Stephanie Fiorenza
Papers
1
Total Citations
3
H-Index
1
About
Stephanie Fiorenza is a researcher whose work bridges wireless communications and intelligent localization systems. Her primary research areas include radio frequency signal processing, target localization in dynamic environments, and the application of associative memory networks to sensor data. Her most notable contribution, the "Associative Search Network for RSSI-Based Target Localization in Unknown Environments," introduces a novel framework that leverages received signal strength indicators (RSSI) to accurately pinpoint targets without prior knowledge of the environment. This work, published in 2015 and garnering 3 citations, addresses a critical challenge in wireless sensor networks—adaptability to unknown or changing surroundings—by combining associative memory with search algorithms. While her citation count is modest, the conceptual innovation of her approach has laid groundwork for further exploration in adaptive localization, particularly in contexts where traditional GPS or map-based methods fail. Fiorenza’s research demonstrates a keen ability to integrate machine learning principles with practical wireless engineering, offering a pathway toward more resilient and autonomous localization systems. Her work is especially relevant for students and researchers interested in the intersection of signal processing, neural networks, and real-world sensor applications.
Research Focus
Key Achievements
Top Papers
- 1