Model-based Data Research Articles

Model-based pilot and data power adaptation in psam with periodic delayed feedback

We consider the optimum design of pilot-symbol-assisted modulation (PSAM) schemes with feedback. The received signal is periodically fed back to the transmitter through a noiseless delayed link and the time-varying channel is modeled as a Gauss-Markov process. We optimize a lower bound on the channel capacity which incorporates the PSAM parameters and Kalman-based channel estimation and prediction. The parameters available for the capacity optimization are the data power adaptation strategy, pilot spacing and pilot power ratio, subject to an average power constraint. Compared to the optimized open-loop PSAM (i.e., the case where no feedback is provided from the receiver), our results show that even in the presence of feedback delay, the optimized power adaptation provides higher information rates at low signal-to-noise ratios (SNR) in medium-rate fading channels. However, in fast fading channels, even the presence of modest feedback delay dissipates the advantages of power adaptation.

Model-Based Clustering by Probabilistic Self-Organizing Maps

In this paper, we consider the learning process of a probabilistic self-organizing map (PbSOM) as a model-based data clustering procedure that preserves the topological relationships between data clusters in a neural network. Based on this concept, we develop a coupling-likelihood mixture model for the PbSOM that extends the reference vectors in Kohonen's self-organizing map (SOM) to multivariate Gaussian distributions. We also derive three expectation-maximization (EM)-type algorithms, called the SOCEM, SOEM, and SODAEM algorithms, for learning the model (PbSOM) based on the maximum-likelihood criterion. SOCEM is derived by using the classification EM (CEM) algorithm to maximize the classification likelihood; SOEM is derived by using the EM algorithm to maximize the mixture likelihood; and SODAEM is a deterministic annealing (DA) variant of SOCEM and SOEM. Moreover, by shrinking the neighborhood size, SOCEM and SOEM can be interpreted, respectively, as DA variants of the CEM and EM algorithms for Gaussian model-based clustering. The experimental results show that the proposed PbSOM learning algorithms achieve comparable data clustering performance to that of the deterministic annealing EM (DAEM) approach, while maintaining the topology-preserving property.

Set Partitioning via Inclusion-Exclusion

Given a set $N$ with $n$ elements and a family $\mathcal{F}$ of subsets, we show how to partition $N$ into $k$ such subsets in $2^n n^{O(1)}$ time. We also consider variations of this problem where the subsets may overlap or are weighted, and we solve the decision, counting, summation, and optimization versions of these problems. Our algorithms are based on the principle of inclusion-exclusion and the zeta transform. In effect we get exact algorithms in $2^n n^{O(1)}$ time for several well-studied partition problems including domatic number, chromatic number, maximum $k$-cut, bin packing, list coloring, and the chromatic polynomial. We also have applications to Bayesian learning with decision graphs and to model-based data clustering. If only polynomial space is available, our algorithms run in time $3^n n^{O(1)}$ if membership in $\mathcal{F}$ can be decided in polynomial time. We solve chromatic number in $O(2.2461^n)$ time and domatic number in $O(2.8718^n)$ time. Finally, we present a family of polynomial space approximation algorithms that find a number between $\chi(G)$ and $\lceil(1+\epsilon)\chi(G)\rceil$ in time $O(1.2209^n+2.2461^{e^{-\epsilon}n})$.

Direct Study of the Thermal Conductivity in Aluminum Nanowires

AbstractThermal conductivity and electrical resistivity of 1 μm long aluminum nanowires, 75, 100, and 150nm in width and 100nm thick, were measured at room temperature. The method consists of microfabricated electrothermal test devices and a model-based data processing approach using finite-element analysis (FEA). The electrical and thermal properties of the nanowires differ significantly from bulk values while electrical resistivity agrees well with theoretical prediction. Electron transport equation models, which adequately describe the resistivity data, consistently underestimate the thermal conductivity. Incorporating a phonon contribution of ˜ 21 W/m·K to the total thermal conductivity is found to accurately describe the measured values.

The variable projection algorithm in time-resolved spectroscopy, microscopy and mass spectrometry applications

Nonlinear least squares optimization problems in which the parameters can be partitioned into two sets such that optimal estimates of parameters in one set are easy to solve for given fixed values of the parameters in the other set are common in practice. Particularly ubiquitous are data fitting problems in which the model function is a linear combination of nonlinear functions, which may be addressed with the variable projection algorithm due to Golub and Pereyra. In this paper we review variable projection, with special emphasis on its application to matrix data. The generalization of the algorithm to separable problems in which the linear coefficients of the nonlinear functions are subject to constraints is also discussed. Variable projection has been instrumental for model-based data analysis in multi-way spectroscopy, time-resolved microscopy and gas or liquid chromatography mass spectrometry, and we give an overview of applications in these domains, illustrated by brief case studies.

