Design of a multiple bloom filter for distributed navigation routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes. © 2013 IEEE

Exploring Effective Advertising Strategies: The Roles of Formats, Content Relevance and Shopping Tasks on Ad Recognition

The widespread application of Web-based technology has contributed not only to the content of advertising but also to the improvement of presentation formats. Animation has become a powerful presentation format on the Web. Despite its potential benefits, however, animation is no panacea. Practitioners and academics have been paying increased attention to the exploration of effective advertising strategies in the e-commerce environment. Drawing on theories in cognitive psychology, this study, by using a laboratory experiment, investigates the roles of presentation formats, content relevance and shopping tasks on ad recognition. The results show that abrupt and looming formats are effective formats in improving ad recognition. There is a significant three-way interaction between the formats, content relevance and shopping tasks on ad recognition. Specifically, the interaction effect of animation formats and content relevance is observed in browsing tasks, but not in searching tasks

Unfalsified visual servoing for simultaneous object recognition and pose tracking

In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation

Does Content Relevance Lead to Positive Attitude toward Websites? Exploring the Role of Flow and Goal Specificity

It is common practice to have Websites interact with consumers by delivering personalized services, among which content relevance is most widely adopted on Internet advertising. However, little is known about the impact of relevance on consumers’ psychological states and the underlying mechanism of relationship between relevance and attitude toward Websites. Building upon flow theory and Hoffman and Novak’s (1996) conceptual model, this study investigates the role of flow and relevance in influencing attitude toward Websites. The findings show that the link between content relevance and attitude toward Web sites is mediated by consumers’ flow experience. In addition, goal specificity moderates the effect of relevance on flow experience. Under the conditions of high goal specificity, the relevance effect on flow is magnified. Theoretical and practical implications of this study are discussed

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties