基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

基于Raspberry Pi及Drone Kit的无人机飞行控制教学实践应用

基于开源硬件Raspberry Pi,Pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用Raspberry Pi作为无人机的机载计算机与Pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

基于Raspberry Pi的实时流媒体直播系统设计

在“全民直播、万物皆可播”时代背景下,阐述了一种实时流媒体直播系统的设计过程.系统以Raspberry Pi 4B作为主控,搭载Linux操作系统.系统由视频采集端、流媒体服务器端、客户端三部分组成.视频采集端实时获取摄像头视频数据,利用FFmpeg协议对视频流封包并推送到流媒体服务器.服务端运行SRS流媒体服务器,提供视频存储、推送与软件更新服务.客户端支持视频流拉取、直播、片段回放功能.系统采用QT配合触摸屏设计人机交互界面,可远程进行软件更新.本系统具有成本低、部署便捷、延迟短等优点,经测试系统的整体延时小于200 ms.

基于Raspberry的3D打印机分布式远程控制系统

Raspberry分布式远程控制系统将树莓派(Raspberry Pi)作为控制端,通过蒲公英x1盒子进行异地组网式的内网穿透实现远程控制树莓派进而控制3D打印机,打破空间限制,再利用开源软件Octoprint和OctoFarm实现对多台3D打印机的分布式控制。同时,介绍了该分布式远程控制系统的实现方法和工作原理,OctoFarm还会将每台打印机的工作日志数据上传到MongoDB数据库,便于后续进行原生数据分析,为3D打印机进行调参纠错提供数据支撑。

Multi-Plate Microbial Monitoring Terminal Based on Raspberry Pi 4B

We utilized Raspberry Pi 4B to develop a microbial monitoring system to simplify the microbial image-capturing process and facilitate the informatization of microbial observation results.The Raspberry Pi 4B firmware,developed under Python on the Linux platform,achieves sum verification of serial data,file upload based on TCP protocol,control of sequence light source and light valve,real-time self-test based on multithreading,and an experiment-oriented file management method.The system demonstrated improved code logic,scheduling,exception handling,and code readability.

Metabolic enzyme inhibitory abilities, in vivo hypoglycemic ability of palmleaf raspberry fruits extracts and identification of hypoglycemic compounds

The unripe palmleaf raspberry,namely Fupenzi(FPZ),is an important medicinal and edible food.This study aims to evaluate the potential of FPZ extracts prepared with different approaches in attenuating hyperglycemia,gout,Alzheimer’s disease,and pigmentation,to obtain the enriching fraction and to identify the major active compounds.Results indicated that FPZ extracts showed weak activity against acetylcholinesterase,considerable ability against tyrosinase and xanthine oxidase,but excellent inhibition onα-glucosidase.Ultrasound-assisted 40%ethanol extract(40EUS)gave the highest phenolics content,and the bestα-glucosidase inhibition(IC_(50)=0.08μg/mL),which is 877-fold higher than that of positive control acarbose.The 40%ethanol eluting fraction of 40EUS showed the strongestα-glucosidase inhibition with the IC_(50) value of 37.79 ng/mL,it could also effectively attenuate the fasting blood glucose level and oral glucose tolerance of C57BL/6 mice.Twenty-six compounds were identified from 40%ethanol fraction by using HPLC-QTOF-MS/MS,hydrolysable tannins(including 11 ellagitannins and 4 gallotannins)were the major compounds,phenolic acids came to the second.Above results could provide important technical supporting for the further application and research of FPZ in health foods and drugs against diabetes.

