A Positioning Scheme Combining Location Tracking with Vision Assisting for Wireless Sensor Networks

This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF)with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedureis employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assistedcalibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracyenhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments.Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KFbasedapproach with the landmark information can calibrate the location estimation and reduce the corner effect of alocation-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimentalresults demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in aZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, theproposed algorithm can achieve reasonably good performance

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)

On the unitarity of higher-dervative and nonlocal theories

We consider two simple models of higher-derivative and nonlocal quantu systems.It is shown that, contrary to some claims found in literature, they can be made unitary.Comment: 8 pages, no figure

Applications of simulation technique on debris-flow hazard zone delineation: a case study in Hualien County, Taiwan

Debris flows pose severe hazards to communities in mountainous areas, often resulting in the loss of life and property. Helping debris-flow-prone communities delineate potential hazard zones provides local authorities with useful information for developing emergency plans and disaster management policies. In 2003, the Soil and Water Conservation Bureau of Taiwan proposed an empirical model to delineate hazard zones for all creeks (1420 in total) with potential of debris flows and utilized the model to help establish a hazard prevention system. However, the model does not fully consider hydrologic and physiographical conditions for a given creek in simulation. The objective of this study is to propose new approaches that can improve hazard zone delineation accuracy and simulate hazard zones in response to different rainfall intensity. In this study, a two-dimensional commercial model FLO-2D, physically based and taking into account the momentum and energy conservation of flow, was used to simulate debris-flow inundated areas. <br><br> Sensitivity analysis with the model was conducted to determine the main influence parameters which affect debris flow simulation. Results indicate that the roughness coefficient, yield stress and volumetric sediment concentration dominate the computed results. To improve accuracy of the model, the study examined the performance of the rainfall-runoff model of FLO-2D as compared with that of the HSPF (Hydrological Simulation Program Fortran) model, and then the proper values of the significant parameters were evaluated through the calibration process. Results reveal that the HSPF model has a better performance than the FLO-2D model at peak flow and flow recession period, and the volumetric sediment concentration and yield stress can be estimated by the channel slope. The validation of the model for simulating debris-flow hazard zones has been confirmed by a comparison of field evidence from historical debris-flow disaster data. The model can successfully replicate the influence zone of the debris-flow disaster event with an acceptable error and demonstrate a better result than the empirical model adopted by the Soil and Water Conservation Bureau of Taiwan

Comparison of PCR ribotyping and multilocus variable-number tandem-repeat analysis (MLVA) for improved detection of Clostridium difficile

<p>Abstract</p> <p>Background</p> <p>Polymerase chain reaction (PCR) ribotyping is one of the globally accepted techniques for defining epidemic clones of <it>Clostridium difficile </it>and tracing virulence-related strains. However, the ambiguous data generated by this technique makes it difficult to compare data attained from different laboratories; therefore, a portable technique that could supersede or supplement PCR ribotyping should be developed. The current study attempted to use a new multilocus variable-number tandem-repeat analysis (MLVA) panel to detect PCR-ribotype groups. In addition, various MLVA panels using different numbers of variable-number tandem-repeat (VNTR) loci were evaluated for their power to discriminate <it>C. difficile </it>clinical isolates.</p> <p>Results</p> <p>At first, 40 VNTR loci from the <it>C. difficile </it>genome were used to screen for the most suitable MLVA panel. MLVA and PCR ribotyping were implemented to identify 142 <it>C. difficile </it>isolates. Groupings of serial MLVA panels with different allelic diversity were compared with 47 PCR-ribotype groups. A MLVA panel using ten VNTR loci with limited allelic diversity (0.54-0.83), designated MLVA10, generated groups highly congruent (98%) with the PCR-ribotype groups. For comparison of discriminatory power, a MLVA panel using only four highly variable VNTR loci (allelic diversity: 0.94-0.96), designated MLVA4, was found to be the simplest MLVA panel that retained high discriminatory power. The MLVA10 and MLVA4 were combined and used to detect genetically closely related <it>C. difficile </it>strains.</p> <p>Conclusions</p> <p>For the epidemiological investigations of <it>C. difficile</it>, we recommend that MLVA10 be used in coordination with the PCR-ribotype groups to detect epidemic clones, and that the MLVA4 could be used to detect outbreak strains. MLVA10 and MLVA4 could be combined in four multiplex PCR reactions to save time and obtain distinguishable data.</p