Adaptive Mesh Refinement for Fast Convergence of EFIE-Based 3-D Extraction

3-D full-wave method of moments (MoM) based electromagnetic analysis is a popular means toward accurate solution of Maxwell's equations. The time and memory bottlenecks associated with such a solution have been addressed over the last two decades by linear complexity fast solver algorithms. However, the accurate solution of 3-D full-wave MoM on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretization may not be fine enough to capture spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates a large number of solution variables and therefore requires an unnecessarily large matrix solution. In this paper, different refinement criteria are studied in an adaptive mesh refinement platform. Consequently, the most suitable conductor mesh refinement criterion for MoM-based electromagnetic package-board extraction is identified and the advantages of this adaptive strategy are demonstrated from both accuracy and speed perspectives. The results are also compared with those of the recently reported integral equation-based h-refinement strategy. Finally, a new methodology to expedite each adaptive refinement pass is proposed.

Gamma oscillations in the midbrain spatial attention network: linking circuits to function

Gamma-band (25-140 Hz) oscillations are ubiquitous in mammalian forebrain structures involved in sensory processing, attention, learning and memory. The optic tectum (01) is the central structure in a midbrain network that participates critically in controlling spatial attention. In this review, we summarize recent advances in characterizing a neural circuit in this midbrain network that generates large amplitude, space-specific, gamma oscillations in the avian OT, both in vivo and in vitro. We describe key physiological and pharmacological mechanisms that produce and regulate the structure of these oscillations. The extensive similarities between midbrain gamma oscillations in birds and those in the neocortex and hippocampus of mammals, offer important insights into the functional significance of a midbrain gamma oscillatory code.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Transgenic Drosophila model to study apolipoprotein E4-induced neurodegeneration

The epsilon 4 isoform of apolipoprotein E (ApoE4) that is involved in neuron-glial lipid metabolism has been demonstrated as the main genetic risk factor in late-onset of Alzheimer's disease. However, the mechanism underlying ApoE4-mediated neurodegeneration remains unclear. We created a transgenic model of neurodegenerative disorder by expressing epsilon 3 and epsilon 4 isoforms of human ApoE in the Drosophila melanogaster. The genetic models exhibited progressive neurodegeneration, shortened lifespan and memory impairment. Genetic interaction studies between amyloid precursor protein and ApoE in axon pathology of the disease revealed that over expression of hApoE in Appl-expressing neurons of Drosophila brain causes neurodegeneration. Moreover, acute oxidative damage in the hApoE transgenic flies triggered a neuroprotective response of hApoE3 while chronic induction of oxidative damage accelerated the rate of neurodegeneration. This Drosophila model may facilitate analysis of the molecular and cellular events implicated in hApoE4 neurotoxicity. (C) 2015 Elsevier B.V. All rights reserved.

Transgenic Drosophila model to study apolipoprotein E4-induced neurodegeneration

The epsilon 4 isoform of apolipoprotein E (ApoE4) that is involved in neuron-glial lipid metabolism has been demonstrated as the main genetic risk factor in late-onset of Alzheimer's disease. However, the mechanism underlying ApoE4-mediated neurodegeneration remains unclear. We created a transgenic model of neurodegenerative disorder by expressing epsilon 3 and epsilon 4 isoforms of human ApoE in the Drosophila melanogaster. The genetic models exhibited progressive neurodegeneration, shortened lifespan and memory impairment. Genetic interaction studies between amyloid precursor protein and ApoE in axon pathology of the disease revealed that over expression of hApoE in Appl-expressing neurons of Drosophila brain causes neurodegeneration. Moreover, acute oxidative damage in the hApoE transgenic flies triggered a neuroprotective response of hApoE3 while chronic induction of oxidative damage accelerated the rate of neurodegeneration. This Drosophila model may facilitate analysis of the molecular and cellular events implicated in hApoE4 neurotoxicity. (C) 2015 Elsevier B.V. All rights reserved.