Xiaonian Wang

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.

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.

Enhancement of image luminance resolution by imposing random jitter

Inspired by biological eyes, silicon retinas with pixel-level processing have been developed to achieve very high-speed and high-quality image processing. Due to the limitation on the fill factor and the dimension of a silicon chip, both spatial and luminance resolutions have to be kept low. For recovering fine images from a silicon retina with a lower resolution, the authors propose a neural network model and its electronic counterpart by imposing random jitter to the sensor and collecting temporal statistics of the firing neurons. Statistical analysis shows that the scheme can enhance resolution of an image and emphasize contrast edges present in the image. It is further proved that the enhancement in luminance resolution and sharpness is a trade-off between recovering bias and variance. Therefore, jitter intensity needs to be optimized by considering the luminance distribution. The simulations illustrate its effect on the fine detail reconstruction using the proposed scheme.

Sensing Semantics of RSS Feeds by Fuzzy Matchmaking

RSS feeds provide a fast and effective way to publish up-to-date information or renew outdated contents for information subscribers. So far RSS information is mostly managed by content publishers but Internet users have less initiative to choose what they really need. More attention needs to be paid on techniques for user-initiative information discovery from RSS feeds. In this paper, a quantitative semantic matchmaking method for the RSS based applications is proposed. Semantic information is extracted from an RSS feed as numerical vectors and semantic matching can then be conducted quantitatively. Ontology is applied to provide a common-agreed matching basis for the quantitative matchmaking. In order to avoid semantic ambiguity of literal statements from distributed and heterogeneous RSS publishers, fuzzy inference is used to transform an individual-dependent vector into an individual-independent vector. Semantic similarities can be revealed as the result.

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.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.

Tumorigenic potential is restored during differentiation in fusion-reprogrammed cancer cells

Detailed understanding of the mechanistic steps underlying tumor initiation and malignant progression is critical for insights of potentially novel therapeutic modalities. Cellular reprogramming is an approach of particular interest because it can provide a means to reset the differentiation state of the cancer cells and to revert these cells to a state of non-malignancy. Here, we investigated the relationship between cellular differentiation and malignant progression by the fusion of four independent mouse cancer cell lines from different tissues, each with differing developmental potentials, to pluripotent mouse embryonic stem (ES) cells. Fusion was accompanied by loss of differentiated properties of the four parental cancer cell lines and concomitant emergence of pluripotency, demonstrating the feasibility to reprogram the malignant and differentiative properties of cancer cells. However, the original malignant and differentiative phenotypes re-emerge upon withdrawal of the fused cells from the embryonic environment in which they were maintained. cDNA array analysis of the malignant hepatoma progression implicated a role for Foxa1, and silencing Foxa1 prevented the re-emergence of malignant and differentiation-associated gene expression. Our findings support the hypothesis that tumor progression results from deregulation of stem cells, and our approach provides a strategy to analyze possible mechanisms in the cancer initiation.

Navigation of mobile robots in an intelligent environment with wireless visual sensors

Today, most autonomous mobile robots rely on centralized perception and processing. They are, however, facing difficulties to be reliable and efficient due to uncertainties introduced from dynamic environments. In this paper, an intelligent environment with distributed visual sensors to support navigation and control of mobile robots, wireless mosaic eyes, is reported. The distributed visual sensors relieve the required massive intelligence to its environment. A multiple Bloom-filter scheme is developed for distributed storage of global routing information into wireless sensors. An error expectation based design for a multiple Bloom-filter is proposed, which exhibits superior performance to the conventional design. Considering dynamic navigation and control of a mobile robot guided by distributed vision, an active contour based scheme is developed to deal with path planning, trajectory generation, and optimal motion control. A working system has been developed to demonstrate the idea.