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BACKGROUND: Blood pressure (BP) is currently diagnosed by cuff-based devices, which are inconvenient and provide discontinuous measurements. Photoplethysmography (PPG)-based cuffless techniques have recently been used to accurately estimate both systolic BP (SBP) and diastolic BP (DBP). However, it is difficult to use these SBP and DBP estimations to reflect the personalized traits in the peripheral vascular condition; thus, their accuracy is limited. OBJECTIVE: The purpose of this study is to describe a technique that can be distinguished simply among three BP categories (normotensive, prehypertensive, and hypertensive) and reflect individual traits using PPG only. METHODS: We measured BP over 120 s using the fingers of 105 subjects. The PPG waveforms varied in size and amplitude over time. Therefore, normalization for uniform features for individual traits was done after the extracted waveforms were divided into multiple windows. The feature is determined by the lowest amplitude in the waveform within each divided window. The features have been applied to distinguish three BP categories using the first-eigenvector (1-EV) and second-eigenvector (2-EV) in linear discriminant analysis. RESULTS: The best decision boundary for each BP category was estimated using 1-EV (-0.02 to +0.02) and 2-EV (>+0.02) in the hypertensive category, 1-EV (< 0) and 2-EV (⩽+0.02) in the prehypertensive category, and 1-EV (⩾-0.02) and 2-EV (⩽+0.02) in the normotensive category. The overlap range with 1-EV (-0.02 to 0) and 2-EV (⩽+0.02) in particular accurately reflected individual traits. CONCLUSION: Discrimination among the three BP categories reflecting individual traits was successfully achieved using PPG. This method could improve limitations of cuff-based techniques.

Cerebral artery stenosis is currently diagnosed by transcranial Doppler (TCD), computed tomographic angiography (CTA), or magnetic resonance angiography (MRA). CTA exposes a patient to radiation, while CTA and MRA are invasive and side effects were related to contrast medium use. This study aims to provide a technique that can simply discriminate between people with normal blood vessels and those with cerebral artery stenosis using photoplethysmography (PPG), which is noninvasive and inexpensive. Moreover, the measurement takes only 120 seconds and is conducted on the fingers. The technique projects the light of a specific wavelength and analyzes the pulse waves which are generated when the blood passes through the blood vessels according to one’s heartbeat using the transmitted light. Normalization was performed after dividing the extracted pulse waveform into windows, and maximum positive and negative amplitudes (MPA, MNA) were extracted from the detected pulse waves as features. The extracted features were used to identify normal subjects and those with cerebral artery stenosis using a linear discriminant analysis. The study results showed that the recognition rate using MPA was 92.2%, MNA was 90.6%, and combined MPA + MNA was 90.6%. The technique proposed is expected to detect early stage asymptomatic cerebral artery stenosis and help prevent ischemic stroke.

Apoptosis is an erratic physiological process whereby cells undergo a self-destruction process. A quantitative analysis of apoptosis, namely counting the number of cells undergoing apoptosis, is required not only to understand the mechanism of this process but also to quantitatively evaluate the effect of anticancer drugs as many anticancer drugs attempt to achieve their effects by inducing the cancer cell to apoptosis. Currently, counting cells undergoing apoptosis is generally carried out manually by the naked eyes. Although these methods are accurate and appropriate in some cases, they can be extremely time consuming and labor intensive. Also, manual counting is subjective and difficult to achieve high throughput. There is compelling necessity for developing an automatic apoptosis-counting method. In this paper, we described an image-based method, which is applied in HCT116 human colon adenocarcinoma cell culture to count the number of apoptosis induced by anticancer drug oxaliplatin. Our methods are mostly based on well-known and mature algorithms in image processing and pattern recognition such as the contrast-limited adaptive histogram equalization, pixel classification, size distribution detection by granulometries, watershed algorithm, and artificial neural networks. We evaluated our segmentation results using the area overlap measurement, and the best achieved performance that can reach over 98%. We also evaluated our apoptosis detection results against the results of four biomedical experts and compared the results with other existing efforts. The final performance shows that our method is more reliable and accurate for detecting apoptosis than the previous methods.

Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for ±20mmHg, 93.5% for ±15mmHg, and 82.3% for ±10mmHg, based on maximum negative amplitude. The recognition performance for diastolic blood pressure was found to be 100% for ±20mmHg, 98.8% for ±15mmHg, and 88.2% for ±10mmHg based on maximum positive amplitude. In our results, systolic blood pressure showed more fluctuation than diastolic blood pressure in terms of individual traits, and subjects with prehypertension or hypertension (systolic blood pressure) showed a stronger steep-slope pattern in 1/3 section of the feature windows than normal subjects. The other side, subjects with prehypertension or hypertension (diastolic blood pressure) showed a steep-slope pattern in front of the feature windows (2/3 section) than normal subjects. This paper presented a novel blood pressure measurement system that can monitor hypertension using personalized traits. Our study can serve as a foundation for reliable hypertension diagnosis and management based on consideration of individual traits.

In this paper, by using steel fiber, polypropylene fiber and these two hybrid fibers, the fire resistance performance and explosive properties of High Strength Concrete (HSC) with specified compressive strength of 40MPa are discussed. The paper also examines the bending resistance of the beam and the shearing resistance properties of non-reinforced HSC beam. This research helps to clarify the fire resistance of fiber HSC and its anti-explosion methods. The test results show that crack generation, explosion and carbonization can be effectively restrained when HSC is mixed with hybrid fibers under high temperature; furthermore, the maximum internal force and ductility are increased and the initial cracking can be restrained in the mechanical test.

Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of human-centered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding (LLE) are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.

BACKGROUND: By using the conventional method of measuring two-points (systolic and diastolic) blood pressure, it is difficult to differentiate a heart disease-prone personality from normals. Recently, an oscillometric method that reflects the personalized trait of blood pressure was developed by one author. OBJECTIVE: By using this new measurement technique, this study intended to test the possibility of differentiating a heart disease-prone personality (type A or type D) from normal people. METHODS: TPA scale in MMPI-2 and DS14 were used for screening type A and D. Oscillometric waveforms created by the cuff pressure were segmented into window blocks based on a single beat at a minimum, allowing this method to extract maximal top and bottom amplitudes in each window block. Then, a spectrogram using a short-time Fourier transform was applied to discriminate between character types in extracted blood pressure patterns with linear discriminant analysis. RESULTS: Compared to a normal personality, type A and type D personalities displayed a lower frequency response on STFT with maximum negative amplitudes than normals. In particular, the type D personality showed a lower frequency response than the type A personality. CONCLUSION: These results could provide a new qualitative method for measuring different biological indices between type A or D personalities and normals.

Unlike TCP Reno, TCP Vegas recognizes network congestion through the measuring of RTT (Round Trip Time) and decides the main congestion control parameters, such as Windows size. But, congestion avoidance scheme of Vegas poorly reflects asymmetric characteristics of packet path because TCP Vegas uses the measuring of RTT that reflects forward/backward packet transmission delay as a forward delay. The RTT can`t infer the forward/backward transmission delay variation because it only measures the packet`s turn around time. In this paper, We have designed and implemented a new Vegas congestion control algorithm that can distinguish forward/backward network congestion. We have modified the source codes of TCP Vegas in Linux 2.6 kernel and verified their performance.

Nonlinear aeroelastic analyses of a missile control fin are performed considering backlash and dynamic stiffness of actuator. Doublet-Hybrid method is used for the calculation of subsonic unsteady aerodynamic forces, and aerodynamic forces are approximated by the minimum-state approximation. For nonlinear flutter analysis backlash is represented by a free-play and is linearized by using the describing function method. Also, dynamic stiffness is function of frequency and is calculated by solving equation of motion for actuator. The linear and nonlinear flutter analyses show that the aeroelastic characteristics are significantly dependent on the backlash and dynamic stiffness. From the nonlinear flutter analysis, various types of limit cycle oscillations are observed in a range of air speeds below the linear divergent flutter boundary. The nonlinear flutter characteristics and the nonlinear aeroelastic responses are also investigated in the time domain.

