10848842
Vision Guided Circumnavigating Autonomous Robots
In this paper, we propose a system for vision guided autonomous circumnavigation, allowing robots to navigate around objects of arbitrary pose. The system performs knowledge-based object recognition from an intensity image using a canonical viewer-centred model. A path planned from a geometric model then guides the robot in circum-navigating the object. This system can be used in many applications where robots have to recognize and manipulate objects of unknown pose and placement. Such applications occur in a variety of contexts such as factory automation, underwater and space exploration, and nuclear power station maintenance. We also define a canonical-view graph to model objects, which is a viewer-centred representation.
7502395
Dynamic image sequence analysis using fuzzy measures
In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.
14780024
Fuzzy causal networks: general model, inference, and convergence
In this paper, we first propose a general framework for fuzzy causal networks (FCNs). Then, we study the dynamics and convergence of such general FCNs. We prove that any general FCN with constant weight matrix converges to a limit cycle or a static state, or the trajectory of the FCN is not repetitive. We also prove that under certain conditions a discrete state general FCN converges to its limit cycle or static state in O(n) steps, where n is the number of vertices of the FCN. This is in striking contrast with the exponential running time 2/sup n/, which is accepted widely for classic FCNs.
207320245
Hidden Markov models with factored Gaussian mixtures densities
We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm and propose a novel method for initializing them. To compare the performances of the proposed models with that of the factorial hidden Markov models and HMMs, we have carried out extensive experiments which show that this modelling approach is effective and robust.
2364735
Embodied categorisation for vision-guided mobile robots
This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be effective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate efficient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more efficient and robust.
45372122
Interrogating the structure of fuzzy cognitive maps
Abstract Causal algebra in fuzzy cognitive maps (FCMs) plays a critical role in the analysis and design of FCMs. Improving causal algebra in FCMs to model complicated situations has been one of the major research topics in this area. In this paper we propose a dynamic causal algebra in FCMs which can improve FCMs' inference and representation capability. The dynamic causal algebra shows that the indirect, strongest, weakest and total effects a vertex influences another in the FCM not only depend on the weights along all directed paths between the two vertices but also the states of the vertices on the directed paths. Therefore, these effects are nonlinear dynamic processes determined by initial conditions and propagated in the FCM to reach a static or cyclic pattern. We test our theory with a simple example.
5196479
Odd Wheels in Graphs
For k?1 the odd wheel of 2k+1 spokes, denoted by W2k+1, is the graph obtained from a cycle of length 2k+1 by adding a new vertex and joining it to all vertices of the cycle. In this paper it is shown that if a graph G of order n with minimum degree greater than 7n/12 is at least 4-chromatic then G contains an odd wheel with at most 5 spokes.
2119274
Dynamical mobile terminal location registration in wireless PCS networks
In this paper, we propose a mobile terminal (MT) location registration/update model. In this model, the registration decision is based on two factors-the time elapsed since last call arrival and the distance the MT has traveled since last registration. It is established that the optimal registration strategy can be represented by a curve. Only when the state of the system reaches this curve is a registration performed. In order for an MT to calculate its traveled distance, an interactive implementation scheme and a distance calculation algorithm are developed. When the call interarrival times are independent and geometrically distributed, the proposed model becomes a distance-based model and, in this case, the optimal registration strategy is of threshold structure. For the distance-based model, a single sample path-based ordinal optimization algorithm is devised. In this algorithm, without any knowledge about the system parameters, the MT observes the system state transitions, estimates the ordinal of a set of strategies, and updates the registration strategy adaptively. Since only a single sample path is used, this algorithm can be implemented online. Several numerical examples are provided to compare the proposed model and the existing ones.
6922682
A Predicative Semantic Model for Integrating UML Models
This paper presents a predicative semantic model for integrating models from UML class diagrams and sequence diagrams. The integrated model is used for dealing with consistency problems of UML class diagrams and sequence diagrams. We also define the notion of consistent refinement of these integrated models.
34215309
Jini-enabled high performance computing
The vision of computational grids promises an exciting future for high performance computing (HPC). In the project presented we make a small but practical step toward the grand vision of pervasive HPC by using some of the prevailing Internet technologies to enable access of HPC anytime and anywhere. Specifically, the project focuses on HPC that could be accessed by using the World Wide Web. We have conducted experiments based on engineering/scientific applications. Examples include simulations of automated guided vehicles (hereafter AGVs for short) in container port operations, N-body and molecular simulations. We initially build systems based on an ad-hoc approach, whose merits and demerits are identified. Then, we employ Jini technology to reconstruct the system architecture and present a more efficient and robust model. Performance studies are also presented. The objectives are to explore and address relevant issues, evaluate various approaches, demonstrate a workable version and conduct experiments with actual Web-enabled applications. The result is that now we are able to partially realize the dream of using the HPCs anytime and anywhere through devices such as a wireless/mobile computer, as long as they are armed with a common Web browser.
8930290
Data mining of gene expression changes in Alzheimer brain
Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.