Different Number Of Sources Research Articles

3-D gamma radiation field reconstruction method using limited measurements for multiple radioactive sources

The reconstruction of 3-D gamma radiation field is important for radiation protection in the presence of unknown radiation sources and limited measurements, such as the decontamination and decommissioning of nuclear facilities. While there may be multiple radioactive sources in these applications, the current 3-D reconstruction methods do not take the multiple radiation sources into consideration. This paper develops a method that has the potential to reconstruct the 3-D gamma radiation field of multiple radioactive sources with limited measurements of the field. This method solves a 3-D fourth-order TV-H-1 equation to promote the continuity of the reconstructed 3-D radiation field, which simultaneously fills the missing dose rates and recovers local details near the radioactive sources. The proposed method was verified against three Monte Carlo simulations with different numbers of sources and validated against a 1-D experiment. The method was tested with different numbers of combinations of the input data, including both randomly and regularly distributed data sets. The results reveal that the method accurately reconstructs the 3-D radiation field and achieves a relative error level as low as 2.18%, using only 3.125% of the data for the simulation cases. The 1-D experiment shows a relative error range between 5.57% and 65.28%. These results preliminary demonstrate the potential of the proposed method for 3-D radiation field reconstruction in practice.

3D marine controlled-source electromagnetic modeling using an edge-based finite element method with a block Krylov iterative solver

Abstract An edge-based finite element method for the numerical modeling of electromagnetic fields in complex media is presented. We used the analytical solution on an electric field in a homogeneous half space to develop a source correct factor to reduce the influence of source singularity and boundary conditions on the numerical accuracy, so that we can minimize the time required to construct the field source term in the scattered field formula. The modeling domain was discretized using an unstructured tetrahedral mesh. We transformed the complex equations of the electrical field into two real-valued equations by decomposing the field into real and imaginary components. Thereafter, we adopted a block conjugate orthogonal conjugate gradient (BL_COCR) iterative solver with an incomplete LU decomposition preconditioner, which was robust for ill-conditioned systems and efficient for multiple source electromagnetic modeling to solve the real-valued equation systems. Using the analytical solution on an electric field in a homogeneous layer model, we evaluated the accuracy of our numerical forward solution and the results showed that the source correct factor can reduce forward modeling errors associated with boundary effects and source singularities. We also applied the developed algorithm to compute the CSEM responses for typical 3D marine geo-electric models with different number of sources and compared with different iterative solvers, and the results showed that the BL_COCR solver has high computational efficiency when solving multiple right-hand term problems.

A Gridless DOA Estimation Method Based on Convolutional Neural Network With Toeplitz Prior

Most existing deep learning (DL) based direction-of-arrival (DOA) estimation methods treat direction finding problem as a multi-label classification task and the output of the neural network is a probability spectrum where the peaks indicate the true DOAs. These methods essentially belong to grid-based methods and may encounter grid mismatch effect. In this paper, we focus on gridless DL based DOA estimation under generalized linear array which can be regarded as a uniform linear array (ULA) with/without “holes”. By using the Toeplitz structure, a deep convolutional neural network (CNN) is proposed to estimate the noiseless covariance matrix of the aforementioned ULA with “no holes,” based on which the DOAs can be retrieved by using root-MUSIC. To increase the generalization, the parameters of the CNN with respect to different number of sources are pre-trained and stored in a database. We then propose another CNN for source enumeration in order to choose suitable parameters from the database. Our method can find more sources than sensors and do not suffer from the grid mismatch effect.

Suppliers’ coalition strategy for green-Resilient supply chain network design

ABSTRACT Risk mitigation strategies have an environmental effect besides their economic benefits. The current research investigates designing a green-resilient supply chain network under the risk of disruptions. A new risk mitigation strategy namely “coalition” is applied between suppliers in a bi-objective mixed-integer linear programming model to minimize total cost and carbon emissions, simultaneously. Along with the coalition strategy between suppliers, the multi-sourcing strategy in the downstream supply chain is applied for retailers. The performance of the coalition strategy is compared with the three reputable risk mitigation strategies in both single-source and multi-source retailers. Results revealed that the average total cost and total carbon emissions of the green-resilient model have been reduced by 14% and 3.6% compared to the non-resilient model, respectively. Besides, multiple-sourcing for retailers has no improvement in the non-resilient model. On the contrary, the economic and environmental performance of the resilient model varies for the different number of sources.

