Electroencephalographic (EEG) analysis has been widely adopted for the monitoring of cognitive state changes and sleep stages because abundant information in EEG recording reflects changes in ...drowsiness, arousal, sleep, and attention, etc. In this study, micro-electro-mechanical systems (MEMS) based silicon spiked electrode array, namely dry electrodes, are fabricated and characterized to bring EEG monitoring to the operational workplaces without requiring conductive paste or scalp preparation. An isotropic/anisotropic reactive ion etching with inductive coupled plasma (RIE-ICP) micromachining fabrication process was developed to manufacture the needle-like micro probes to pierce the stratum corneum of skin and obtain superior electrically conducting characteristics. This article reports a series of prosperity testing and evaluations of continuous EEG recordings. Our results suggest that the dry electrodes have advantages in electrode-skin interface impedance, signal intensity and size over the conventional (wet) electrodes. In addition, we also developed an EEG-based drowsiness estimation system that consists of the dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression to estimate driverpsilas drowsiness level in a virtual-reality-based dynamic driving simulator to demonstrate the potential applications of the MEMS electrodes in operational environments.
In this work, numerical simulations have been conducted to investigate the particle mixing feature in a stirred vessel driven by an impeller. The Eulerian multi-fluid model has been employed along ...with the standard k–ε turbulence model to simulate the gas-liquid-solid three-phase flow in the stirred vessel. The effects of impeller speed and immersion depth of impeller on the particle distribution are discussed. The results show that the particle volume fractions nearby the vessel bottom are large on the vicinity of the side walls of the vessel and small in the vessel middle region at different impeller speeds and immersion depths.
碩士
國立交通大學
控制工程系
84
Music synthesis by physical modeling methods becomes the major
research topic in the related area when FM synthesis and
Wavetable synthesis cannot satisfy the demanding users.
...Combining the property of wave propagation and the associate
discrete-time implementation, it is possible to generate
realistic and dynamic musical tones. We first advance the
Karplus-Strong plucked-string algorithm into a 2-D membrane
extension. In order to model?sHeal instrument, we propose a
class of neural network called Linear Scattering Recurrent
Network (LSRN) which employs the measurement of the response of
a string as the learning data such that the model can be trained
to be a counterpart of the string in the synthesis domain. The
correspondent learning algorithm and computer simulations are
given to demonstrate the encouraging modeling results. Musical
instrumental nonlinearity which points to our future works is
also discussed.
The presence of an on-line seizure detection system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In ...this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4% computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100% detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.
Extracion of hemicelluloses from sugarcane bagasse under microwave radiation was studied in this paper. The effects of liquid to solid ratio ( V ( water ) : m ( stover) ) , alkali consumption ( m( ...NaOH): m ( stover ) ) , time, microwave power, and temperature on the extraction yield of hemicelluloses were investigated. The optimal parameters were: liquid to solid ratio of 25, alkali consumption of 2.0, time of 20 min, and microwave power of 1200 W. The obtained hemicelluloses was qualitatively analyzed by gas chromatography, and results showed that prepared hemicelluloses is a kind of xylan hemicelluloses, mainly consisting of simple sugars like D-xylose, D- arabinose, L- arabinose, D- galacturonic acid, D-galactose and D-mannose.
Long-term training will change the brain activity due to plasticity of the human brain. In this paper, an EEG-based neural network was proposed to assess neuroplasticity induced by musical training. ...A musical interval perception experiment was designed to acquire and compare the behavioral and neural responses of musicians and non-musicians. The auditory event related potentials (AEP) elicited by the consonant and dissonant intervals were combined and the PCA was used to extract discriminable features to classify the EEG recordings. Various linear and nonlinear classifiers were utilized for EEG classification and the results were also compared. The average accuracies of LDA, RBFSVM and BPNN are 94.6% (PCs = 8), 95.9% (PCs = 6), and 97.2% (PCs = 20). ANOVA analysis of the classification results shows that the performance of BPNN is significantly better than the results of LDA (p<;0.05). But there is no significantly difference with RBFSVM. The RBFSVM performs better stability if redundant principle components were included in the feature vector. The experimental results demonstrate the feasibility of assessing effects of musical training by AEP signals elicited by musical chord perception.
Serious sleep disorder may interfere with people's daily life, so monitoring the physiological signals and analyzing the recorded data are to find a solution to improve sleep quality. According to ...the survey of National Institute of Neurological Disorders and Stroke (NINDS) of United States, about 60 million Americans a year have insomnia frequently or for extended periods of time. A polysomnography (PSG) is a common approach with comprehensive multi-parametric physiological recording for sleep testing, but a subject sleeping in an unfamiliar environment with many wires attached will not feel comfortable and the recorded data cannot reflect the real sleeping quality. A wrist-watch actigraph recorder was developed for using within a living environment, while 35 healthy and 11 insomnia subjects were monitored and recorded by both PSG and actigraphy all night 8 hours for validation. The results show 91% in overall accuracy, 92% in sensitivity, 80% in specificity, 97% in predictive value for sleep and 54 % in predictive value for wake.
Supertetrahedral clusters: A family of lanthanide oxide supertetrahedral T3{Ln20} clusters (Ln = Tb, Dy, Ho, Er; see figure) were obtained from the solvothermal reaction of lanthanide(III) salts with ...polytriazolate ligands that could be methylated and oxidized in situ.
The existing bio-signal monitoring systems are mostly designed for signal recording without the capability of automatic analysis so that their applications are limited. The goal of this paper is to ...develop a real-time wireless embedded electroencephalogram (EEG) monitoring system that includes multi-channel physiological acquisition, wireless transmission, and an embedded system. The wireless transmission can overcome the inconvenience of wire routing and the embedded multi-task scheduling for the dual-core processing system is developed to realize the real-time processing. The whole system has been applied to detect the driver’s drowsiness for demonstration since drowsiness is considered as a serious cause of many traffic accidents. The electroencephalogram (EEG) features changes from wakefulness to drowsiness are extracted to detect the driver’s drowsiness and an on-line warning feedback module is applied to avoid disasters caused by fatigue.
This paper applies fuzzy vector quantization (FVQ) to the modeling of observation-based Discrete Hidden Markov Model (DHMM) and then to improve the speech recognition rate for the Mandarin speech. ...Vector quantization based on a codebook is a fundamental process to recognize the speech signal by DHMM. A codebook will be first trained by K means algorithms using Mandarin training speech. Then, based on the trained codebook, the speech features are quantized by the fuzzy sets defined on each vectors of the codebook. Subsequently, the quantized speech features are statistically applied to train the model of DHMM for the speech recognition. All the speech features to be recognized should go through the FVQ based on the fuzzy codebook before being fed into the DHMM model for recognition. Experimental results in this paper shows that the speech recognition rate can be improved by using FVQ algorithm to train the model of DHMM.
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