Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr Yunqiang Sun, 中北大学, China

Prof. Dr. Yunqiang Sun🌐📡 is a distinguished scholar specializing in automatic modulation recognition (AMR), wireless communications, and intelligent sensor networks. He has contributed groundbreaking research, including the development of the Multimodal Parallel Hybrid Neural Network (MPHNN), which achieves 93.1% recognition accuracy with reduced complexity. His expertise spans spatio-temporal signal processing, attention mechanisms, and hybrid neural networks. Prof. Sun has published extensively, with works featured in prestigious journals like Electronics (Switzerland) and IEEE Access. His research also explores gait recognition algorithms, millimeter-wave cavity filters, and ultrasonic signal transmission. A dedicated innovator, Prof. Sun’s work advances technologies in communication and sensing systems. 📊📖✨

Publication Profile

Scopus

Proposed Solution 🤖✨

The Multimodal Parallel Hybrid Neural Network (MPHNN) is an advanced model designed to address limitations in processing modulated signals. It preprocesses these signals in multimodal formats, enhancing data interpretation. By combining Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Gated Recurrent Units (Bi-GRU) for temporal feature processing, MPHNN efficiently captures both spatial and temporal dependencies. This innovative approach enables more accurate and robust signal processing, making it highly effective in various applications. Prof. Dr. Yunqiang Sun’s work highlights the power of integrating multiple neural network models for improved performance. 🧠🔧📡📊

Attention Mechanisms 

Prof. Dr. Yunqiang Sun’s research leverages advanced deep learning techniques to enhance recognition accuracy. By integrating the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA), his work in the Multi-Path Hierarchical Neural Network (MPHNN) effectively combines both temporal and spatial features. This fusion allows for improved recognition performance in complex tasks, as the model focuses on the most relevant information across time and space. Prof. Sun’s innovative approach showcases the power of attention mechanisms in modern neural networks. 🤖📊🧠🔍

Results 

Prof. Dr. Yunqiang Sun, MPHNN, has achieved an impressive 93.1% accuracy across multiple datasets, setting a new benchmark in model performance. His work stands out due to its lower complexity and reduced number of parameters compared to existing models, making it more efficient and scalable. This breakthrough represents a significant advancement in the field, offering a solution that balances high accuracy with computational efficiency. Prof. Sun’s innovative approach holds great promise for a wide range of applications, offering potential improvements in performance and resource utilization. 🔬📊💡📈

Diverse Publication Record

Prof. Dr. Yunqiang Sun is an accomplished researcher with a focus on AMR, gait recognition algorithms, and plasmonic waveguide-coupled systems. He has published extensively in prestigious journals such as IEEE Access, Electronics (Switzerland), and Advanced Composites and Hybrid Materials. Notable works include impactful publications like CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network and Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism. Prof. Sun’s research continues to push the boundaries of technology, contributing significantly to the fields of signal processing and machine learning. 📚🔬📈💡

Citations and Recognition

Prof. Dr. Yunqiang Sun has contributed significantly to the field, with some recent works gaining traction and fewer citations, while others, like his paper on MEMS sensors in Cluster Computing, showcase a higher citation count, reflecting their enduring influence. His research spans various areas, where his innovative approaches and technical expertise continue to shape discussions and advancements in the field. Despite the varying citation impact, Prof. Sun’s work maintains its relevance and continues to inspire future developments in the areas he studies. 🌟📚🔬🧠📈

Research Focus

Prof. Dr. Yunqiang Sun’s research focuses on advanced signal processing, modulation recognition, and sensor technologies. He explores machine learning models like transformers and convolutional neural networks (CNNs) for automatic modulation recognition and signal analysis, with applications in communication systems. His work also extends to gait recognition using algorithms based on compressed sensing and MEMS sensors, which contribute to innovations in human-computer interaction and health monitoring. Prof. Sun’s expertise spans across ultrasonic wave transmission in negative refractive materials and advanced filter designs in millimeter-wave systems, with a strong emphasis on the intersection of signal processing and emerging technologies. 📡🤖📊

Publication Top Notes

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Quadrule-passband millimeter-wave cavity filter based on non-resonant node

Transmission characteristics of ultrasonic longitudinal wave signals in negative refractive index materials

Numerical calculus solution of gait recognition algorithm based on compressed sensing

Application and research of MEMS sensor in gait recognition algorithm