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)

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

Homologous Muscle Contraction during Unilateral Movement Does Not Show a Dominant Effect on Leg Representation of the Ipsilateral Primary Motor Cortex

Co-activation of homo- and heterotopic representations in the primary motor cortex (M1) ipsilateral to a unilateral motor task has been observed in neuroimaging studies. Further analysis showed that the ipsilateral M1 is involved in motor execution along with the contralateral M1 in humans. Additionally, transcranial magnetic stimulation (TMS) studies have revealed that the size of the co-activation in the ipsilateral M1 has a muscle-dominant effect in the upper limbs, with a prominent decline of inhibition within the ipsilateral M1 occurring when a homologous muscle contracts. However, the homologous muscle-dominant effect in the ipsilateral M1 is less clear in the lower limbs. The present study investigates the response of corticospinal output and intracortical inhibition in the leg representation of the ipsilateral M1 during a unilateral motor task, with homo- or heterogeneous muscles. We assessed functional changes within the ipsilateral M1 and in corticospinal outputs associated with different contracting muscles in 15 right-handed healthy subjects. Motor tasks were performed with the right-side limb, including movements of the upper and lower limbs. TMS paradigms were measured, consisting of short-interval intracortical inhibition (SICI) and recruitment curves (RCs) of motor evoked potentials (MEPs) in the right M1, and responses were recorded from the left rectus femoris (RF) and left tibialis anterior (TA) muscles. TMS results showed that significant declines in SICI and prominent increases in MEPs of the left TA and left RF during unilateral movements. Cortical activations were associated with the muscles contracting during the movements. The present data demonstrate that activation of the ipsilateral M1 on leg representation could be increased during unilateral movement. However, no homologous muscle-dominant effect was evident in the leg muscles. The results may reflect that functional coupling of bilateral leg muscles is a reciprocal movement

The Supersonic Project: SIGOs, A Proposed Progenitor to Globular Clusters, and Their Connections to Gravitational-wave Anisotropies

Supersonically induced gas objects (SIGOs), are structures with little to no dark-matter component predicted to exist in regions of the universe with large relative velocities between baryons and dark matter at the time of recombination. They have been suggested to be the progenitors of present-day globular clusters. Using simulations, SIGOs have been studied on small scales (around 2 Mpc) where these relative velocities are coherent. However, it is challenging to study SIGOs using simulations on large scales due to the varying relative velocities at scales larger than a few Mpc. Here, we study SIGO abundances semi-analytically: using perturbation theory, we predict the number density of SIGOs analytically, and compare these results to small-box numerical simulations. We use the agreement between the numerical and analytic calculations to extrapolate the large-scale variation of SIGO abundances over different stream velocities. As a result, we predict similar large-scale variations of objects with high gas densities before reionization that could possibly be observed by JWST. If indeed SIGOs are progenitors of globular clusters, then we expect a similar variation of globular cluster abundances over large scales. Significantly, we find that the expected number density of SIGOs is consistent with observed globular cluster number densities. As a proof-of-concept, and because globular clusters were proposed to be natural formation sites for gravitational wave sources from binary black-hole mergers, we show that SIGOs should imprint an anisotropy on the gravitational wave signal on the sky, consistent with their distribution

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

The Supersonic Project: The Early Evolutionary Path of Supersonically Induced Gas Objects

Supersonically induced gas objects (SIGOs) are a class of early universe objects that have gained attention as a potential formation route for globular clusters. SIGOs have recently begun to be studied in the context of molecular hydrogen cooling, which is key to characterizing their structure and evolution. Studying the population-level properties of SIGOs with molecular cooling is important for understanding their potential for collapse and star formation, and for addressing whether SIGOs can survive to the present epoch. Here, we investigate the evolution of SIGOs before they form stars, using a combination of numerical and analytical analysis. We study timescales important to the evolution of SIGOs at a population level in the presence of molecular cooling. Revising the previous formulation for the critical density of collapse for SIGOs allows us to show that their prolateness tends to act as an inhibiting factor to collapse. We find that simulated SIGOs are limited by artificial two-body relaxation effects that tend to disperse them. We expect that SIGOs in nature will be longer lived compared to our simulations. Further, the fall-back timescale on which SIGOs fall into nearby dark matter halos, potentially producing a globular-cluster-like system, is frequently longer than their cooling timescale and the collapse timescale on which they shrink through gravity. Therefore, some SIGOs have time to cool and collapse outside of halos despite initially failing to exceed the critical density. From this analysis we conclude that SIGOs should form stars outside of halos in nonnegligible stream velocity patches in the universe

Always trust in old friends? Effects of reciprocity in bilateral asset specificity on trust in international B2B partnerships

Grounded in Social Exchange Theory (SET), this study is motivated by two unresolved issues. First, scholars find mixed results on how relationship duration facilitates business-to-business (B2B) trust. The lack of consensus results from the assumption that relationship duration is a measure of prior trust-building efforts. We contend that trust-building lies in exchanges between B2B partners, and relationship duration moderates the effects of reciprocal exchanges. Second, although Transaction Cost Analysis (TCA) is one of the most used theoretical lens in the study of B2B trust, TCA is criticized for neglecting the exchange process in B2B trust-building. To provide clarity to these issues, we validate the expectation that bilateral asset specificity constitutes social exchange processes, which communicate goodwill reciprocity and equivalence reciprocity. Empirical findings suggest that, within bilateral asset specificity: (1) achieving goodwill reciprocity always enhances trust, regardless of the duration contingency; and (2) violating equivalence reciprocity impairs trust over the duration