Recent research:
Our work spans intelligent sensing, smart city infrastructure, and data-driven optimization across telecommunications, sensing, and agriculture to improve efficiency, resilience, and safety in urban systems. In wireless sensor networks for smart water metering, we propose a connectivity-aware deployment that couples geospatial constraints with set-cover and enhanced minimum spanning tree techniques to minimize fragmentation, energy use, and infrastructure costs while ensuring reliable data aggregation through a scalable, hierarchical topology connected to a single cellular base station. In wireless communications, we analyze MIMO-GFDM under nonlinear power amplification, leveraging PAPR reduction techniques and a Markovian channel allocation approach to optimize transmitter back-off, OOB emissions, BER, and energy trade-offs, with multicast considerations under Rayleigh conditions guiding transmit power limits. Channel allocation for smart meter data in MVNO contexts combines Hungarian matching with cumulative tokens to balance channel availability and congestion, enabling dynamic, cost-efficient, reliable transmission in heterogeneous networks. Intelligent monitoring for older adults uses computer vision and ML to detect activities with high accuracy on a proprietary video dataset, illustrating practical deployment of AI in elder care. Automated agriculture applications include an automated rose counting system using image processing and an IoT-based smart irrigation solution for roses, leveraging LoRaWAN and Pybytes for real-time visibility. Soil nutrient sensing in cocoa crops demonstrates real-time nutrient monitoring via RS485, WiFi, and local visualization to support nutrient management decisions. WSN design demonstrates how routing (LEACH, PEGASIS) and MST-based approaches influence latency and packet loss in ZigBee deployments. In finance, gradient boosting models (XGBoost) with undersampling improve credit risk prediction, showing robust performance on imbalanced consumer loan datasets. SDR-backed time synchronization and PLL filter design improvements enhance radio prototyping, while D2D and three-level cellular allocations optimize resource usage and fairness (BCQI-based, Best Channel Quality Indicator, Round Robin) in heterogeneous networks. Across these topics, the emphasis is on scalable architectures, energy efficiency, spectrum and resource optimization, and AI-enabled decision support to advance smart grids, water management, health monitoring, and precision agriculture.

Key Topics:

• Telecommunications, Wireless Systems & Antennas
• Optimization, Operations Research & Decision Support
• Smart Grids, IoT, Edge/Fog & Industry 4.0
• Sensors, Instrumentation & Measurement Systems
• Agriculture, Precision Farming, Food Systems & Agroecology
• Artificial Intelligence, Machine Learning & Data Science
• Computer Vision, Medical Imaging & Signal Processing