TCP／IP与NETBIOS

简述了ＴＣＰ／ＩＰ用于基于ＰＣＬＡＮ时所存在的问题，阐明了ＮＥＴＢＩＯＳ在ＰＣＬＡＮ中的作用和它作为一种ＨＦＰ（ＨｏｓｔｔｏＦｒｏｎｔｅｎｄＰｒｏｔｏｃｏｌ）与ＴＣＰ／ＩＰ的关系。最后介绍了用于ＰＣＬＡＮ的ＴＣＰ／ＩＰ产品和一个简单实例     

FLASH电子盘驱动软件设计

用新型ＦＬＡＳＨ ＭＥＭＯＲＹ芯片作为可读写电子盘存储人质是本系统创新之处,本文简要地说明了Ｆｌａｓｈ电子盘驱动软件的设计方法,种类和组成,详细地介绍了针对ＦＬＡＳＨ ＭＥＭＯＲＹ芯片的特性编写电子盘读／写算法的方法,并给出了读／写算法流程图。此读／写算法已在ＤＯＳ设备驱动程序和修改的ＩＮＴ１３ｈ中成功实现。     

CF-920 FFT信号分析仪与IBM-PC及其兼容机之间的数据通信

主要介绍了利用ＧＰＩＢ－ＰＣ接口机实现ＣＦ－９２０ＦＦＴ信号分析仪与ＩＢＭ－ＰＣ及其兼容机之间的二进制数据（时域波形、自相关系数、互相关系数、自功率谱、互功率谱、幅值概率密度函数等）通信,以及由二进制数据转换成十进制数据的公式。文中讨论了由振动计、磁带记录仪、ＣＦ－９２０ＦＦＴ信号分析仪、计算机所组成的监测与分析系统的量值标定方法。     

焊接缺陷超声检测回波信号的双谱分析

针对焊接缺陷超声检测中信号处理的特征提取问题，应用高阶谱方法对三类压力容器焊接缺陷的超声回波信号进行了分析，在焊接缺陷超声检测中，回波信号的相位携带有被检对象重要的结构特征信息。高阶谱方法与常规的功率谱分析方法不同，它不仅有振幅而且包含有相位，能揭示常规功率谱分析所不能表现的重要信息。本文应用高阶累积量技术对缺陷回波信号进行双谱分析，提取出缺陷回波基于双谱的平均相位信息作为特征参量，取得了较好的识别结果。     

基于VHDL高级综合的条码阅读预处理器的FPGA设计

文中定义了条码阅读处理器的功能，给出其ＶＨＤＬ语言的行为源描述，讨论了在ＶＨＤＬ高级综合系统ＨＬＳ／ＢＩＴ的支持下面向ＦＰＧＡ，从算法级行为描述开始，自顶向下地进行条码阅读预处理器的设计过程，从中可见，ＶＨＤＬ高级综合和ＦＰＧＡ的结合，是一种简化设计复杂度，提高设计时效的ＡＳＩＣ的简便方法。     

脉象信号的频谱分析

根据人体脉象信号的特点,介绍了信号采集系统对脉象的采集,提供了脉象信号的一种频域分析方法--功率谱分析,应用快速傅里叶变换方法对脉搏信号进行分析,并通过读功率谱特征的分析和比较,最后,提出了利用径向型网络对4种脉象信号进行分类,以脉象信号的频谱特征作为神经网络输入时的训练结果的差异.尽管文中的训练样本有限,但仿真结果表明:对脉象信号的一些特定的特征值,利用神经网络进行识别是一种可行而有效的方法,在自适应、自学习能力方面较传统的模式识别方法具有明显的优越性.     

基于VB和Matlab接口的海浪谱分析软件设计

在海浪研究中,海浪功率谱的获取是一个非常重要的内容;Visual Basic与Matlab的接口技术为编写一种人性化界面与高效率计算引擎相结合的应用程序提供了关键的条件;为实现随机信号的频谱和功率谱,随机信号频谱和功率谱分析中常用到快速傅里叶变换（FFT）,而Matlab内部提供了这种函数。为了快速方便地获取随机信号的功率谱,介绍了一种基于VB6．0与Matlab的接口技术实现FFT进而实现功率谱的方法,并在此基础上编写了实时海浪功率谱分析软件。该软件已经成功应用到海浪数据的处理中。实践证明：该方法具有一定的通用性,可用于其它随机信号频谱分析。     

