Effect ofθ-alumina formation on the growth kinetics of alumina-forming superalloys

Alumina-forming ODS superalloys are excellent oxidation-resistant materials. Their resistance relies upon the establishment of a stable, slow-growing, and adherent -alumina. In the present investigation, these alloys exhibited unstable and relatively less adherent -alumina phase, which increased the oxidation rate in the transient stage and converted into -alumina in the later part of the exposure. The oxide-growth process was found to depend upon various parameters such as temperature, time, and presence of an active elecment in the superalloy. Characterization carried out by XRD, SEM/EDAX, and AES on oxidized ODS and non-ODS alloys demonstrated a significant influence of the active element, Y, on the transformation of - to -alumina. SIMS analysis of two-stage oxidation at 900°C for two different durations evidently showed that the change in the transport process is due to -to--alumina transformation. On the basis of these results, a new and consistent mechanism is proposed to explain the influence of -alumina and its transformation on growth kinetics and the effect of yttrium on the transformation leading to good scale adherence and oxidation resistance.     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

EPR study of Cu2+ in Na2O-NaF-B2O3-Bi2O3 glasses

Electron paramagnetic resonance (EPR) studies have been performed on the glass system (30–x) NaF-xNa₂O-50B₂O₃-20Bi₂O₃ doped with CuO as a paramagnetic probe. The calculated g values indicate that Cu²⁺ is in a tetragonally elongated octahedral co-ordination, and the Jahn-Teller character gives rise to anisotropic hyperfine structure. The spin-Hamiltonian parameters and -bonding parameter, ², are calculated. Varying the fluoride ion concentration and its effect on these parameters is also discussed.     

High order parameter-robust numerical method for time dependent singularly perturbed reaction–diffusion problems

We introduce a high order parameter-robust numerical method to solve a Dirichlet problem for one-dimensional time dependent singularly perturbed reaction-diffusion equation. A small parameter ε is multiplied with the second order spatial derivative in the equation. The parabolic boundary layers appear in the solution of the problem as the perturbation parameter ε tends to zero. To obtain the approximate solution of the problem we construct a numerical method by combining the Crank–Nicolson method on an uniform mesh in time direction, together with a hybrid scheme which is a suitable combination of a fourth order compact difference scheme and the standard central difference scheme on a generalized Shishkin mesh in spatial direction. We prove that the resulting method is parameter-robust or ε-uniform in the sense that its numerical solution converges to the exact solution uniformly well with respect to the singular perturbation parameter ε. More specifically, we prove that the numerical method is uniformly convergent of second order in time and almost fourth order in spatial variable, if the discretization parameters satisfy a non-restrictive relation. Numerical experiments are presented to validate the theoretical results and also indicate that the relation between the discretization parameters is not necessary in practice.     

A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

Identifying some important success factors in adopting agile software development practices

Agile software development (ASD) is an emerging approach in software engineering, initially advocated by a group of 17 software professionals who practice a set of “lightweight” methods, and share a common set of values of software development. In this paper, we advance the state-of-the-art of the research in this area by conducting a survey-based ex-post-facto study for identifying factors from the perspective of the ASD practitioners that will influence the success of projects that adopt ASD practices. In this paper, we describe a hypothetical success factors framework we developed to address our research question, the hypotheses we conjectured, the research methodology, the data analysis techniques we used to validate the hypotheses, and the results we obtained from data analysis. The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past. The study indicates that nine of the 14 hypothesized factors have statistically significant relationship with “Success”. The important success factors that were found are: customer satisfaction, customer collaboration, customer commitment, decision time, corporate culture, control, personal characteristics, societal culture, and training and learning.