Observational backreaction in discrete black holes lattice cosmological models

Applying the Sachs formalism, the optical properties encoded in the distance modulus are studied along curves exhibiting local rotational symmetry for some closed inhomogeneous cosmological models whose mass content is discretized by Schwarzschild-like sources. These models may challenge the concordance model in its use of the distance modulus data of type Ia supernovae, because they do not violate any energy condition. This result relies only on the symmetry properties considered, and not on the way in which the mass is discretized. The models with different number of sources are then compared among themselves and with a Friedmann–Lemaitre–Robertson–Walker model with the same total mass content by introducing a compactness parameter. The analysis shows that observational backreaction occurs because increasing the number of sources the features of a universe with a continuous matter distribution are not recovered. Our models are shown to exhibit a non-trivial relationship between kinematical, dynamical and observational backreactions, the kinematical one being asymptotically decreasing while the latter two are present. Furthermore, the electric part of the Weyl tensor contributes to the luminosity distance by affecting the evolution of the scale factor, while the magnetic part has an indirect role by affecting only the evolution of the former.

A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

ANy-way Independent Component Analysis.

Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require the same number of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions.Clinical Relevance-This establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.

Robust Source Counting and DOA Estimation Using Spatial Pseudo-Spectrum and Convolutional Neural Network

Many signal processing-based methods for sound source direction-of-arrival estimation produce a spatial pseudo-spectrum of which the local maxima strongly indicate the source directions. Due to different levels of noise, reverberation and different number of overlapping sources, the spatial pseudo-spectra are noisy even after smoothing. In addition, the number of sources is often unknown. As a result, selecting the peaks from these spectra is susceptible to error. Convolutional neural network has been successfully applied to many image processing problems in general and direction-of-arrival estimation in particular. In addition, deep learning-based methods for direction-of-arrival estimation show good generalization to different environments. We propose to use a 2D convolutional neural network with multi-task learning to robustly estimate the number of sources and the directions-of-arrival from short-time spatial pseudo-spectra, which have useful directional information from audio input signals. This approach reduces the tendency of the neural network to learn unwanted association between sound classes and directional information, and helps the network generalize to unseen sound classes. The simulation and experimental results show that the proposed methods outperform other directional-of-arrival estimation methods in different levels of noise and reverberation, and different number of sources.

A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array

In this paper, a multi sample compressive sensing (CS) technique is presented for the direction of arrival (DOA) estimation using sparse antenna array that has applications in several fields including radars and sonars. Two different types of sparse antenna arrays are considered. One is linear sparse array for DOA estimation in one dimension and other is L shaped sparse array for DOA estimation in two dimensions. To make the algorithm robust against impulsive and Gaussian noise, a preprocessing stage is introduced. First, in the preprocessing stage median difference correntropy is used that combines median difference and the generalized correntropy. This suppresses the amplitude of impulsive noise. Second, the strength of weighted moving average filter is exploited before applying the CS technique to make the algorithm more robust. In the CS techniques, the source energy is distributed among the adjacent grid due to grid mismatch. Therefore, a fitness function based on the difference of the source energy among the adjacent grid is introduced. This provides the best discretization value through iterative grid refinement for the grid. The effectiveness and robustness of the proposed method is verified through exhaustive simulations for different number of sources and noise scenarios using one dimensional and two-dimensional sparse array structures.

Perfect partial reconstructions for multiple simultaneous sources

ABSTRACTA major focus of research in the seismic industry of the past two decades has been the acquisition and subsequent separation of seismic data using multiple sources fired simultaneously. The recently introduced method of signal apparition provides a new take on the problem by replacing the random time‐shifts usually employed to encode the different sources by fully deterministic periodic time‐shifts. In this paper, we give a mathematical proof showing that the signal apparition method results in optimally large regions in the frequency–wavenumber space where exact separation of sources is achieved. These regions are diamond shaped and we prove that using any other method of source encoding results in strictly smaller regions of exact separation. The results are valid for arbitrary number of sources. Numerical examples for different number of sources (three, respectively, four sources) demonstrate the exact recovery of these diamond‐shaped regions. The implementation of the theoretical proofs in the field is illustrated by the results of a conducted field test.

