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Probing the chiral magnetic wave in pPb and PbPb collisions at root S-NN=5.02 TeV using charge-dependent azimuthal anisotropies
No full textCharge-dependent anisotropy Fourier coefficients (v(n)) of particle azimuthal distributions are measured in pPb and PbPb collisions at root S-NN = 5.02 TeV with the CMS detector at the LHC. The normalized difference in the second-order anisotropy coefficients (v(2)) between positively and negatively charged particles is found to depend linearly on the observed event charge asymmetry with comparable slopes for both pPb and PbPb collisions over a wide range of charged particle multiplicity. In PbPb, the third-order anisotropy coefficient v(3) shows a similar linear dependence with the same slope as seen for v(2). The observed similarities between the v(2) slopes for pPb and PbPb, as well as the similar slopes for v(2) and v(3) in PbPb, are compatible with expectations based on local charge conservation in the decay of clusters or resonances, and constitute a challenge to the hypothesis that, at LHC energies, the observed charge asymmetry dependence of v(2) in heavy ion collisions arises from a chiral magnetic wave
Planted models for k-way edge and vertex expansion
No full textGraph partitioning problems are a central topic of study in algorithms and complexity theory. Edge expansion and vertex expansion, two popular graph partitioning objectives, seek a 2-partition of the vertex set of the graph that minimizes the considered objective. However, for many natural applications, one might require a graph to be partitioned into k parts, for some k > 2. For a k-partition S1,..., Sk of the vertex set of a graph G = (V, E), the k-way edge expansion (resp. vertex expansion) of S1,..., Sk is defined as maxi�k Φ(Si), and the balanced k-way edge expansion (resp. vertex expansion) of G is defined as min {S1,...,Sk}�Pk max i�k Φ(Si) , where Pk is the set of all balanced k-partitions of V (i.e each part of a k-partition in Pk should have cardinality |V |/k), and Φ(S) denotes the edge expansion (resp. vertex expansion) of S � V. We study a natural planted model for graphs where the vertex set of a graph has a k-partition S1,..., Sk such that the graph induced on each Si has large expansion, but each Si has small edge expansion (resp. vertex expansion) in the graph. We give bi-criteria approximation algorithms for computing the balanced k-way edge expansion (resp. vertex expansion) of instances in this planted model
Computational Modelling-Based Device Design for Improved mmWave Performance and Linearity of GaN HEMTs
No full textIn this work, a comprehensive, TCAD based design approach for mmWave (mmW) GaN HEMTs is presented. Unique trade-offs between epi-layer design and HEMT's mmW performance are discussed. Effect of surface states on cut off frequency is modeled and presented. We have found that carrier trapping by the donor type interface states causes RF performance drift at high drain fields, which particularly leads to the non-linear behavior of mmW HEMTs at high drain bias. Moreover, we have observed that channel electrostatics, barrier layer, and UID GaN channel design govern the linearity and scaling behavior of such GaN HEMTs. To improve channel electrostatics, which improves the linearity and cut-off frequency, a partially recessed barrier under the gate is studied. A relative study of AlN/GaN HEMT and AlGaN/GaN HEMTs is performed to investigate the nonlinearity behavior. In addition, the dependence of cut-off frequency on contact resistance and lateral scaling is studied for partially-recessed barrier and conventional design for both AlN and AlGaN barrier types. The mmW performance is found to be a strong function of barrier design in the gate and recess regions. Unique design trends and physical behavior was observed for AlN and AlGaN barriers, which signifies that design guidelines derived for one epi-stack can't be deployed to the other
Partial informational correlation-based band selection for hyperspectral image classification
No full textHyperspectral (HS) data are enriched with highly resourceful abundant spectral bands. However, analyzing and interpreting these ample amounts of data is a challenging task. Optimal spectral bands should be chosen to address the issue of redundancy and to capitalize on the absolute advantages of HS data. Partial informational correlation (PIC)-based band selection approach is proposed for feature selection-based classification of HS images. PIC measure appears to be more skillful compared to mutual information for estimation of nonparametric conditional dependency. In this proposed approach, HS narrow bands are selected in an innovative way utilizing the PIC. This approach is more efficient in terms of computational time and in generalizing the applicability of selected spectral bands. Further, these optimal spectral bands are used in the support vector machine (SVM) and random forest classifier for performance evaluation. The optimum performance is accomplished with SVM classifier, and the achieved average overall accuracies are 82.89, 91.4, and 91.29 for the Indian Pines, Pavia University, and Botswana datasets, respectively. The proposed band selection approach is compared with different state-of-the-art techniques. This methodology improves the classification performances compared to the existing techniques, and the advancement in performances is proven to be statistically significant
Performance Analysis of Optical MEMS Based Pressure Sensor Using Ring Resonators Structure on Circular Diaphragm
No full textProposed work here consists of Micro Opto Electro Mechanical System based pressure sensor integrated with ring resonators. Single, double and triple ring resonators are integrated with photonic crystal sensing layer have been investigated for different sensitivity and minimum detectability by coupling FEA modelling with optical system. Photonic crystal sensing layer is modelled using Ansys Multiphysics for modelling and analysis. For applied pressure in the range of 1Mpa to 6Mpa, deformation and Maximum stress developed in different direction is calculated. Influence of mechanical deformation and stress developed during application of pressure for peak resonance wavelength shift is explored for each combination of ring resonators. Minimum observable deformation of 1.0073μ \mathrmm for single ring resonator at pressure of 6.3μ \mathrmPa. Deformation of 1.051μ \mathrmm for double ring resonator at pressure of 6.5\ μ \mathrmPa and 1.009\ μ \mathrmm deformation for triple ring resonator at pressure of 6.9\ μ \mathrmPa is detected. Maximum sensitivity of 450nm/RIU and Quality factor of 12,245 is observed for single ring resonator. Proposed type of sensor investigation having remarkable application in biomedical instruments with appropriate design
