Efficient FBMC-OQAM Channel Equalization Through Pruned DFT Vian S. Al-Doori, Ali Al-Shuwaili, Thamer M. Jamel Proceedings International Conference on Developments in Esystems Engineering Dese, 2023 Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (OQAM) is seen as a potential candidate to replace the conventional Cyclic Prefixed-Orthogonal Frequency Division Multiplexing (CP-OFDM) in the future wireless communications standards like 6G and beyond. This modulation scheme relies mainly on the utilization of pulse shaping which relax the system from CP overhead but requires more complex receiver structure. Moreover, in time-varying channels and in high-SNR regime, the classical single-tab equalizer is not adequate to compensate for channel impairments in FBMC-OQAM which entails the utilization of more complex equalization techniques. Therefore, this work first proposes to leverage the unique properties of FBMC-OQAM and the power of the pruned DFT technique—an efficient algorithm to compute DFT where only subset of the data is involved during computation—to improve the system performance. Then, the equalization performance of the proposed scheme is enhanced with several sophisticated equalization algorithms including the multi-tab frequency sampling, parallel Analysis Filter Bank (AFB), frequency spreading and overlap-and-save equalizers. Through simulations, the performance of the proposed scheme shows considerable improvement, in term of Bit Error Rate (BER) metric, compared to the conventional non pruned-DFT FBMC system.
Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing Hayder Mahmood Salman, Vian S. Al Al-Doori, Hayder sharif, Wasfi Hameed4, Rusul S. Bader Fusion Practice and Applications, 2023 To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.