A Jacobi Generalized Orthogonal Joint Diagonalization Algorithm for Joint Blind Source Separation

Joint blind source separation (J-BSS) has emerged as a data-driven technique for multi-set data fusion applications. In this paper, we propose a Jacobi generalized orthogonal joint diagonalization (GOJD) algorithm for J-BSS of multiset signals. By the use of second-order statistics, we can obtain multiple sets of auto-covariance and cross-covariance matrices from the multi-set signals, which together admit a GOJD formulation. For computing the GOJD, we propose a computationally efficient Jacobi algorithm, which uses a sequence of Givens rotations to simultaneously diagonalize the covariance matrices. In comparison with other GOJD algorithms, the proposed algorithm is shown to have fast convergence. Moreover, as the optimal Givens rotation matrix in each update is calculated in closed-form, this algorithm is computationally very efficient. In the application aspect, we have considered the scenario where different data sets in J-BSS may have different number of sources, among which there exist both similar components that are consistently present in multiple data sets, and diverse components that are uniquely present in each data set. We have shown how J-BSS based on the proposed GOJD algorithm can effectively extract both similar and diverse source components. Simulation results are given to show the nice performance of the proposed algorithm, with regards to both speed and accuracy, in comparison with other algorithms of similar type.

UAV-based multiple source localization and contour mapping of radiation fields

This paper proposes an efficient approach to the multiple source localization and contour mapping problem of radiation fields using Unmanned Aerial Vehicles (UAVs). A typical radiation field originating from a single hotspot can be generated by three spatial distributions of sources; scattered, clustered and biased. Of these, the clustered sources are relatively easy to localize, because the sources are located in a close proximity to the center of distribution. In other cases, it is not very straightforward, because, when multiple radiating sources generate a hotspot in a cumulative manner, sources do not coincide with the hotspot position. Regardless of our knowledge about the hotspot position, we attempt to solve the multiple radiation localization problem in two steps: the Region Of Interest (ROI) selection and the source localization. Existing algorithms eventually explore whole area, causing the problem of excessive use of UAV resources. We therefore propose a framework to reduce ROI in a radiation field that not only optimizes the resources but also increases the localization accuracy. For the source localization process, two different methods are employed interchangeably. Those methods are called the Hough Transform and the Variational Bayesian, adaptively selected with a switching technique and the overall performance is evaluated by balancing between the localization accuracy and the required exploration. In favor of the optimization, the prediction model defines the type of sources in a way that the adaptive switching methodology can converge to an optimal solution by selecting an appropriate method. Thus, the proposed framework enables the UAV to accurately localize the radiation sources in a fast manner. In order to verify the validity and the performance of the proposed strategies, we performed extensive numerical experiments with different numbers of sources and their positions. Our empirical results clearly show that the proposed approach outperforms existing individual approaches.

Certain investigations on power generation using repulsive magnets and new stepped DC coupled quasi Z-inverter

This paper presents a novel approach for power generation from rotating machines in textile mills using externally coupled repulsive magnets and a new stepped DC coupled Quasi Z-inverter. Power is obtained as a byproduct of the effective harnessing of rotational energy with the use of repulsive magnets. This derived power is stored in a battery arrangement and is retrieved, buck/boosted, and converted to multilevel AC voltage with the new stepped DC coupled quasi Z-inverter. The proposed inverter is powered by isolated voltage sources, uses fewer switches, and produces less distortion in the resulting multilevel voltage compared to its counterpart, and thus reduces the filter requirements. The derived AC power from the system can be used for light-load applications in textile mills, thus compensating for the energy demands. Simulation of the entire proposed setup is performed in MATLAB/Simulink and the results are presented. The repulsive force produced by the magnets is assessed with K & J Magnet software. To validate the simulation, experimentation is done using rotating machines available in the laboratory. The prototype model of the stepped DC coupled quasi Z-inverter is used to study the performance of the system and the results are evaluated. The optimum modulation index is identified for different numbers of sources at the input.