基于VB和Matlab接口的海浪谱分析软件设计

在海浪研究中,海浪功率谱的获取是一个非常重要的内容;Visual Basic与Matlab的接口技术为编写一种人性化界面与高效率计算引擎相结合的应用程序提供了关键的条件;为实现随机信号的频谱和功率谱,随机信号频谱和功率谱分析中常用到快速傅里叶变换(FFT),而Madab内部提供了这种函数.为了快速方便地获取随机信号的功率谱,介绍了一种基于VB6.0与Matlab的接口技术实现FFT进而实现功率谱的方法,并在此基础上编写了实时海浪功率谱分析软件.该软件已经成功应用到海浪数据的处理中.实践证明:该方法具有一定的通用性,可用于其它随机信号频谱分析.     

Windows NT HAL的结构与移植

文中介绍ＷｉｎｄｏｗｓＮＴ的硬件抽象层ＨＡＬ，具体讨论ＨＡＬ的结构、功能及其移植方法     

WindowsNT HAL的结构与移植

文中介绍ＷｉｄｎｏｗｓＮＴ的硬件抽象层ＨＡＬ，具体讨论ＨＡＬ的结构、功能及其移植方法。     

Identification of sleep stages from heart rate variability using a soft-decision wavelet-based technique

This work is concerned with a new technique to find identification factors for the different sleep stages based on a soft-decision wavelet-based estimation of power-spectral density (PSD) contained in the main frequency bands of Heart Rate Variability (HRV).A wavelet-based PSD distribution of HRV in different sleep stages is implemented on an epoch basis. Four sleep stages (S1–S4), “REM sleep” (with “rapid eye movements”), and wakefulness are considered in this work. The data used, including electro-cardiograms and sleep stage monitoring hypnograms, are provided by the sleep laboratory of the department of Psychiatry and Psychotherapy of Christian-Albrechts University Kiel, Germany. The data, taken from 12 healthy people and containing enough epochs of the above 5 different sleep stages plus the wake state, is divided into almost equal sets for training and test.The results show that the PSD of the very-low-frequency (VLF) band and the low-frequency (LF) band are reduced as sleep stages vary from the wake state to REM sleep and further to light sleep (S1–S2) and deep sleep (S3–S4). The variation of the PSD in the high-frequency (HF) band is almost the opposite. The ratio of the VLF/HF PSD is found to be a good identification factor between the different sleep stages, showing better results than other, commonly used factors such as the LF/HF and VLF/LF PSD ratios.     

The effects of physical vibration on heart rate variability as a measure of drowsiness

We investigated the effects of low frequency whole body vibration on heart rate variability (HRV), a measure of autonomic nervous system activation that differentiates between stress and drowsiness. Fifteen participants underwent two simulated driving tasks for 60?min each: one involved whole-body 4–7?Hz vibration delivered through the car seat, and one involved no vibration. The Karolinska Sleepiness Scale (KSS), a subjective measure of drowsiness, demonstrated a significant increase in drowsiness during the task. Within 15–30?min of exposure to vibration, autonomic (sympathetic) activity increased (p?p?Practitioner summary: The effects of physical vibration on driver drowsiness have not been well investigated. This laboratory-controlled study found characteristic changes in heart rate variability (HRV) domains that indicated progressively increasing neurological effort in maintaining alertness in response to low frequency vibration, which becomes significant within 30?min.

Abbreviations: ANS: autonomic nervous system; Ctrl: control; EEG: electroencephalography; HF: the power in high frequency range (0.15 Hz-0.4Hz) in the PSD relected parasympathetic activity only; HRV: heart rate variability; KSS: karolinska sleepiness scale; LF: the power in low frequency range (0.04 Hz-0.15Hz) in the PSD reflected both sympathetic and parasympathetic activity of the autonomic nervous system; LF/HF ratio: the ratio of LF to HF indicated the balance between sympathetic and parasympathetic activity; RMSSD: the root mean square of difference of adjacent RR interval; pNN50: the number of successive RR interval pairs that differed by more than 50 ms divided by the total number of RR intervals; RR interval: the differences between successive R-wave occurrence times; PSD: power spectral density; RTP: research training program; SD: standard deviation; SEM: standard error of the Mean; Vib: vibration     

