Speeding up Fault Simulation using Parallel Fault Simulation

AbstractIn this paper, a novel approach is introduced on accelerating the fault simulation speed on field programmable gate array (FPGA). The approach is based on parallel simulation methodology. More than one faulty circuit is handled in the fault simulation system, but the relative area overhead is low and it will accelerate the simulation process. A new metrics – Speedup relative to the Ratio of Hardware Overhead (SRHO) is introduced, by which the experimental results are evaluated. Experimental results in terms of simulation time, hardware overhead and SRHO for ISCAS-85 benchmark circuits are compared to a previous work to show its advantage

Application of Fractal Dimension for Quantifying Noise Texture in Computed Tomography Images

Purpose Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization. Methods The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential box‐counting algorithm applied to images of the uniform section of ACR phantom. The two‐dimensional Noise Power Spectrum (NPS) and one‐dimensional‐radially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPS‐peak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom. Results Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rank‐order coefficient of 0.98 (P‐value \u3c 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 × 64 pixels or one ROI of 128 × 128 pixels. Fractal dimension was found to be sensitive to non‐noise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction. Conclusions Fractal dimension correlated with the NPS‐peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64‐pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images

CT Automated Exposure Control Using A Generalized Detectability Index

Purpose Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index (d\u27gen) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches. Methods This study proposes a task‐based automated exposure control (AEC) method using a generalized detectability index (d\u27gen). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d\u27gen metric is calculated using lookup tables of task‐based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in‐house iterative reconstruction algorithm with different regularization strengths (IR1–IR4). The task‐based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d\u27gen and noise standard deviation. The performance of the proposed d\u27gen‐AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images. Results The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d\u27gen predicted by the lookup table and d\u27 measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d\u27gen‐AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the d\u27gen‐AEC method, the observers’ IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P \u3e 0.35). The d\u27gen‐AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans. Conclusions A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d\u27gen‐AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels

Self-Lubricating Polytetrafluoroethylene/Polyimide Blends Reinforced with Zinc Oxide Nanoparticles

ZnO nanoparticle reinforced polytetrafluoroethylene/polyimide (PTFE/PI) nanocomposites were prepared and their corresponding tribological and mechanical properties were studied in this work. The influences of ZnO loading, sliding load, and velocity on the tribological properties of ZnO/PTFE/PI nanocomposites were systematically investigated. Results reveal that nanocomposites reinforced with 3 wt% ZnO exhibit the optimal tribological and mechanical properties. Specifically, the wear loss decreased by 20% after incorporating 3 wt% ZnO compared to unfilled PTFE/PI. Meanwhile, the impact strength, tensile strength, and elongation-at-break of 3 wt% ZnO/PTFE/PI nanocomposite are enhanced by 85, 5, and 10% compared to pure PTFE/PI blend. Microstructure investigation reveals that ZnO nanoparticles facilitate the formation of continuous, uniform, and smooth transfer film and thus reduce the adhesive wear of PTFE/PI

Identification of wounding and topping responsive small RNAs in tobacco (Nicotiana tabacum)

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) and short interfering RNAs (siRNAs) are two major classes of small RNAs. They play important regulatory roles in plants and animals by regulating transcription, stability and/or translation of target genes in a sequence-complementary dependent manner. Over 4,000 miRNAs and several classes of siRNAs have been identified in plants, but in tobacco only computational prediction has been performed and no tobacco-specific miRNA has been experimentally identified. Wounding is believed to induce defensive response in tobacco, but the mechanism responsible for this response is yet to be uncovered.</p> <p>Results</p> <p>To get insight into the role of small RNAs in damage-induced responses, we sequenced and analysed small RNA populations in roots and leaves from wounding or topping treated tobacco plants. In addition to confirmation of expression of 27 known miRNA families, we identified 59 novel tobacco-specific miRNA members of 38 families and a large number of loci generating phased 21- or 24-nt small RNAs (including ta-siRNAs). A number of miRNAs and phased small RNAs were found to be responsive to wounding or topping treatment. Targets of small RNAs were further surveyed by degradome sequencing.</p> <p>Conclusions</p> <p>The expression changes of miRNAs and phased small RNAs responsive to wounding or topping and identification of defense related targets for these small RNAs suggest that the inducible defense response in tobacco might be controlled by pathways involving small RNAs.</p

Deformed Two-Mode Quadrature Operators in Noncommutative Space

Starting from noncommutative quantum mechanics algebra, we investigate the variances of the deformed two-mode quadrature operators under the evolution of three types of two-mode squeezed states in noncommutative space. A novel conclusion can be found and it may associate the checking of the variances in noncommutative space with homodyne detecting technology. Moreover, we analyze the influence of the scaling parameter on the degree of squeezing for the deformed level and the corresponding consequences.Comment: 11 pages, no figure