Effects of refractive index mismatch in optical CT imaging of polymer gel dosimeters

Purpose: Proposing an image reconstruction technique, algebraic reconstruction technique-refraction correction (ART-rc). The proposed method takes care of refractive index mismatches present in gel dosimeter scanner at the boundary, and also corrects for the interior ray refraction. Polymer gel dosimeters with high dose regions have higher refractive index and optical density compared to the background medium, these changes in refractive index at high dose results in interior ray bending. Methods: The inclusion of the effects of refraction is an important step in reconstruction of optical density in gel dosimeters. The proposed ray tracing algorithm models the interior multiple refraction at the inhomogeneities. Jacob's ray tracing algorithm has been modified to calculate the pathlengths of the ray that traverses through the higher dose regions. The algorithm computes the length of the ray in each pixel along its path and is used as the weight matrix. Algebraic reconstruction technique and pixel based reconstruction algorithms are used for solving the reconstruction problem. The proposed method is tested with numerical phantoms for various noise levels. The experimental dosimetric results are also presented. Results: The results show that the proposed scheme ART-rc is able to reconstruct optical density inside the dosimeter better than the results obtained using filtered backprojection and conventional algebraic reconstruction approaches. The quantitative improvement using ART-rc is evaluated using gamma-index. The refraction errors due to regions of different refractive indices are discussed. The effects of modeling of interior refraction in the dose region are presented. Conclusions: The errors propagated due to multiple refraction effects have been modeled and the improvements in reconstruction using proposed model is presented. The refractive index of the dosimeter has a mismatch with the surrounding medium (for dry air or water scanning). The algorithm reconstructs the dose profiles by estimating refractive indices of multiple inhomogeneities having different refractive indices and optical densities embedded in the dosimeter. This is achieved by tracking the path of the ray that traverses through the dosimeter. Extensive simulation studies have been carried out and results are found to be matching that of experimental results. (C) 2015 American Association of Physicists in Medicine.

Domain size correlated magnetic properties and electrical impedance of size dependent nickel ferrite nanoparticles

We report here the investigations on the size dependent variation of magnetic properties of nickel ferrite nanoparticles. Nickel ferrite nanoparticles of different sizes (14 to 22 nm) were prepared by the sol-gel route at different annealing temperatures. They are characterized by TGA-DTA, XRD, SEM, TEM and Raman spectroscopy techniques for the confirmation of the temperature of phase formation, thermal stability, crystallinity, morphology and structural status of the nickel ferrite nanoparticles. The magnetization studies revealed that the saturation magnetization (M-s), retentivity (M-r) increase, while coercivity (H-c) and anisotropy (K-eff) decrease as the particle size increases. The observed value of M-s is found to be relatively higher for a particle size of 22 nm. In addition, we have estimated the magnetic domain size using magnetic data and correlated to the average particle size. The calculated magnetic domain size is closely matching with the particle size estimated from XRD. Impedance spectroscopy was employed to study the samples in an equivalent circuit to understand their transport phenomena. It shows that nickel ferrite nanoparticles exhibit a non-Debye behavior with increasing particle size due to the influence of increasing disorders, surface effects, grain size and grain boundaries, etc. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

Belief propagation for minimum weight many-to-one matchings in the random complete graph

In a complete bipartite graph with vertex sets of cardinalities n and n', assign random weights from exponential distribution with mean 1, independently to each edge. We show that, as n -> infinity, with n' = n/alpha] for any fixed alpha > 1, the minimum weight of many-to-one matchings converges to a constant (depending on alpha). Many-to-one matching arises as an optimization step in an algorithm for genome sequencing and as a measure of distance between finite sets. We prove that a belief propagation (BP) algorithm converges asymptotically to the optimal solution. We use the objective method of Aldous to prove our results. We build on previous works on minimum weight matching and minimum weight edge cover problems to extend the objective method and to further the applicability of belief propagation to random combinatorial optimization problems.

Super Resolution Using a Single Image Dictionary

To perform super resolution of low resolution images, state-of-the-art methods are based on learning a pair of lowresolution and high-resolution dictionaries from multiple images. These trained dictionaries are used to replace patches in lowresolution image with appropriate matching patches from the high-resolution dictionary. In this paper we propose using a single common image as dictionary, in conjunction with approximate nearest neighbour fields (ANNF) to perform super resolution (SR). By using a common source image, we are able to bypass the learning phase and also able to reduce the dictionary from a collection of hundreds of images to a single image. By adapting recent developments in ANNF computation, to suit super-resolution, we are able to perform much faster and accurate SR than existing techniques. To establish this claim, we compare the proposed algorithm against various state-of-the-art algorithms, and show that we are able to achieve b etter and faster reconstruction without any training.

