Using fuzzy logic to improve a clustering technique for function approximation

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging

Acousticelectric brain imaging (ABI), which is based on the acoustoelectric (AE) effect, is a potential brain function imaging method for mapping brain electrical activity with high temporal and spatial resolution. To further enhance the quality of the decoded signal and the resolution of the ABI, the decoding accuracy of the AE signal is essential. An adaptive decoding algorithm based on Fourier fitting (aDAF) is suggested to increase the AE signal decoding precision. The envelope of the AE signal is first split into a number of harmonics by Fourier fitting in the suggested aDAF. The least square method is then utilized to adaptively select the greatest harmonic component. Several phantom experiments are implemented to assess the performance of the aDAF, including 1-source with various frequencies, multiple-source with various frequencies and amplitudes, and multiple-source with various distributions. Imaging resolution and decoded signal quality are quantitatively evaluated. According to the results of the decoding experiments, the decoded signal amplitude accuracy has risen by 11.39% when compared to the decoding algorithm with envelope (DAE). The correlation coefficient between the source signal and the decoded timing signal of aDAF is, on average, 34.76% better than it was for DAE. Finally, the results of the imaging experiment show that aDAF has superior imaging quality than DAE, with signal-to noise ratio (SNR) improved by 23.32% and spatial resolution increased by 50%. According to the experiments, the proposed aDAF increased AE signal decoding accuracy, which is vital for future research and applications related to ABI.

Acoustoelectric Signal Decoding Based on Fourier Approximation

The acoustoelectric (AE) effect is that ultrasonic wave causes the conductivity of electrolyte to change in local position. AE imaging is an imaging method that utilizes AE effect. The decoding accuracy of AE signal is of great significance to improve the decoded signal quality and resolution of AE imaging. At present, the envelope function is adopted to decode AE signal, but the timing characteristics of the decoded signal and the source signal are not very consistent. In order to further improve the decoding accuracy, based on envelope decoding, the decoding process of AE signal is investigated. Considering with the periodic property of AE signal in time series, the upper envelope signal is further fitted by Fourier approximation. Phantom experiment validates the feasibility of AE signal decoding by Fourier approximation. And the time sequence diagram decoded with envelope is also compared. The fitted curve can represent the overall trend curve of low-frequency current signal, which has a significant correspondence with the current source signal. The main performance is of the same frequency and phase. Experiment results validate that the proposed decoding algorithm can improve the decoding accuracy of AE signal and be of potential for the clinical application of AE imaging.