COMPUTERIZED OPTICAL EXPERIMENTS WITH PORTABLE SPECTROMETERS AND AMAZON WEB SERVICES CLOUD INTEGRATION

Background. This paper presents a system for automating optical experiments using the StellarNet spectrometer and AWS cloud services. The relevance of this research is driven by the need for remote access to laboratory equipment, which became particularly critical during the COVID-19 pandemic. The proposed solution enables real-time optical experiments, automates spectral data collection and processing, and ensures secure cloud storage for further analysis. By leveraging cloud technologies, the system enhances research efficiency and accessibility, reducing dependence on physical presence in the laboratory. Materials and Methods. The study reviews modern approaches to laboratory automation, focusing on both hardware and software solutions. The integration of embedded processors like Raspberry Pi, development environments such as LabVIEW, and IoT-based architectures is considered. A key aspect is the implementation of remote access, allowing seamless operation and real-time monitoring. The system hardware includes the StellarNet VIS-50 spectrometer, the SL1-LED light source, and the Synco PC Box mini-PC. Software tools, such as the StellarNet Python SDK and AWS IoT Core, are used to enable automated data processing and cloud communication. Results and Discussion. A Python script was developed for integrating the spectrometer with AWS IoT Core, enabling seamless data transmission. The paper describes the setup, calibration, and real-time spectral data collection process. Using the MQTT protocol, experimental data is securely transmitted to the cloud, allowing remote access and analysis. The implementation details, including secure connection protocols, message publication, and cloud integration, are discussed to highlight system reliability and efficiency. Conclusions. The proposed system enhances laboratory experiment automation, improving research flexibility and efficiency in STEM disciplines. It reduces equipment maintenance costs and increases accessibility to experimental data. The scalable solution can be adapted to various scientific applications requiring remote equipment access and continuous data collection.