Gaussian-distributed Source Research Articles

Improvement of multisource localization of magnetic particles in an animal

In this simulation work, the linearized Bregman iterative algorithm was applied to solve the magnetic source distribution problem of a magnetic particle imaging (MPI) system for small animals. MPI system can apply an excitation magnetic field, and the induced magnetic field from the magnetic nanoparticles (MNPs) can be detected by the sensors of MPI system. With a gaussian distribution source at the upper side of the mouse brain, sensors set above the mouse brain and the constant excitation magnetic field, the average deviation of the calculated source distribution from the multiplane scanning along the axis away from the mouse brain and the closest plane scanning are 2.78 × 10–3 and 2.84 × 10–3 respectively. The simulated result showed that combination of multiplane scanning hardly improves the accuracy of the source localization. In addition, a gradient scan method was developed that uses gradient magnetic field to scan the mouse brain. The position of the maximum of the lead field matrix will be controlled by the gradient field. With a set up gaussian distribution source at the bottom of the mouse brain, the average deviation of the calculated source distribution from the gradient scan method and the constant field are 4.42 × 10–2 and 5.05 × 10–2. The location error from the two method are 2.24 × 10–1 cm and 3.61 × 10–1 cm. The simulation showed that this method can improve the accuracy compared to constant field when the source is away from the sensor and having a potential for application.

Sparse Bayesian learning for time-varying DOA estimation

Sparse Bayesian learning (SBL) provides sparse direction-of-arrival (DOA) estimation performance, and an SBL scheme with sequential processing is proposed for time-varying DOA estimation. SBL employs a Gaussian prior for the source signals and models variances of the source amplitudes as a hyperparameter. For sequential processing, the Gaussian-distributed source signals are modeled as Gaussian processes, and we consider the variances of the source amplitudes as the parameter of the covariance function in the Gaussian process. The sequential SBL estimates the variance parameter that evolves sequentially over time based on a state-space model. The suggested SBL with the sequential processing provides high-resolution capabilities for time-varying DOAs with varying source strengths or moving sources over time. The present method is evaluated by using simulated and real data (SWellEx-96 Event S5).

Spatial information theory of sensor array and its application in performance evaluation

The direction of arrival (DOA) and amplitude-phase (AP) of source signal can be estimated through array signal processing, while the process of information acquisition has not been described by information theory yet. In this study, the authors propose a novel spatial information theory framework to describe the DOA and AP estimation process. Firstly, they give a sensor array system model, where the spatial information is defined mathematically. Furthermore, in a single source scenario, the derivations of theoretical expression and an asymptotic upper bound for DOA information are presented. It also proves that the AP information accords with Shannon channel capacity when the source follows complex-Gaussian distribution. In addition, when in an uncorrelated multi-sources scenario, the AP information is the sum of that of each source for Gaussian distributed sources. Finally, they propose a definition of entropy error to evaluate the information acquisition capability of a sensor array. Numerical results verify that the entropy error is consistent with mean square error and approaches Cramer–Rao bound in high signal-to-noise ratio. It also verifies the correctness of theoretical derivation and shows that the spatial information can be employed as an evaluation index to quantitatively analyse the system performance which has significant guidance.

Monte-Carlo simulation of the Siemens Artiste linear accelerator flat 6 MV and flattening-filter-free 7 MV beam line.

The aim of our work is to provide the up-to-now missing information on the Siemens Artiste FFF 7 MV beam line using a Monte-Carlo model fit to the realistic dosimetric measurements at the linear accelerator in clinical use at our department. The main Siemens Artiste 6MV and FFF 7MV beams were simulated using the Geant4 toolkit. The simulations were compared with the measurements with an ionization chamber in a water phantom to verify the validation of simulation and tuning the primary electron parameters. Hereafter, other parameters such as surface dose, spectrum, electron contamination, symmetry, flatness/unflatness, slope, and characteristic off-axis changes were discussed for both Flat and FFF mode. The mean electron energy for the FFF beam was 8.8 MeV and 7.5 MeV for Flat 6 MV, the spread energy and spot size of the selected Gaussian distribution source were 0.4 MeV and 1mm, respectively. The dose rate of the FFF beam was 2.8 (2.96) times higher than for the flattened beam for a field size of 10×10 (20×20) cm2. The electron contamination has significant contribution to the surface dose especially for the flattened beam. The penumbra, surface dose and the mean energy of photons decrease by removing the flattening filter. Finally, the results show that off-axis changes have no strong effect on the mean energy of FFF beams, while this effect was more considerable for the flattened beam.

Monte Carlo simulations of elemental imaging using the neutron-associated particle technique.

The purpose of this study is to develop and employ a Monte Carlo (MC) simulation model of associated particle neutron elemental imaging (APNEI) in order to determine the three-dimensional (3D) imaging resolution of such a system by examining relevant physical and technological parameters and to thereby begin to explore the range of clinical applicability of APNEI to fields such as medical diagnostics, intervention, and etiological research. The presented APNEI model was defined in MCNP by a Gaussian-distributed and isotropic surface source emitting deuterium + deuterium (DD) neutrons, iron as the target element, nine iron-containing voxels (1 cm3 volume each) arranged in a 3-by-3 array as the interrogated volume of interest, and finally, by high-purity germanium (HPGe) gamma-ray detectors anterior and posterior to the 9-voxel array. The MCNP f8 pulse height tally was employed in conjunction with the PTRAC particle tracking function to not only determine the signal acquired from iron inelastic scatter gamma-rays but also to quantitate each of the nine target voxels' contribution to the overall iron signal - each detected iron inelastic scatter gamma-ray being traced to the source neutron which incited its emission. With the spatial, vector, and timing information of the series of events for each relevant neutron history as collected by PTRAC, realistic grayscale images of the distribution of iron concentration in the 9-voxel array were simulated in both the projective and depth dimensions. With an overall 225 ps timing resolution, 6.25 mm2 imaging plate pixels assumed to have well localized scintillation, and a DD neutron, Gaussian-distributed source spot with a diameter of 2 mm, projective and depth resolutions of < 1 cm and <3 cm are achievable, respectively, for iron-containing voxels on the order of 1,000 ppm Fe. The imaging resolution offered by APNEI of target elements such as iron lends itself to potential applications in disease diagnosis and treatment planning (high resolution) as well as to ordnance and contraband detection (low resolution). However, experimental study beyond simulation is required to optimize the layout and electronic configuration of APNEI system components - including realistic shielding and phantom materials - for background signal reduction in order to accurately determine the detection limits and spatial resolution of iron and other elements of interest on a case-by-case basis.

