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。

The Design of Multi-Connector IoT Central Gateway Based on Raspberry Pi and Docker

With the vigorous development of Internet of Things(IoT)technology,the demand for communication and data exchange between different types of IoT devices is increasing day by day.To solve the problems of diversity and complexity of communication protocols between devices,this paper proposes a design scheme of a multi-connector IoT central gateway based on Raspberry Pi and Docker.Through the research and application of related technologies,by integrating multiple communication interfaces and utilizing containerization technology,an efficient,flexible,and scalable IoT central gateway has been realized,which can support the connection and data interaction of multiple communication protocols and provide strong support for the stable operation and development of the IoT system.

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.

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.

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.

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.

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.

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.

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.

基于Raspberry Pi的智能家居系统设计

针对普通智能家居系统接口标准多、维护成本高、移动性差等缺点,提出一种基于Raspberry Pi嵌入式平台的智能家居系统设计方案。该系统由控制器、Web服务器、Web浏览器、APP移动终端和监控终端组成,采用ZigBee技术实现监控终端与控制器之间的通信。实验表明,该系统能使用户通过Web浏览器或手机APP终端实现对家居的远程监控,具备较好的应用价值。

基于Raspberry Pi和Haar分类器的AUV目标搜寻

文中使用Raspberry Pi开源硬件平台搭建了水下机器人视觉系统,通过Open CV对所要搜寻的特定目标物体进行Haar分类器训练,AUV在进行特定目标搜寻任务时加载训练文件,并通过所开发程序在嵌入式视觉系统上完成了水下特定目标的搜寻任务。文中对训练流程进行了分析,并对分类器做出了评价,通过实际的水下实验,验证了所用训练分类器具有较低的误检率和较高的检测率,同时,所搭建的视觉系统具有较好的实时性。

基于Raspberry Pi的远程监测系统的设计与实现

为解决普通计算机爱好者及在校学生难以入门物联网和嵌入式技术的问题,介绍了一种基于ARM的简单而强大的微型计算机,并利用该计算机平台设计和实现了智能监控、火警预警和温湿度监测。

基于Raspberry Pi的室内智能灌溉系统设计与研究

为提高灌溉的精确度与精细化种植的用水效率,研究设计了一种基于 Raspberry 的精准灌溉系统。采用Raspberry Pi 3b与相关传感器设计了系统的硬件,应用pyhton语言编写系统相关程序,并搭建数据库,编写了二维模糊控制器的模糊算法控制程序,其输入量为土壤湿度和土壤湿度的变化值,输出量为直流电机的 PWM 值。最后通过黄瓜幼苗的种植进行试验,达到了预期的节水目标。为智能灌溉的全面实现提供理论基础与参考依据。

基于FPGA与Raspberry Pi的SEU测试系统设计

为了检测在大气粒子辐照条件下,FPGA产生翻转效应的误码率,设计了一种基于FPGA与Raspberry Pi的单粒子翻转效应测试系统。该设计采用FPGA作为系统主控,接收GPS实时信息并根据GPS的秒脉冲进行工作;主控FPGA对被测FPGA进行数据下载与回读,并将比较结果发送到接口FPGA;首次采用Raspberry Pi作为上位机与接口FPGA进行通信,并将测试结果实时屏显同时保存到Micro SD卡,提高了存储的灵活性。通过青藏高原实测结果表明,该设计能实时显示测试结果,中子测试结果与中子探测仪保持一致,系统工作长时间保持稳定。现场获得了有效的测试数据,该系统满足设计要求。

基于Raspberry Pi的轮式移动机器人设计及运动控制

基于卡片电脑Raspberry Pi完成了移动机器人从硬件搭建到运动控制的工作.通过Raspberry Pi与传感器的配合对机器人运动环境进行检测.采用模糊控制算法,根据不同环境设计了不同的运动机制,保障移动机器人运动的安全性.利用实验检验并完善了机器人的避障性能,实现了机器人在复杂环境中的运动控制,包括机器人的遥控以及自主漫游运动.

基于Raspberry Pi的智能灭蚊飞行机器人

设计了一种基于Raspberry Pi的智能灭蚊虫飞行机器人,其能够携带灭蚊虫药水喷洒地面和沟渠区域,实现自动高效灭蚊虫的功能。主要介绍飞行机器人的设计及开发,包括飞行控制器设计、Raspberry Pi通信系统设计、Android控制端设计、电机驱动设计、喷药系统设计等。本系统设计及实现可以移植到其他农林药物喷洒作业中,具有一定的创新意义。