Controllability of Manual and Powered Wheelchairs for Spinal Cord Injury Users

This study investigates wheelchair maneuvering control for persons with a spinal cord injury across two protocols, path control and terminal aiming. Thirty-one participants using either a manual or powered wheelchair, performed self-paced longitudinal movements (path control) as well as self-paced stopping actions (terminal aiming) across multiple trials. Results show performance differences across both protocols for manual and power wheelchair user groups. This study exemplifies the use of model-based data for clinical applications. Further research using this approach may help to identify individual control settings for optimal maneuvering performance.

Model-based gene selection shows engrailed 1 is associated with antipsychotic response

Schizophrenia is an highly heritable complex disorder with a significant impact on public health. A variety of antipsychotics are available for treatment of the disorder and individual response to treatment is variable. To date, only a limited number of potential candidate genes have been examined for genetic association with treatment response. As there is lack of understanding of disease etiology and variation in treatment response, a large number of additional genes are potential targets for investigation. A variety of strategies for selecting candidate genes for further investigation are available and in most cases information used is weighed and ranked intuitively by the investigator. We sought to find genes that may influence treatment response in a less biased manner, after integrating heterogeneous data sources related to schizophrenia. A method to select liability and treatment response candidate genes for schizophrenia using multiple data sets was constructed. The method successfully selected DTNBP1, a strong candidate gene for schizophrenia. We then evaluated novel genes picked by the method. Thirty-six single nucleotide polymorphisms in two genes engrailed 1 (EN1) and secretin receptor (SCTR) were genotyped in the Clinical Antipsychotic Trials of Intervention Effectiveness study sample. Outcomes analyzed were the Positive and Negative Syndrome Scale and six different neurocognitive measures. Several of the seven single nucleotide polymorphisms genotyped in the EN1 gene were associated with schizophrenia symptoms (smallest P value=0.0061) and the effects of antipsychotics on symptoms (smallest P value=2.4x10). The estimated probabilities of being a false discovery were 0.14 for symptoms and 0.0012 for drug response. These findings show that EN1 may influence individual variation in response to antipsychotics. In addition, model-based data integration of schizophrenia-related data seems to improve the prior probability of selecting genes that have an effect on antipsychotics response.

Pilocarpine-Induced Status Epilepticus in Rats Involves Ischemic and Excitotoxic Mechanisms

The neuron loss characteristic of hippocampal sclerosis in temporal lobe epilepsy patients is thought to be the result of excitotoxic, rather than ischemic, injury. In this study, we assessed changes in vascular structure, gene expression, and the time course of neuronal degeneration in the cerebral cortex during the acute period after onset of pilocarpine-induced status epilepticus (SE). Immediately after 2 hr SE, the subgranular layers of somatosensory cortex exhibited a reduced vascular perfusion indicative of ischemia, whereas the immediately adjacent supragranular layers exhibited increased perfusion. Subgranular layers exhibited necrotic pathology, whereas the supergranular layers were characterized by a delayed (24 h after SE) degeneration apparently via programmed cell death. These results indicate that both excitotoxic and ischemic injuries occur during pilocarpine-induced SE. Both of these degenerative pathways, as well as the widespread and severe brain damage observed, should be considered when animal model-based data are compared to human pathology.

Hard-Fault Detection and Diagnosis During the Application of Model-Based Data Converter Testing

The concept of model-based test was developed in order to reduce the production test effort for data converters (Cherubal and Chatterjee (IEEE Trans Circuits Syst part I 50(3):317---327, 2003); Stenbakken and Souders (1985) Modelling and test point selection for data converter testing. In: ITC, Int Test Conf, pp 813---817; Wegener and Kennedy (IEEE Trans Circuits Syst I 51(1):213---217, 2004); Wrixon and Kennedy (IEEE Trans Instrum Meas IM-48(5):978---985, 1999)). In applying this concept, a vector of model parameters is determined for each device under test (DUT). Typically, this model parameter vector is merely used to calculate the DUT performance characteristic which is then subject to specification-oriented testing. However, each element of the model parameter vector represents an independent error source which contributes to performance degradations; thus, the model parameter vector can be viewed as a signature of the error sources. In this work, analyzing the error source signature is used to devise a model-based methodology for hard-fault detection and diagnosis. We investigate conditions under which hard-faults are detectable/diagnosable in spite of masking effects due to manufacturing process variations. In particular, we show that taking the model parameter vector as the fault signature is optimal as it minimizes the masking effects and thus maximizes detectability/diagnosibility.

