Szerző szerinti böngészés "Xie, Yu"
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Tétel Szabadon hozzáférhető A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals(2024) Xie, Yu; Oniga, István LászlóTétel Szabadon hozzáférhető A Review of Processing Methods and Classification Algorithm for EEG Signal(2020) Xie, Yu; Oniga, István LászlóTétel Szabadon hozzáférhető Advancements in EEG Signal Classification: Deep Learning Approaches and Hardware Implementation on FPGA(2025) Xie, Yu; Oniga, Istvan; Xie, Yu; Informatikai tudományok doktori iskola; Informatikai KarThis dissertation addresses the challenges of EEG signal processing by proposing novel methods for signal preprocessing, feature extraction, classification, and hardware acceleration. First, a comprehensive review of traditional and state-of-the-art EEG techniques is presented, providing both theoretical foundations and practical guidance for future studies. We then analyze EEG signal characteristics and design MLP and SCNN models, demonstrating that beta waves offer more reliable indicators than alpha waves for eye state classification, with MLP achieving higher accuracy and efficiency while CNN ensures robustness through integrated preprocessing and feature extraction. Furthermore, EEG data collected via OpenBCI are used to introduce CWT-CNN and STFT-CNN networks, confirming that time–frequency analysis significantly improves classification accuracy, and highlighting the potential of branch learning strategies. Parallel CNN structures leveraging CWT, CSP, and STFT further enhance feature representation and surpass existing methods. To mitigate data scarcity, we propose a data enhancement technique specialized to EEG signal, achieving superior performance with our binary branch CWT-CNN. Finally, the study transitions from software development to hardware realization by implementing a lightweight CNN on the PYNQ-Z2 FPGA. Comparative evaluations with GPU and ARM platforms demonstrate that while GPUs achieve higher speed, FPGAs offer distinct advantages in energy efficiency, portability, and cost-effectiveness, making them highly suitable for real-time EEG-based applications in edge computing.Tétel Szabadon hozzáférhető Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks(2023) Xie, Yu; Oniga, István LászlóTétel Szabadon hozzáférhető Comparison of EEG Data Processing Using Feedforward and Convolutional Neural Network(2021) Xie, Yu; Oniga, István László; Majoros, TamásTétel Szabadon hozzáférhető Enhancing Motor Imagery Classification in Brain-Computer Interfaces Using Deep Learning and Continuous Wavelet Transform(2024) Xie, Yu; Oniga, István LászlóTétel Szabadon hozzáférhető FPGA-Based Hardware Accelerator on Portable Equipment for EEG Signal Patterns Recognition(2022) Xie, Yu; Majoros, Tamás; Oniga, István LászlóTétel Korlátozottan hozzáférhető Performance Evaluation of Infocommunication SystemsXie, Yu; Sztrik, János; DE--Informatikai KarIn this thesis, we introduce the theory of Call Center and Key technology, then consider the Call Center system as a queue model and using them to study to how to evaluate the performance of Call Center. We analyze four main queue model including Erlang-C queue model, Erlang-B queue model, Erlang-A queue model and M/M/S/K+M model separately, using Matlab applicaton to programe their performance formulas, suming up their characteristic, using conditon, advantagae, disadvantage and the effect of each factor to the system.