Unknown Intensity Research Articles

An inverse problem for equations of cerebral oxygen transport

A continuum steady-state model of cerebral oxygen transport is considered. It consists of two semilinear elliptic equations describing the distributions of blood and tissue oxygen concentrations. The inverse problem is to find unknown intensities of the sources of the equation for the blood oxygen transport. As conditions of overdetermination, the average values of the blood oxygen concentration in some neighborhoods of the sources, are taken. In addition, the reconstruction of the blood and tissue oxygen concentrations are also required. The existence theorem is proved without assumptions of smallness. The conditions for the uniqueness of the solution are established. A numerical algorithm based on the Tikhonov regularization method is constructed and its convergence is shown by a numerical example.

Target Birth Intensity Estimation in Dense Clutter

In order to solve the problem of unknown target birth intensity in multi-target tracking, an improved GM-PHD filtering algorithm based on virtual track is proposed. According to the measurement set in the monitoring area, the virtual tracks of the birth targets are established by using the motion model of the targets. The false tracks modeled by the clutter are eliminated by PHD filter, and the states of the birth targets are extracted. The simulation results show that the algorithm performs well in dense clutter environment.

Rapid Blind Denoising Method for Grating Fringe Images Based on Noise Level Estimation

Object profile measurement theory and technology based on grating fringe image projection are popular topics of research. However, due to the presence of noise, the phase extracted from captured grating fringe images may be wrong, affecting the measurement results. To manage noise with unknown intensity levels, we use Gaussian distribution to model the noise, and propose a novel rapid blind denoising method. First, we establish an energy expression model for grating fringe images. After performing singular value decomposition (SVD) on the grating fringe image, we establish a linear relationship between the average energy of the image and the noise variance. Second, we propose a noise variance estimation method based on a single frame grating fringe image. By adding an auxiliary image of zero-mean Gaussian noise, we can obtain the estimated noise variance. Third, a Gaussian filtering method of the phase maps using the estimated variance, is employed to reduces the amount of data processing. The experimental results show that the proposed method can complete noise processing faster than five other methods, and extract the phase profile of two measured models with minimum root-mean-square (RMS) error of 0.0686 and 0.0673 rad.

Using a learned algorithm for detecting a deterministic signal in a noise of unknown intensity

The main sources of noise and interference, the features of detecting signals in noises, methods of dealing with them during transmission over various distances are considered. Passive methods of dealing with noise and interference are to reduce the length of cable networks to a reasonable minimum and reduce the number of cables; using only high quality cables and connectors from well-known manufacturers; laying cables with large bending radii to avoid interference from the so-called triboelectric effect; separation of signal and power cable trunks; using matched loads; in such a use of equipment so that its operating conditions are significantly lower than the limiting ones; in using the most interference-resistant interface. Active methods of dealing with noise and interference consist in the use of intermediate signal amplifiers that compensate for their attenuation in the line due to ohmic resistance and losses at high frequencies due to cable reactivity; switching to a twisted pair; switching to fiber-optic cable when it is necessary to transmit a signal over long distances. In a number of problems of signal reception in the presence of noise, one cannot be limited by such a general criterion as the signal-to-noise ratio. There is a need to use more subtle statistical properties of processes that allow one to quantify the reliability of the data. Noise is an important problem in science and technology because it defines lower limits both in terms of the accuracy of any measurement and in terms of the magnitude of signals that can be processed electronically. An adaptive detection of signals in noise is defined, using an optimal detection algorithm for analysis. An algorithm is proposed for detecting a signal in noise both in discrete observation and in the transition to continuous observation of signals. Probabilistic characteristics of signal detection in noise of unknown and known intensity of the learning algorithm are constructed.

Modeling the pressure distribution in a spatially averaged cerebral capillary network

<p style='text-indent:20px;'>A boundary value problem for the Poisson's equation with unknown intensities of sources is studied in context of mathematical modeling the pressure distribution in cerebral capillary networks. The problem is formulated as an inverse problem with finite-dimensional overdetermination. The unique solvability of the problem is proven. A numerical algorithm is proposed and implemented.</p>

Hypoxic Ischemic Encephalopathy in Units Reporting to the Ibero-American Society of Neonatology Network: Prevalence and Mortality.