A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing on Pre-Beamformed RF Signals
No full textRecent advances in the unsupervised and generative models of deep learning have shown promise for application in biomedical signal processing. In this work, we present a portable resource-constrained ultrasound (US) system trained using Variational Autoencoder (VAE) network which performs compressive-sensing on pre-beamformed RF signals. The encoder network compresses the RF data, which is further transmitted to the cloud. At the cloud, the decoder reconstructs back the ultrasound image, which can be used for inferencing. The compression is done with an undersampling ratio of 1/2, 1/3, 1/5 and 1/10 without significant loss of the resolution. We also compared the model by state-of-the-art compressive-sensing reconstruction algorithm and it shows significant improvement in terms of PSNR and MSE. The innovation in this approach resides in training with binary weights at the encoder, shows its feasibility for the hardware implementation at the edge. In the future, we plan to include our field-programmable gate array (FPGA) based design directly interfaced with sensors for real-time analysis of Ultrasound images during medical procedures
Trend Statistics Network and Channel invariant EEG Network for sleep arousal study
No full textSleep is a very important part of life. Lack of sleep or sleep disorder can cause a negative impact on day to day life and can have long term serious consequences. In this work, we propose an end-to-end trainable neural network for automated sleep arousal scoring. The network consists of two main parts. Firstly, a trend statistics network computes the moving average of the filtered signals at different scales. Secondly, we propose a channel invariant EEG network to detect the arousals in any Electroencephalography (EEG) channel. Finally, we combine the features from various channels through a convolution network and a bi-directional long short-term memory to predict the probability of arousal. Further, we propose an objective function that uses only respiratory effort related arousal (RERA) and non-arousal regions to optimize the network. We also propose a method to estimate the respiratory disturbance index (RDI) from the probability predicted by the network. Evaluation on Physionet Challenge 2018 database shows that the proposed method detects RERA with mean area under the precision-recall curve (AUPRC) of 0.50 in a 10-fold cross validation setup. The mean absolute error of RDI prediction is 6.11, while a two-class RDI severity prediction yields a specificity of 75 and a sensitivity of 83
Effect of hole doping on the structure and magnetic properties of hexagonal Zr0.2Lu0.8FeO3 ceramics
No full textZr4+ doped multiferroic LuFeO3 (LFO) samples were prepared by a sol-gel reaction method and the effect of doping on structural, electric and magnetic properties are evaluated and compared with pure LFO. X-ray diffraction and Rietveld refinement confirm that the pure and Zr doped LFO exists in the hexagonal crystal structure with space group P63cm. The unit cell parameters and the volume of Zr0.2Lu0.8FeO3 (ZLFO) are greater than that of the undoped LFO. Magnetic measurements for ZLFO sample show a weak ferromagnetic-like behaviour at room temperature with M r � 0.013 emu/g and finite coercivity. Hence, the paramagnetic LFO transformed to the weak ferromagnetic at room temperature upon Zr doping
A Novel Technique to Investigate the Role of Traps in the Off-State Performance of AlGaN/GaN High Electron Mobility Transistor on Si Using Substrate Bias
No full textLeakage mediated by GaN buffer traps is identified and studied using a novel characterization technique. Through back-gating measurement, the effect of buffer trap states on the lateral leakage is determined by probing mesa-isolated Ohmic pads. Time-dependent leakage measurements are carried out to study the extent of the increase in buffer leakage due to the traps. It is observed that the mesa leakage is more prominent at very slow sweep rates and high substrate bias. The temperature-dependent measurements show that the mesa leakage and the substrate leakage are characterized by thermionic emission from the traps with an activation barrier of 0.34 and 0.2 eV, respectively
Cone snail prolyl-4-hydroxylase alpha-subunit sequences derived from transcriptomic data and mass spectrometric analysis of variable proline hydroxylation in C. amadis venom
No full textPutative prolyl-4-hydroxylase (P4H) alpha-subunit sequences have been extracted by mining transcriptomic data obtained from seven cone snail species C. amadis, C. monile, C. araneosus, C. miles, C. litteratus, C. frigidus, and C. ebraeus. Sequences ranging from 518 to 559 residues have been compared with representative animal P4H sequences. The alpha-subunitconsists of an N-terminus double domain, involved in dimerization and substrate binding, while the C-terminus contains the catalytic domain. Definitive functional annotation of the cone snail sequences has been achieved by an analysis of conserved residues responsible for catalytic function, specific conformational features, and subunit interactions, using two independent structures of the double domain, and the catalytic domain, previously reported in the literature. The variability of proline hydroxylation in conotoxins is illustrated by a mass spectrometric analysis of C. amadis venom. Site specific hydroxylation and the presence of peptides with multiple proline residues, resistant to modification, suggests that sequence and conformational effects may determine the substrate specificity of the Conus prolyl-4-hydroxylases. Significance: Proline hydroxylation is a widely observed post translational modification, with collagen being the pre-eminent example. Hydroxylation of proline is also widely observed in conotoxins, which are a major component of marine cone snail venom. This paper describes newly identified prolyl-4-hydroxylase sequences, using transcriptome data from seven Corms species. The predicted functional annotation of prolyl-4-hydroxylase sequences was carried out using two available crystal structures of independent domains. The mass spectrometric characterisation of proline/hydroxyproline containing peptides in C. amadis venom confirms sequence specific hydroxylation in Conus venom as shown previously by others