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Closed Circuit Television (CCTV) system has been popular in daily life such as traffic, airport, street and public place. The common goal of CCTV system is the prevention of crime and disorder by observing objects. In the future, smart CCTV camera combined with mobile phone will be used to protect human from crime and dangerous situations. Intelligent CCTV system in public place will monitor human behavior in real-time and transfer image data to control tower for the security purpose. In this paper, we propose an abnormal behavioral tracking model for prediction of abnormal situation by using Expectation Maximization (EM) algorithm combined with Viterbi algorithm. The tracking model will detect objects from CCTV image in dynamic environment for the prediction of dangerous situation. This tracking system has five main steps. (1) The detection of object and their environment, (2) Feature extraction from objects and situations such as human body posture, weather, and time (3) Location information such as object trajectory and area safety level (4) knowledge update and decision making (5) prediction of abnormal situation and maximized risk rates.

Recently, mobile applications which provide health services at anytime and anywhere are on demand due to the growth of mobile wireless technologies. For the health service, an inspection service middleware is needed for monitoring health condition such as observing and analyzing EEG (electroencephalography), ECG (electrocardiography) and EMG (Electrocardiogram) waveforms from wearable ECG devices under the coverage of a wireless sensor network (WSN). For the inspection service middleware, we propose a new notion of prediction model based on risk ratio Expectation Maximization (EM) by monitoring real-time bio-signals. The prediction model can detect abnormal health condition by the monitoring system. In this paper, we explain the detail algorithms and results for these steps based on EM. There are the five modules as follows: (1) The measurement of bio-signals such as body temperature, EEG, ECG and EMG, (2) Object assessment from measurement wavelength, (3) Situation assessment from GPS in smart device, (4) Maximized health condition using risk ratio EM, (5) Knowledge update and decision making for healthy life.

The media presentation with temporal dependencies in the different media data, such as still image, text, video and audio is an open research area. This is an important issue for emerging distributed multimedia system. In this paper, we propose a new notion, called SRF (Synchronization Relation Flow) model that supports the multi-media synchronization in multi-screen service. The SRF considers of four main aspects as follows; Flexible Querying, Location and Trajectories, Interoperability among Multi-Screen and Reasoning in effectively intelligent service. The SRF provides full functionalities for intelligent multi-screen service.