Percorrer por autor "Jvarsheishvili, Mariam"
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- Multi-sensor pattern recognition and real-time data processing for autonomous smart waste managementPublication . Jvarsheishvili, Mariam; Ibrahim, Ahmad; Ahmadi, Mahdia; Igrejas, Getúlio; Soares, Caio; Izidorio, Felipe; Lopes, Rui Pedro; Rodrigues, Pedro JoãoUrban waste management systems require intelligent monitoring solutions that can process multi-modal sensor data in real-time while operating autonomously. This paper presents RAICYCLE, a comprehensive smart waste management system that integrates advanced pattern recognition techniques with real-time operating systems for autonomous urban deployment. The system employs eight BME688 environmental sensors with distinct heater profiles (50°C to 350°C) for volatile organic compound (VOC) pattern classification, combined with VL53L0X Time-of-Flight sensors and GPS tracking. The embedded architecture utilizes FreeRTOS dual-core task scheduling on ESP32 microcontrollers, enabling concurrent sensor data processing, LoRaWAN communication, and system monitoring. Data serialization through Protocol Buffers achieves 70% payload reduction compared to JSON formats, while kinetic energy harvesting from container lid movements enables autonomous operation. The system demonstrates effective real-time processing of 32 dimensional feature vectors for waste classification and environmental monitoring in urban deployments.
- Synthetic data generation for volatile organic compounds recognitionPublication . Ahmadi, Mahdia; Ibrahim, Ahmad Gamal; Jvarsheishvili, Mariam; Igrejas, Getúlio; Izidorio, Felipe; Lopes, Rui Pedro; Soares, Caio; Rodrigues, Pedro JoãoThe fact that machine learning (ML) models to recognize volatile organic compounds (VOC) are typically developed with limited datasets and can be expensive to gather scaled sensor data is an obstacle in their development. The Bosch BME688 is a multi-gas sensor that can give detailed environmental data, but needs large experimental campaigns to construct representative data sets. To overcome this issue, we introduce a Python library on synthetic data generation to the BME688. The tool uses the Kernel Density Estimation (KDE) to generate an empirical gas resistance distribution according to various heater profiles and uses mathematical gas mixing to generate self-configurable multi-gas simulations. Experiments by validation on coffee and oil gases show that the resulting datasets retain the statistical characteristics of actual measurements, both at the stepwise level of gas resistance distributions and at the multivariate level with Principal Component Analysis (PCA). The library generates machine learning reproducible experimentation, machine learning algorithm prototyping on mixtures of percentages, and provision of systematic evaluation of VOC recognition systems. The contribution of the work is a modular and lightweight framework to address the problem of the lack of data, facilitate the reproducible research and speed up the creation of air quality monitoring solutions based on ML.
- VOC pattern recognition in waste using AI-Enabled BME688 sensorsPublication . Izidorio, Felipe; Jvarsheishvili, Mariam; Igrejas, Getúlio; Rodrigues, Pedro João; Lopes, Rui PedroUrban waste management constitutes a critical infrastructure challenge for modern cities. Traditional waste collection methodologies operate on fixed schedules regardless of actual container fill levels, resulting in inefficient resource allocation, unnecessary carbon emissions, and occasional overflow incidents. Additionally, the inability to detect hazardous materials creates environmental and safety risks, particularly with improperly disposed hot ash that can cause fires in waste containers. Smart sensors, artificial intelligence, and optimization algorithms offer a transformative approach to monitor waste conditions in real-time and provide actionable insights to stakeholders.