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。     

基于短声和扫频音的听觉脑干诱发电位比较研究

听觉脑干诱发电位检测(Auditory Brainstem Response,ABR)是一种客观检测听力损失的常用方 法。短声作为一种宽频信号,其诱发的 ABR 通常被认为是 ABR 检测的金标准。但由于人体耳蜗基底 膜行波具有延迟特性,短声不能同时刺激整个耳蜗基底膜产生兴奋,导致诱发的 ABR 波形因不同相而 产生衰减。为解决这一局限性,该研究设计了一种扫频音刺激,根据耳蜗基底膜的延迟特性重新调整 不同频率成分出现的时间,以诱发 ABR 信号。实验通过将所设计的扫频 ABR 方法和传统的短声 ABR 方法的波质量进行比较发现,在不同的刺激强度和刺激速率下,扫频 ABR 的波形形态均明显优于短声 ABR 的。此外,在不同的响应叠加次数下,所设计的扫频音比短声诱发 ABR 能更快地诱发出 ABR 波 形。该文所提出的扫频 ABR 方法有望提高 ABR 在听力损失诊断中的灵敏度。     

声表面波温度传感器频率分级扫描方法研究

针对声表面波(SAW)谐振腔体的频率响应单侧单调特征,为提高传感器的温度响应速度,提出了一种频率分级扫描方法,将频率扫描分为粗扫频和精扫频两个阶段,以不同的扫频步长依次进行.利用粗扫频确定中心谐振频率所处的频率区间,再在该频率区间内利用精扫频得到腔体中心谐振频率,可将温度传感器扫频时间降为原来的1/7.利用提出的频率扫描方法开展实验研究,分别研究了单个传感器的温度量测不确定度和多个传感器的温度量测一致性,并分析了实验结果.     

基于振动和声发射技术的滚动轴承故障分析

对声发射和振动两种方法在滚动轴承裂纹故障诊断中的应用进行了研究,并比较了这两种方法在早期故障诊断中的优越性。采用电火花技术在滚动轴承内圈加工宽度为0.5 mm和0.8 mm的两种裂纹,模拟在实际情况下的滚动轴承内圈裂纹故障。在低速(10 r/min)和高速(1 000 r/min)两种情况下,通过试验分别测得滚动轴承两种裂纹故障状态下的声发射信号和振动信号。分析比较了不同转速、不同裂纹故障状态下的轴承振动和声发射信号的时频特征。对信号数据进行波形和包络谱分析。试验结果表明:声发射信号与调制信号无关。对声发射信号进行包络分析,可以有效分析信号的故障特征频率。在故障微弱情况下,声发射信号比振动信号更具敏感性;当故障强度较大时,采用振动方法诊断更为准确,即声发射技术更有利于故障的早期检测。声发射技术可作为振动分析技术的补充,用于故障检测。     

仿体表面磁共振声辐射力成像位移精度检测研究

磁共振声辐射力成像(Magnetic Resonance Acoustic Radiation Force Imaging,MR-ARFI)是一种超声焦点定位技术,可通过检测声辐射力作用到生物体内时在局部组织区域产生的微米级位移,实现对超声焦点的定位。基于 MR-ARFI 位移检测结果,可实现经颅超声焦点定位及相位矫正。但目前仍较 少研究对 MR-ARFI 定量技术的位移检测精度进行标定。该文在超声作用参数(输入功率、时长、脉冲重复频率)完全一致的条件下,以激光多普勒技术测得的位移作为金标准,与 MR-ARFI 测得的位移值进行比对,探究 MR-ARFI 的位移检测精度。研究结果显示,激光多普勒测量的最大位移值和位移分布 图与 MR-ARFI 测量结果具有良好的一致性。这表明 MR-ARFI 定位技术具有较高的位移检测精度,可反映检测对象的真实运动情况,有望在脑科学领域发挥重要作用。     

滑坡体临滑声发射监测主机的设计

基于滑坡体临滑时因岩石断裂、岩石摩擦而产生声发射的特点,提出一种基于声发射监测的滑坡体临滑监测主机的设计方案。系统能监测的声发射频率范围为1Hz～20kHz,能监测的最小声压为0.002Pa。通过安装在滑坡体附近的声发射传感器,连续监测和记录滑坡体临滑时产生的声发射,可用于分析其变化规律和特征,从而实现对滑坡灾害的预测预报。     

声发射技术在罐底腐蚀检测中的应用与研究

在利用声发射技术对四个大型原油储罐底板腐蚀情况进行现场检测的基础上,对采集到的声发射信号进行处理分析,并对比了不同时段的用于罐底信号定位的声发射事件.结果表明:同样一个储罐在白天和夜间得到的声发射事件数量差异巨大,在夜间采集的声发射数据更能代表罐底腐蚀的实际情况.可用于判别检测过程是否受到噪声影响;可以为以后的声发射检测时间的选择提供指导意义.     