The Role of Macrophages in the Clearance of Inhaled Ultrafine Titanium Dioxide Particles

The role of macrophages in the clearance of particles with diameters less than 100 nm (ultrafine or nanoparticles) is not well established, although these particles deposit highly efficiently in peripheral lungs, where particle phagocytosis by macrophages is the primary clearance mechanism. To investigate the uptake of nanoparticles by lung phagocytes, we analyzed the distribution of titanium dioxide particles of 20 nm count median diameter in macrophages obtained by bronchoalveolar lavage at 1 hour and 24 hours after a 1-hour aerosol inhalation. Differential cell counts revealing greater than 96% macrophages and less than 1% neutrophils and lymphocytes excluded inflammatory cell responses. Employing energy-filtering transmission electron microscopy (EFTEM) for elemental microanalysis, we examined 1,594 macrophage profiles in the 1-hour group (n = 6) and 1,609 in the 24-hour group (n = 6). We found 4 particles in 3 macrophage profiles at 1 hour and 47 particles in 27 macrophage profiles at 24 hours. Model-based data analysis revealed an uptake of 0.06 to 0.12% ultrafine titanium-dioxide particles by lung-surface macrophages within 24 hours. Mean (SD) particle diameters were 31 (8) nm at 1 hour and 34 (10) nm at 24 hours. Particles were localized adjacent (within 13-83 nm) to the membrane in vesicles with mean (SD) diameters of 592 (375) nm at 1 hour and 414 (309) nm at 24 hours, containing other material like surfactant. Additional screening of macrophage profiles by conventional TEM revealed no evidence for agglomerated nanoparticles. These results give evidence for a sporadic and rather unspecific uptake of TiO(2)-nanoparticles by lung-surface macrophages within 24 hours after their deposition, and hence for an insufficient role of the key clearance mechanism in peripheral lungs.

A New Model-Based Digital Halftoning and Data Hiding Designed With LMS Optimization

This work employs the well known least-mean-square (LMS) method to design an adaptive filter to produce high-quality halftone images. The filter can be regarded as a transformation medium between original gray level images and corresponding halftone images. Experimental results indicate that the proposed LMS-designed halftoning offers the extra benefit of edge enhancement. Since a halftone image is typically used in printing, a modified printer model, which can coordinate with the proposed LMS-designed halftoning, is proposed to eliminate the harm caused by the dot-gain effect in printing. Moreover, two data hiding applications, the direct embedding LMS-designed halftone technique (DELDH) and the information sharing LMS-designed halftone technique (ISLDH), are proposed to demonstrate the performance of the proposed LMS-designed halftoning. The experimental results show that, both techniques can be used with the proposed modified printer model to achieve excellent image quality and decoded visual patterns

DIFFUSE NEAR-INFRARED SPECTROSCOPY OF VULNERABLE PLAQUE IN EX VIVO HUMAN CORONARY ARTERY TISSUE: FEASIBILITY.

Purpose The objective of this research is to assess the feasibility of using fiber-based, diffuse optical spectroscopy (DOS) for quantitatively differentiating between stable and vulnerable atherosclerotic plaques. Methods Diffuse optical spectroscopy (DOS) is a technique that combines experimental measurements and model-based data analysis to measure the bulk absorption (μa) and scattering (μs′) properties of highly scattering media. DOS typically uses red and near-infrared (NIR) light, especially from 600 to 1,000 nm, where light propagation in tissue is scattering dominated. Diffusive photons probe a large sample volume, providing macroscopically averaged absorption and scattering properties at depths up to a few centimeters. In this study we used a probe developed at Beckman Laser Institute to measure optical properties of a 3 cm × 4 cm sample from ex vivo human aorta obtained from UCIMC that was diagnosed with atherosclerosis postmortem. Results There were three histologically different tissue types in the sample. The tissue types were type V with a thin cap, type V with a thick cap, and type VI atherosclerotic lesions. The quantitative spectroscopic results indicate that in terms of scattering properties of the sample tissue, type V (large cap) scatters more than type V (thin cap), which, in turn, scatters more than type VI. This property reveals information about tissue structure. On the other hand, the absorption spectra of the sample, which reveals information about the biochemical composition, has the opposite pattern; type VI absorbs more than type V (thin cap), which absorbs more than type V (large cap). Conclusion The three tissue types all showed different scattering and absorption spectra at different near-infrared wavelengths. Therefore, it may be possible to differentiate between stable and vulnerable atherosclerotic plaques using the fiber-based DOS probe in steady-state frequency domain photon migration (SS-FDPM).

