Dr. Paolo Dini | Data Analysis | Best Researcher Award

Dr. Paolo Dini | Data Analysis | Leading Researcher at Centre Tecnològic de Telecomunicacions de Catalunya | Spain

Dr. Paolo Dini is a distinguished researcher in the field of information engineering, currently affiliated with the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), where he leads research at the intersection of sustainable computing, wireless communication, and artificial intelligence. Dr. Paolo Dini holds a Ph.D. in Information and Communication Technologies, and his academic foundation has enabled him to make impactful contributions to the development of energy-efficient and intelligent network infrastructures. Over the years, he has amassed a prolific research portfolio with more than 60 peer-reviewed publications and over 2,300 citations, earning him an h-index of 25 and an i10-index of 54, according to Scopus. Professionally, Dr. Paolo Dini has held research and leadership roles in multiple European and international collaborative projects, contributing both to academia and industrial innovation. He has worked alongside prominent researchers from institutions like Ericsson, Politecnico di Bari, University of Padova, and CTTC, fostering multidisciplinary research in areas such as mobile traffic modeling, green networking, and edge intelligence. His expertise includes machine learning for network optimization, distributed systems, multi-agent systems, 5G and beyond architectures, and sustainable AI. These skills are further demonstrated by his role in developing algorithms and models for energy harvesting in mobile networks and predictive analytics for traffic anomaly detection. Dr. Paolo Dini’s research interests continue to evolve with the current technological landscape, focusing on combining AI with wireless systems to enable smarter, greener, and more adaptive communication environments.

Professional Profile: ORCID | Google Scholar

Selected Publications:

Mobile traffic prediction from raw data using LSTM networks (2018) – 245 Citations

HetNets powered by renewable energy sources: Sustainable next-generation cellular networks (2012) – 201 Citations

SolarStat: Modeling photovoltaic sources through stochastic Markov processes (2014) – 108 Citations

Detecting mobile traffic anomalies through physical control channel fingerprinting: A deep semi-supervised approach (2019) – 81 Citations

Mr. Xincheng Guo | Time Series Awards | Best Machine Learning for Sensing Award

Mr. Xincheng Guo, Shanghai University of Engineering Science, China

Xincheng Guo is a graduate student pursuing a Master’s degree in Electronic Information at Shanghai University of Engineering Science, with research focused on intelligent signal processing, deep learning, and IoT systems. His notable contributions include the development of an innovative CEEMDAN-WT-VMD framework for multi-source noise suppression in power load data and the design of advanced neural network models such as Bidirectional Temporal Convolutional Networks and Attention-based BiGRU for spatiotemporal modeling and signal denoising. He has published first-authored research on short-term power load forecasting in the journal Electronics (2025, Q1). Xincheng has also engineered a multi-sensor fire detection and patrol system integrating improved YOLOv5s vision algorithms with sensor fusion and high-precision positioning technologies. His technical expertise spans sensing algorithms, embedded systems, and AI frameworks like PyTorch and TensorFlow. He has received multiple honors, including the 2nd Prize in the 19th China Graduate Electronics Design Competition (Shanghai Division) and the National Graduate Scholarship.

Professional Profile:

ORCID

Summary of Suitability for Best Machine Learning for Sensing Award conclusion

Xincheng Guo is a highly promising candidate for the Research for Best Machine Learning for Sensing Award, demonstrating strong expertise in intelligent signal processing and deep learning applied to multi-modal sensing systems. Currently pursuing a Master’s degree in Electronic Information at Shanghai University of Engineering Science, Guo has developed innovative methods for sensing signal denoising and prediction, including a novel CEEMDAN-WT-VMD framework that achieves significant noise reduction and a Bidirectional Temporal Convolutional Network that outperforms state-of-the-art models in power load forecasting. His research is supported by the National Natural Science Foundation of China, reflecting its scientific merit and relevance. Beyond theoretical contributions, Guo has designed practical embedded sensing systems integrating advanced vision algorithms and multi-sensor fusion for real-time fire detection, showcasing his ability to translate machine learning innovations into impactful applications. With published Q1 journal papers, recognized technical skills in AI frameworks, and awards in national electronics competitions, Xincheng Guo embodies the excellence and innovation that the Best Machine Learning for Sensing Award seeks to honor.

🎓 Education

Master of Electronic Information (2023.09 – 2026.09)
Shanghai University of Engineering Science
Focus: Intelligent Signal Processing, Deep Learning, IoT Systems

💼 Work Experience

Graduate Student
China Education and Research Network (CERNET), Beijing (Since 2023.09)

🏆 Achievements & Key Contributions

Developed CEEMDAN-WT-VMD framework for multi-source noise suppression, achieving a 46.3% noise reduction (SNR 227.1 dB)
Created Bidirectional Temporal Convolutional Network (BiTCN) with 0.65% MAPE on power load forecasting, outperforming top models
Designed an Attention-based BiGRU model for dynamic temporal feature weighting in noisy data
Published first-author paper:
“Short-Term Power Load Forecasting Based on CEEMDAN-WT-VMD Joint Denoising” in Electronics (2025, Q1, IF=3.0)
Built a Multi-Sensor Fire Detection and Patrol System using Raspberry Pi and MM32 with improved YOLOv5s vision algorithm (+8.2% mAP), flame/smoke sensor fusion, and GPS positioning

🎖️ Awards & Honors

🥈 2nd Prize, 19th China Graduate Electronics Design Competition (Shanghai Division), 2024
🥉 3rd Prize, 6th Yangtze River Delta Smart City Competition, 2024
🎓 National Graduate Scholarship, 2023-2024
🛫 Aerospace Inspirational Scholarship, 2022-2023
🏅 CET-4 Certificate (English Proficiency)
💻 National Computer Technology and Software Professional Qualification (Primary Level)