SU‐E‐T‐705: The Effects of Applicator Displacement on Dose Distribution around Cs‐137 Brachytherapy Sources

Purpose: The uncertainties in brachytherapy source localization can cause significant errors in dose estimation around the sources because of the high dose gradient in brachytherapy. The effects of applicator movement on the dose values in gynecological brachytherapy are investigated in this study. Methods: The MCNP4C Monte Carlo code was used for estimation of the effects of applicator movements on the dose distribution around Selectron Cs‐137 LDR source (Nucletron Trading BV, Netherlands). The simulations were done for vaginal applicator and tandem‐ovoid set. The applicator movements of 1 to 5 mm in both longitudinal and transverse planes and the applicator rotation of 1o to 5o were also simulated in both cases. The small spherical tally cells were used in dose estimation around LDR sources using *F8 tally. Results: For typical source configurations in vaginal and tandem‐ovoid applicator, an upward superior movement of 5 cm in vaginal applicator causes an error of 1.9% in dose of reference points, while 5 cm movement in transverse direction would led to an error of about 54.9%. In case of tandem‐ovoid set, those errors were 8.1 % and 27.6 % respectively. The uncertainties caused by 5o rotation of vaginal and tandem‐ovoid were 73.1% and 2.8% at reference points. The rotation angle of 1o caused a dose error of 11.9% and 1.2% at reference points for vaginal and tandem‐ovoid applicators. Conclusions: The results obtained for Cs‐137 source indicate that the displacement of the applicator in longitudinal direction has less pronounced effect on doses than in transverse. The difference in dose error values of vaginal and tandem‐ovoid is mainly due to the more complicated geometry of tandem‐ovoid set and the different number of sources inside the applicators.

HYBRID SCENARIO BASED PERFORMANCE ANALYSIS OF DSDV AND DSR

The area of mobile ad hoc networking has received considerable attention of the research community in recent years. These networks have gained immense popularity primarily due to their infrastructure-less mode of operation which makes them a suitable candidate for deployment in emergency scenarios like relief operation, battlefield etc., where either the pre-existing infrastructure is totally damaged or it is not possible to establish a new infrastructure quickly. However, MANETs are constrained due to the limited transmission range of the mobile nodes which reduces the total coverage area. Sometimes the infrastructure-less ad hoc network may be combined with a fixed network to form a hybrid network which can cover a wider area with the advantage of having less fixed infrastructure. In such a combined network, for transferring data, we need base stations which act as gateways between the wired and wireless domains. Due to the hybrid nature of these networks, routing is considered a challenging task. Several routing protocols have been proposed and tested under various traffic conditions. However, the simulations of such routing protocols usually do not consider the hybrid network scenario. In this work we have carried out a systematic performance study of the two prominent routing protocols: Destination Sequenced Distance Vector Routing (DSDV) and Dynamic Source Routing (DSR) protocols in the hybrid networking environment. We have analyzed the performance differentials on the basis of three metrics - packet delivery fraction, average end-to-end delay and normalized routing load under varying pause time with different number of sources using NS2 based simulation.

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

We address the inverse problem of source reconstruction for the difficult case of multiple sources when the number of sources is unknown a priori. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release time and duration) that characterize each source. A mapping (source–receptor relationship) that relates a multiple source distribution to the concentration measurements made by an array of detectors is formulated based on a forward-time Lagrangian stochastic model. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source–receptor relationship and realized in terms of a backward-time Lagrangian stochastic model, is described. An efficient computational algorithm based on a parallel tempered Metropolis-coupled reversible-jump Markov chain Monte Carlo (MCMC) method is formulated and implemented to draw samples from the posterior probability density function of the source parameters. This methodology allows the MCMC method to initiate jumps between the hypothesis spaces corresponding to different numbers of sources in the source distribution and, thereby, allows a sample from the full joint posterior distribution of the number of sources and the parameters for each source to be obtained. The proposed methodology for source reconstruction is tested using synthetic concentration data generated for cases involving two and three unknown sources.

