GaAs/InGaAs/GaAs nanowire core-multishell heterostructures with a strained radial In0.2Ga0.8As quantum well were fabricated by metal organic chemical vapor deposition. The quantum well exhibits a dislocation-free phase-pure zinc-blende structure. Low-temperature photoluminescence spectra of a single nanowire exhibit distinct resonant peaks in the range from 880 to 1000 nm, corresponding to the longitudinal modes of a Fabry-Pérot cavity. This suggests a decoupling of the gain medium and resonant cavity so that the quantum well provides the gain while the nanowire acts as the cavity. The resonant modes were observed at temperatures up to 240 K, exhibiting high power- and temperature-stability. The modes were blueshifted while decreasing the quantum well thickness due to enhanced quantum confinement. The results make the GaAs-based nanowire/quantum well hybrid structure promising for wavelength-tunable near-infrared nanolasers.

Since most of the contact conduction type of heart sound sensors don’t take into account the acoustic signal attenuation problem caused by the heart sound signal transmitting to a sensor whose filling materials’ impedance is different to human soft tissue, the signal-to-noise ratio (SNR) of the heart sound sensors is not very well. Human heart is immersed in blood. If the sensor’s core sensitive element can be immersed in fluid, the attenuation of heart sound signal may be decreased greatly. Inspired by the principle of hydroacoustic signal’s detection, this paper proposes the design of heart sound sensor based on the bionic vector hydrophone. Then theoretical analysis and finite element method (FEM) simulation about the sensor have been carried out. Combined sensitivity with resonant frequency, the optimum dimension of the sensor’s structure has been determined. The sensor’s micro-structure has been fabricated by using Micro-Electro-Mechanical System (MEMS) technology and coupling encapsulated by choosing a kind of medical coupling agent as the filling material. Finally, the performance of the proposed sensor is tested. The fact is that the proposed sensor can work well with either healthy people or patients with heart disease. The obtained data clearly show that: the SNR of the proposed heart sound sensor is superior to 3200-type of 3M Littmann 8.2 dB.

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It is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused on the detection and classification of the abnormities of S1 and given out in detail the essential differences between abnormal and normal S1. This work applied Empirical Wavelet Transform (EWT) to decompose S1 and extracted the instantaneous frequency (IF) of mitral component (M1) and tricuspid component (T1) by using Hilbert Transform. Firstly, the heart sound signal is preprocessed following these processes: filtering, resampling, normalization and segmentation. Secondly, S1 is decomposed into several modes based on EWT. First two maximal points with a distance greater than 20Hz in Fourier Spectrum of S1 are selected and the nearest minimal points on both sides of the maximal points are found out as the boundaries for segmentation of the spectrum. S1 is decomposed into 5 modes and every mode's IF are calculated through Hilbert transformation. At last, a k-mean cluster algorithm is applied to cluster the IF of different modes. TD and A peak_ratio are calculated for decision tree classifier and S1s are divided into three categories: normal S1, S1 with abnormal split and S1with abnormal amplitude change. When the proposed method is applied to detect normal S1, Se=94.6%, Pp=98.6% and Oa=93.3%; When it is applied to detect S1 with abnormal split, Se=92.6%, Pp=96.9% and Oa=90%; When it is applied to detect S1 with abnormal amplitude change, Se=94.4%, Pp=95.7% and Oa=90.6%; Comparison experiments are carried out between the proposed method and HVD method. The results show Oa of the proposed method is higher than HVD method when detecting the three different S1s. INDEX TERMSFirst heart sound (S1), abnormities, empirical wavelet transform (EWT), mitral component (M1), tricuspid component (T1), Instantaneous frequency (IF).