Efficient solution for frequency band decomposition problem using wavelet packet in HRV

Heart rate variability (HRV) is a very significant noninvasive tool for assessment of sympathovagal balance (SB) that reflects variation of parasympathetic and sympathetic activities in autonomic nervous system (ANS). Low frequency/high frequency (LF/HF) power ratio provides information about these activities. Because of nonstationary characteristic of HRV, analyses based on wavelet transform were typically preferred in previous studies. There is an important problem that required frequency ranges for LF and HF cannot be obtained using discrete wavelet transform (DWT). Different sampling frequencies do not remove this problem. In this study, a solution based on wavelet packet (WP) is presented for removing this problem. In addition, effect of WP on SB values is investigated. Method was applied to spontaneous ventricular tachyarrhythmia database and variation of energy values and LF/HF energy ratios were compared for DWT and WP. WP provides absolutely excellent approximation to required frequency bands and exposes different and impressive SB results.     

How to avoid misinterpretation of heart rate variability power spectra?

Spectral analysis of R-R Interval time series is increasingly used to determine periodic components of heart rate variability (HRV). Particular diagnostic relevance is assigned to a low-frequency (LF) component, associated with blood pressure regulation, and a high-frequency (HF) component, also referred to as respiratory sinus arrhythmia (RSA) in the HRV power spectra. Frequency ranges for parametrisation of power spectra have been defined for either component in numerous publications.Results obtained from examinations with standardised psychic load in which ECG and respiratory signal are continuously recorded and adequately processed have shown that the true individual frequency range of the HF component can be reliably determined only by means of characteristics of respiration (respiratory rate (RR), range and median value of RR, tidal depth). Respiratory rhythms are interindividually extremely differentiated and of individual-specific nature. In many cases LF and HF components may be totally superimposed on each other and, consequently, cannot be diagnostically evaluated.     

Effect of hypoxia,safety shoe type,and lifting frequency on cardiovascular and ventilation responses

ObjectiveThere is limited work on the physiological demands of lifting activities at different altitudes and different lifting frequencies when wearing different types of shoes. This study aimed to examine the heart rate variability (HRV) and ventilation responses of individuals in normobaric hypoxia (ambient oxygen of 15%, 18%, and 21%) while doing lifting tasks and wearing three types of different safety shoes (“light, medium, and heavy-duty”) at two different lifting frequencies (“1 lift/min and 4 lifts/min”).MethodsUsing an experimental study design, two sessions were conducted by ten male university students that included an acclimatization and training session followed by experimental lifting. The study used a four-way repeated measures design (4 independent and twenty-one responses, i.e., twelve HRV and nine ventilation responses).ResultsThe findings highlighted substantial low HRV and ventilation parameters for the light workload stress in the form of higher ambient oxygen content and lowered lifting frequency while wearing light safety shoe type. It also presented an increase in the physical demand, followed by increased lifting frequency and replication with increased mean heart rate and decreased mean RR, very low frequency (VLF) power, low frequency (LF) power, and low frequency to a high-frequency ratio (LF/HF).ConclusionOur findings suggest that if a safe lifting load limit is applied for workers in the industrial environment, the risk of musculoskeletal disorders will be mainly decreased, and the rate of production will be better with ambient oxygen content and appropriate safety shoes. This research would safeguard industrial workers' physical capacities and future health risks.     

Estimation and evaluation of sub-bands on LF and HF base-bands in HRV for Ventricular Tachyarrhythmia patients

Heart Rate Variability (HRV) is an efficient tool for assessment of Sympathovagal Balance (SB) and classification of cardiac disturbances. However, its index may be not enough for classification and evaluation of some disease. This study presents 32 new sub-bands over LF and HF base-bands that are accepted in the literature. Moreover, it determines dominant sub-bands over both base-bands in VTA database. These sub-bands are obtained using Wavelet Packet Transform (WPT) and evaluated using Multilayer Perceptron Neural Networks (MLPNN). Results are compared with obtained results from normal datasets. The domination effects of these sub-bands are assessed according to comparison of each other related to MLPNN training and test accuracy percentages by selecting different width of windows. As a result, obtained results showed that the LF zone including LF₁, LF₂ and LF₃ sub-bands on 0.0390625–0.0859375 Hz frequency range is the most dominant over the LF base-band and, the HF zone including HF1, HF2 and HF3 on 0.1953125–0.28125 Hz frequency range is the most dominant over the HF base-band. In normal datasets, distinctive domination effect has not been determined.     

