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Papers by Krishnamurthy Vemuru

In FY 2016, the research team evaluated Security Cards, STRIDE (Spoofing identity, Tampering with... more In FY 2016, the research team evaluated Security Cards, STRIDE (Spoofing identity, Tampering with data, Repudiation, Information disclosure, Denial of service, Elevation of privilege), and persona non grata (PnG) for effectiveness in threat identification. Security Cards is an approach that emphasizes creativity and brainstorming over more structured approaches such as checklists. STRIDE involves modeling a system and subsystem and related data flows. PnGs represent archetypal users who behave in unwanted, possibly nefarious ways. The team used two scenarios: an aircraft maintenance scenario and a drone swarm scenario, both described in this technical note in detail, along with the project outcomes. No individual threat modeling method included all identified threats. The research team subsequently developed the Hybrid Threat Modeling Method (hTMM), considering the desirable characteristics for a Threat Modeling Method. At a high level, the hTMM includes the following steps, describ...

Neuromorphic architectures enable machine learning on a faster timescale compared to conventional... more Neuromorphic architectures enable machine learning on a faster timescale compared to conventional processors and require encoding of spike trains from images for computer vision applications. We report low-exposure image representation algorithms that can generate multiple short-exposure frames from a given long exposure image. The frame deconvolution is non-linear in the sense that the difference between adjacent short-exposure frames change with exposure time, however the frames have a structural representation of the original image such that the image reconstructed from these frames has a Peak Signal-to-Noise Ratio (PSNR) of over 300 and a Structural Similarity Index Metric (SSIM) close to unity. We show that the low-exposure frames generated by our algorithms enable feature extraction for machine learning or deep learning, e. g., classification using convolutional neural networks. The validation accuracy for classification depends on the range of the random subtraction parameter...

$Research paper thumbnail of Spin and orbital magnetic moments of Fe and Co in Co/Fe and Fe/Co multilayers on Si from L$_{\mathrm{2,3}}$ edge X-ray Magnetic Circular Dichroism Spectroscopy$

Bulletin of the American Physical Society, 2016

Stephen Adams, University of Virginia Bryan Carter, University of Virginia Krishnamurthy Vemuru, ... more Stephen Adams, University of Virginia Bryan Carter, University of Virginia Krishnamurthy Vemuru, University of Virginia Carl Elks, Virginia Commonwealth University Tim Bakker, Virginia Commonwealth University Kryzsztof Cios, Virginia Commonwealth University Georgios Bakirtzis, Virginia Commonwealth University Aidan Collins, Virginia Commonwealth University Nancy Mead, Carnegie Mellon University Forrest Shull, Carnegie Mellon University

International Conference on Neuromorphic Systems 2021, 2021

Algorithms, 2020

We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired n... more We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower...

Physical Review Letters, 2002

ABSTRACT We report muon spin relaxation (μ+SR) studies on the magnetic phase diagram of Ce(Cu1-xN... more ABSTRACT We report muon spin relaxation (μ+SR) studies on the magnetic phase diagram of Ce(Cu1-xNix)2Ge2 polycrystals for 0.5≤ x ≤ 0.8. A sharp magnetic transition, evidenced by the appearance of a fast Gaussian relaxation component σ, has been observed in the x = 0.5 alloy at 4.0 K in zero applied field. The average local field < Bμ> at the stopping sites of the muons, extracted from σ, exhibits a linear temperature dependence. We associate these features with an incommensurate spin density wave (SDW) ordering. Magnetic ordering, either long range or short range, and signatures of non-Fermi liquid behaviour have not been observed down to 2.0 K at x = 0.8.

The magnetic behavior of Eu and Fe in the filled skutterudite ferromagnet EuFe4Sb12 has been inve... more The magnetic behavior of Eu and Fe in the filled skutterudite ferromagnet EuFe4Sb12 has been investigated using Eu L2,3 edge and Fe K edge x-ray magnetic circular dichroism (XMCD) spectroscopy. Eu L3 edge x-ray absorption spectra (XAS) in EuFe4Sb12 clearly show that Eu is in a mixed valence state with about 15% non-magnetic Eu^3+ states at 5 K. By comparing

$Research paper thumbnail of Magnetic order in the polar intermetallic CaMn2Sb2 studied via powder neutron diffraction$

Journal of Magnetism and Magnetic Materials, 2009

This paper reports a neutron powder diffraction study of CaMn2Sb2 in the temperature range of 20-... more This paper reports a neutron powder diffraction study of CaMn2Sb2 in the temperature range of 20-300 K. Short-range magnetic order, characterized by a broad diffraction peak corresponding to a d spacing of 4 , is observed between 120 and 200 K. Collinear long-range antiferromagnetic order of manganese ions occurs below 85 K, where a transition is observed in the DC

Physical Review Letters - PHYS REV LETT, 2007

We combine x-ray magnetic circular dichroism spectroscopy at Fe L{sub 2,3} edges, at Eu M{sub 4,5... more We combine x-ray magnetic circular dichroism spectroscopy at Fe L{sub 2,3} edges, at Eu M{sub 4,5} edges, x-ray absorption spectroscopy (XAS) investigation of Eu valence, and local spin density calculations, to show that the filled skutterudite Eu{sub 0.95}FeSb is a ferrimagnet in which the Fe 3d moment and the Eu{sup 2+} 4f moment are magnetically ordered with dominant antiferromagnetic coupling. From Eu L edge XAS, we find that about 13% of the Eu have a formal valence of 3{sup +}. We ascribe the origin of ferrimagnetism at a relatively high transition temperature T{sub c} of 85 K in Eu{sub 0.95}FeSb to f-electron interaction with the nearly ferromagnetic [FeSb]{sup 2.2-} host lattice.