利用扫频音的刺激频率耳声发射实验研究

耳声发射是一种可以被记录的弹性波能量，产生于耳蜗，可以用于检测耳蜗外毛细胞的健康 情况。目前，临床上使用的瞬态诱发耳声发射和畸变产物耳声发射各有优缺点，而该文研究的对象是一种比其他耳声发射应用更广泛的刺激频率耳声发射(Stimulus Frequency Otoacoustic Emissions， SFOAE)。因此，该文提出了一种基于扫频的 SFOAE 测量方法。所谓扫频是一种频率随时间线性变化的刺激声，利用其所测量的 SFOAE 频率范围更宽，而分辨率取决于扫频率。该文中，首先，采用三间隔范式产生 SFOAE，并用跟踪滤波器从背景噪声中提取扫频 SFOAE。其次，依次通过对相同实验对象在不同时间所测得结果、与传统方法结果和对不同扫描时间所测得结果进行对比，以验证扫频 SFOAE 的可靠性、兼容性和有效性。研究结果表明，所提出的 SFOAE 可以用于改进临床上现有的听力损失的检测方法。     

利用扫频畸变产物耳声发射实现听力损失的快速检测

畸变产物耳声发射(Distortion Product Otoacoustic Emission,DPOAE)是在两个固定频率的纯音刺激下,由健康人 耳主动向外发射的声音能量。DPOAE 耳声发射信号的存在与人耳的健康状况密切相关,因而它的检测被广泛用于新生儿 听力筛查和成人听力检测。然而,目前的 DPOAE 检测技术存在操作复杂、分辨率低、测试时间长等不足。本文提出用频 率随时间线性增加的扫频信号替代纯音作为刺激声诱发 DPOAE 耳声发射,以克服当前方法的不足。该扫频方法采用“三 段式”播放组合消除刺激声的干扰,并用动态跟踪滤波器从记录到的信号中提取高信噪比的扫频 DPOAE 信号。实验结果 表明,扫频 DPOAE 检测方法可以大大缩短 DPOAE 的测量时间,提高检测结果的分辨率,并能灵敏反映受试者听力损失 的频率范围。扫频 DPOAE 检测方法的提出,有利于研制一种快速高效的新型耳声发射检测仪,在临床上协助医生进行准 确的听力诊断和治疗。     

基于耳蜗基底膜特性的语音特征提取方法及在智能轮椅上的应用

从人耳听觉特性出发,对能模拟耳蜗基底膜滤波特性的伽马通滤波器组进行了研究、修正,并以修正的滤波器组为基础,提出了一种基于耳蜗基底膜特性的GT-ERBCC(equivalent rectangular bandwidth cepstrum coeffi-cient based on GammaTone filters)语音特征提取方法。该方法能准确地表征出语音信号的特征,降低语音识别系统的难度,并将该方法应用于智能轮椅人机交互实验中。结果表明,基于耳蜗基底膜特性的特征提取方法能有效提高语音识别系统的识别率。     

微弱信号混沌检测的自跟踪扫频控制方法

当利用混沌理论进行微弱信号的检测时,针对不同频率的信号只能分别构建不同的检测系统进行检测,势必使其检测效率低下.本文阐述了一种分频段阈值变换的混沌检测方法,并基于该方法实现了自跟踪扫频检测.为此,首先分析了微弱信号混沌检测方法中的变阈值法和定阈值法,指出了这两种方法的优缺点,然后提出了分频段阈值变换的混沌检测方法,并基于该方法开展了微弱信号的自跟踪扫频检测控制的研究,设计制作了微弱信号自跟踪扫频检测控制电路,并进行了微弱信号自跟踪扫频混沌检测的实验研究.结果表明该检测控制系统可以实现在噪声背景下的中低频率微弱周期信号的自跟踪扫频检测.     

基于计算机PCI总线的虚拟信号源设计

虚拟仪器是基于个人计算机(PC)作为硬件平台的一种新型仪器,为了充分利用PC机的硬件资源,设计通用性强的虚拟仪器,研究了PCI总线的典型特点及专用接口芯片,建立了基于PCI总线的PCI板卡设计方法,并设计了“多波形信号发生器插卡”。实验结果表明:该设计方法充分利用了PCI总线高速的特点,将直接数字式频率合成器(DDS)软件化,能产生正弦波、三角波、方波、噪声、扫频及其它复杂波形。由该PCI卡构建的虚拟多波形信号发生器可以方便、准确地选择波形和调节波形参数,具有较强的通用性。     

