Wavelet has been used as a powerful tool in the signal processing and function approximation recently. This paper presents the application of wavelets for solving two key problems in 3-D audio simulation. First, we em...Wavelet has been used as a powerful tool in the signal processing and function approximation recently. This paper presents the application of wavelets for solving two key problems in 3-D audio simulation. First, we employ discrete wavelet transform (DWT) combined with vector quantization (VQ) to compress audio data in order to reduce tremendous redundant data storage and transmission times. Secondly, we use wavelets as the activation functions in neural networks called feed-forward wavelet networks to approach auditory localization information cues (head-related transfer functions (HRTFs) are used here). The experimental results demonstrate that the application of wavelets is more efficient and useful in 3-D audio simulation.展开更多
文摘Wavelet has been used as a powerful tool in the signal processing and function approximation recently. This paper presents the application of wavelets for solving two key problems in 3-D audio simulation. First, we employ discrete wavelet transform (DWT) combined with vector quantization (VQ) to compress audio data in order to reduce tremendous redundant data storage and transmission times. Secondly, we use wavelets as the activation functions in neural networks called feed-forward wavelet networks to approach auditory localization information cues (head-related transfer functions (HRTFs) are used here). The experimental results demonstrate that the application of wavelets is more efficient and useful in 3-D audio simulation.
文摘目的探讨阿尔茨海默病(Alzheimer's disease,AD)患者大脑灰质体积、灰质皮层厚度及基于皮层厚度的结构协变网络(structural covariance network,SCN)的拓扑属性改变。材料与方法本研究共筛选了250例来自ADNI数据库的被试,包括AD组100人,健康对照(healthy controls,HCs)组150人。首先,利用基于体素的形态学分析方法(voxel-based morphometry,VBM)和基于表面的形态学分析方法(surface-based morphometry,SBM)分别计算每组被试的灰质体积和皮层厚度并比较其组间差异。其次,将有组间差异的脑区定义为感兴趣区(region of interest,ROI),提取每一个ROI的灰质体积和皮层厚度值,与认知量表进行偏相关分析。最后,构建基于皮层厚度的SCN并利用图论分析方法分析该网络的全局属性及局部属性的变化特征。结果第一,相较于HCs组,AD组的灰质体积和皮层厚度显著下降[体素和顶点水平总体误差(family-wise error,FWE)校正后P<0.001]。AD组灰质体积下降的脑区主要包括双侧海马、双侧眶额皮层、左侧岛叶、右侧枕下回、左侧楔前叶、左侧中央前回、左侧中央扣带回。AD组皮层厚度变薄的脑区主要包括双侧颞叶、双侧额叶、双侧顶叶、双侧扣带回、双侧梭状回、双侧岛回、双侧楔前叶等。第二,偏相关分析表明,AD组简易精神状态检查量表(Mini-Mental State Examination,MMSE)得分分别与右侧海马体积[rs=0.35,错误发现率(false discovery rate,FDR)校正后P<0.001]、左侧海马体积(r_(s)=0.38,FDR校正后P<0.001)、右侧梭状回皮层厚度(r_(s)=0.38,FDR校正后P<0.001)呈正相关;临床痴呆评定量表(Clinical Dementia Rating Sum of Boxes,CDR-SB)评分与左侧梭状回皮层厚度(r_(s)=-0.39,FDR校正后P<0.001)呈负相关。第三,脑网络分析表明,AD组SCN的全局效率(P<0.001)、局部效率(P=0.03)及小世界属性(P<0.001)高于HCs组,最短路径低于HCs组(P<0.001)。结论联合VBM、SBM的形态学分析及SCN的图论分析有助于全面理解AD患者脑网络的重组及其意义,进而为AD患者神经影像学改变提供新的见解和证据。