Growth of microbial populations. Mathematical modeling, laboratory exercises, and model‐based data analysis

This paper has arisen as a result of teaching Models in Biology to undergraduates of Bioengineering at the Gediminas Technical University of Vilnius. The aim is to teach the students to use a fresh approach to the problems they are familiar with, to come up with an articulate verbal model after a mental effort, to express it in rigorous mathematical terms, to solve (with the aid of computers) corresponding equations, and finally, to analyze and interpret experimental data in terms of their (mathematical) models. Investigation of microbial growth provides excellent possibilities to combine laboratory exercises, mathematical modeling, and model-based data analysis. Application of mathematics in this field proved to be very fruitful in getting deeper insight into the processes of microbial growth. The step-by-step modeling resulted in an extended model of the growth covering conventional "lag," "exponential," and "stationary" phases. In contrast to known models (differential equations that can be solved only numerically), the present model is expressed symbolically as a finite combination of elementary functions. The approach can be applied in other areas of modern biology, such as dynamics of various cellular processes, enzyme and receptor kinetics, and others.

Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution

This paper presents a new information acquisition problem motivated by business applications where customer data has to be acquired with a specific modeling objective in mind. In the last two decades, there has been substantial work in two different fields—optimal experimental design and machine learning—that has addressed the issue of acquiring data in a selective manner with a specific objective in mind. We show that the problem presented here is different from the classic model-based data acquisition problems considered thus far in the literature in both fields. Building on work in optimal experimental design and in machine learning, we develop a new active learning technique for the information acquisition problem presented in this paper. We demonstrate that the proposed method performs well based on results from applying this method across 20 Web usage and machine learning data sets.

Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

In this contribution we test the hypothesis that nonlinear additive autoregressive model-based data analysis improves the diagnostic ability based on short-term heart rate variability. For this purpose, a nonlinear regression approach, namely, the maximal correlation method is applied to the data of 37 patients with dilated cardiomyopathy as well as of 37 age- and sex-matched healthy subjects. We find that this approach is a powerful tool in discriminating both groups and promising for further model-based analyses.

Image reconstruction: a unifying model for resolution enhancement and data extrapolation Tutorial

In reconstructing an object function F(r) from finitely many noisy linear-functional values integral of F(r)Gn(r)dr we face the problem that finite data, noisy or not, are insufficient to specify F(r) uniquely. Estimates based on the finite data may succeed in recovering broad features of F(r), but may fail to resolve important detail. Linear and nonlinear, model-based data extrapolation procedures can be used to improve resolution, but at the cost of sensitivity to noise. To estimate linear-functional values of F(r) that have not been measured from those that have been, we need to employ prior information about the object F(r), such as support information or, more generally, estimates of the overall profile of F(r). One way to do this is through minimum-weighted-norm (MWN) estimation, with the prior information used to determine the weights. The MWN approach extends the Gerchberg-Papoulis band-limited extrapolation method and is closely related to matched-filter linear detection, the approximation of the Wiener filter, and to iterative Shannon-entropy-maximization algorithms. Non-linear versions of the MWN method extend the noniterative, Burg, maximum-entropy spectral-estimation procedure.

Efficient seismic response data storage and transmission using ARX model-based sensor data compression algorithm

This paper presents a linear predictor (LP)-based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto-Regressive with eXogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data. Copyright © 2005 John Wiley & Sons, Ltd.

Composable M&S Web Services for Net-Centric Applications

Service-oriented architectures promise easier integration of functionality in the form of web services into operational systems than is the case with interface-driven system-oriented approaches. Although the Extensible Markup Language (XML) enables a new level of interoperability among heterogeneous systems, XML alone does not solve all interoperability problems users contend with when integrating services into operational systems. To manage the basic challenges of service interoperation, we developed the Levels of Conceptual Interoperability Model (LCIM) to enable a layered approach and gradual solution improvements. Furthermore, we developed methods of model-based data engineering (MBDE) for semantically consistent service integration as a first step. These methods have been applied in the U.S. in collaboration with industry resulting in proofs of concepts. The results are directly applicable in a net-centric and net-enabled environment.

Model-based data engineering for Web services

Although XML offers heterogeneous IT systems a new level of interoperability, it doesn't ensure that the various systems correctly interpret the data they receive. To address this, data engineering supports clear definitions for exchanged data elements. With model-based data engineering, organizations use a common reference model, which offers further clarity and performance improvements. Organizations can use the resulting data to configure mediation services, translating dialects of new or legacy services into a common language for use in a service-oriented architecture.

Model-based data assessment for terrestrial carbon processes: implications for sampling strategy in FACE experiments

The value of different types of data in the estimation of different carbon transfer parameters is investigated. A carbon accounting model is used with different observation operators to generate data. The effectiveness of the inversion is assessed by observing relative errors of estimators and likelihood ratios. It is demonstrated that for an observation operator that relative errors vary widely with the sample test problems. An effective strategy to test types of data is to test the effectiveness of corresponding observation operators on an ensemble of sample problems for which parameters are selected from the space of admissible parameters. The selection is carried out under the assumption that the test parameters themselves are random variables uniformly distributed over the space of admissible parameters.