We present a new texture retrieval algorithm that, for the first time, performs content-based image retrieval (CBIR) in the modulation domain by computing powerful low-level texture features based on computed AM-FM im...
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We present a new texture retrieval algorithm that, for the first time, performs content-based image retrieval (CBIR) in the modulation domain by computing powerful low-level texture features based on computed AM-FM image models. Performance of the new algorithm is analyzed with respect to competing methods where texture features are computed from Gabor filter magnitude responses. Our experimental results show that the new algorithm achieves a significant performance advantage. We describe how the new algorithm will be used for the texture component of a novel CBIR service called DIRECT, which is designed to provide image based searches in distributed digital libraries without the need for manual entry of annotations and image metadata.
In this paper, neural network-based methods incorporating ensemble learning techniques are presented that estimate chlorophyll /spl alpha/ (chl /spl alpha/) concentration in the coastal waters of the Gulf of Maine (GO...
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In this paper, neural network-based methods incorporating ensemble learning techniques are presented that estimate chlorophyll /spl alpha/ (chl /spl alpha/) concentration in the coastal waters of the Gulf of Maine (GOM). A dataset was constructed consisting of in situ chl measurements from the GOM matched with satellite data from the sea-viewing wide-field-of-view sensor (SeaWiFS). These data were used to develop models using diverse neural network ensembles for estimation of chl /spl alpha/ concentration from satellite-retrieved ocean reflectances. Results indicate that the models are able to generalize across geographical and temporal variation, and are resilient to uncertainty such as that introduced by poor atmospheric correction, or radiance contributions from non-chl /spl alpha/ components in case 2 waters.
Using the lattice Boltzmann method, we study fluid flow in a two-dimensional (2D) model of fracture network of rock. Each fracture in a square network is represented by a 2D channel with rough, self-affine internal su...
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Using the lattice Boltzmann method, we study fluid flow in a two-dimensional (2D) model of fracture network of rock. Each fracture in a square network is represented by a 2D channel with rough, self-affine internal surfaces. Various parameters of the model, such as the connectivity and the apertures of the fractures, the roughness profile of their surface, as well as the Reynolds number for flow of the fluid, are systematically varied in order to assess their effect on the effective permeability of the fracture network. The distribution of the fractures’ apertures is approximated well by a log-normal distribution, which is consistent with experimental data. Due to the roughness of the fractures’ surfaces, and the finite size of the networks that can be used in the simulations, the fracture network is anisotropic. The anisotropy increases as the connectivity of the network decreases and approaches the percolation threshold. The effective permeability K of the network follows the power law K∼〈δ〉β, where 〈δ〉 is the average aperture of the fractures in the network and the exponent β may depend on the roughness exponent. A crossover from linear to nonlinear flow regime is obtained at a Reynolds number Re∼O(1), but the precise numerical value of the crossover Re depends on the roughness of the fractures’ surfaces.
A family of Markov models for analyzing the performance of parallel processors that execute a job consisting of N independent tasks using P fault-prone processors is presented in this paper. This study extends our pre...
In this paper, we have described a model to parallelize the resampling routine, which is used in the geometric correction of data provided by remote sensing satellites. Our model is a typical master-slave model consis...
In this paper, we have described a model to parallelize the resampling routine, which is used in the geometric correction of data provided by remote sensing satellites. Our model is a typical master-slave model consisting of N machines termed as hosts out of which one is designated as the master. The input image data resides on the master. Processing of the input image data is done in parallel on the N machines. Issues related to load-balancing and various error conditions that may occur during execution like one of the machines going down have been studied and are incorporated in the model. It also provides the flexibility to add or delete the hosts during the execution of the resampling routine. The serial version of this routine involves huge amount of computations and takes substantial amount of time even for an image of 473 MB. We have implemented our model with the help of PVM which is most often used in distributed computing environment. Our approach has been tested for geometric correction on LISS-III 4 band data of size 473 MB. It is found that if one uses 2. 3 or 4 hosts the overall execution time is reduced by 33%, 42% and 49%, respectively.
The HFC model for evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness. Individuals move from low-fitness subpopulations to hig...
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A generalized statistical signal processing framework developed in [2] is utilized for interference suppression for Joint Multiuser Detection (MUD). Previous work on the efficient correlations subtractive architecture...
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A generalized statistical signal processing framework developed in [2] is utilized for interference suppression for Joint Multiuser Detection (MUD). Previous work on the efficient correlations subtractive architecture form of the reduced rank multistage Wiener filter (CSA-MWF) is extended by considering the multiple signal constraint case. The reduced rank algorithm is not based on an eigen-decomposition, which requires the signal subspace rank to be greater than or equal to the number of signals present in the system. The solution meets or exceeds full rank MMSE at a significantly reduced rank. System performance is characterized for a highly loaded synchronous DS-CDMA system in the presence of multi path. The bit error rate (BER) performance of the Joint CSA-MWF (JCSA-MWF) is compared to MMSE and RAKE receivers.
In ad hoc networks, each node utilizes its limited resources to carry out the collective operation of the network. It is not always in the best interests of the network's nodes to demand the continuous participati...
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In ad hoc networks, each node utilizes its limited resources to carry out the collective operation of the network. It is not always in the best interests of the network's nodes to demand the continuous participation of all nodes in the network operations. We propose an energy dependent participation (EDP) scheme, where a node periodically re-evaluates its participation in the network based on the residual energy in its battery. More importantly, a node gives special consideration to supporting the communication needs of its active network applications and preventing further network partitioning. EDP's localized partition checking algorithm is particularly well suited for the zone routing protocol, where the link-state information is proactively maintained within each node's local zone and routes to faraway nodes are reactively obtained via global queries. Through simulations, we evaluate the impact of our proposed scheme on battery life and network connectivity. Our results suggest that the EDP scheme can increase the usable lifetime of a battery-constraint ad hoc network by over 50%.
Ultrasonic backscatter can provide information on the density of scatterers within biological media, and is therefore an important tool in tissue characterization. In this paper, a novel neural network approach to est...
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Ultrasonic backscatter can provide information on the density of scatterers within biological media, and is therefore an important tool in tissue characterization. In this paper, a novel neural network approach to estimate scatterer density from generalized entropy is proposed. Neural estimation compares favorably with nonlinear least-squares models.
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