A Deep Learning Model for Heart Sound Classification Fusing Time-Frequency Features [0.03%]
融合时频特征的心音分类深度学习模型
Nuo Liu,Xiayu Chen,Yueyi Yu et al.
Nuo Liu et al.
Objective: Cardiovascular diseases (CVDs) are a leading global health threat. The automatic classification of phonocardiogram (PCG) signals is crucial for their early diagnosis, yet existing models are often limited by an...
Deep Learning-based Surrogate Model of Subject-Specific Finite-Element Analysis for Vertebrae [0.03%]
基于深度学习的脊椎精细化有限元分析的代理模型方法研究
Yuanrui Cai,Enrico Dall&#x;Ara,Damien Lacroix et al.
Yuanrui Cai et al.
Subject-specific finite-element analysis (FEA) models enable accurate simulation of vertebral biomechanics but are often time-consuming to construct and solve under varying conditions. This study presents a novel deep learning (DL)/machine ...
A Similarity-Constrained Multi-way Gated Attention Network for Focused Ultrasound-induced Blood-brain Barrier Opening Evaluation [0.03%]
一种约束相似性的多路门控注意力网络用于聚焦超声开启血脑屏障的评估
Haixin Dai,Wenjing Li,Yan Wei et al.
Haixin Dai et al.
Objective: The blood-brain barrier (BBB) poses a significant challenge for central nervous system drug delivery due to its selective permeability. Focused ultrasound (FUS) combined with bubble agents enables non-invasive,...
Fully-Flexible Multifunctional Polydimethylsiloxane (PDMS) Neural Probe With a U-Turn Polyester Microchannel [0.03%]
一种具有U型聚酯微通道的全功能柔性聚二甲基硅氧烷神经探针
Mohammad Makhdoumi Akram,Amir Aghajani,Jonathan Levesque et al.
Mohammad Makhdoumi Akram et al.
Objective: This study aims to develop a flexible, implantable neural probe with tunable stiffness and multifunctionality for electrophysiology, drug delivery, and optogenetics, while minimizing immune response. ...
Quasi-static Elastography-driven Automated Robotic Ultrasound Screening and Localization [0.03%]
基于准静态弹性图像的自动化机器人超声筛查与定位
Hanying Liang,Shipeng Zhang,Guochen Ning et al.
Hanying Liang et al.
Robotic ultrasound imaging systems primarily focus on enhancing automation in grayscale image acquisition but lack essential functional information, which restricts their clinical effectiveness and efficiency. In this regard, we propose a n...
Deep Transfer Learning in Intra-subject and Inter-subjects for Intracortical Brain Machine Interface Decoding [0.03%]
面向皮层脑机接口的深度迁移学习方法研究
Zhongzheng Fu,Peng Zhang,Xinrun He et al.
Zhongzheng Fu et al.
Objective: This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain machine interface (iBMI) systems while reducing the rel...
ZS-KAN: Zero-shot Image Denoising with Lightweight Kolmogorov-Arnold Networks [0.03%]
基于轻量级科诺多罗夫-阿纳托尔网络的零样本图像去噪(ZS-KAN)
Jianxu Wang,Ge Wang
Jianxu Wang
Current learning-based image denoising methods have achieved impressive performance. However, their reliance on deep neural architectures and large paired datasets limits their applicability in data-limited or edge computing scenarios. Moti...
Assessing the robustness of deep learning based brain age prediction models across multiple EEG datasets [0.03%]
基于深度学习的脑年龄预测模型在多个EEG数据集上的鲁棒性评估
Thomas Tveitstol,Mats Tveter,Christoffer Hatlestad-Hall et al.
Thomas Tveitstol et al.
The increasing availability of large electroencephalography (EEG) datasets enhances the potential clinical utility of deep learning (DL) for cognitive and pathological decoding. However, dataset shifts due to variations in the population an...
Biophysical Circuit Modeling of Electro-Quasistatic Multi-Human Body Communication [0.03%]
电准静态多人体通信的生物物理电路模型
David Yang,Sukriti Shaw,Samyadip Sarkar et al.
David Yang et al.
Human body communication, particularly of the Electro-Quasistatic variety, has gained traction among low-power wireless circuit designers due to its benefits in terms of power and physical security compared to conventional electromagnetic o...
From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification [0.03%]
从语音到声学影像：超声微结构分类的光谱网络
Ali K Z Tehrani,An Tang,Mirco Ravanelli et al.
Ali K Z Tehrani et al.
The frequency dependence of backscattered radiofrequency (RF) signals produced by ultrasound scanners carries rich information related to the tissue microstructure (i.e., scatterer size, attenuation). This information can be sue to classify...