Automating IEEE 1500 Core Test—An EDA Perspective

Editor's note:Standardized design and test practices enable automation. This article describes a methodology and corresponding tool set that combines automated support for IEEE Std 1500 and test data compression in one.—Erik Jan Marinissen, IMEC     

Simulation of optical model parameters with reduced model deficiency by D-optimal criterion

The nucleon optical model is widely used to calculate the elastic scattering cross-sections and polarizations. The only requirement, in using the optical model for a reliable prediction of the nuclear data is the unique set of optical model parameters (OMP). The uncertainties in the OMP lead to uncertainties in calculated cross-sections and thus the deficiency in the model itself. To our knowledge, there is no technique, to estimate the limits of variation of OMP to establish good fits with consequent reduction of optical model deficiency between these limits. In this paper, we propose D-optimal criterion and the technique of Determinant Inequalities developed by us to simulate OMP with reduced optical model deficiency. Our technique enables one to simulate the limits of variation of the OMP with the existing covariance data. The simulation of these limits would aid to establish good fits among the OMP. Further, the variation in the range of deficiency in the optical model can be ascertained well in advance. We demonstrate the utility of our technique to the OMP of ²³⁹Pu.     

Wear Performance of Laser Processed Tantalum Coatings

This first generation investigation evaluates the in vitro tribological performance of laser-processed Ta coatings on Ti for load-bearing implant applications. Linear reciprocating wear tests in simulated body fluid showed one order of magnitude less wear rate, of the order of 10(-4)mm(3)(N.m)(-1), for Ta coatings compared to Ti. Our results demonstrate that Ta coatings can potentially minimize the early-stage bone-implant interface micro-motion induced wear debris generation due to their excellent bioactivity comparable to that of hydroxyapatite (HA), high wear resistance and toughness compared to popular HA coatings.     

Experimental Comparison of the Effects of Nanometric and Micrometric Particulates on the Tensile Properties and Fracture Behavior of Al Composites at Room and Elevated Temperatures

This article studies the influence of nanometric (n-SiC_p) and micrometric-scale SiC particulates (μ-SiC_p) on the tensile properties of the Al 7075 alloy. The unreinforced Al and its composites were synthesized using the powder metallurgy (P/M) route and were tested uniaxially in tension at both room and elevated temperatures. Aging behavior was studied to observe any effect of the reinforcement on the aging kinetics and hardness of the composites. X-ray diffraction was performed to determine the crystal structures of the raw materials and any reaction phase formed in the composites. The n-SiC_p were not dispersed uniformly in the Al matrix and clustered mainly at the grain boundaries. The stiffness of the composites increased and the ductility decreased with an increase in the volume fraction of the n-SiC_p. The n-SiC_p proved to be a better reinforcement than the traditional μ-SiC_p in terms of imparting higher ductility to the composite. Fractography and microscopy using optical, scanning electron, and transmission electron microscopes were performed for failure and microstructural analysis of all the materials. At room temperature, the fracture altered from ductile in the unreinforced Al to brittle in the composites. At an elevated temperature, the fracture mechanism transformed from brittle to ductile rupture in the composites.     

Electrical and Thermal Properties of Twin-Screw Extruded Multiwalled Carbon Nanotube/Epoxy Composites

This paper presents the experimental results of dispersing multiwalled carbon nanotubes (MWNTs) into epoxy (space grade structural adhesive) nanocomposites using co-rotating twin screw extrusion process. Two sets of specimens were prepared; set 1 with ultrasonication for predispersing MWNT before extrusion and set 2 direct dispersion of MWNT in the extruder. MWNT was loaded up to 8 vol.% in both the sets. The specimens were characterized for room temperature volume and surface resistivities as per ASTM D257 using Keithley Model 6517 and for thermal conductivity in the temperature range ?50 to 150 °C as per ASTM E 1530 using Thermal Conductivity Instrument (TCI) 2022 SX211. The volume resistivity of sets 1 and 2 decreased to an extent of 10¹¹ and 10⁹ respectively. The surface resistivity drop was of the order of 10⁹ for both the sets. These drops corresponded to the maximum MWNT loading of 8 vol.%. Electrical conductivity values of the specimens were fitted into the Power Law Model to evaluate the critical exponent. Both sets 1 and 2 showed increase in thermal conductivity with increase in temperature in the testing range. Thermal conductivity increased with increase in filler loading and the maximum increase was 60% at 150 °C in case of 8 vol.% MWNT nanocomposites for set 1. The corresponding value for the set 2 was 25%. Thermal conductivity values were predicted using Lewis Nielson model. DSC of the specimens showed increase in glass transition temperature with increase in filler loading. The dispersion of the nanofillers was studied using SEM and the surface morphology using AFM.     