On the Ablowitz–Ladik equations with self-consistent sources

Darboux transformations and explicit solutions to Ablowitz–Ladik (AL) equations with self-consistent sources (ALESCS) are studied. Based on the Darboux transformation (DT) for the AL problem, we construct three types of non-auto-Bäcklund transformations connected with AL systems with different numbers of sources. The degenerate cases of DT and their applications to the reduced systems of ALESCS, for instance, discrete nonlinear Schrödinger with self-consistent sources (D-NLSSCS) and discrete mKdV equation with self-consistent sources (D-mKdVSCS), are discussed. Many types of solutions of ALESCS, D-NLSSCS and D-mKdVSCS, including solitons, positons, negatons can be derived from DTs and their degenerate cases.

Health Risk Assessment Associated to Domestic Wood Burning Emissions

P-638 Introduction: Houses equipped with wood burning domestic heating systems are potential sources of pollutants such as polycyclic aromatic hydrocarbons (PAHs), particulate matter (PM), volatile organic compounds (VOCs), dioxins and furans (PCDD/Fs), heavy metals, nitrogen oxides (NOx). According to previous investigations, wood burning domestic heating could be considered as one of the major sources of fine particulate and PAHs in ambient air. The aim of this study was to evaluate in which conditions associated emissions could be considered as an issue in terms of population exposure. Methods: A list of pollutants of interest was defined depending on reference toxicology values collected in international databases. The target compounds were the following: PAHs, PM10, PM2.5, PCDD/Fs, benzene, toluene, ethylbenzene, acetaldehyde, formaldehyde, NOx and several heavy metals. Emission factors based on measurements were then determined for the selected compounds emitted from stoves and closed hearth operating in nominal and reduced modes. Dispersion and deposition of pollutants emitted from different numbers of sources functioning discontinuously throughout the day and year were modelled with atmospheric dispersion software ADMS 3.3. Children and adults exposure scenarios were drafted considering acute and chronic inhalation, and chronic exposure due to ingestion of soil and vegetables grown locally. Exposure parameters were collected in national databases. Results: Preliminary results showed that in a small village (50 houses) the levels of concentration calculated for PM10, PAHs, benzene and acetaldehyde reach ambient air levels measured near traffic areas in Paris. As far as human exposure and health impact are concerned, hazard quotients linked to chronic particles inhalation are above guideline values for people living in the neighbourhood. Discussion: Work is still in process to refine exposure scenarios and consolidate health risk assessment.

Pharmacological properties of alpha 1-adrenoceptor-mediated vasoconstrictions in dog and monkey lingual arteries: evidence for subtypes of alpha 1-adrenoceptors.

We examined whether alpha 1-adrenoceptor-mediated vasoconstrictions were due to extra- or intracellular Ca2+ movements, and whether they were subdivided into alpha 1-adrenoceptor subtypes in dog and monkey lingual arteries. The NE-induced vasoconstriction in dog lingual arteries was mostly dependent on Ca2+ influx from the extracellular space, since it was readily blocked by a Ca2+ entry blocker, diltiazem, and the NE-induced response was attenuated approximately 90% in Ca(2+)-free solution. On the other hand, in monkey lingual arteries, diltiazem failed to depress the NE-induced dose-response curve, and the response was attenuated only about 60% in Ca(2+)-free solution. The vasoconstrictor response to 10mg caffeine in normal KHS was almost the same as that to 1 micrograms NE in Ca(2+)-free solution in both kinds of arteries, suggesting that alpha 1-adrenoceptor-mediating vasoconstrictions require a different number of sources of Ca2+ in different blood vessels. Pretreatment of preparations with CEC (an alpha 1B-antagonist) significantly suppressed and shifted the dose-response curve for NE to the right in monkey lingual arteries, but it had no significant effect in dog lingual arteries. However, WB4101 (an alpha 1A-antagonist) showed almost the same potency in blocking vasoconstrictor responses as bunazosin in both kinds of arteries (the pA2 values were not significantly different). Moreover, responses to ME (an alpha 1A-agonist) were blocked by diltiazem as well as by bunazosin and WB4101, while CEC had no blocking effect on ME-induced responses.(ABSTRACT TRUNCATED AT 250 WORDS)

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<h3>Abstract</h3> Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require the <i>s</i>a<i>me</i> number of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions. <h3>Clinical Relevance</h3> This establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.