Like/dislike analysis using EEG: Determination of most discriminative channels and frequencies

In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of “like” decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, …, 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4–19 Hz and high frequency (HF) 20–40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential in determining the outcome, i.e., like/dislike decision. In the LF band, 4 and 5 Hz were found to be the most discriminative frequencies (MDFs). In the HF band, none of the frequencies seemed offer significant information. When both male and female data was used, in the LF band, a frontal channel on the left (F7-A1) and a temporal channel on the right (T6-A2) were found to be the most discriminative channels (MDCs). In the HF band, MDCs were central (Cz-A1) and occipital on the left (O1-A1) channels. The results of like timings suggest that male and female behavior for this set of stimulant images were similar.     

The effects of air temperature on office workers’ well-being,workload and productivity-evaluated with subjective ratings

Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate office worker’s productivity remains to be a challenge for ergonomists. In this study, the effect of indoor air temperature (17 °C, 21 °C, and 28 °C) on productivity was investigated with 21 volunteered participants in the laboratory experiment. Participants performed computerized neurobehavioral tests during exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroencephalograph (EEG) were also measured. Several subjective rating scales were used to tap participant’s emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm discomfort negatively affected participants’ well-being and increased the ratio of low frequency (LF) to high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by tasks increased and participants had to exert more effort to maintain their performance and they also had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air temperature had negative influence on office workers’ productivity and the subjective rating scales were useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on productivity.     

The effects of air temperature on office workers’ well-being, workload and productivity-evaluated with subjective ratings

Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate office worker’s productivity remains to be a challenge for ergonomists. In this study, the effect of indoor air temperature (17 °C, 21 °C, and 28 °C) on productivity was investigated with 21 volunteered participants in the laboratory experiment. Participants performed computerized neurobehavioral tests during exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroencephalograph (EEG) were also measured. Several subjective rating scales were used to tap participant’s emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm discomfort negatively affected participants’ well-being and increased the ratio of low frequency (LF) to high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by tasks increased and participants had to exert more effort to maintain their performance and they also had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air temperature had negative influence on office workers’ productivity and the subjective rating scales were useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on productivity.     

A Computer Algorithm to Impute Interrupted Heart Rate Data for the Spectral Analysis of Heart Rate Variability—The ARIC Study

The shorter term beat-to-beat heart rate data collected from the general population are often interrupted by artifacts, and an arbitrary exclusion of such individuals from analysis may significantly reduce the sample size and/or introduce selection bias. A computer algorithm was developed to label as artifacts any data points outside the upper and lower limits generated by a 5-beat moving average ±25% (or set manually by an operator using a mouse) and to impute beat-to-beat heart rate throughout an artifact period to preserve the timing relationships of the adjacent, uncorrupted heart rate data. The algorithm applies Fast Fourier Transformation to the smoothed data to estimate low-frequency (LF; 0.025–0.15 Hz) and high-frequency (HF; 0.16–0.35 Hz) spectral powers and the HF/LF ratio as conventional indices of sympathetic, vagal, and vagal–sympathetic balance components, respectively. We applied this algorithm to resting, supine, 2-min beat-to-beat heart rate data collected in the population-based Atherosclerosis Risk in Communities study to assess the performance (success rate) of the algorithm (N= 526) and the inter- and intra-data-operator repeatability of using this computer algorithm (N= 108). Eighty-eight percent (88%) of the records could be smoothed by the computer-generated limits, an additional 4.8% by manually set limits, and 7.4% of the data could not be processed due to a large number of artifacts in the beginning or the end of the records. For the repeatability study, 108 records were selected at random, and two trained data operators applied this algorithm to the same records twice within a 6-month interval of each process (blinded to each other's results and their own prior results). The inter-data-operator reliability coefficients were 0.86, 0.92, and 0.90 for the HF, LF, and HF/LF components, respectively. The average intra-data-operator reliability coefficients were 0.99, 0.99, and 0.98 for the HF, LF, and HF/LF components, respectively. These results indicate that this computer algorithm is efficient and highly repeatable in processing short-term beat-to-beat heart rate data collected from the general population, given that the data operators are trained according to standardized protocol.