An intelligent home is likely in the near future. An important ingredient in an intelligent environment such as a home is prediction – of the next low-level action, the next location, and the next high-level task that an inhabitant is likely to perform. In this paper we model inhabitant actions as states in a simple Markov model. We introduce an enhancement to this basic approach, the Task-based Markov model (TMM) method. TMM discovers high-level inhabitant tasks using the supplied unlabeled data. We investigate clustering of actions to identify tasks, and integrate clusters into a hidden Markov model that predicts the next inhabitant action. We validate our approach and observe that for simulated data we achieve good accuracy using both the simple Markov model and the TMM, whereas on real data we see that simple Markov models outperform the TMM. We also perform an analysis of the performance of the HMM in the framework of the TMM when diverse patterns are introduced into the data.

In real-time remote diagnosis of emergency medical events, mobility can be enabled by wireless video communications. However, clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video. Because the medical domain has its own fidelity criteria, it is important to incorporate diagnostic video quality criteria into any video compression system design. To this end, we used flexible algorithms for region-of-interest (ROI) video compression and obtained feedback from medical experts to develop criteria for diagnostically lossless (DL) quality. The design of the system occurred in three steps-measurement of bit rate at which DL quality is achieved through evaluation of videos by medical experts, incorporation of that information into a flexible video encoder through the notion of encoder states, and an encoder state update option based on a built-in quality criterion. Medical experts then evaluated our system for the diagnostic quality of the video, allowing us to verify that it is possible to realize DL quality in the ROI at practical communication data transfer rates, enabling mobile medical assessment over bit-rate limited wireless channels. This work lays the scientific foundation for additional validation through prototyped technology, field testing, and clinical trials.

Technology assisted safe living has great potential in revolutionizing the way healthcare is provided to elderly and needy population. A wireless sensor network (WSN) based system for sensing and reporting events based on context is presented in this paper. It is demonstrated that by proper use of architectures for supporting WSNs and by exploiting very recent technological advances, it is now possible to build and deploy extremely energy efficient systems with very long and dependable battery life. The system evolves over most existing approaches by highly localizing the computations for detecting events and transmitting only positive events where care providers may need to be alerted.

Image denoising is used to eliminate the noise while retaining as much as possible the important signal features. The function of image denoising is to calculate approximately the original image form the noisy data. Image denoising still remains the challenge for researchers because noise removal introduces artifacts and causes blurring of the images. Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). MR images are typically corrupted with noise, which hinder the medical diagnosis based on these images. The presence of noise not only causes as undesirable visual quality as well as lowers the visibility of low contrast objects. in this paper noise removal approach has proposed using hybridization of three filter with DWT method. Results calculated in terms of PSNR,MSE & TIME.

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