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PET scanner calibration is a routine procedure that is performed on a daily basis. The normalization data acquisition ideally should take as little time as possible. Consequently, normalization data are noisy and a component-based model is used to battle related issues. Most normalization components except scanner crystal efficiencies are fixed for a given scanner type. In this paper we propose a...
Total variation (TV) minimization problems are widely used for solving incomplete data problems in computed tomography (CT) image reconstruction. The present paper investigates a primal dual proximal point method of Chambolle-Pock algorithm to solve the CT image reconstruction problem which consisting the sum of £2 data fidelity term and TV regularization term. We tested these methods on computer...
For metal artifact reduction (MAR) in X-ray cone-beam computed tomography (CBCT), we propose a penalized weighted least-squares (PWLS) image reconstruction method [1] based on scatter correction (SC) method. Our main contribution is to incorporate the inaccuracy of the scatter correction data in PWLS method, and to accelerate the PWLS image reconstruction with a preconditionned conjugate gradient...
This paper proposes the idea of a local linear controller for voltage regulation of DC-DC buck converters. The control strategy is based on the data-driven identification of the converter model through the local model networks (LMNs) and use of the identified model for local linear control (LLC) of the voltage controlled buck converter. The LMN is compose of the local linear models (LLMs) and models...
We investigate the effects of missing observations on the robust Bayesian model for spectral analysis introduced by Christmas [2013]. The model assumes Student-t distributed noise and uses an automatic relevance determination prior on the precisions of the amplitudes of the component sinusoids and it is not obvious what their effect will be when some of the otherwise temporally uniformly sampled data...
The Variational Garrote is a promising new approach for sparse solutions of ill-posed linear inverse problems (Kappen and Gomez, 2012). We reformulate the prior of the Variational Garrote to follow a simple Binomial law and assign a Beta hyper-prior on the parameter. With the new prior the Variational Garrote, we show, has a wide range of parameter values for which it at the same time provides low...
As the high-speed I/O (HSIO) and serial link data rate keeps increasing, the requirements for accuracy and advanced capabilities of its modeling and simulation techniques get more stringent. Emerging requirements such as comprehending process, voltage, and temperature (PVT) variations at deep sub-micron process nodes or smaller, fully accounting for all the circuit blocks of the link, gap closing...
In Wireless Sensor Networks (WSN), Node Localization is of great importance for location aware services. In this paper we propose the use of Time of Arrival (TOA) information with two popular machine learning algorithms M5 tree Model (M5P) and Sequential Minimal Optimization for Regression (SMOreg) for more accurate node localization in WSN. In this paper we also applied the same node localization...
A novel sampling method is proposed for estimating a continuous multi-scale mixture model. The multi-scale mixture models we assume have a hierarchical structure in which each component of the mixture is represented by a Gaussian mixture model (GMM). In speaker modeling from speech, this GMM represents intra-speaker dynamics derived from the difference in the attributes such as phoneme contexts and...
In this paper a comparative analysis of compact noise model formulations for intrinsic bipolar transistors is presented. The analysis includes the approximated transport noise model feasible for compact model implementations and a correlated noise model derived from the non quasi-static theory of bipolar transistors. The models are first compared at the intrinsic device level, taking as reference...
A new model and a theory to capture the effects of halo (pocket) implants on the flicker noise of the advanced-node MOSFETs have been proposed and verified with measurements. The model can accurately capture the bias dependence of the drain-current flicker-noise (FN) power density. Also for the first time, we explain and model the unexpected channel-length dependence of FN power density in strong-halo...
Modeling of reasoning in intelligent systems on the example of intelligent decision support system of real time by means of integration of methods based on case-based reasoning (accumulated experience) and inductive notion formation in the presence of noisy data are considered.
Network protocols on transport layer of Open System Interconnection (OSI) model of data transmission solve very difficult problems for delivery all messages in necessary places at designated time. There are no accurate mathematical methods for searching of solution for different problems of optimization for dynamical network characteristics. Some problems may be successfully solved by means of modeling,...
This paper presents a new RANSAC based method for extracting planes from 3D range data. The generic RANSAC Plane Extranction (PE) method may over-extract a plane. It may fail in the case of a multi-step scene where the RANSAC process results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC algorithm overcomes the latter limitation if the inlier patches are separate...
The conventional power delivery analysis applying Icc(t) approach has the propensity to yield pessimistic outcome that leads to power delivery network (PDN) over-design. In addition, the noise profile captured using Icc(t) approach has high prospect of miscorrelation with the lab measurement data. Recent works adopting the signal integrity and power delivery (SIPD) co-simulation approach was found...
The use of online data for steady-state detection is required to solve problems like statistical data reconciliation, real time optimization and controller performance monitoring. In this paper, a new method for univariate system is proposed, which makes use of successive differences of time series (single variate) data. The method is simple because the parameters on which it is based are easy to...
Linear prediction methods, based on a Hankel data matrix, suffer from subspace leakage and degraded resolution when applied to data models that do not result in a mode matrix with Vandermonde structure, such as the constant-Q model. In the absence of noise, the Vandermonde structure ensures the equivalence between the number of backscattered signals and the rank of the data matrix. This paper first...
A novel automatic respiratory gating (ARG) algorithm for ultrasound is presented. The ARG algorithm was applied on dual contrast imaging (contrast/tissue) loops in order to remove respiratory motion artifacts from dynamic contrast enhanced ultrasound (DCEUS) of liver lesion images. The ARG algorithm is fully automated and it only requires the operator to define the breathing cycle phase to be extracted...
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical...
Recent advances in sensing and communication technologies enable us to collect round-the-clock monitoring data from a wide-array of distributed systems including data centers, manufacturing plants, transportation networks, automobiles, etc. Often this data is in the form of time series collected from multiple sensors (hardware as well as software based). Previously, we developed a time-invariant relationships...
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