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Communication is a fundamental human need for conveying information and ideas. However, individuals who are deaf and mute face difficulties in communicating with the broader community that does not understand sign language. This study aims to design and implement a real-time static sign language translator into speech using five flex sensors, an MPU6050 sensor, a Raspberry Pi Pico, an ADS1115 ADC module, and a DFPlayer Mini module as the audio output medium. Testing results show that the device successfully recognizes finger movements and hand orientation. The system is capable of playing audio output corresponding to recognized gestures, with the shortest latency recorded at 1.1 seconds and the longest at 2.8 seconds, achieving a detection accuracy rate of 75% based on 60 tests across 12 sign words. This device supports the translation of 12 simple sign words. The implementation demonstrates potential as an assistive communication tool, although further development is needed to improve accuracy, expand vocabulary, and conduct trials directly with deaf or mute users.

Power transformer theft, a pervasive issue disrupting critical infrastructure, necessitates the development of cost-effective and energy-autonomous security solutions. This paper presents the design and implementation of a detection-focused anti-theft framework that integrates a Raspberry Pi Zero W, camera module, and passive infrared (PIR) motion sensors powered by a solar system for continuous monitoring. The system is designed for remote, off-grid deployment, utilizing a headless Raspberry Pi powered by a 5V solar panel and power bank to ensure energy autonomy. Upon motion detection, captured images are processed on the edge device using OpenCV’s Haar Cascade classifier, optimized for upper-body detection to minimize false positives and verify human presence. Captured images are processed locally on the edge device using OpenCV’s Haar Cascade classifier to confirm human presence before an alert is sent to the mobile application, emphasizing real-time operation and low latency. Once an intrusion is confirmed, the images are saved locally and uploaded via the Secure File Transfer Protocol to a custom-developed Android application. The app provides a dedicated remote monitoring interface, enabling secure file transfer and system access, while providing users with immediate notifications and image management capabilities. The system emphasizes low power consumption, real-time operation, and low deployment cost. Tests over 200 triggered events under varied environmental conditions achieved 90% detection accuracy with an average latency of 4.5 s. Solar autonomy was maintained for approximately 24 h under normal operation. It is concluded that the integration of solar power, edge computing of images, and mobile monitoring provides a feasible, scalable, and financially viable framework for securing transformers, especially in resource-constrained environments.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

This study aims to develop an automatic detection system to improve lift safety through early detection and rapid response to sling failure. The research method uses an experimental approach by designing a 3-story lift prototype that integrates a Raspberry Pi Pico microcontroller as the main processing unit, an HC-SR04 ultrasonic sensor to detect lift position, a limit switch to detect sling failure, and a mechanical braking system using a servo motor. The system is equipped with an I2C LCD for real-time display, Telegram notifications for remote monitoring, and a buzzer alarm as an early warning. The test results show that the ultrasonic sensor has a high accuracy of 97.58% with an average error of 2.42%. The system successfully detects sling failure and activates mechanical braking automatically, preventing the basket from falling freely and allowing it to stop on the nearest floor. All control functions such as navigation buttons, motor rotation direction, and limit switch accuracy work well. This system provides an innovative solution to improve elevator safety through the integration of automatic detection technology, real-time monitoring, and direct physical response that can be applied to the development of future elevator safety technology.

This final project presents the design and development of a digital microscope based on Raspberry Pi 4 integrated with Internet of Things (IoT) technology. The aim of this project is to create a digital microscope that allows real-time observation and data sharing over the internet, enhancing accessibility for researchers and students, especially in remote areas. The digital microscope utilizes a high-quality camera (Sony IMX307) for capturing images, which are processed by the Raspberry Pi 4 and displayed on a touchscreen LCD. This project not only facilitates remote learning and collaborative research but also aims to improve the efficiency of scientific observations. The methodology includes the design, assembly, and testing of the microscope, with a focus on functionality and user experience. The results indicate that the digital microscope successfully meets the objectives, providing an affordable and innovative solution for educational and research purposes. Further development and testing will enhance its capabilities, making it more suitable for diverse scientific applications.

This study aims to develop an intuitive and efficient smart home control system by utilizing hand tracking and speech recognition technologies. These technologies employ the OpenCV, Mediapipe, PyAudio, and Speech Recognition libraries to recognize hand gestures and voice commands in real-time. The system is developed using a Raspberry Pi connected to a webcam and microphone as input devices, and a relay to control electronic appliances. The results show a high accuracy rate at optimal light intensity for hand tracking and a specific distance for speech recognition. This system is implemented in an IoT environment to control devices such as lights and door locks. The research is expected to contribute to the development of smarter and more user-friendly smart homes.