A vital sign signal noise suppression method for wearable piezoelectric devices.

This paper tackles the problem of noise suppression during vital sign signal monitoring. Physiological signal monitoring is a significant and promising medical monitoring method, and wearable medical monitoring devices based on piezoelectric polymer sensors are a trending way for their advantages of being flexible in the shape, portable to use, and comfortable to wear. However, this raises the question that the measured signal contains much more noise components. To avoid the following shortcoming of low signal to noise ratio (SNR), a noise suppression method based on improved wavelet threshold and empirical mode decomposition combined with singular value decomposition (SVD) screening the intrinsic mode function (IMF) components is proposed. A wavelet transform is first used under the combination of hard and soft thresholds to focus the target range in the low-frequency region where the energy of the physiological signal is concentrated. Then, a complete ensemble empirical mode decomposition is used to decompose the signal effectively, which can resist the influence of random noises. Meanwhile, a SVD decomposition procedure was used to filter out the lower correlated IMF components to retain the validity of the original signal. We verified the effectiveness of the proposed method through simulated and measured experiments as well as the advantages and disadvantages of the algorithm compared with other physiological signal denoising algorithms through SNR filtering results, power spectrum distribution, and other perspectives. The results proved that the proposed method could effectively remove more detailed noise and improve the SNR of the signal efficiently, which is more conducive to the demand for auxiliary medical diagnosis in the future.

Electrochemical performance of a new all solid-state ultra-low noise electrospray electrode as a marine electric field sensor.

A new type of Ag/AgCl electrode as a marine electric field sensor is prepared using electrospray. The surface of the electrode is porous, and the particle size of AgCl is small and uniform with an average particle size of 1.43 µm, which accelerated the speed of the oxidation-reduction reactions. Therefore, the electrode with large specific surface area has high stability and low noise. The impedance, sensitivity, self-noise, and stability of the electrode are measured to study the electrochemical performance of the electrode. The impedance of the electrode is 7.9 Ω, and the electrode shows resistance characteristics, meaning that the electrode can well receive the weak ocean electric field signals with low signal distortion. The sensitivity experiment result shows that the electrode can well restore the sinusoidal electric field signal of 1 Hz (10 mV). The voltage drift is less than 5 µV/100 h, the self-potential is between -51 and 56 µV, and the self-noise of the electrode is 2.48 nV @ 1 Hz. The AgCl layer on the surface of the electrode is porous and thick, and the particle size of AgCl is small and uniform. This makes the electrode have excellent electrochemical performance. All the experimental results show that the electrode has ultra-low noise and excellent response to low frequency weak electric field signals. The electrode is of great significance to the exploitation of marine resources as the marine electric field sensor.

A bionic piezoelectric tactile sensor for features recognition of object surface based on machine learning.

Based on the tactile mechanism of human fingertips, a bionic tactile sensor fabricated from polyvinylidene fluoride piezoelectric film is proposed, which can identify the surface softness, viscoelasticity, thermal conductivity, and texture roughness of the object. The tactile sensor is mounted on the fingertip of the bionic manipulator, which obtains the surface features by touching and sliding the object. The time-domain features of the output signal are used for preliminarily discriminating the softness, viscoelasticity, and heat conduction of the object. Finally, based on the Back Propagation and the Particle Swarm Optimization-Back Propagation neural network algorithm, the recognition experiment of texture roughness is carried out using the PSO algorithm to improve the BP neural network so that the optimized BP algorithm has a higher convergence accuracy. The results show that the PSO-BP algorithm achieved the highest accuracy of 98% for identifying samples with different roughnesses and the average recognition achieved an accuracy of 94%. The bionic piezoelectric tactile sensor proposed in this paper has a good application development prospect in recognizing the surface features of objects and intelligent robots.

Visible-light-promoted conversion of alkyl benzyl ether to alkyl ester or alcohol via O-α-sp(3) C-H cleavage.

A mild and high-yielding visible-light-promoted conversion of alkyl benzyl ethers to the alkyl esters or alkyl alcohols was developed. Mechanistic studies provided evidence for a radical chain reaction involving the homolytic cleavage of O-α-sp(3) C-H bonds in the substrate as one of the propagation steps. We propose that α-bromoethers are key intermediates in the transformation.