Regulation of A 9-cis-epoxycarotenoid Dioxygenase(NCED)Gene from Raspberry on High Salinity and Cold Tolerance

Abscisic acid(ABA)is a plant hormone that plays an important role in plant development and abiotic stress.The 9-cis-epoxycarotenoid dioxygenase(NCED)is a key rate-limiting enzyme in the ABA biosynthetic pathway.The physiological and molecular mechanisms of NCED regulating plant development and abiotic stress tolerance have been reported in many plant species,but gene function of RiNCEDs in Rubus idaeus L.is rarely reported.In this study,the open reading frame(ORF)sequence of RiNCED2 in red raspberry fruit was isolated and the function of this gene under abiotic stress was investigated.While RiNCED2 was induced by cold,high salinity,drought and ABA,it was highly expressed in new leaves as measured by real-time qPCR.Overexpression of RiNCED2 in Arabidopsis under both high-salt and cold stress increased ABA content,demonstrating that RiNCED2 was involved in ABA biosynthesis.Meanwhile,the leaf wilting degree of transgenic Arabidopsis was less,while the content of malondialdehyde(MDA)was significantly reduced,and the chlorophyll content,proline content,peroxidase(POD),catalase(CAT)and superoxide dismutase(SOD)activities were significantly increased.These results indicated that overexpression of RiNCED2 enhanced the resistance of transgenic Arabidopsis to high salt and cold.

Design of Li River Water Quality Dynamic Monitoring System Based on Raspberry Pi and TinyML

In order to solve the problem of scientific monitoring of water quality, a trophic monitoring system for Li River water quality is developed to improve the decision-making of related environmental management departments. The system is based on embedded computing, deep learning and Internet of Things technology, combined with software and hardware design, to automatically obtain real-time water quality parameters with Raspberry Pi equipped with sensors and positioning modules. A camera is employed to capture the screen, and yolo-tiny image recognition is implemented in the Raspberry Pi. Lastly, the cloud storage is used for interaction to realize real-time monitoring of water quality, real-time positioning of the boat, real-time return of image recognition and visualization. The system is proven efficient and intelligent in facilitating water quality protection.

Securing Transmitted Color Images Using Zero Watermarking and Advanced Encryption Standard on Raspberry Pi

Image authentication techniques have recently received a lot of attention for protecting images against unauthorized access.Due to the wide use of the Internet nowadays,the need to ensure data integrity and authentication increases.Many techniques,such as watermarking and encryption,are used for securing images transmitted via the Internet.The majority of watermarking systems are PC-based,but they are not very portable.Hardwarebased watermarking methods need to be developed to accommodate real-time applications and provide portability.This paper presents hybrid data security techniques using a zero watermarking method to provide copyright protection for the transmitted color images using multi-channel orthogonal Legendre Fourier moments of fractional orders(MFrLFMs)and the advanced encryption standard(AES)algorithm on a low-cost Raspberry Pi.In order to increase embedding robustness,the watermark picture is scrambled using the Arnold method.Zero watermarking is implemented on the Raspberry Pi to produce a real-time ownership verification key.Before sending the ownership verification key and the original image to the monitoring station,we can encrypt the transmitted data with AES for additional security and hide any viewable information.The receiver next verifies the received image’s integrity to confirm its authenticity and that it has not been tampered with.We assessed the suggested algorithm’s resistance to many attacks.The suggested algorithm provides a reasonable degree of robustness while still being perceptible.The proposed method provides improved bit error rate(BER)and normalized correlation(NC)values compared to previous zero watermarking approaches.AES performance analysis is performed to demonstrate its effectiveness.Using a 256×256 image size,it takes only 2 s to apply the zero-watermark algorithm on the Raspberry Pi.

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured,such as viewing angles,blurring,sensor noise,etc.However,in this paper,a prototype for text detection and recognition from natural scene images is proposed.This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus(USB)camera and embedded our text detection and recognition model,which was developed using the Python language.Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detec-tor(EAST)model for text localization and detection and the Tesseract-OCR,which is used as an Optical Character Recognition(OCR)engine for text recog-nition.Our prototype is controlled by the Virtual Network Computing(VNC)tool through a computer via a wireless connection.The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75%with low computational complexity.Furthermore,our proto-type is more performant than the Tesseract software in terms of the recognition rate.Besides,it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89%compared to the EasyOCR software on the Raspberry Pi 4 board.