We present the educational possibilities of the Web based virtual experiment (VE) environments in the science education. As there are a lot of things that cannot be experimented with in the lab among the contents of experiment of science education, there are a lot of difficulties in the teaching-learning process. Therefore, we have developed virtual experiment environments on the Web designed to be compatible to the learner levels through level analysis in the learning contents. The students can select the learning level in the exploring step of learning cycle model: regular, advanced and remedial courses, according to the degree of their understanding or interest about the learning topic. The virtual experiment environments will support students to learn scientific phenomena and concepts focusing on: radiation balance, the earthquake waves, the earth’s crust structure, the movement of sea water, and solar system in the science field of middle school. The VE environments have been evaluated to the responses of learners on a Web. This study shows that the Web-based VE environments in science education can be effectively used as a virtual class.

This article presents the educational possibilities of Web-based science education using a desktop virtual reality (VR) system. A Web site devoted to science education for middle school students has been designed and developed in the areas of earth sciences: meteorology, geophysics, geology, oceanography, and astronomy. Learners can establish by themselves the pace of their lessons using learning contents considered learner level and they can experiment in real time with the concepts they have learned, interacting with VR environments that we provide. A VR simulation program developed has been evaluated with a questionnaire from learners after learning freely on the Web. This study shows that Web-based science education using VR can be effectively used as a virtual class. When we consider the rapid development of VR technology and lowering of cost, the study can construct more immersive environments for the education in the near future. © 2002 Wiley Periodicals, Inc. Comput Appl Eng Educ 10: 18–25, 2002; Published online in Wiley InterScience (www.interscience.wiley.com.); DOI 10.1002/cae.10014

We present a new human identification system based on PCA using geometric features of teeth such as the size and shape of the jaws, size of the teeth and teeth structure. In this paper we try to set forth the foundations of a biometric system for information encrypting of living people using dental features. To create a biometric matching system, a template based on principal component analysis(PCA) is created from dental data collected the plaster figures of teeth which were done at dental hospital, department of oral medicine. Templates of dental images based on PCA representation include the 100 principle components as the features for individual identification. The PCA basis vectors reflects well the features for individual identification in the whole of teeth and the part of teeth. The classification for human identification is generated based on the distance between the whole of teeth and the part of teeth with the nearest neighbor(NN) algorithm. The identification performance in 300 dental image is 97% for the part of teeth missed the right-molar and back teeth, 98.3% for the part of teeth missed the front teeth and 96.6% for the part of teeth missed the left-molar and back-teeth.

Oscillometric method uses a pressure sensor instead of a stethoscope to record the pressure oscillations within the cuff. Oscillations observed from the pressure sensor show personalized traits of systolic pressure, which indicates the maximum amount of work the heart has to perform per stroke in order to move blood through the arteries. For a given person, the oscillation waveforms extracted from the cuff pressure have an oscillation pattern that varies in size over time. Thus, to extract uniform features from a given person, we normalize the variability of the corresponding oscillation patterns for different frequencies, and evaluate systolic blood pressure using a feedforward neural network based on the extracted features. The validity of measuring systolic blood pressure with a neural network was compared with the average values of systolic pressure obtained by two nurses using the auscultatory method. The recognition performance was found to be 98.2% for ±20 mmHg, 93.5% for ±15 mmHg, and 82.3% for ±10 mmHg based on the difference between the systolic pressures measured by the auscultation method and the proposed method with a neural network. The collected database for our experiment includes 85 participants with ages ranging from 10–80 years. In this paper, we present a novel personalized traits monitoring system that can monitor personalized traits of systolic blood pressure. This study can serve as a foundation for the diagnosis and management of isolated systolic hypertension.

In this paper, we present an exact error analysis for circle approximation by Bézier curve. The approximation method is a special case of Floater's conic approximation method (Comput. Aided Geom. Design 8 (1991)135). Using this, we propose an approximation method of the offset curve of given plane Bézier curve by Bézier curve of the same degree.