Raspberry Pi-based HIL simulators for control education

The main source of urban waste is the daily life activities of residents, and the waste sorting of residents’ waste is important for promoting economic recycling, reducing labor costs, and protecting the environment. However, most residents are unable to make accurate judgments about the categories of household waste, which severely limits the efficiency of waste sorting. We have designed an intelligent waste bin that enables automatic waste sorting and recycling, avoiding the extensive knowledge required for waste sorting. To ensure that the waste-classification model is high accuracy and works in real time, GECM-EfficientNet is proposed based on EfficientNet by streamlining the mobile inverted bottleneck convolution (MBConv) module, introducing the efficient channel attention (ECA) module and coordinate attention (CA) module, and transfer learning. The accuracy of GECM-EfficientNet reaches 94.54% and 94.23% on the self-built household waste dataset and TrashNet dataset, with parameters of only 1.23 M. The time of one recognition on the intelligent waste bin is only 146 ms, which satisfies the real-time classification requirement. Our method improves the computational efficiency of the waste-classification model and simplifies the hardware requirements, which contributes to the residents’ waste classification based on intelligent devices.

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“…The Raspberry Pi 4B [ 41 , 42 ] is widely used in the intelligence field. We use it as the main control device in this research.…”

Section: Materials and Methodologymentioning

confidence: 99%

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

Feng

Yang

Chen

et al. 2022

IJERPH

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“…Raspberry Pi 3B is functioning as a mini-PC. It is supported with a GPIO port of 40 pins to interact with the outside world [19]. In other words, the Raspberry Pi 3B controls and monitors.…”

Section: B Hardware and Software Designmentioning

confidence: 99%

3D Geometric Shape and Colors Interactive Learning Media using Raspberry Pi, OpenCV, and TensorFlow Lite

Wicaksono,

Rahmatya

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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This study aims to create learning media for 3D geometric shapes and colors for early childhood. Early childhood can enter 3D objects into the device. Then, the device will mention and explain the shape and color of the object in question. As a contribution, this research provides a brief introduction and learning related to geometric shapes for early childhood. The method used is experimental. The hardware components of this system are Raspberry Pi 3, RPi Camera, PIR Sensor, 3.5inch LCD, and Speaker. Python, OpenCV, and TensorFlow Lite are used from the software side. OpenCV is used to detect colors. TensorFlow Lite is used to detect the shape of geometric objects. In this study, the model used is a custom model specifically for TensorFlow Lite, which was trained through Google Colab. This media has a learning mode and a question mode. In learning mode, early childhood only needs to insert objects into the learning media and will get information in the form of images and sounds related to the object. In question mode, the learning media will provide instructions and questions to enter objects with a specific color. After that, the learning media will determine whether the answer is right or wrong and give the score. The results of tests that cover both modes reach a success percentage of 100%, where this learning media can recognize and explain every shape and color of objects in both modes.

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“…In this study, the Raspberry Pi 3B is used as the system's main brain, which will process all input and output. Raspberry Pi 3B is a mini PC and it has peripheral ports and GPIO pins [9]. It has SoC BCM2837 and 1GB RAM [10].…”

Section: B Designmentioning

confidence: 99%

IoT Implementation for Server Room Security Monitoring Using Telegram API

Wicaksono¹,

Rahmatya²,

Ilham³

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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This research aims to create a system that can monitor server room security by utilizing IoT technology, controllers, sensors, actuators, and Telegram API. The system made includes security for the indoor and outdoor parts. The method used in this study is experimental. The output obtained by the user is telegram text messages, photos, and video. The main controller on this system is the Raspberry Pi. This study showed that if the server room temperature is >230C, the system will send a message via Telegram. If the PIR sensor_1 or PIR sensor_2 detects an intruder or the measured distance is <300cm from the side of the door, the system will send text messages, photos, and videos via Telegram. That way, the user can determine the next steps related to the condition of the server room. However, if the RFID tag is recognized, the solenoid door lock, PIR, and ultrasonic will be deactivated. Other features, Users can ask the system to record a video of the room conditions and also messages related to room temperature through commands sent via Telegram messages. Testing is carried out using 3 scenarios: testing command, testing regarding valid access, and testing regarding invalid temperature and intruder detection. Overall test results show all these tests were successful 100% and followed the objectives and program scenarios. This tool has fulfilled the objective where this tool can monitor the condition and security of the server room by utilizing IoT technology.

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Raspberry Pi-based HIL simulators for control education

Cited by 19 publications

References 15 publications

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet

3D Geometric Shape and Colors Interactive Learning Media using Raspberry Pi, OpenCV, and TensorFlow Lite

IoT Implementation for Server Room Security Monitoring Using Telegram API

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