Nanopore sequencing device analysis systems simultaneously generate multiple picoamperage current signals representing the passage of DNA or RNA nucleotides ratcheted through a biomolecule nanopore array by motor proteins. Squiggles are a noisy and time-distorted representation of an underlying nucleotide sequence, "gold standard model", due to experimental and algorithmic artefacts. Other research fields use dynamic time warped-space averaging (DTWA) algorithms to produce a consensus signal from multiple time-warped sources while preserving key features distorted by standard, linear-averaging approaches. We compared the ability of DTW Barycentre averaging (DBA), minimize mean (MM) and stochastic sub-gradient descent (SSG) DTWA algorithms to generate a consensus signal from squiggle-space ensembles of RNA molecules Enolase, Sequin R1-71-1 and Sequin R2-55-3 without knowledge of their associated gold standard model. We propose techniques to identify the leader and distorted squiggle features prior to DTWA consensus generation. New visualization and warping-path metrics are introduced to compare consensus signals and the best estimate of the "true" consensus, the study's gold standard model. The DBA consensus was the best match to the gold standard for both Sequin studies but was outperformed in the Enolase study. Given an underlying common characteristic across a squiggle ensemble, we objectively evaluate a novel "voting scheme" that improves the local similarity between the consensus signal and a given fraction of the squiggle ensemble. While the gold standard is not used during voting, the increase in the match of the final voted-on consensus to the underlying Enolase and Sequin gold standard sequences provides an indirect success measure for the proposed voting procedure in two ways: First is the decreased least squares warped distance between the final consensus and the gold model, and second, the voting generates a final consensus length closer to the known underlying RNA biomolecule length. The results suggest considerable potential in marrying squiggle analysis and voted-on DTWA consensus signals to provide low-noise, low-distortion signals. This will lead to improved accuracy in detecting nucleotides and their deviation model due to chemical modifications (a.k.a. epigenetic information). The proposed combination of ensemble voting and DTWA has application in other research fields involving time-distorted, high entropy signals.

Limited research exists for identifying data races under the specific characteristics found in embedded systems. E-RACE is a new style of data-race identification tool which directly utilizes specialized hardware capabilities to monitor the flow of data and instructions. Compared to existing data race analysis approaches, the hardware-assisted E-RACE tool has advantages of recognizing data-race issues without requiring extensive software code instrumentation. The tool is integrated into an Embedded Unit Testing Driven Development Framework to encourage the construction of testable code and early identification of data-races.

Software reliability can be improved by using code coverage analysis to ensure that all statements are executed at least once during the testing process. When full code coverage information is obtained through software code instrumentation, high runtime performance overheads are incurred. Techniques that perform deferred or selective code instrumentation have shown success in reducing run-time overheads; however, the execution profile remains distorted. Techniques have been proposed that use internal processor hardware during the data gathering process, e.g. program counter logging. These approaches have been shown to reduce overheads; but currently trade swift execution for sparse code coverage. By combining the branch-vector hardware designed for debugging modern embedded processors with on-demand code coverage analysis, we have developed a new tool which provides full code coverage, while minimizing performance distortions. Experimental results show a performance impact of only 8 - 12%, while still providing 100% code coverage information.

Learning embedded programming is a highly demanding exercise. The beginner is bombarded with complexity from the start: embedded production based around a myriad of C++ constructs with low-level elements integrated onto ever more complicated processor architectures. The picture is further compounded by tool support having unfamiliar roles and appearances from previous student experiences. This demanding situation often has the student bewildered; seeking for "a crutch" or the simplest way forward regardless of the overall consequences. To control this potentially chaotic picture, the instructor needs to introduce devices to combat this complexity. We argue that test driven development (TDD) should become the instructor's principal weapon in this fight. Reasons for this belief combined with our, and the students', experiences with this novel approach are discussed.

Geometric and intensity distortions due to the presence of metallic implants in magnetic resonance imaging impede the full exploitation of this advanced imaging modality. The aim of this study is to provide a method for (a) quantifying and (b) reducing the implant distortions in patient images. Initially, a set of reference images (without distortion) was obtained by imaging a custom-designed three-dimensional grid phantom. Corresponding test images (containing the distortion) were acquired with the same imaging parameters, after positioning a specific metallic implant in the grid phantom. After determining: 1) the nonrecoverable; 2) the distorted, but recoverable; and 3) the unaffected areas, a point-based thin-plate spline image registration algorithm was employed to align the reference and test images. The calculated transformation functions utilized to align the image pairs described the implant distortions and could therefore be used to correct any other images containing the same distortions. The results demonstrate successful correction of grid phantom images with a metallic implant. Furthermore, the calculated correction was applied to porcine thigh images bearing the same metallic implant, simulating a patient environment. Qualitative and quantitative assessments of the proposed correction method are included.