A Deep Trash Classification Model on Raspberry Pi 4

Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloading of garbage dis-posal systems and efficiently promote recycling activities;thus,development of such a classification system is topical and urgent.This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time.An image dataset isfirst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classification module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classification system.This proves the efficiency and high applicability of the proposed system.

一种基于Raspberry Pi的智能助盲导航系统

以提高盲人的生活便捷性为目的,开发出了一款基于树莓派的智能助盲导航系统。该系统能够将多维视觉信息转化为有效听觉信息,通过对盲人日常生活场景的各种实体进行图像的采集和处理,并最终以语音的形式传输给盲人。

Raspberry Pi的DRAM PUF密钥生成系统研究

介绍了基于树莓派B+硬件平台实现DRAM PUF密钥发生系统的研究与设计。系统从嵌入式设备广泛使用的动态随机存储器中提取带有自身硬件指纹的物理不可克隆函数,通过模糊提取算法和Hash算法消除由于环境噪声及老化效应带来的错误信息,稳定生成128位DRAM PUF安全密钥。该系统生成的DRAM PUF安全密钥可靠性高达99%,唯一性为48.44%,均匀性为50%,并且密钥的信息熵值为1.0。

基于代谢组学的‘海尔特兹’红树莓果实中核苷、核苷酸和类似物含量的变化规律研究

本研究基于代谢组学,采用液相色谱-质谱联用法,针对‘海尔特兹’红树莓4个不同时期果实中17种核苷、核苷酸和类似物含量及变化规律进行研究。结果表明:在海尔特兹’红树莓果实发育过程中,脱氧胞苷酸、5-甲基脱氧胞苷、玉米素核苷、N-[(5-羟基-2-吡啶基)甲基]腺苷、反式玉米素-O-葡萄糖苷核糖苷、1-甲基腺苷、鸟苷、2-苯基氨基腺苷、1,7-二甲基鸟苷和胞苷2’,3’-环磷酸的含量在青果时期最高;UDP-4-脱氢-6-脱氧-D-葡萄糖和dTDP-D-葡萄糖的含量在黄果时期最高;尿苷二磷酸葡萄糖、8-羟基鸟苷、胞苷的含量在红果时期最高;1-甲基肌苷和脂酰腺苷酸的含量在深红果时期最高。本研究阐明了17种核苷、核苷酸和类似物在‘海尔特兹’红树莓在果实发育中的含量变化规律,为后期相关研究及应用提供了参考。

Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA

To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits.

基于树莓派和OpenCV视觉技术的自动捡网球机器人

网球场地的网球收集依靠人力处理,耗时长、效率不高。针对此问题,设计了一种基于OpenCV视觉技术的自动捡网球机器人。该机器人配置树莓派4B主控板、STM32C8T6单片机、树莓派USB摄像头、L298N电机驱动板、37GB3530直流电机及超声波传感器。利用OpenCV视觉识别技术和树莓派Linux系统相结合以及场景内的网球识别和挤压滚筒式的拾捡机械结构功能特点,对网球进行拾取和储存,以满足对球场内的网球进行捡拾和搬运的需求。经过对相关机械结构设计和软件实现结果进行检验,所设计的自动捡网球机器人能有效识别地面的网球,并对网球场中的网球进行收集与储存,执行效果良好。该智能自动捡网球机器人可应用于网球场地,以提高拾捡效率、节约时间、节省人力资源。

不同光质对树莓组培苗生长的影响

该研究以树莓‘秋红’为试验材料,探究在红蓝光基础上添加不同单色光质对其组培苗生长及光合特性的影响,筛选有利于树莓组培苗生长的较佳光质。试验结果表明,红蓝光(2∶1)+15%绿光处理更有利于树莓组培苗的生长,其茎粗、生根率、根长、单株鲜重和干重均优于荧光灯和红蓝光(2∶1)处理的,叶绿素含量、净光合速率(Pn)、实际光化学效率(Y[II])、相对电子传递速率(rETR)均表现出较佳的优势,可推荐作为树莓组培苗较优的组培光质。

