智慧的学生进入意义场域的结构之中,继而彰显教师的社会生命意义。

智能汽车内外饰造型意象耦合度评价方法研究

为帮助设计人员在智能汽车造型设计早期更好地控制内外饰传达的整体意象,提出一种基于眼动实验,结合感性工学理论的智能汽车内外饰造型意象耦合度评价方法。以耦合理论为核心,通过调研获取感性意象词汇集和内外饰并存图库,引入观察距离作为调剂因子统一内外饰意象评价,运用语义差分法构建内外饰总体意象空间并获取意象过渡线;运用眼动实验获取车内外关键设计要素及客观认知权重,经专家评审修正后计算其综合权重并与意象评分结合,探索基于意象认知耦合的智能汽车内外饰造型耦合度评价方法。该方法以3款智能汽车为评价对象,将评价结果与语义差分所得评分进行对比验证,结果表明该方法准确性良好,可以指导后续智能汽车内外饰造型耦合设计。

基于FLFR-SCP算法的井下图像去雾研究

煤矿井下环境复杂,煤炭的掘进生产、除尘降尘等环节容易产生大量的粉尘颗粒和雾气悬浮在空气当中,导致井下监控系统无法获得较为清晰的视频图像。基于暗道先验的去雾算法无法完成井下图像的实时处理和尘雾图像的局部去雾。对暗道先验算法进行改进,改进透射率图的处理算法、获取大气光值算法、重新设计大气透射率函数和局部尘雾处理算法。实验表明,改进后算法能够较为准确地计算出大气光值和透射率,可以获得较为清晰的无雾图像,改进后图像的处理时间有较大幅度缩短,基本满足井下实时处理的要求。针对井下图像尘雾区域不均匀的问题,算法也有较好的处理效果。

基于深度学习的农作物病虫害研究进展

病虫害严重影响农业和环境的可持续发展,导致农作物产量损失和品质下降。深度学习技术为病虫害识别和防治提供了新方法,在识别准确率和效率方面呈现出独特优势。在归纳总结深度学习技术发展历史、算法的优缺点基础上,探讨其在农作物病虫害研究中的应用现状、存在的问题和发展趋势。CNN(convolutional neural networks)架构是病虫害识别的核心技术,基于迁移学习的深度学习技术是研究热点。为加快深度学习技术在农业领域的应用,促进智慧农业的发展,应加快建设农作物病虫害数据集、优化深度学习架构、搭建移动平台、研究单一和混合病虫害图像分割技术、融合无人机和卫星遥感影像与地面观测数据,实现大面积农作物病虫害的识别与检测。通过深度学习技术在农作物病虫害识别中的应用,可以在保护生态平衡的基础上加强对病虫害防治,保障农作物的产量和质量。

基于OpenMV的智能送药小车的设计

为提高现代智慧药房的智能化服务水平,设计了一款以STM32F103处理器为核心的智能送药小车。该系统利用OpenMV图像识别技术来确认配送目标、循迹前进、路口转向、择位而停,完成送药任务。利用神经网络TensorFlow lite训练模式对图像中的数字和线路信息进行深度学习,得到准确率达到92.4%的模型,识别时间低于0.5 s,近端药房送药任务平均时长14.48 s,双车远端病房协作送药完成时间48.35 s。相对于传统自动化送药系统,该系统智能化程度较高。

基于Segformer与特征融合的水下养殖鱼类图像分割方法

水产养殖管理中,精准分割图像中的鱼类对生长管理至关重要,但水下环境复杂,图像质量低,现有分割方法面临精度低、泛化能力弱等挑战。提出了一种改进Segformer模型(FT-Segformer,简称SegFT)的水下鱼类图像分割方法。首先,利用四层transformer block提取输入图像高分辨率到低分辨率的不同尺度特征。在解码器部分,借助特征金字塔融合机制增强上下文感知;然后,利用转置卷积还原特征图维度,进一步提升特征学习的效果;最后,构建了一个用于模型评估的真实水下养殖环境的锦鲤数据集(UAGF),并在该数据集上进行相关验证试验。结果显示:该模型在mIoU、mPA和mRecall等评估指标上均优于现有方法,分别提升了1.76%、0.39%和0.19%,在mIoU指标上,SegFT分别超越了U-Net、PSPNet、HRNet、Deeplabv3+模型1.92、3.73、3.07和3.58个百分点。研究表明,所提出的方法在复杂的水下环境下,具有显著的有效性和鲁棒性。分割性能上优于现有的监督图像分割方法。