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.     

潜艇自航式声诱饵发射方向的仿真研究

如何在实战中更快更好地确定声诱饵的可行发射方向是该文的研究目的.由于被动声自导鱼雷采用能量检测来探测目标,首先建立被动声自导鱼雷检测模型,仿真分析了鱼雷对不同航速的潜艇和模拟潜艇噪声的声诱饵的发现概率及声自导作用距离,然后建立声诱饵对抗干扰模型,通过数学仿真分析研究了声诱饵朝水下空间各个方向发射时所产生的对抗效果,并得到了不同鱼雷报警舷角下声诱饵的可行发射区域以及最不利发射方向角.研究结果是对声诱饵作战使用方法研究的深入.     

双幅正弦波激励频移法的压电石英传感器用于测定凝血时间

快速凝血检测技术在医学诊断领域具有重要应用价值，为了降低样本加入过程的扰动，文章开发了一种双幅激励的压电石英体新型凝血监测系统。该系统采用两种振幅正弦波分别对压电石英体进行激励，通过快速扫频技术测得其在不同激励电压下的谐振频率，所获得的频率差作为测量信号与液体粘度存在良好的线性关系。在血液凝固过程中，介质粘度会发生显著变化，压电石英体双幅频移信号可实现对血液凝固过程的实时动态监测。采用直径8 mm（电极直径3 mm）、基频10 MHz小型镀钛石英晶体设计出一次性微流控PQC检测片，成功用于微滴（15μL）毛细自动进样凝血过程的监测。对比传统单振幅激励PQC血凝监测系统，压电石英体双幅频移监测系统具有灵敏度高、重现性好的优势。使用该系统与商用电化学凝血仪分别对血样凝血时间进行了检测，结果显示两者数据具有很好的相关性。     

EarNet: Biometric Embeddings for End to End Person Authentication System Using Transient Evoked Otoacoustic Emission Signals

Neural Processing Letters - Transient Evoked Otoacoustic Emissions (TEOAE) are a class of oto-acoustic emissions that are generated by the cochlea in response to an external stimulus. The TEOAE...     

Analog electronic cochlea design using multiplexingswitched-capacitor circuits

A new design methodology is proposed to realize a real cochlea using the multiplexing switched-capacitor circuits. The proposed technique is based upon the transmission-line model proposed by Zwislocki (1950). At the cost of the increase in the number of clock phases, the decay rate in the transition region of the filter section can be increased by adding only a few components. Therefore, the components and chip area of the designed silicon cochlea can be small. An experimental chip containing four filter sections has been designed and fabricated. The measured frequency responses from the 32-section cochlea formed by cascading nine fabricated chips are consistent with both theoretical calculation results and observed behavior of a real cochlea. Moreover, the designed silicon cochlea has the dynamic range of 67 dB in each section and a low sensitivity to process variations. Thus it is suitable for VLSI implementation with the associated neural network.     

The Effect of Stimulus Size and Position on the Task Performance of an Eye Mouse: Comparing Blink and Dwell Methods in a Click Task

Eye mouse devices are operated by ocular movements, which have proven to be useful for disabled people who find eye control to be the easiest input method. However, there are few studies comparing the two typical click methods for eye mouse (blink and dwell) in identical experimental conditions for general object selection operations. Especially, investigation on task performance (effectiveness and efficiency) of selecting objects is not sufficient. Therefore, this study aims to investigate the relationships between task performance of selecting a stimulus and independent variables with comparing the two different click methods. To investigate the relationship, movement distance, eye input method, stimulus position, and stimulus size were considered as independent variables. Results of the blink method revealed a higher efficiency than the dwell method, particularly as the stimulus size increased. Also, both efficiency and effectiveness of stimuli in upper parts of screen was higher than for those in lower ones. Additionally, efficiency was remarkably higher when the stimulus size increased. The results of this study may be used as guidelines to design interfaces using an eye mouse device. Especially, it can be applied to various conditions of selecting an object in the working environment using the eye mouse.     