脉冲式连续微波干燥树莓果浆的控制系统和品质优化

为改善微波加热均匀性,提高连续式微波干燥树莓果浆干后品质,在自行研制脉冲式微波输出可编程控制器(Programmable Logic Controller,PLC)控制系统上,采用中心组合试验设计方法,分析微波占空比、脉冲周期、风速和干燥时间四个实验因素对树莓果浆的平均温度、温度离散值、含水率、水分含量离散值、花青素保留率和花青素保留率离散值指标的影响规律,获得脉冲式微波干燥树莓果浆的最佳工艺。对比脉冲式微波干燥与常用的反馈控温微波干燥、间歇微波干燥和连续式微波干燥工艺的果浆干后品质及均匀性指标,进而研制连续式微波干燥树莓果浆品质优化控制系统。结果表明,脉冲式微波干燥通过PLC控制系统,使磁控管以脉冲形式输出矩形波形式功率,间歇性地开启磁控管比连续微波加热更有效,并且采用脉冲式微波干燥树莓果浆具有更优产品品质和加热效率,对比反馈控温系统成本更低可靠性更好。脉冲式微波干燥树莓果浆的最佳工艺为:微波占空比0.6、脉冲周期17 s、风速1.9 m/s、干燥时间12.5 min。相比于连续式微波干燥,脉冲式微波干燥果浆品质最优,脉冲式干后果浆花青素保留率提高35.31%±0.55%,反馈控温下提高33.59%±0.23%,间歇微波下提高16.42%±0.18%。脉冲式微波干燥通过控制功率降低果浆干后温度,改善干后品质均匀性,为树莓干制品工业化生产提供依据。

基于树莓派和视觉图像的钻井振动筛倾角调节系统

目前,我国的钻井振动筛均采用人工监测筛面固液分离状态和手动调节筛面倾角的方式进行操作,无法实现自适应工作,常常出现钻井液“跑浆”现象。针对这一问题,提出了一种基于树莓派和视觉图像的钻井振动筛倾角调节系统。该系统以树莓派及专用摄像头、电机驱动板和2台步进电机为硬件平台,搭载基于OpenCV和改进AlexNet模型开发的图像识别软件,用于实现钻井振动筛的筛面固液分离状态视觉检测与筛面倾角自动调节。首先,根据钻井振动筛固液分离过程中液相终止线的位置特征,将采集的筛面图像分为正常、少浆和跑浆状态三种类别,并构建筛面图像数据集。然后,使用TensorFlow平台搭建基于迁移学习的AlexNet模型,以实现钻井振动筛的筛面固液分离状态的自动识别。最后,基于识别结果,由树莓派的GPIO(general purpose input/output,通用型输入/输出)接口控制2台步进电机同步工作,以实现钻井振动筛的倾角调节。结果表明,所设计的倾角调节系统对筛面固液分离状态的识别准确率达到97.33%,响应时间约为1.5 s,可满足钻井振动筛的倾角调节要求。该倾角调节系统设备体积小,成本低,且便于调试与维护,可有效提高钻井振动筛的自动化水平。

中大型停车场车位管理系统的设计与实现

随着现代工业的快速发展,我国汽车行业迅速崛起,但停车难、停车乱以及用户停取车困难和停车场停车效率低下的问题日益凸显。针对当前中大型停车场车位管理系统软硬件协调困难、系统老旧和发展缓慢等问题,提出了一种基于物联网技术及设备的中大型车位管理系统的设计与实现方案。系统采用STM32单片机、树莓派及红外传感器等硬件组件,构建了以ZigBee为网络层的硬件系统,并结合停车场场景实时特性,利用Web系列技术开发了与之适配的操作软件。测试结果表明,该系统能够实现车位检测、车位管理以及用户高效停取车等核心功能,有效提升了停车场的管理效率和用户体验。