Fabrication of novel Ag3PO4/BiOBr heterojunction with high stability and enhanced visible-light-driven photocatalytic activity

Herein, we report a facile and effective method to enhance the photocatalytic activity of bismuth oxybromide (BiOBr) semiconductor through the fabrication of heterojunction with Ag3PO4. The as synthesized Ag3PO4/BiOBr microspheres were characterized with transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD) and UV-vis diffuse reflectance spectroscopy (DRS). The new Ag3PO4/BiOBr heterojunctions exhibited wide absorption in the visible-light region and compared to pure BiOBr and Ag3PO4 samples displayed exceptionally high photocatalytic activity for the degradation of typical organic pollutants such as Rhodamine B (RhB) and phenol. The optimal Ag/Bi weight ratio in Ag3PO4/BiOBr microsphere (AB7) was found to be 0.7. The enhanced photocatalytic activity was related to the efficient separation of electron-hole pairs derived from matching band potentials between BiOBr and Ag3PO4 which results into the generation of natural energy bias at heterojunction and subsequent transfer of photoinduced charge carriers. Moreover, the synthesized samples exhibited almost no loss of activity even after 6 recycling runs indicating their high photocatalytic stability. Considering the facile and environment friendly route for the synthesis of Ag3PO4/BiOBr hybrids with enhanced visible-light induced photocatalytic activity, it is possible to widely apply these hybrids in various fields such as waste water treatment. (C) 2015 Elsevier B.V. All rights reserved.

Rainbow matchings in strongly edge-colored graphs

A rainbow matching of an edge-colored graph G is a matching in which no two edges have the same color. There have been several studies regarding the maximum size of a rainbow matching in a properly edge-colored graph G in terms of its minimum degree 3(G). Wang (2011) asked whether there exists a function f such that a properly edge-colored graph G with at least f (delta(G)) vertices is guaranteed to contain a rainbow matching of size delta(G). This was answered in the affirmative later: the best currently known function Lo and Tan (2014) is f(k) = 4k - 4, for k >= 4 and f (k) = 4k - 3, for k <= 3. Afterwards, the research was focused on finding lower bounds for the size of maximum rainbow matchings in properly edge-colored graphs with fewer than 4 delta(G) - 4 vertices. Strong edge-coloring of a graph G is a restriction of proper edge-coloring where every color class is required to be an induced matching, instead of just being a matching. In this paper, we give lower bounds for the size of a maximum rainbow matching in a strongly edge-colored graph Gin terms of delta(G). We show that for a strongly edge-colored graph G, if |V(G)| >= 2 |3 delta(G)/4|, then G has a rainbow matching of size |3 delta(G)/4|, and if |V(G)| < 2 |3 delta(G)/4|, then G has a rainbow matching of size |V(G)|/2] In addition, we prove that if G is a strongly edge-colored graph that is triangle-free, then it contains a rainbow matching of size at least delta(G). (C) 2015 Elsevier B.V. All rights reserved.

Sensing of Stimulus Artifact Suppressed Signals From Electrode Interfaces

Stimulus artifacts inhibit reliable acquisition of biological evoked potentials for several milliseconds if an electrode contact is utilized for both electrical stimulation and recording purposes. This hinders the measurement of evoked short-latency biological responses, which is otherwise elicited by stimulation in implantable prosthetic devices. We present an improved stimulus artifact suppression scheme using two electrode simultaneous stimulation and differential readout using high-gain amplifiers. Substantial reduction of artifact duration has been shown possible through the common-mode rejection property of an instrumentation amplifier for electrode interfaces. The performance of this method depends on good matching of electrode-electrolyte interface properties of the chosen electrode pair. A novel calibration algorithm has been developed that helps in artificial matching of impedance and thereby achieves the required performance in artifact suppression. Stimulus artifact duration has been reduced down to 50 mu s from the stimulation-cum-recording electrodes, which is similar to 6x improvement over the present state of the art. The system is characterized with emulated resistor-capacitor loads and a variety of in-vitro metal electrodes dipped in saline environment. The proposed method is going to be useful for closed-loop electrical stimulation and recording studies, such as bidirectional neural prosthesis of retina, cochlea, brain, and spinal cord.

