CARDENAS AMAYA, JORGE DANIEL
INGENIERÍA · Nivel C
Desde 2025 es Postdoctoral fellow en Tongji University
Jorge Cárdenas received the B.Sc. degree in electronic engineering with a minor in digital signal processing from the Universidad Autónoma de San Luis Potosı́ (UASLP), Mexico, in 2016, the M.Sc. degree in applied sciences with a major in photonics and...
Jorge Cárdenas received the B.Sc. degree in electronic engineering with a minor in digital signal processing from the Universidad Autónoma de San Luis Potosı́ (UASLP), Mexico, in 2016, the M.Sc. degree in applied sciences with a major in photonics and a minor in image processing from the Instituto de Investigación en Comunicación Óptica (IICO), Mexico, in 2018, and the Ph.D. degree in engineering sciences with a major in electronics from UASLP, in 2023. From September 2023 to July 2025, he was a Research Assistant with the Faculty of Sciences, UASLP. He is currently a Postdoctoral Researcher with the School of Electronic and Information Engineering, Tongji University, Shanghai, China. His current research interests include integrated sensing and communications, radio frequency signal processing, channel propagation, machine learning, and vehicular communications. He is a member of the Mexican National System of Researchers (SNII, candidate level) at UASLP. He is also a member of the IEEE Communications Society and the IEEE Vehicular Technology Society.
| Nombre | JORGE DANIEL CARDENAS AMAYA |
|---|---|
| Área | INGENIERÍAS Y DESARROLLO TECNOLÓGICO |
| Campo | INGENIERÍA |
| Disciplina | INGENIERÍA ELECTRÓNICA |
| Especialidad | ELECTRONICA Y TELECOMUNICACIONES |
| CVU | 787951 |
| Institución |
SIN INSTITUCIÓN
|
|---|---|
| Dependencia | NO APLICA |
| Entidad | NO APLICA |
| Nivel | C |
| Vigencia | Inicio: 01/01/2026 |
| Fin: 31/12/2029 |
Publicaciones ORCID
- 2025
- Head-On Vehicle Collision Prevention With Machine Learning and a Fully Centralized Radio Sensing Approach IEEE Open Journal of the Communications Society
- 2024
- Fall Detection Using WiFi Signals With Doppler Frequency Diversity IEEE Consumer Electronics Magazine
- Experimental Evaluation of a Head-On Collision Warning System Fusing Machine Learning and Decentralized Radio Sensing IEEE Sensors Journal
- 2023
- Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures International Journal of Environmental Research and Public Health
- 2022
- Doppler Spectrum Measurement Platform for Narrowband V2V Channels IEEE Access
- 2021
- Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems Sensors
- 2020
- Effects of Antenna Orientation in Fall Detection Systems Based on WiFi Signals 2020 IEEE Latin-American Conference on Communications (LATINCOM)