A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler

NOx emissions from power plants pose terrible threat to the surrounding environment. The aim of this work is to achieve low NOx emissions form a coal-fired utility boiler by using combustion optimization. Support vector regression (SVR) was proposed in the first stage to model the relation between NOx emissions and operational parameters of the utility boiler. The grid search method, by comparing with GA, was preferably chosen as the approach for the selection of SVR’s parameters. A mass of NOx emissions data from the utility boiler was employed to build the SVR model. The predicted NOx emissions from SVR model were in good agreement with the measured. In the second stage, two variants of ant colony optimization (ACO) as well as genetic algorithm (GA) and particle swarm optimization (PSO) were employed to find the optimum operating parameters to reduce the NOx emissions. The results show that the hybrid algorithm by combining SVR and optimization algorithms with the exception of PSO can effectively reduce NOx emissions of the coal-fired utility boiler below the legislation requirement of China. Comparison among various algorithms shows the performance of the well-designed ACO outperforms those of classical GA and PSO in terms of the quality of solution and the convergence rate.     

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.     

VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]

We described the three stimulus modulation approaches used in current VEP based BCIs: time modulation (t-VEP), frequency modulation (f-VEP), and pseudorandom code modulation (c-VEP). We then carried out a detailed comparison of system performance between an f-VEP BCI and a c-VEP BCI. The results show that an f-VEP BCI has the advantage of little or no training and simple system configuration, while the c-VEP based BCI has a higher communication speed. The stimulus modulation design is the crux of VEP based BCI systems. In future work, other stimulus modulation techniques, such as various multiple access methods used in communication systems, may be used to improve BCI performance.     

The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

ABSTRACT

The traditional area extraction method mainly depends on manual field survey methods, it is workload, slow and high cost. While remote sensing technology has the advantages of accuracy, rapidity, macroscopic and dynamic, which has become an effective means to extract crop growing area. In this paper, we took Kaifeng City in Henan Province as the study area. Firstly, we explored the advantages of Sentinel-2A RENDVI in crop identification. Then used the supervised classification SVM, object-oriented classification method and assisted with field measured data to extract the winter wheat planting area, the characteristics of the two methods were compared and analysed. Finally, we combined the above two classification methods and proposed a new classification method V2OAE to remove unnecessary influencing factors. The experiment results showed that RENDVI has better recognition ability than the NDVI (Normalized Difference Vegetation Index) in distinguishing vegetation with similar spectrum, the classification effect of object-oriented classification is better than supervised classification SVM, and our classification method removes unnecessary influence factors in the results of object-oriented classification, which is further improve the monitoring accuracy.

Firstly, we have preprocessed the Sentinel-2A image data, its steps are: (1) In the first step, we made radiation calibration for remote sensing images to eliminate the image distortion caused by external factors, data acquisition and transmission systems and so on; (2) In the second step, we made atmospheric correction to eliminate changes in the spectral feature of remote sensing images caused by atmospheric absorption or scattering; (3) In the third step, we made band resampling to unify the resolution of remote sensing images and facilitate the mathematical combination operation of vegetation index; (4) In the fourth step, we made mosaic and cutting to get preprocessed remote sensing images of Kaifeng City. Secondly, we analysed the spectral features of each object and established the interpretation mark with the field measured data. then we explored the ability to identify the ground objects based on NDVI(Normalized Difference Vegetation Index) and RENDVI. Third, we used the rule-based object-oriented classification method and SVM classification to extract the planting area of the study area, the input definition of SVM is spectral feature images of ground objects and the output definition of SVM is the recognition result of ground objects in the process of data training. Then the advantages and disadvantages of the two methods in classification results were analysed. Finally, In order to extract winter wheat information more accurately, we combined the above two classification methods and proposed a new classification method V2OAE (Vector Object Oriented Area Extraction) to remove unnecessary influencing factors, then the winter wheat planting area in Kaifeng City was statistically obtained.     

Rejecting hydro-biogeochemical model structures by multi-criteria evaluation

This work presents a novel way for assessing and comparing different hydro-biogeochemical model structures and their performances. We used the LandscapeDNDC modelling framework to set up four models of different complexity, considering two soil-biogeochemical and two hydrological modules. The performance of each model combination was assessed using long-term (8 years) data and applying different thresholds, considering multiple criteria and objective functions. Our results show that each model combination had its strength for particular criteria. However, only 0.01% of all model runs passed the complete rejectionist framework. In contrast, our comparatively applied assessments of single thresholds, as frequently used in other studies, lead to a much higher acceptance rate of 40–70%. Therefore, our study indicates that models can be right for the wrong reasons, i.e., matching GHG emissions while at the same time failing to simulate other criteria such as soil moisture or plant biomass dynamics.