Currently, how to equip machines with the ability for properly recognizing users' felt-emotion during multimedia presentation is a growing issue. In this study we focused on the approach for recognizing music-induced emotional responses from brain activity. A comparative study was conducted to testify the feasibility of using hierarchical binary classifiers to improve the classification performance as compared with nonhierarchical schemes. According to our classification results, we not only found that using one-against-one scheme of hierarchical binary classifier results in an improvement to performance, but also established an alternative solution for emotion recognition by proposed model-based scheme depending on 2D emotion model.

In this technical demonstration, we showcase an interactive content presentation (ICP) system that integrates media-expressed-emotion-based composition, user-perceived preference feedback, and interactive digital art creation. ICP harmonizes the browsing of multimedia contents by presenting them in the form of music videos (photos, blog articles with accompanied music) based on their expressed emotion similarity. ICP facilitates content browsing by automatically and dynamically selecting the media to be played next in real time, responding to user's preference feedback measured from physiological signals. In addition, ICP enhances the enjoyments of content browsing by incorporating interactive digital art creation. ICP achieves these goals by properly integrating recent researches on media-expressed emotion classification,cross-media composition, and physiological signal processing.

An approach to recognize the emotion responses during multimedia presentation using the electroencephalogram (EEG) signals is proposed. The association between EEG signals and music-induced emotion responses was investigated in three factors, including: 1) the types of features, 2) the temporal resolutions of features, and 3) the components of EEG. The results showed that the spectrum power asymmetry index of EEG signal was a sensitive marker to reflect the brain activation related to emotion responses, especially for the low frequency bands of delta, theta and alpha components. Besides, the maximum classification accuracy was obtained around 92.73% by using support vector machine (SVM) based on 60 features derived from all EEG components with the feature temporal resolution of one second. As such, it will be able to provide key clues to develop EEG-inspired multimedia applications, in which multimedia contents could be offered interactively according to the userspsila immediate feedback.

A genetic algorithm (GA) embedded search strategy over a colored timed Petri net (CTPN) for wafer fabrication is proposed. Through the CTPN model, all possible behaviors of the wafer manufacturing systems, such as WIP status and machine status, can be completely tracked down by the reachability graph of the net. The chromosome representation of the search nodes in GA is constructed directly from the CTPN model, recording information about the appropriate scheduling policy for each workstation in the fabrication. A better chromosome found by GA is received by the CTPN based schedule builder, and a near-optimal schedule is then generated.

A significant amount of risk is involved in the wafer fabrication due to huge investment costs, long production cycle time, and short production life cycle. In this paper, a genetic algorithm (GA) embedded search strategy over a hybrid color-timed Petri-net (HCTPN) for wafer fabrication is proposed. Through the HCTPN model, all possible behaviors of the wafer manufacturing systems such as WIP status and machine status can be completely tracked down by the reachability graph of the net. The chromosome representation of the search nodes in GA is constructed directly from the HCTPN model, recording the information about the appropriate scheduling policy for each workstation in the fabrication. A better chromosome found by GA is received by the HCTPN based schedule builder, and then a near-optimal schedule is generated.

To solve the inconvenience of routine transportation of chronically ill and handicapped patients, this paper proposes a platform based on a hybrid fiber coaxial (HFC) network in Taiwan designed to make a home telecare system feasible. The aim of this home telecare system is to combine biomedical data, including three-channel electrocardiogram (ECG) and blood pressure (BP), video, and audio into a National Television Standard Committee (NTSC) channel for communication between the patient and healthcare provider. Digitized biomedical data and output from medical devices can be further modulated to a second audio program (SAP) subchannel which can be used for second-language audio in NTSC television signals. For long-distance transmission, we translate the digital biomedical data into the frequency domain using frequency shift key (FSK) technology and insert this signal into an SAP band. The whole system has been implemented and tested. The results obtained using this system clearly demonstrated that real-time video, audio, and biomedical data transmission are very clear with a carrier-to-noise ratio up to 43 dB.

Conventional model-based or statistical analysis methods for functional MRI (fMRI) suffer from the limitation of the assumed paradigm and biased results. Temporal clustering methods, such as fuzzy clustering, can eliminate these problems but are difficult to find activation occupying a small area, sensitive to noise and initial values, and computationally demanding. To overcome these adversities, a cascade clustering method combining a Kohonen clustering network and fuzzy, means is developed. Receiver operating characteristic (ROC) analysis is used to compare this method with correlation coefficient analysis and t test on a series of testing phantoms. Results shown that this method can efficiently and stably identify the actual functional response with typical signal change to noise ratio, from a small activation area occupying only 0.2% of head size, with phase delay, and from other noise sources such as head motion. With the ability of finding activities of small sizes stably this method can not only identify the functional responses and the active regions more precisely, but also discriminate responses from different signal sources, such as large venous vessels or different types of activation patterns in human studies involving motor cortex activation. Even when the experimental paradigm is unknown in a blind test such that model-based methods are inapplicable, this method can identify the activation patterns and regions correctly.

A new approach for analysis of RF coils in magnetic resonance (MR) experiments is reported. Instead of assuming current distribution in conventional quasi-static algorithm, this approach transforms the coil geometry into an equivalent circuit for complex current calculation. Self and mutual inductance are taken into consideration. Frequency responses of RF coils and transverse magnetic field (B1) maps can be simulated. This approach is especially efficient for phased array coil design for its small matrix size when implemented on computers. Experiments on both single surface coil and phased array coils are consistent with simulation results. Index Terms-Magnetic resonance, method of moment, phased array coil, RF coil.

A unified approach to managing multimedia medical databases in a telemedicine system is proposed. In order to manage, search, and display patient information more efficiently, we define a patient information package (PIP) as a concise data set of a patient's medical information from each visit. By means of PIP's, both patient-oriented and problem-oriented query strategies, which are most frequently used in daily clinical practice and medical education, can be accommodated. We also provide a unified methodology for accessing various types of patient medical records as well as design two types of user interfaces, high-quality data display and web-based interface, for different medical service purposes. The PIP-based management of databases has been successfully implemented between the National Taiwan University (NTUH), Taipei, and the Chinshan health care center, Chinshan, Taiwan, for teleconsultation, telediagnosis, and tele-education.