Sample statistics and model parameters can be used to infer the properties, or characteristics, of the underlying population in typical data-analytic situations. Confidence intervals can provide an estimate of the range within which the true value of the statistic lies. A narrow confidence interval implies low variability of the statistic, justifying a strong conclusion made from the analysis. Many statistics used in software metrics analysis do not come with theoretical formulas to allow such accuracy assessment. The Efron bootstrap statistical analysis appears to address this weakness. In this paper, we present an empirical analysis of the reliability of several Efron nonparametric bootstrap methods in assessing the accuracy of sample statistics in the context of software metrics. A brief review on the basic concept of various methods available for the estimation of statistical errors is provided, with the stated advantages of the Efron bootstrap discussed. Validations of several different bootstrap algorithms are performed across basic software metrics in both simulated and industrial software engineering contexts. It was found that the 90 percent confidence intervals for mean, median, and Spearman correlation coefficients were accurately predicted. The 90 percent confidence intervals for the variance and Pearson correlation coefficients were typically underestimated (60-70 percent confidence interval), and those for skewness and kurtosis overestimated (98-100 percent confidence interval). It was found that the Bias-corrected and accelerated bootstrap approach gave the most consistent confidence intervals, but its accuracy depended on the metric examined. A method for correcting the under-/ overestimation of bootstrap confidence intervals for small data sets is suggested, but the success of the approach was found to be inconsistent across the tested metrics.

This distribution is counter-intuitive for at least two reasons. First it would seem “obvious” that the numbers drawn from a list generated from widely different arbitrary processes would have roughly equally probabilities for the digits 1 and 9 to be first digits. This is not normally the case. If the list of numbers does not have artificial limits, or include invented numbers such as postal codes, then approximately 30% of the numbers will have 1 as their first digit, but only 5% will have 9 as their first digit. Deviations from the expected Benford Distribution indicate the presence of some special characteristic of the data. The second, more theoretically challenging, problem is: What is the underlying property associated with so many widely different processes which generates lists of numbers that follow Benford's Law?

The Internet creates new opportunities for component distribution. Infrastructure for dynamic, Web-based composition of software components appears to be a very impelling need. This demonstration focuses on a Web-based system that supports dynamic component composition.

Motion artefact suppression remains an active topic in MRI. In this paper, we suggest that certain nonrigid, or spatially variant, characteristics of motion of an object can be represented by extending the work of Mitsa et al. This empirical extension uses a ghost distortion transfer function (GTDF) applied to the k-space (frequency domain) data. We demonstrate the variety of ghost characteristics that can be generated from various two-dimensional (2-D) GTDF's. The distortion transfer function for periodic motion along the Z-axis can be determined from the nonoverlapped portions of the ghost and central image. It required a GDTF with the shape of a belt bandpass filter to produce an image corresponding to the ghosts of a volunteer's abdomen image corrupted by unknown respiratory motion artefacts. The preliminary results of a composite method of motion artefact suppression are presented. The artefact suppression was successful for ghost images described by a GDTF have a low-pass nature, but less successful with ghosts have a GDTF of a bandpass nature.

A recent paper by Yan and Mao (see ibid., vol.12, no.1, p.73-7, 1993) provided the results of using a neural network based nonlinear prediction algorithm to extrapolate truncated magnetic resonance data. The extrapolation is intended to reduce the truncation artifacts that result when reconstructing an image from a limited k-space magnetic resonance data set using the discrete Fourier transform. When attempting to quantitatively compare Yan and Mao's method with the authors' own existing constrained modeling algorithm, the authors discovered a systematic error in Yan and Mao's analysis. With the error corrected, it was found that Yan and Mao's approach worked significantly better than they have reported and was more stable in the presence of noise.

M.R. Smith et al. (see IEEE Trans. Biomed. Imaging, vol.BM5-3, no.3, p.132-139, 1986) and E.M. Haacke et al. (see IEEE Trans. Acoust., Speech, and Signal Proc., to appear) have reported success with modeling techniques that overcome the problem of reduced data sets in MR data (magnetic resonance) imaging. The present authors detail the assumptions made by the Smith and Haacke reconstruction methods and their success in modeling MR data. By taking into account experimental phase distortions, they generate an improved model that makes the basic assumptions of the above models more realistic. The authors present the theory behind determining phase corrections from the gathered data and show the effect of applying the modified algorithms. Experimental results confirm that the correction of the phase distortions reduces the required model order of both the Smith and Haacke methods.>

The problem of estimating the parameters of noisy multicomponent signals using parametric modeling technique is considered in this paper. The multicomponent signal of interest is formed by a superposition of basic functions having the same location in time but different widths and amplitudes. Based on the modified Gardner transformation, some samples of deconvolved data are derived from the multicomponent signals. The deconvolved data are then modeled using a special nonstationary autoregressive moving average (ARMA) process in which the parameters of the ARMA model are obtained by linear least-squares procedure. The least-squares procedure is based on the singular value decomposition (SVD) to overcome the limitations of the transient error method (TEM) of analysis that uses cholesky decomposition to determine its AR coefficients. The moving average (MA) coefficients corresponds to the initial residual error sequences so as to account for the nonstationary noise in the deconvolved data. This new method of analysis, termed the SVD-based transient error method, produces high resolution estimates of the exponents of multicomponent signals at both low and high signal to noise (SNR) ratios.