Modified Greedy Pursuits for Improving Sparse Recovery

Compressive Sensing (CS) theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate. In recent years, many recovery algorithms have been proposed to reconstruct the signal efficiently. Subspace Pursuit and Compressive Sampling Matching Pursuit are some of the popular greedy methods. Also, Fusion of Algorithms for Compressed Sensing is a recently proposed method where several CS reconstruction algorithms participate and the final estimate of the underlying sparse signal is determined by fusing the estimates obtained from the participating algorithms. All these methods involve solving a least squares problem which may be ill-conditioned, especially in the low dimension measurement regime. In this paper, we propose a step prior to least squares to ensure the well-conditioning of the least squares problem. Using Monte Carlo simulations, we show that in low dimension measurement scenario, this modification improves the reconstruction capability of the algorithm in clean as well as noisy measurement cases.

India-Madagascar paleo-fit based on flexural isostasy of their rifted margins

The present study contributes new constraints on, and definitions of, the reconstructed plate margins of India and Madagascar based on flexural isostasy along the Western Continental Margin of India (WCMI) and the Eastern Continental Margin of Madagascar (ECMM). We have estimated the nature of isostasy and crustal geometry along the two margins, and have examined their possible conjugate structure. Here we utilize elastic thickness (Te) and Moho depth data as the primary basis for the correlation of these passive margins. We employ the flexure inversion technique that operates in spatial domain in order to estimate the spatial variation of effective elastic thickness. Gravity inversion and flexure inversion techniques are used to estimate the configuration of the Moho/Crust-Mantle Interface that reveals regional correlations with the elastic thickness variations. These results correlate well with the continental and oceanic segments of the Indian and African plates. The present study has found a linear zone of anomalously low-Te (1-5 km) along the WCMI (similar to 1680 km), which correlates well with the low-Te patterns obtained all along the ECMM. We suggest that the low-Te zones along the WCMI and ECMM represent paleo-rift inception points of lithosphere thermally and mechanically weakened by the combined effects of the Marion hotspot and lithospheric extension due to rifting. We have produced an India-Madagascar paleo-fit representing the initial phase of separation based on the Te estimates of the rifted conjugate margins, which is confirmed by a close-fit correlation of Moho geometry and bathymetry of the shelf margins. The matching of tectonic lineaments, lithologies and geochronological belts between India and Madagascar provide an additional support for the present plate reconstruction. (C) 2014 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Synthetic image super resolution using FeatureMatch

In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

Part II: A Fully Integrated RF PA in 28-nm CMOS With Device Design for Optimized Performance and ESD Robustness

In this paper, we report drain-extended MOS device design guidelines for the RF power amplifier (RF PA) applications. A complete RF PA circuit in a 28-nm CMOS technology node with the matching and biasing network is used as a test vehicle to validate the RF performance improvement by a systematic device design. A complete RF PA with 0.16-W/mm power density is reported experimentally. By simultaneous improvement of device-circuit performance, 45% improvement in the circuit RF power gain, 25% improvement in the power-added efficiency at 1-GHz frequency, and 5x improvement in the electrostatic discharge robustness are reported experimentally.

Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning

Despite significant advances in recent years, structure-from-motion (SfM) pipelines suffer from two important drawbacks. Apart from requiring significant computational power to solve the large-scale computations involved, such pipelines sometimes fail to correctly reconstruct when the accumulated error in incremental reconstruction is large or when the number of 3D to 2D correspondences are insufficient. In this paper we present a novel approach to mitigate the above-mentioned drawbacks. Using an image match graph based on matching features we partition the image data set into smaller sets or components which are reconstructed independently. Following such reconstructions we utilise the available epipolar relationships that connect images across components to correctly align the individual reconstructions in a global frame of reference. This results in both a significant speed up of at least one order of magnitude and also mitigates the problems of reconstruction failures with a marginal loss in accuracy. The effectiveness of our approach is demonstrated on some large-scale real world data sets.

Interrelationship between Interphase Boundaries and Phase Contents near the Critical Compositions of Lead-Free Ferroelectric (Na0.5Bi0.5)TiO3-BaTiO3

Heterophase structures in lead-free perovskite-type ferroelectric solid solutions of (1 - z)(Na0.5Bi0.5)TiO3 - zBaTiO(3) are analysed for a few critical compositions near the morphotropic phase boundary (z = 0.05-0.07). Examples of the phase coexistence and elastic matching of the phases from different symmetry groups are considered to find optimum volume fractions of specific domain types and coexisting phases at the complete stress relief in two-phase samples. Some interrelations between these volume fractions are described using variants of the domain arrangement at changes in the composition and unit-cell parameters. The evaluated room-temperature volume fractions of the ferroelectric monoclinic (Cm symmetry) and tetragonal (P4mm symmetry) phases near the morphotropic phase boundary are in agreement with experimental data.