Analog Coded SoftCast: A Network Slice Design for Multimedia Broadcast/Multicast

This paper presents a network slice design for ultra high definition (UHD) video broadcast/multicast to achieve higher network efficiency and improved quality of experience (QoE). The proposed network slice design consists of a rateless source compression scheme and an analog-coded SoftCast scheme. The rateless Spinal code is adopted to compress the video source at content server and the compressed source is transmitted from content server across wireless core network to the base station. An a priori information-assisted Spinal decoder is designed to utilize the sparsity of bit planes for compression. In the analog-coded SoftCast scheme, we design a new chaotic function-based analog code with negligible power penalty for the generalized Gaussian-distributed source in SoftCast because the existing chaotic functions designed for uniformly distributed sources suffer from serious power penalty in SoftCast. We also design a maximum a posteriori probability decoding algorithm for the proposed analog code in order to exploit the statistics of video source as a priori information to improve the performance. The experimental results show that the proposed rateless code-based compression scheme achieves efficient compression and approaches the bound of binary erasure channel. In particular, the 1/2 analog-coded SoftCast has almost 2 dB gain over conventional SoftCast with two repetitions, and the 1/3 analog-coded SoftCast has almost 3 dB gain over conventional SoftCast with three repetitions. The system simulations for the broadcast system show higher network capacity and improved QoE in the proposed UHD slice, because the reconstructed video quality of each user is commensurate with its channel condition.

Solar flux density calculation for a heliostat with an elliptical Gaussian distribution source

The calculation of solar flux density is a key work for the design and optimization of solar tower system. Because of the great amount of calculation, the source distribution is often regarded as a radial distribution, which is not consistent with the reality. This paper presents a new method to calculate the flux density distribution by a focusing heliostat with an elliptical Gaussian distribution source. The two-dimensional convolution integration is proposed and converted into a one-dimensional integration. We use the Gauss-Legendre integration method to reduce the amount of calculation and accelerate the speed of integration. This method can be used to calculate the solar flux at image and receiver plane by most of the heliostat. It needs only 0.1% time of the ray tracing method for calculating the efficiency of the heliostat. It can be applied for design optimization of the solar heliostat field which is superior to the present methods in both accuracy and computation requirements.

A numerical study on dual-phase-lag model of bio-heat transfer during hyperthermia treatment

The success of hyperthermia in the treatment of cancer depends on the precise prediction and control of temperature. It was absolutely a necessity for hyperthermia treatment planning to understand the temperature distribution within living biological tissues. In this paper, dual-phase-lag model of bio-heat transfer has been studied using Gaussian distribution source term under most generalized boundary condition during hyperthermia treatment. An approximate analytical solution of the present problem has been done by Finite element wavelet Galerkin method which uses Legendre wavelet as a basis function. Multi-resolution analysis of Legendre wavelet in the present case localizes small scale variations of solution and fast switching of functional bases. The whole analysis is presented in dimensionless form. The dual-phase-lag model of bio-heat transfer has compared with Pennes and Thermal wave model of bio-heat transfer and it has been found that large differences in the temperature at the hyperthermia position and time to achieve the hyperthermia temperature exist, when we increase the value of τT. Particular cases when surface subjected to boundary condition of 1st, 2nd and 3rd kind are discussed in detail. The use of dual-phase-lag model of bio-heat transfer and finite element wavelet Galerkin method as a solution method helps in precise prediction of temperature. Gaussian distribution source term helps in control of temperature during hyperthermia treatment. So, it makes this study more useful for clinical applications.

Nonlinear behavior of thermal lagging in concentric living tissues with Gaussian distribution source

The high-order effects of thermal lagging were instated in describing non-equilibrium heating in tissues. This work theoretically studies the temperature rise behavior in living tissues during magnetic tumor hyperthermia treatment within the non-linear form of the dual-phase-lag model. It was experimentally discovered that the concentration of magnetic particles is in Gaussian distribution along the radial direction as magnetic fluid is injected into the tissue structure. Therefore, the non-linear high order partial differential equation in concentric spherical domain, which would introduce the complexity and cause some mathematical difficulties, is numerically solved. The comparison between the linear and non-linear effects of phase lag times on bio-heat transfer has been done. The results show that the effect of τ q 2 strengthens the behavior of thermal wave as well as the effect of τ T 2 affects the behavior of diffusion. The effect of τ q 2 on the behavior of non-Fourier bio-heat transfer would fade away for τ T > τ q .

Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method.

THE OPTICAL TRANSFER FUNCTION OF CONFOCAL SCANNING LASER MICROSCOPY WITH GAUSS SOURCE

Taking into account the size effect of the source and the detector, we have calculated the Optical transfer function of a real system with Gaussian distribution of the source. Because of the Gaussian distribution source , the size of source and detector can be enlarged, and the signal-to-noise ratio will be increased.