scholarly journals Phaseless Gauss-Newton Inversion for Microwave Imaging

2020 ◽  
Author(s):  
Chaitanya Narendra ◽  
Puyan Mojabi
Keyword(s):  
Field Data ◽  
Prior Information ◽  
Structural Information ◽  
Microwave Imaging ◽  
The Other ◽  
Total Field ◽  
Field Phase ◽  
Full Data ◽  
L2 Norm ◽  
Phase Data

<p>A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.</p>

scholarly journals Phaseless Gauss-Newton Inversion for Microwave Imaging

2020 ◽  
Author(s):  
Chaitanya Narendra ◽  
Puyan Mojabi
Keyword(s):  
Field Data ◽  
Prior Information ◽  
Structural Information ◽  
Microwave Imaging ◽  
The Other ◽  
Total Field ◽  
Field Phase ◽  
Full Data ◽  
L2 Norm ◽  
Phase Data

<p>A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.</p>

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

ABSENCE OF PHASE TRANSITIONS ON TREE STRUCTURES

1993 ◽  
Vol 07 (29n30) ◽  
pp. 1947-1950 ◽  
Author(s):  
RAFFAELLA BURIONI ◽  
DAVIDE CASSI
Keyword(s):  
Long Range ◽  
Nearest Neighbor ◽  
Linear Chain ◽  
Mean Field ◽  
Range Order ◽  
The Other ◽  
Long Range Order ◽  
Field Phase ◽  
Tree Structures ◽  
Bethe Lattices

We rigorously prove that the correlation functions of any statistical model having a compact transitive symmetry group and nearest-neighbor interactions on any tree structure are equal to the corresponding ones on a linear chain. The exponential decay of the latter implies the absence of long-range order on any tree. On the other hand, for trees with exponential growth such as Bethe lattices, one can show the existence of a particular kind of mean field phase transition without long-range order.

scholarly journals Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

2020 ◽  
Vol 34 (04) ◽  
pp. 6704-6711
Author(s):  
Zheng Yu ◽  
Xuhui Fan ◽  
Marcin Pietrasik ◽  
Marek Z. Reformat
Keyword(s):  
Prior Information ◽  
Structural Information ◽  
Real World Data ◽  
Posterior Inference ◽  
Current Formulation ◽  
Proposed Model ◽  
Community Evolution ◽  
Stochastic Blockmodel ◽  
Community Information ◽  
Group Information

The Mixed-Membership Stochastic Blockmodel (MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlying the network data. However, the current formulation of MMSB suffers from the following two issues: (1), the prior information (e.g. entities' community structural information) can not be well embedded in the modelling; (2), community evolution can not be well described in the literature. Therefore, we propose a non-parametric fragmentation coagulation based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs entity-based clustering to capture the community information for entities and linkage-based clustering to derive the group information for links simultaneously. Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP). By integrating the community structure with the group compatibility matrix we derive a generalized version of MMSB. An efficient Gibbs sampling scheme with Polya Gamma (PG) approach is implemented for posterior inference. We validate our model on synthetic and real world data.

scholarly journals Integrative pharmacogenomics to infer large-scale drug taxonomy

2016 ◽  
Author(s):  
Nehme El-Hachem ◽  
Deena M.A. Gendoo ◽  
Laleh Soltan Ghoraie ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Prior Information ◽  
Structural Information ◽  
Drug Efficacy ◽  
Basic Drug ◽  
Flexible Tool ◽  
Experimental Drugs ◽  
New Compounds ◽  
Novel Drug

ABSTRACTIdentification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterlzed small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose a new integrative computational pharmacogenomlc approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. We demonstrate that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. We highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposlng.

scholarly journals Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

2020 ◽  
Vol 6 (10) ◽  
pp. 103
Author(s):  
Ali S. Awad
Keyword(s):  
Gaussian Noise ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
The Other ◽  
Noisy Image ◽  
Simulation Experiments ◽  
Two Dimensional Image ◽  
Pixel Location ◽  
Image Pixels

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

Response of Soybean (Glycine max) Cultivars to Metribuzin in the Field and Greenhouse

1989 ◽  
Vol 3 (4) ◽  
pp. 566-572 ◽  
Author(s):  
W. W. De Weese ◽  
L. M. Wax ◽  
W. C. Carlson ◽  
J. A. Ciarletta
Keyword(s):  
Glycine Max ◽  
Correlation Coefficient ◽  
Field Data ◽  
The Other ◽  
Screening Procedure ◽  
Soybean Cultivars ◽  
Greenhouse Screening ◽  
Significant Injury ◽  
Good Agreement

Experimental objectives were to assess metribuzin tolerance of predominately privately developed soybean cultivars and to evaluate a greenhouse screening procedure. In field results in 1982, ‘Vinton 81’, ‘Northrup King 1884’ and ‘L77-1863’ soybean cultivars were extremely sensitive to metribuzin at 0.56 kg/ha, averaging 34% injury. The other 45 cultivars showed no significant injury at the 0.56 kg/ha rate, from 9 to 46% injury at 1.4 kg/ha, and 18 to 72% injury at 2.2 kg/ha. In greenhouse hydroponic studies, these same three soybean cultivars were killed, while the other 45 cultivars were injured from 15 to 82%. A good agreement of greenhouse and field data was determined, with a correlation coefficient of r = 0.82.

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