基于微处理器和FPGA的波形生成器

波形生成器是振动控制仪、液压振动伺服控制器等仪器设备的重要组成部分。基于微处理器和FPGA的硬件架构,以两种方法实现了波形生成器：一种是FPGA实时逐点计算生成波形;另一种基于两种运算部件的结合——微处理器先将生成的一个周期的波形数据发给FPGA,FPGA根据相位对数据波形进行查表和插值后输出波形。该波形生成器可输出正弦波、矩形波、锯齿波、线性扫频和对数扫频。最后在资源使用、正弦波失真度等方面对两种方法进行了对比。     

基于USB声卡的便携式AEP检测系统的设计

听觉诱发电位(AEP)是研究听力疾病的重要手段,可用于客观评价听觉通路完整性。针对AEP检测的要求,设计一种基于USB声卡的便携式听觉诱发电位检测系统。利用计算机作为主要工作平台,控制USB声卡同步完成声音刺激发放和AEP数据采集,配合Visual C#平台所编写的上位机程序完成AEP数据的处理分析、结果显示及数据管理。听性脑干反应(ABR)是AEP成分中幅度最弱而采集带宽要求最高的,因此ABR是AEP成分中最难检测的。通过本系统完成一个ABR检测,并与目前市场上顶级AEP电位仪的检测结果进行对比,结果证明了本系统性能的可靠性及稳定性。     

基于ICL8038函数信号发生器的设计

本设计是以ICL8038和AT89C2051为核心设计的数控及扫频函数信号发生器。ICL8038作为函数信号源结合外围电路产生占空比和幅度可调的正弦波、方波、三角波;该函数信号发生器的频率可调范围为1￣100kHz,步进为0.1kHz,波形稳定,无明显失真。     

基于小波变换的岩体AE波形特征研究

文章对室内声发射事件能率与应力的关系及波形特性进行了分析。通过小波变换,将室内岩体声发射等波形分解成低频和高频信号,对其原始声发射波形、细节信号和近似信号进行比较与分析,结果表明岩体在变形损伤破裂过程中不同时期的声发射波形特征不同。据此,可建立声发射波形特征库,用于预测预报岩体受损变形过程的发展趋势和规律。对含有信号部分的小波系数进行重构后,该信号与原始信号的误差小于10-10,说明小波分析可使岩体声发射波形特征预测预报的准确度和可靠性得以提高。     

基于51单片机信号发生器的设计

该文介绍一种用STC89C52RC单片机组成的波形信号发生器,可产生波锯齿波信号、方波信号、正弦波信号、三角波信号四种信号波形,信号波形的占空比和频率可由键盘改变,并可选择单双极性输出,具有电路简单、性能优良、便于操作等特点。通过测试,其指标性能达到了设计的要求。     

EEG based automated detection of auditory loss: A pilot study

Auditory dysfunction is one of the most common deficiencies present in the newborn. Recent studies show that significant bilateral hearing loss is present in ∼1 to 3 per 1000 newborn infants in the well-baby nursery population and in ∼2 to 4 per 1000 infants in the ICU population. The ignorance of hearing screening test at the initial stage will impede speech, language and cognitive development. It has been further noted that direct physician observation as well as parental recognition has not been significantly successful until today in identifying the hearing loss in the first year of life. To overcome such problems, early screening is essential. This paper presents a pilot study on detection of hearing loss by applying electroencephalography (EEG) signals as the key indicator. The effect of auditory evoked potential (AEP) is exploited on EEGs by introducing an external stimulus to the subject’s auditory canal. Two time domain features, spike rhythmicity, autoregressive model using Levinson-Durbin algorithm and frequency domain features such as power spectral density estimation by Burg’s and Yule-Walker methods are applied. Feed forward and feedback neural network models are used to distinguish the stimuli and non-stimuli EEGs. The neural network models are configured optimally by varying the hidden neurons and learning algorithms and their performance are evaluated in terms of specificity, sensitivity and classification accuracy. It can be concluded from the experimental study that the proposed methodology can be applied for neonatal healthcare applications.     

希尔伯特-黄变换增强高刺激率AEP的信噪比

针对高刺激率模式下运用连续循环去卷积的方法提取的听觉诱发电位(AEP)可能需要比常规方法更长的记录时间,受试者要忍受大量的声音刺激的问题,本文提出运用希尔伯特-黄变换的去噪方法来减少刺激个数。首先对采集到的每个扫程的脑电信号进行经验模态分解,得到一组固有模态函数(IMFs),然后将这些IMFs分为有用信号层和噪声层,采取非线性阈值方法分层进行去噪处理,以提高信号去卷积前的信噪比。处理结果的评估以1 014个扫程的脑电数据按照常规方法处理的提取AEP为基准,计算去噪后AEP和基准AEP的信噪比与相关系数。结果表明该方法能够有效减少约2/3的刺激个数,是一种十分有前景的高刺激率下AEP信号的提取方法。     