Hypoxic ischemic encephalopathy is a neurological condition occurring immediately after birth following a perinatal asphytic episode. Therapeutic hypothermia is a safe and effective intervention to reduce mortality and major disability in survivors. In Latin America, perinatal asphyxia is a major problem, but no data are available characterizing its current situation in the region or the impact of hypoxic ischemic encephalopathy on its management. Understand the prevalence, mortality and use of therapeutic hypothermia in newborns at ≥36 weeks gestational age with hypoxic ischemic encephalopathy admitted to neonatal units reporting to the Ibero-American Society of Neonatology Network. The Ibero-American Society of Neonatology Network groups various neonatology centers in Latin America that share information and collaborate on research and medical care. We evaluated data on newborns with ≥36 weeks gestational age reported during 2019. Each unit received a guide with definitions and questions based on the Society's 7th Clinical Consensus. Evaluated were encephalopathy frequency and severity, Apgar score, need for resuscitation at birth, use of therapeutic hypothermia and clinical evolution at discharge. Our analysis includes descriptive statistics and comparisons made using the chi-square test. We examined reports of 2876 newborns from 33 units and 6 countries. In 2849 newborns with available data, hypoxic encephalopathy prevalence was 5.1% (146 newborns): 27 (19%) mild, 36 (25%) moderate, 43 (29%) severe, and 40 (27%) of unknown intensity. In those with moderate and severe encephalopathy, frequencies of Apgar scores ≤3 at the first minute (p = 0.001), Apgar scores ≤3 at the fifth minute (p ⟨0.001) and advanced resuscitation (p = 0.007) were higher. Therapeutic hypothermia was performed in only 13% of newborns (19). Neonatal mortality from encephalopathy was 42% (61). Hypoxic ischemic encephalopathy is a neonatal condition that results in high mortality and severe neurological sequelae. In this study, the overall prevalence was 5.1% with a mortality rate of 42%. Although encephalopathy was moderate or severe in 54% of reported cases, treatment with hypothermia was not performed in 87% of newborns. These data reflect a regional situation that requires urgent action.

Estimating the Position of an Image with Unknown Intensity Shape

The problem considered is to estimate the image position of a spatially extended object. It is assumed that the shape of the image intensity is a priori unknown, but it can be predicted with some error. In order to synthesize the estimate of the image position, the quasi-likelihood version of the maximum likelihood method is used. Behavior of the signal function in the neighborhood of the real image position is studied. Characteristics of the resulting estimate, such as bias and dispersion, are found by means of the local Markov approximation method. Influence of non-uniformity of the received image intensity upon the estimate accuracy is demonstrated by an example of receiving the rectangular image with linearly varying intensity.

Measurement Extraction of Two Targets With Unequal and Unknown Intensities in an FPA

This article extends previous work on location and intensity estimation for measurement extraction of targets in a focal plane array. Prior work has been done to extract single targets and two targets of equal intensity, whereas this work explores the case where two targets have unequal and unknown intensities. Here, we assume a Gaussian point spread function (PSF) with spread $\sigma _{\text{PSF}}$ , but our approach could be applied to other PSF shapes. We present a maximum likelihood (ML) method for target extraction under resolved and unresolved assumptions. In the unresolved case, we estimate the parameters of a single target that represents the centroid of the two unresolved targets. We also present the Cramer–Rao lower bound (CRLB) of the estimation variances for both cases. Our simulation results show that resolved targets have their parameter vectors estimated efficiently (i.e., the variance meets the CRLB) when the targets are separated by $0.9\sigma _{\mathrm{PSF}}$ , or about 1.8 pixel widths. We also find that estimation of the centroid parameters is efficient below a target separation of $0.65\sigma _{{\mathrm{PSF}}}$ . Furthermore, we find that increased difference in the SNR of two targets causes the variances in the resolved scenario to be lower, and in the case of the unresolved scenario, to increase. We also derive and characterize a decision about target cardinality as a hypothesis testing problem, and develop a generalized likelihood ratio test to perform the decision making. The performance of this test is evaluated via Monte Carlo simulations, and matches well to theoretical predictions. Finally, we explore the effect of separation between targets, and individual target SNR on resolvability.

Recent Studies on Two-Dimensional Radiative Transfer Problems in Anisotropic Scattering Media

In this work, an explicit formulation to solve two-dimensional radiative transfer problems in anisotropic scattering media is developed. A nodal technique along with the Analytical Discrete Ordinates (ADO) method are used to solve the discrete ordinates approximation of the radiative transfer equation, in Cartesian geometry. To make it possible, the discrete ordinates equations are transversally integrated over regions of the domain reducing the complexity of the model, yielding two one-dimensional equations for average angular intensities in x and y directions. The one-dimensional equations, with approximations for the unknown intensities on the contours of the regions, are then explicitly solved by the ADO method, with respect to the spatial variables, whose solutions are written in terms of eigenvalues and eigenfunctions. The phase function is expanded in terms of Legendre polynomials up to arbitrary order L, to model higher order anisotropy. The eigenvalue problem is derived for this general case and it preserves a relevant feature of the ADO method, which is the reduced order equal to half of the number of discrete directions. Numerical results for the average radiation density and radiative heat flux are presented, for test cases in which the degree of anisotropy can be up to twelve and the albedo assumes different values. A comparative analysis with results available in the literature allows the verification of the formulation and indicates a good performance of the proposed method in coarser meshes.

Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction

Linguistic modifiers such as negators (e.g., not), intensifiers (e.g., very) and modals (e.g., would) are commonly used in expressing opinions. These modifiers play an important role in recognizing the sentiment intensity of multi-word phrases because they may lead to an intensity shift and polarity reversal for the words they modify. Appropriately modeling the effect of such modifiers on the intensity shift can greatly improve the performance of phrase-level sentiment intensity prediction. To this end, this paper proposes two neural network (NN) models organized in a pipelined fashion to determine 1) the intensity of individual words and 2) the shift weights of modifiers representing the degrees of intensity change for the words they modify. The intensity of a phrase can then be determined by combining the intensity of the constituent word and the shift weight of the modifier within the phrase. When measuring the word intensity, the first NN model introduces a hidden layer as a filter to select appropriate similar seed words in the prediction process. Automatic word intensity prediction can address the unknown intensities of words not covered in sentiment lexicons. In learning the modifier weights, the second NN model considers both the weights of individual modifiers and groups of modifiers to capture various intensity shift effects caused by them. Experiments on a SemEval-2016 dataset showed that the proposed method yielded better prediction performance for both single words and multi-word phrases.

The Algorithm of the Vortex Sheet Intensity Determining in 3D Incompressible Flow Simulation around a Body

An original algorithm is developed for vortex methods of computational fluid dynamics for determining the intensity of the vortex sheet on the surface of a body in the flow of an incompressible medium. Unlike the common in the vortex methods approach to satisfying the no-slip boundary condition on a streamlined surface, which is based on ensuring that the normal velocity component of the medium is zero, the proposed procedure is based on a mathematically equivalent condition of equality to zero of the tangent velocity component on the body surface. The unknown intensity of the vortex sheet is assumed to be piecewise constant on triangular panels that approximate the surface of the body. The resulting integral equation is approximated by a system of linear algebraic equations, which dimension is twice the number of panels. The coefficients of the system matrix are expressed through double integrals over the panels. An algorithm is proposed for calculating these integrals for the case of neighboring panels, when these integrals are improper. An additive singularity exclusion is performed and analytical expressions for the integrals of them are obtained. The smooth parts of integrands are integrated numerically using Gaussian quadrature formulae. The proposed algorithm makes it possible to improve significantly the accuracy of the vortex sheet intensity reconstruction when flow simulating around complex-shaped bodies by using vortex methods for arbitrary triangular surface meshes, including essentially non-uniform and having cells with high aspect ratio.

Making Soft Optical Sensors More Wearable

This paper discusses new components and approaches to make stretchable optical fiber sensors better meet the power and washability requirements of wearables. First, an allpolymer quick connector allows the light source and photosensor to be quickly detached for washing. Second, the paper investigates the possibility of driving the sensors using ambient light instead of an onboard light source. While optical strain sensors and touch sensors have advantages over electronic ones in wet environments, and the intrinsic stretchability of the fibers is useful for soft robotics and highly conformal wearables, the typical light-emitting diode (LED) light source consumes more power than an electronic resistive or capacitive strain sensor. In this work, ambient light of uniform but unknown intensity is demonstrated to drive an elastomeric optical touch sensor in a differential configuration.

Nearly Optimal Adaptive Sequential Tests for Object Detection

Object detection in a cluttered environment, involving noisy measurements of signal over time, is a central problem in radar, sonar, optical, and communications applications. We consider the problem of detecting an object assuming that the distributions of the observed data are not exactly specified. As a result, the hypotheses to be tested are composite. We propose an adaptive version of the SPRT, named adaptive double SPRT developed by Tartakovsky (2014), who showed that this test is uniformly asymptotically optimal in the sense that it minimizes the average sample size for all parameter values when the probabilities of errors are small. This test is based on the parallel implementation of two one-sided adaptive SPRTs, each of these SPRTs is based on the comparison of the adaptive likelihood ratio statistic to a threshold. An alternative, also asymptotically optimal test is the popular generalized SPRT that has certain drawbacks in selecting thresholds to guarantee the upper bounds on the probabilities of errors but may appear to be more efficient than the adaptive SPRT if the error probabilities match. We study the relative efficiency of the adaptive SPRT, the generalized SPRT, and the non-adaptive 2-SPRT asymptotically as well as for realistic probabilities of errors using Monte Carlo simulations in a problem of detecting a signal with unknown intensity in clutter. The results demonstrate the robustness and high performance of the adaptive double SPRT algorithm.

Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity.

Nonparametric estimation of the pair correlation function of replicated inhomogeneous point processes

We consider the nonparametric estimation of the isotropic pair correlation function (PCF) of inhomogeneous point processes when replicates are available. Based on carefully designed estimating equations, two types of nonparametric estimators, i.e., the local polynomial estimator and the orthogonal series estimator, are proposed and studied. The proposed estimators circumvent the problems caused by the need for estimating the unknown intensity function for kernel smoothed PCF estimators and they are free of edge correction terms. Asymptotic properties are investigated for both estimators and valid point-wise confidence bands are derived. Finite sample performances of the proposed estimators are demonstrated by simulation as well as an application to the Sina Weibo posting data.

