目前,许多基于深度学习和神经网络的算法被应用于脑电(electroencephalogram, EEG)信号情感识别.然而,现有研究大多采用提取单维脑电信号特征的方法.随着多传感技术的更新,更具全面性和系统性的多维信号特征提取需求出现.本文尝试将复杂网络研究应用到多维情感脑电识别中,提出一种基于水平可视图多元联合模体熵的情感识别算法,该方法可以有效避免人工选取特征对实验结果的影响,保持原始序列的非线性动力学特征.首先利用水平可视图算法将多维情感脑电信号分别转换为多路可视图网络,提取模体熵特征识别情感脑电研究中的关键频带和关键通道.在此基础上,将水平可视图网络两两联合,提取多元水平联合模体熵向量,作为输入参数对情感脑电信号进行识别.由于情感脑电序列长度会对识别效果产生影响,我们将脑电信号切割成大小不一的窗口,对比不同窗口大小对分类准确率的影响.实验结果表明,当切割窗口大小为10 s时,多元水平联合模体熵对情感脑电信号的识别效果最佳,对积极脑电/消极脑电、积极脑电/中性脑电、消极脑电/中性脑电的分类准确率分别达到95.07%, 97.73%, 90.26%,优于其他二维连接参数.同时,三分类的准确率为93.67%,本文算法无论在识别复杂度和准确率上,与已有算法相比均有较大提高.

Acoustofluidic technology combines acoustic and microfluidic technologies to realize particle manipulation in microchannels driven by acoustic waves, and the acoustic radiation force (ARF) with boundaries is important for particle manipulation in an acoustofluidic device. In the work reported here, the ARF on a free cylinder immersed in a viscous fluid with an incident plane wave between two impedance boundaries is derived analytically and calculated numerically. The influence of multiple scattering between the particle and the impedance boundaries is described by means of image theory, the finite-series method, and the translational addition theorem, and multiple scattering is included partly in image theory. The ARF on a free rigid cylinder in a viscous fluid is analyzed by numerical calculation, with consideration given to the effects of the distances from cylinder edge to boundaries, fluid viscosity, cylinder size, and boundary reflectivity. The results show that the interaction between the two boundaries and the cylinder makes the ARF change more violently with different frequencies, while increasing the viscosity can reduce the amplitude of the ARF in boundary space. This study provides a theoretical basis for particle manipulation by the ARF in acoustofluidics. © 2022 Author(s).

Ultrasonic imaging technologies possess extensive applications in modern medical diagnosis and treatment. In this paper, a designed artificial bubble array is utilized to establish a multi-scattering sound field model to describe the nonlinear vibration of the artificial bubble array, combining nonlinear acoustics and multi-scattering theory. With the results of the effective medium model, the correctness and reliability of the multi-scattering model are verified. Furthermore, under different incident frequencies and different bubble array parameters, the distributions of fundamental and second harmonic sound pressure fields produced by nonlinear vibration of the artificial bubble array are investigated. Via a judicious design of structural parameters, both fundamental and second harmonic acoustic focusing at specific locations inside the bubble array and second harmonic acoustic focusing outside the array under a specific incident frequency are realized. © 2022 Author(s).

Acoustic coding metamaterials have important applications in simplifying design procedure and providing a flexible approach to realize complicated functions. Here, we design a 1-bit coding metamaterial for flexibly manipulating the sound propagation path. The capability of subwavelength acoustic propagation control on coding metamaterial is attributed to the dipole-like characteristic of the Mie resonator. The Mie resonator with a subwavelength scale is constructed with a non-uniform structure, which can generate Mie resonance with dipole-like characteristic. Two kinds of coding elements are introduced by horizontally or vertically reversing the Mie resonator in each element. To verify the performance of the designed coding metamaterials, three specific metamaterial patterns are fabricated to give different trajectories of sound propagation. Our finding may open an avenue for designing acoustic metamaterials and is expected to design intelligent acoustic devices with exciting reconfigurable and programmable applications. Published under an exclusive license by AIP Publishing.

