Narrow Results

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

Considering the continuous development of the film industry and the improvement of the living standard among people, movies have gradually come to the civilians. A good movie poster can effectively reflect the content of the movie, attract the audience, stimulate the demand and achieve a good publicity effect. The current movie poster design work is mainly carried out by professional designers, which requires a lot of time and labor cost. In this paper, we propose a context-aware image generation method for assisted design of movie posters using generative adversarial network (named as MPAD-CIP for short). First, the basic information and visual contents of the movie are perceived, and the representative images are extracted, with the use of convolution operations. Then, a backbone network of deep convolutional generative neural network is formulated to generate images for summary of movies. The backbone network is composed of two components: a generator and a discriminator. Their combination realizes the computer-assisted movie poster design by sensing visual context. In the experimental part, the proposed MPAD-CIP method is compared with several benchmark models to demonstrate that the posters generated by this paper are more realistic and versatile, and some of the generated posters are exhibited.

Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.

This paper presents a context-aware, multi-agent system called “Confidence” that helps elderly people remain independent longer by detecting falls and unusual movement, which may indicate a health problem. The system combines state-of-the-art sensor technologies and four groups of agents providing a reliable, robust, flexible monitoring system. It can call for help in case of an emergency, and issue warnings if unusual behavior is detected. The first group gathers data from the location and inertial sensors and suppresses noise. The second group reconstructs the position and activity of a person and detects the context. The third group assesses the person's condition in the environment and reacts to critical situations such as falls. The fourth group detects unusual behavior as an indicator of a potential health problem. The system was successfully tested on a scenario consisting of events that were difficult to recognize as falls, as well as in a scenario consisting of normal days and days when the person was ill. It was also demonstrated live several times, with excellent performance in complex situations.

Collaborative work is characterized by frequently changing situations and corresponding demands for tool support and interaction behavior provided by the collaboration environment. Current approaches to address these changing demands include manual tailoring by end-users and automatic adaptation of single user tools or for individual users. Few systems use context as a basis for adapting collaborative work environments, mostly focusing on document recommendation and awareness provision. In this paper, we present, firstly, a generic four layer framework for modeling and exploiting context. Secondly, a generic adaptation process translating user activity into state, deriving context for a given focus, and executing adaptation rules on this context. Thirdly, a collaboration domain model for describing collaboration environments and collaborative situations. Fourthly, examples of exploiting our approach to support context-based adaptation in four typical collaboration situations: co-location, co-access, co-recommendation, and co-dependency.

In an effort to improve the efficiency and recommendation accuracy of mobile learning resources, the study proposes a hybrid mobile learning strategy based on Collaborative Filtering (CF), context and interest. Analyse from the perspective of situational awareness, construct a personalised recommendation model for text learning resources based on Gimbal^TM, and obtain a recommendation form. The experimental results show that the RMSE and MAE of Context-Collaborative filtering (C-CF) are lower than those of traditional CF. The Precision and Recall values of C-CF are higher than those of CF at 10 s, the recommendation growth rates of traditional CF and C-CF are 2.09% and 1.67%, respectively. The Gimbal software enables a certain degree of learner location detection and can trigger contextual rules based on time and location contexts to provide users with personalised text-based learning resources. The research results indicate that in specific applications, over time, under the recommendation system, students’ grades steadily increase, which is also beneficial for improving their learning efficiency.

Humanoid robots that share the same space with humans need to be socially acceptable and effective as they interact with people. In this paper we focus our attention on the definition of a behavior-based robotic architecture that (1) allows the robot to navigate safely in a cluttered and dynamically changing domestic environment and (2) encodes embodied non-verbal interactions: the robot respects the users personal space (PS) by choosing the appropriate distance and direction of approach. The model of the PS is derived from human–robot interaction tests, and it is described in a convenient mathematical form. The robot's target location is dynamically inferred through the solution of a Bayesian filtering problem. The validation of the overall behavioral architecture shows that the robot is able to exhibit appropriate proxemic behavior.

The novel mobile scenarios encourage the design and development of context-aware middleware that provides on demand appropriate semantic services services to mobile user's access in a dynamic smart environment, since wireless hotspots and mobile devices are permeating our workplace, home and public places. As mobile users will be immersed in thousands of different kinds of pervasive services, it's of paramount importance to provide appropriate personalized services to the right person in the right form. In this paper we present a collaborative-based service provisioning approach which can enable effective service provision based on social semantic characteristics of user profile, device capability, situation context and services in WLAN enabled environment. We illustrate our ideas through a scenario of service discovery and provision in unfamiliar public places (e.g. airport), and present an OSGi-based middleware to instantiate the service provisioning process based on semantic matchmaking.