In order to improve the speech quality and auditory perceptiveness of electronic cochlear implant under strong noise background, a speech enhancement system used for electronic cochlear implant front-end was constructed. Taking digital signal processing (DSP) as the core, the system combines its multi-channel buffered serial port (McBSP) data transmission channel with extended audio interface chip TLV320AIC10, so speech signal acquisition and output with high speed are realized. Meanwhile, due to the traditional speech enhancement method which has the problems as bad adaptability, slow convergence speed and big steady-state error, versiera function and de-correlation principle were used to improve the existing adaptive filtering algorithm, which effectively enhanced the quality of voice communications. Test results verified the stability of the system and the de-noising performance of the algorithm, and it also proved that they could provide clearer speech signals for the deaf or tinnitus patients.
Clinical studies had demonstrated that early diagnosis of lesion could significantly reduce the risk of cancer. Magneto-acoustic-electrical tomography (MAET) is expected to become a new detection method due to its advantages of high resolution and high contrast. Based on thinking of modular design, a low-cost, digital magneto-acoustic conductivity detection system was designed and implemented in this study. The theory of MAET using chirp continuous wave excitation was introduced. The results of homogeneous phantom experiment with 0.5% NaCl clearly showed that the conductivity curve of homogeneous phantom was highly consistent with the actual physical size, which indicated that the chirp excitation theory in our proposed system was correct and feasible. Besides, the resolution obtained by 1 000 μs sweep time was better than that obtained by 500 μs and 1 500 μs, which means that sweep time is an important factor affecting the detection resolution of the conductivity. The same result was obtained in the experiments carried out on homogeneous phantoms with different concentrations of NaCl, which demonstrated the repeatability of our proposed MAET system.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.