Maximum-likelihood time-of-arrival estimation for saturated optical transient signals.

In this paper, we deal with the problem of detecting the signal of interest in the presence of Gaussian disturbance with symmetric spectrum and assuming that the cell under test (primary data) and the training samples (secondary data) share the same covariance matrix up to an unknown power scaling factor. Moreover, we exploit the symmetric spectral property of the disturbance to transfer the binary hypothesis testing problem from the complex to the real domain and derive an adaptive detector relying on the two-step Generalized Likelihood Ratio Test design procedure. A preliminary performance assessment, conducted by Monte Carlo simulation, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterpart which ignores the spectrum symmetry.

Numerical relays have become essential tools for carrying out the protection and surveillance tasks of electrical power systems. These relays implement their functions from the phasors of the electrical network signals, estimated by digital processing using digital filters. Digital filters must meet certain requirements, such as providing a fast and effective response to increasingly complex transient signals made up of components that make the estimation process difficult. In addition to the decreasing exponential (decaying dc offset), harmonics, and signal noise, it must be added that the interharmonic components that in recent years have acquired great relevance, mainly because of the increase in non-linear loads and the extensive use of power electronic systems. The presence of these interharmonic components causes a poor response in most of the filters implemented today. This article presents the design of a new digital filter, C-CharmDF (Cleaned Characteristic Harmonic Digital Filter) for phasor estimation on noisy transient signals with decreasing exponential components, harmonics, and interharmonics. A detailed study was carried out for severe transient situations and stationary signals. It was found that the method can be suitable for relays that implement both fault location functions and system protection functions.

An extension of a previously presented algorithm for estimating instantaneous 2-D motion fields in the ultrasonography of internal organs is proposed. In the previous algorithm, the motion field was modeled as a regression random process with respect to the respiratory signal, and utilized spatially independent unknown coefficients. However, the value of these coefficients should vary with spatial smoothness, which enables a spatial constraint to be applied. In the proposed extension, the regression coefficients are defined as random variables, i.e. Gaussian-Markov random field (GMRF), with unknown scale factors, allowing a computationally stable estimation algorithm to be constructed.

We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.

We propose an analytic solution for the self-calibration of five-parameter linear camera from three pairs of views of a scene, of four-parameter linear camera from two pairs of views and of two-parameter linear camera from one pairs of views based on Kruppa equations, respectively. The approach does not need any initial value, and can calculate accurate solution. The analysis was performed first by writing a suitable set of equations under introducing unknown scale factors from Kruppa's equations. Then, by an elimination scheme and symbolic computation, only one unknown scale factor was retained, so the equation set was reduced to a 30th order univariate polynomial equation for five-parameter or four-parameter camera, and a 3rd order univariate polynomial equation for two-parameter camera. After scale factors were attained, the camera intrinsic parameters were computed by a linear method. A numerical example was reported that confirms the theoretical results by "Mathematica" software

BackgroundMicroarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (216 - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.ResultsWe developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.ConclusionsThe presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

A design scheme of transient light signal simulator based on Field Programmable gate Array (FPGA) was proposed in this paper. Based on the characteristics of transient light signals and measured feature points of optical intensity signals, a fitted curve was created in MATLAB. And then the wave data was stored in a programmed memory chip AT29C1024 by using SUPERPRO programmer. The control logic was realized inside one EP3C16 FPGA chip. Data readout, data stream cache and a constant current buck regulator for powering high-brightness LEDs were all controlled by FPGA. A 12-Bit multiplying CMOS digital-to-analog converter (DAC) DAC7545 and an amplifier OPA277 were used to convert digital signals to voltage signals. A voltage-controlled current source constituted by a NPN transistor and an operational amplifier controlled LED array diming to achieve simulation of transient light signal. LM3405A, 1A Constant Current Buck Regulator for Powering LEDs, was used to simulate strong background signal in space. Experimental results showed that the scheme as a transient light signal simulator can satisfy the requests of the design stably.

