Earticle

Healthcare targeted for home activity is playing an important role in our daily lives as the number of elderly person is increasing sharply. Researches pointed out that one third of 65-and-over aged person fall per-year. Some detection related work made use of accelerometers or gyroscopes, some use passive infrared or acoustic sensors. But there were some false alarms. Falls can be an unpredicted and dangerous event. A system based on wireless sensor networks to detect the falls for old and resident was proposed in this paper. We provided a method to minimize the false alarm rate of fall detection as well. Besides, we can detect falls that did not trigger the alarm which was intervened by outside forces.

This paper initially proposes a technique for identifying a moving object in a video clip of stationary background for real time content based multimedia communication systems [2]. It deals with identifying an object of interest. Dynamic objects are identified using both background elimination and background registration techniques. Post processing techniques are applied to reduce the noise. The background elimination method uses concept of least squares to compare the accuracies of the current algorithm with the already existing algorithms. The background registration method uses background subtraction which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments.

When applied to precipitation forecasting, the mean generating function - optimal subset regression (MGF-OSR) model is limited by its low accuracy and high error, while the back propagation (BP) neural network model has difficulty in learning for matrix selection. This paper proposes a new MGF-OSR-BP model, which uses a MGF to extend original data, an OSR to select the best series as the BP neural network input node and learning matrix, and the resultant data for training. The training procedure determines the number of hidden layers and uses an optimal number of hidden layers for model training. This paper uses the MGF-OSR-BP model to analyze precipitation data from Hangzhou, China, for 53 years, from 1956 to 2008. The 1956-2006 precipitation data are used as the training sample, and the 2007-2008 data are used as the test set data to verify the practicality of the forecast system. A fitting verification is performed using the forecasted data against field measurement data, and the results show that the forecast accuracy is better than that of the MGF-OSR model or the MGF stepwise multiple regression model.

Research on implementing several projects and proposals of U learning are carried out for the past 10 years. The methods for learning are changing according to the novel inventions in the both hardware and software methods. One such method we concentrate in this paper is novel examination scenarios’ where the participant need not physically appeared the examination but can write the test / present a seminar/ evaluate a test paper / submit the feedback / participate in a quiz etc remotely either login in their laptops or through their android. In this paper we concentrated on developing these U learning applications using a new approach called characteristicUML. We proposed a some Characteristics which help designers in developing these scenarios and enable the Teachers / Researchers / Students to login remotely, download the content, submit the seminars/projects/assignments, actively participate in webinars, test forums etc either through their local desktop or androids.

In this paper, we present a novel Ad hoc network management mechanism UANM to provide unified management functionalities for nodes in an Ad hoc network. UANM adopts a cross layer cooperating management scheme to coordinate the pertinent behaviors of routing and application layer modules. It employs manager and agent model as SNMP. Nevertheless, when manager node needs to send management data packets to agent nodes and there is no available communication route, it will send out the management data packets in the process of route discovery. Thus manager node can finish part of the management work with much less delay and there will be significant energy consumption reduction for the nodes that are involved in the related communications. The simulation results verified the efficiency of UANM.