Measurement Extraction for Two Closely-Spaced Objects Using an Imaging Sensor

This paper considers measurement extraction for two closely-spaced objects with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accuracy given by the Cramer Rao lower bound (CRLB). The second part of the measurement extractor, target detector, selects among the hypotheses of two resolved targets and a single one using information-theoretic criteria and hypothesis tests. Simulation results have been conducted to evaluate the measurement extraction performance including the probability of resolving the two hypotheses, and the efficiency and unbiasedness of the target location estimates for the selected hypothesis using different hypothesis detection schemes. The generalized likelihood ratio test (GLRT) based on linearized observation model using second order Taylor series expansion is most appealing as it provides an explicit expression of the probability of detecting two targets as a function of the target separations, the signal-to-noise ratio at a given false resolution probability. It is shown that the simulation-based resolution performance for the GLRT using the estimated center location of the two targets matches well with the analytic performance assuming known center.

Interval estimation of the ruin probability in the classical compound Poisson risk model

Estimating ruin probability is an important problem in insurance. Zhang et al. (2014) proposed a novel nonparametric estimation method for the ruin probability in the classical risk model with unknown claim size distribution, based on the Fourier transform and the kernel density estimation. However, asymptotic distributions of their estimators are unknown, which hampers statistical inference for the ruin probability. The authors establish asymptotic normal distributions of the estimators with known and with unknown intensity. Since the standard deviations of estimators are hard to estimate, a bootstrap method is advanced to estimate them. This allows one to construct a confidence interval estimate of the ruin probability. Furthermore, a new method is proposed to fast calculate the estimates, and the numerical results are stable and free of the “curse of large initial surplus” problem. Simulations are conducted to demonstrate nice finite sample performance of the estimators. A real dataset from a car insurance company is analyzed for illustrating the use of the proposed methodology.

Symmetrical positron annihilation lifetime spectrometer and single-sided measurements

Positron annihilation lifetime (PAL) spectroscopy is a useful tool in material defect studies. In this work, a symmetrical PAL spectrometer was developed based on a conventional system and 22Na radioactive source. Both detectors provide start and stop signals to double the counting rate without adding devices. Zero-channel position is calibrated automatically during the measurement to combine the two sub-spectra, which can be applied to larger systems with more detectors. The differences of zero-channel positions and time resolutions of two sub-spectra were simulated to confirm the feasibility of our work. Then we used polyimide film (Kapton film, the packaging material of 22Na source) as reference sample to collect PAL spectra with only one piece of tested sample (single-sided). The influences of high and unknown intensity of source components were simulated to check the importance of enough counts and accurate source correction. Symmetrical PAL spectrometer provides higher counting rate to meet the requirement of single-sided sample. Experimental results confirmed that our PAL spectrometer performs well for both double-sided and single-sided measurements.

Realistic sub-Rayleigh imaging with phase-sensitive measurements

As the separation between two emitters is decreased below the Rayleigh limit, the information that can be gained about their separation using traditional imaging techniques, photon counting in the image plane, reduces to nil. Assuming the sources are of equal intensity, Rayleigh’s ‘curse’ can be alleviated by making phase-sensitive measurements in the image plane. However, with unequal and unknown intensities the curse returns regardless of the measurement, though the ideal scheme would still outperform image plane counting (IPC), i.e. recording intensities on a screen. We analyze the limits of the super-resolved position localization by inversion of coherence along an edge (SPLICE) phase measurement scheme as the intensity imbalance between the emitters grows. We find that SPLICE still outperforms IPC for moderately disparate intensities. For larger intensity imbalances we propose a hybrid of IPC and SPLICE, which we call ‘adapted SPLICE’, requiring only simple modifications. Using Monte Carlo simulation, we identify regions (emitter brightness, separation, intensity imbalance) where it is advantageous to use SPLICE over IPC, and when to switch to the adapted SPLICE measurement. We find that adapted SPLICE can outperform IPC for large intensity imbalances, e.g. 10 000:1, with the advantage growing with greater disparity between the two intensities. Finally, we also propose additional phase measurements for estimating the statistical moments of more complex source distributions. Our results are promising for implementing phase measurements in sub-Rayleigh imaging tasks such as exoplanet detection.

Soft-Decision Algorithm in Radio Communication Systems with Frequency Hopping

An expression for soft decisions is obtained for incoherent demodulation of signals with m-digit frequency-shift keying in radio communication systems with character frequency hopping in the presence of additional noise with unknown intensity in the frequency channels of the system. Results of simulation are presented.