为了量化分析样本中的缺陷,利用卷积神经网络（Convolutional Neural Network,CNN）结合阈值分割和深度优先搜索算法实现了对超声检测图像中样品内部缺陷的识别,将实际图像输入神经网络模型中,成功完成了对缺陷的标记,验证了模型的可靠性.利用Field Ⅱ对全矩阵捕获（Full Matrix Capture,FMC）过程及对数据以全聚焦成像方法（Total Focus Method,TFM）进行成像的过程进行了仿真模拟,获得了可用于机器学习的数据集.基于方向梯度直方图（Histogram of Oriented Gradient,HOG）提取了全聚焦成像结果图的图像特征,利用改进的支持向量机（Support Vector Machine,SVM）获得由图像预测缺陷半径的模型并对该模型进行了评价.结果表明,利用上述方法提取的缺陷半径信息精准度在0.1 mm,能够应用于缺陷半径的量化分析,预测误差主要来源于数据集两端,可以通过预处理算法进一步提升检测精度.

In this study, we present a tunable metamaterial consisting of rotatable non-uniform Mie resonators (NMRs) with identical structures. The metamaterial can in real-time manipulate the direction of acoustic radiation and guarantee high transmission efficiency by simply changing the rotation angle of the NMR unit cells, which is induced by the anisotropic property of NMR. In addition, according to generalized Snell's law, the arbitrarily direction-scanning capability is realized by tuning the phase shift distribution along the metamaterial. Our proposed anisotropic metamaterial could contribute to designing a device for the emission and reception of acoustic waves in real-time.

Magnetic field is one efficient approach to realize non-contact control of materials. Here, we propose a type of membrane-based metasurface consisting of several parts of magnetorheological films embedded with magnetic particles, which enable non-contact control of sound insulation through magnetic field. The abundant degree of freedoms in membrane-type metamaterials are explored by considering composite structures. Through numerical calculations, we have investigated the effect of magnetic field excitation and film composition on the acoustic properties and the performance for sound insulation. We find an optimized model for better adjustability, wider acoustic band, and more balanced performance. Copyright © 2022 Xia, Lai and Liu.

Background: Phototherapy is a promising strategy for cancer therapy by reactive oxygen species (ROS) of photodynamic therapy (PDT) and hyperthermia of photothermal therapy (PTT). However, the therapeutic efficacy was restricted by tumor hypoxia and thermal resistance of increased expression of heat shock protein (Hsp). In this study, we developed albumin nanoparticles to combine hypoxia relief and heat shock protein inhibition to overcome these limitations for phototherapy enhancement. Results: Near-infrared photosensitizer (IR780) and gambogic acid (GA, Hsp90 inhibitor) were encapsulated into albumin nanoparticles via hydrophobic interaction, which was further deposited MnO₂ on the surface to form IGM nanoparticles. Both in vitro and in vivo studies demonstrated that IGM could catalyze overexpress of hydrogen peroxide to relive hypoxic tumor microenvironment. With near infrared irradiation, the ROS generation was significantly increase for PDT enhancement. In addition, the release of GA was promoted by irradiation to bind with Hsp90, which could reduce cell tolerance to heat for PTT enhancement. As a result, IGM could achieve better antitumor efficacy with enhanced PDT and PTT. Conclusion: This study develops a facile approach to co-deliver IR780 and GA with self-assembled albumin nanoparticles, which could relive hypoxia and suppress Hsp for clinical application of cancer phototherapy. [Figure not available: see fulltext.].
© 2020, The Author(s).

可视图（Visibility Graph， VG）算法已被证明是将时间序列转换为复杂网络的简单且高效的方法，其构成的复杂网络在拓扑结构中继承了原始时间序列的动力学特性。目前，单维时间序列的可视图分析已趋于成熟，但应用于复杂系统时，单变量往往无法描述系统的全局特征。本文提出一种新的多元时间序列分析方法，将心梗和健康人的12导联心电图（Electrocardiograph， ECG）信号转换为多路可视图，以每个导联为一个节点，两个导联构成可视图的层间互信息为连边权重，将其映射到复杂网络。由于不同人群的全连通网络表现为完全相同的拓扑结构，无法唯一表征不同个体的动力学特征，根据层间互信息大小重构网络，提取权重度和加权聚类系数，实现对不同人群12导联ECG信号的识别。为判断序列长度对识别效果的影响，引入多尺度权重度分布熵。由于健康受试者拥有更高的平均权重度和平均加权聚类系数，其映射网络表现为更加规则的结构、更高的复杂性和连接性，可以与心梗患者进行区分，两个参数的识别准确率均达到93.3%。