This paper describes a novel design of a threshold logic gate (a binary perceptron) and its implementation as a standard cell. This new cell structure, referred to as flash threshold logic (FTL), uses floating gate (flash) transistors to realize the weights associated with a threshold function. The threshold voltages of the flash transistors serve as a proxy for the weights. An FTL cell can be equivalently viewed as a multi-input, edge-triggered flipflop which computes a threshold function on a clock edge. Consequently, it can be used in the automatic synthesis of ASICs. The use of flash transistors in the FTL cell allows programming of the weights after fabrication, thereby preventing discovery of its function by a foundry or by reverse engineering. This paper focuses on the design and characteristics of the FTL cell. We present a novel method for programming the weights of an FTL cell for a specified threshold function using a modified perceptron learning algorithm. The algorithm is further extended to select weights to maximize the robustness of the design in the presence of process variations. The FTL circuit was designed in 40nm technology and simulations with layout-extracted parasitics included, demonstrate significant improvements in the area (79.7%), power (61.1%), and performance (42.5%) when compared to the equivalent implementations of the same function in conventional static CMOS design. Weight selection targeting robustness is demonstrated using Monte Carlo simulations. The paper also shows how FTL cells can be used for fixing timing errors after fabrication.

Effects of heterogeneities in dose distributions under nonreference conditions: Monte Carlo simulation vs dose calculation algorithms

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, ${\bf R}$ , is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, ${\bf R}_{0}$ , is assumed to be equal to $\lambda{\bf R}$ , i.e., ${\bf R}_{0}=\lambda{\bf R}$ . On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor $\lambda$ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

Comparison of reconstructed prompt gamma emissions using maximum likelihood estimation and origin ensemble algorithms for a Compton camera system tailored to proton range monitoring

The response of a Silicon Photomultiplier (SiPM) to optical signals is affected by many factors including photon-detection efficiency, recovery time, gain, optical crosstalk, afterpulsing, dark count, and detector dead time. Many of these parameters vary with overvoltage and temperature. When used to detect scintillation light, there is a complicated non-linear relationship between the incident light and the response of the SiPM. In this paper, we propose a combined discrete-time discrete-event Monte Carlo (MC) model to simulate SiPM response to scintillation light pulses. Our MC model accounts for all relevant aspects of the SiPM response, some of which were not accounted for in the previous models. We also derive and validate analytic expressions for the single-photoelectron response of the SiPM and the voltage drop across the quenching resistance in the SiPM microcell. These analytic expressions consider the effect of all the circuit elements in the SiPM and accurately simulate the time-variation in overvoltage across the microcells of the SiPM. Consequently, our MC model is able to incorporate the variation of the different SiPM parameters with varying overvoltage. The MC model is compared with measurements on SiPM-based scintillation detectors and with some cases for which the response is known a priori. The model is also used to study the variation in SiPM behavior with SiPM-circuit parameter variations and to predict the response of a SiPM-based detector to various scintillators.

The adaptive subspace detector (ASD), which is developed to handle detection problems in partially homogeneous environments where test and training data samples share the same noise covariance matrix up to an unknown scaling factor, has been evaluated in terms of probabilities of false alarm and detection. However, the probability of false alarm of the ASD is provided in an integral form. Moreover, the detection probability of the ASD is approximate in certain cases. In this correspondence, a finite-sum expression for the probability of false alarm of the ASD is obtained, and an exact expression for the detection probability of the ASD is also derived. These expressions are all validated using Monte Carlo simulations.

The paper discusses the problem of detecting transient signals of unknown waveforms in white Gaussian noise. The signals are modeled as impulse responses of rational transfer functions with unknown parameters. A modified generalized likelihood ratio test (MGLRT) is proposed and its statistical properties are analyzed for both known and unknown noise variances. The MGLRT involves constrained maximum likelihood estimation of the signal parameters. The performance of the MGLRT is compared to that of an optimal matched filter and an energy detector, for some test cases. Also, the theoretical distributions of the likelihood ratios under H 0 and H 1 are compared to experimental distributions obtained by Monte Carlo simulations.

We address the problem of ecient resolution, detection and estimation of weak tones in a potentially massiveamount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in the envi-ronment in time and frequency. The requirements for this problem are that the process must be computationallyecient, high gain and able to resolve signals and eciently compress the signal information into a form thatmay be easily displayed and further processed. To meet these requirements, we propose a concentrated peak rep-resentation (CPR) in which the spectral energy is concentrated in spectral peaks, and only the magnitudes andlocations of the peaks are retained. We base our process on the cross spectral representation we have previouslyapplied to other problems. In selecting this method, we have considered other representations and estimationmethods such as the Wigner distribution and Welchs method. We compare our method to these methods. Thespectral estimation method we propose is a variation of Welchs method and the cross-power spectral (CPS)estimator which was “rst applied to signal estimation and detection in the mid 1980s. The CPS algorithm andthe method we present here are based on the principles “rst described by Kodera et al.Keywords: signal detection, signal estimation, transient signals, time-frequency representations