Jialiang Zhu

Good understanding of telecommunication principles and communication networks. Work experience with communication related algorithms. Several research experiences in the area of telecommunication and signal processing during university. Experience with machine learning course projects. Hardworking and passionate PhD seeker with voracious appetite for knowledge. Skilled in working under pressure and adapting to new situations and challenges to achieve research achievements.

Feb. 2022 - June 2022 Speech envelope extraction method for improving EEG-based auditory attention detection

· The microphone array signal is processed by the MUSIC algorithm to identify the number of speakers and determine their location. The microphone array signal is denoised by the GSC algorithm, and then a noise-free speaker's voice signal is obtained.

· The CNN neural network model is trained using the tested EEG data and the corresponding labels of the speakers of interest.

· Combining the sound signal processing model with the CNN model, through the input EEG signal, we can know which direction to pay attention to the speaker and play the person's pure speech signal.

Mar. 2019 – May 2020 Research on Overlapped Frequency Domain Multiplexing (OvFDM) and Its Sequence Detection Algorithm

· Established the communication model of OvFDM system and completed the theoretical analysis and formula derivation of signal transmission.

· Deduced the principle of GLRT (Generalized Likelihood Ratio Test) sequence detection algorithm and applied to OvFDM communication system; simulated the error rate and algorithm complexity of the decoding results with MATLAB, and compared with the traditional maximum likelihood decoding algorithm.

Sept. 2018 Design of anti-lost locator based on STM32 (Team Leader)

· Debugged the GPS module and GSM module with AT instruction

· Tested the GPS antenna module and completed the positioning function

· Programmed to integrate microcontroller and GSM and GPS to position and send messages

May 2017 - May 2018 Design and Implementation of Intelligent Mirror based on Image Recognition and Comparison

· Established the bone point extraction model by using the open source Kinect function library, OpenCV and C++, and realized the comparison between the calculated data and the standard data

· Designed electronic and online questionnaires, and explored people’s demands of fitness through conducting market research

· Purchased the equipment needed for the final equipment and accomplished the appearance design and assembly of the product

Y. Li, J. Zhu, Y. Liu, Z. Wang. “Fitness Coach”: Design and Implementation of a Smart Mirror Based on Automatic Image Recognition and Action Model Comparison. International Journal of Web Engineering and Technology, 2020 Vol.15 No.3, pp.265 - 282