Multi-Criteria Decision Making Approach for Watershed Prioritization Using Analytic Hierarchy Process Technique and GIS

Watershed prioritization based on the natural resources and physical processes involves locating critical areas of erosion, which produce maximum sediment yield to take up conservation activities on priority basis. The present study was taken up with a specific objective of prioritization of micro-watersheds using Multi-Criteria Decision Approach – Analytic Hierarchy Process (AHP) based SYI model (AHPSYI) under GIS environment for a case study area of Mayurakshi watershed in India. This method basically uses information of Potential Erosion Index (PEI) and Sediment Delivery Ratio (SDR), indicative of transport capacity. In the present study, sediment delivery factors viz., topography, vegetation cover, proximity to water courses and soil were translated into GIS layers and integrated using Boolean conditions to create a data layer of spatially distributed SDIs’ across the watershed. For assessment of PEI, important watershed parameters viz., land use/land cover, soil, slope, and drainage density maps were integrated in the GIS environment using Weighted Linear Combination method (WLC) by assigning weights to themes and ranks to features of individual theme using AHP technique. A comparison between AHPSYI based sub watershed prioritization map with that of prioritization map based on the observed sediment yield data revealed that about 78 % of the area showed concurrence. Thus, it can be inferred that the watershed prioritization based on only thematic layers can be dependable to maximum extent. Subsequently, proposed approach was adopted for prioritization of the study area at micro watershed scale, where area under high and very high categories together constitutes around 33 % of the study area. Around 100 micro-watersheds out of 276 watersheds are under moderate to very high category respectively, signifying the need for watershed management.     

SOLUTION OF TRANSIENT LAMINAR NATURAL CONVECTION IN A SQUARE CAVITY BY AN EXPLICIT FINITE ELEMENT SCHEME

This paper discusses the application of a finite element method based on the first-order projection scheme, which is an extension ofChorin's algorithm, to transient laminar natural convection in a square cavity. Results have been presented for a fluid of Prandtl number 0.71 in the Rayleigh number range of 10³-10⁶ in terms of the variation of vertical velocity component, nondimensional temperature, and average Nusselt number with time. Various features of the scheme that render it economical in terms of CPU time and storage requirements are discussed. The steady state results have been compared with benchmark solutions, and the agreement appears to be good.     

Automated cell classification and visualization for analyzing remyelination therapy

Remyelination therapy is a state-of-the-art technique for treating spinal cord injury (SCI). Demyelination—the loss of myelin sheath that insulates axons, is a prominent feature in many neurological disorders resulting in SCI. This lost myelin sheath can be replaced by remyelination. In this paper, we propose an algorithm for efficient automated cell classification and visualization to analyze the progress of remyelination therapy in SCI. Our method takes as input the images of the cells and outputs a density map of the therapeutically important oligodendrocyte-remyelinated axons (OR-axons) which is used for efficacy analysis of the therapy. Our method starts with detecting cell boundaries using a robust, shape-independent algorithm based on iso-contour analysis of the image at progressively increasing intensity levels. The detected boundaries of spatially clustered cells are then separated using the Delaunay triangulation based contour separation method. Finally, the OR-axons are identified and a density map is generated for efficacy analysis of the therapy. Our efficient automated cell classification and visualization of remyelination analysis significantly reduces error due to human subjectivity. We validate the accuracy of our results by extensive cross-verification by the domain experts.     

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.