基于融合多尺度标记信息的深度交互式图像分割

现有深度交互式图像分割算法通过对单击点计算距离映射或者高斯映射,然后将其与图像进行拼接作为网络的输入.每个单击点的影响范围是相同的,而每个交互的目的并不相同,早期交互的主要目的为选择,后期则更侧重微调.基于此,提出了融合多尺度标记信息的深度交互图像分割算法.首先,通过设置不同高斯半径,对每个单击点计算2组不同尺度的高斯映射.然后,融合小尺度高斯映射,并移除基础分割网络中的部分下采样模块,使网络提取更丰富的细节特征.同时,为了保持目标分割结果的完整性,提出了非局部特征注意力模块,该模块融合了大尺度高斯映射.最后,根据高斯映射提供的概率信息,提出了概率单击损失,提升目标在单击附近的分割表现.实验结果表明：提出的算法既能保持分割的完整性,又能得到目标细节的分割结果,大大降低了用户的交互负担.     

Web挖掘在网络广告点击欺诈检测中的应用

为有效解决网络广告中存在的点击欺诈问题,提出了一种基于Web挖掘算法的解决方案,并设计了一套点击欺诈检测模型.该模型通过对点击流进行时序分析、离群点挖掘、非线性分析等操作,能有效检测或屏蔽各类点击欺诈,有效屏蔽无意识的无效点击,并且在不影响广告展示速度的基础上显著提高检测点击欺诈的效率.实验结果表明,该解决方案可以有效检测采用手动或者利用计算机程序的方法模仿正常用户进行点击欺诈的行为,表明了模型的可行性和方案的有效性.     

Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection

This paper presents a novel system for automatic recognition of auditory brainstem responses (ABR) to detect hearing threshold. ABR is an important potential signal for determining objective audiograms. Its detection is usually performed by medical experts with often basic signal processing techniques. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients and a set of discrete wavelet transform (DWT) approximation coefficients are calculated and extracted from signals separately as three different sets of feature vectors. These features are then selected by a modified adaptive method, which mainly supports to the input dimension reduction via selecting the most significant feature components. In the second stage, the feature vectors are classified by a support vector machine (SVM) classifier which is a powerful advanced technique for solving supervised binary classification problem due to its generalization ability. After that the proposed system is applied to real ABR data and it is resulted in a very good sensitivity, specificity and accuracy levels for DCT coefficients such as 99.2%, 94.0% and 96.2%, respectively. Consequently, the proposed system can be used for recognition of ABRs for hearing threshold detection.     

Ping-pong flow control for ATM ABR traffic

In this paper, we propose an improved technique for congestion control, named as ping-pong flow control (PPFC), for asynchronous transfer mode (ATM) available bit rate (ABR) traffic. This is a rate-based flow control scheme, in which the rate regulation is achieved by directly adjusting the transmission rate in the source end station. The proposed algorithm uses a bipolar feedback strategy, which employs positive and negative feedbacks to control the transmission rate for different switch states. These states are determined using the traditional threshold-based method. We also introduce state early detection (SED), which enables the PPFC to control traffic flows more precisely and accurately at critical moments. The simulation results show that the proposed algorithm provides a higher throughput and lower cell loss ratio when compared to the well-known backward explicit congestion notification (BECN). Furthermore, these results also show that PPFC is robust against feedback losses.     

OSPF协议中ABR动态选举研究

在开放式最短路径优先(OSPF)协议网络中,人为指定区域边界路由器(ABR)不能适应网络负荷的动态改变。为此,提出一种依据当前流量需求和链路带宽的动态选举ABR的算法。该算法根据网络流量的改变动态地选择ABR,实现了避免链路拥塞和提高网络性能的目标。NS2仿真结果表明,该算法在端到端时延、丢包率等方面均能获得较好的性能。     

Quantification of haemodynamic response to auditory stimulus in intensive care

Measuring effects of sensory stimuli on haemodynamics could provide information about the interplay between central and autonomous nervous system (ANS). However, ANS response to sensory stimulus has received little attention. In this paper we present a signal processing scheme to extract the responses of heart rate and systemic arterial pressure on auditory stimulus in intensive care patients (N=5). In short, the effect of mechanical ventilation is rejected by optimal linear modelling. Other disturbances are attenuated by filtering and efficient rejection of outlying sweeps of data. The results show identifiable responses on three out of five cases. The response characteristics may be explained by synchronisation of spontaneous variability in systemic arterial pressure to auditory stimulus.