CAS-BUS: A Test Access Mechanism and a Toolbox Environment for Core-Based System Chip Testing

As System on a Chip (SoC) testing faces new challenges, some new test architectures must be developed. This paper describes a Test Access Mechanism (TAM) named CAS-BUS that solves some of the new problems the test industry has to deal with. This TAM is scalable, flexible and dynamically reconfigurable. The CAS-BUS architecture is compatible with the IEEE P1500 standard proposal in its current state of development, and is controlled by Boundary Scan features.This basic CAS-BUS architecture has been extended with two independent variants. The first extension has been designed in order to manage SoC made up with both wrapped cores and non wrapped cores with Boundray Scan features. The second deals with a test pin expansion method in order to solve the I/O bandwidth problem. The proposed solution is based on a new compression/decompression mechanism which provides significant results in case of non correlated test patterns processing. This solution avoids TAM performance degradation.These test architectures are based on the CAS-BUS TAM and allow trade-offs to optimize both test time and area overhead. A tool-box environment is provided, in order to automatically generate the needed component to build the chosen SoC test architecture.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

Six Sigma experience as a stochastic process

This study empirically investigates the states of Six Sigma from a stochastic point of view. By the means of an advanced survey, 97 respondents are asked to rate the effect of Six Sigma on different performance categories, the cost of implementing Six Sigma, the level of enthusiasm and expectations from Six Sigma over 20 years. The autocorrelation and cross-correlation functions of these processes are analyzed to investigate the stages of Six Sigma. Consequently, new concepts namely steady state of Six Sigma and Six Sigma experience functions are introduced which shed light on the life cycle of Six Sigma within the companies.     

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.     

Synthesis,characterization and polymerization of novel sugars based on methacrylate

The present study describes the synthesis and characterizations of polymerizable vinyl sugars. Glucose, mannose, galactose and fructose are abundant and sustainable natural compounds. As it is not possible to make many derivatives of sugars without using protective groups, first of all, diacetone derivatives [diacetone-d-glucose (1), diacetone-d-mannose (2), diacetone-d-galactose (3) and diacetone-d-fructose (4)] were synthesized according to the literature as starting compounds. The remaining free hydroxyl groups on C-3 (diacetone glucose), C-6 (diacetone galactose), C-1 (diacetone fructose) and C-1 (diacetone mannose), were reacted with epichlorohydrin (1-chloro-2,3-epoxypropane) to produce then “-O-(2′,3′-epoxypropane-1′-yl)” ether derivatives (5, 6, 7, and 8) which are epoxy sugars in the basic medium. Next, the epoxy rings of the ethers (5, 6, 7, and 8) were opened with methacrylic acid in DMF to produce new sugar based methacrylates (9, 10, 11, and 12). Finally, free radical polymerization of these sugar based methacrylate monomers was performed, producing related polymers (13, 14, 15 and 16). The polymerizations were carried out using AIBN as an initiator at 70 °C in DMF. All the products were characterized by FTIR, ¹HNMR and ¹³CNMR techniques. Thermal properties of all polymers were investigated by TG, DTG and DSC. The data obtained has suggested that thermal stability of the synthesized polymers has changed with the structure of the sugar and increase in molecular weight.     

Understanding the Corrosion Inhibition Mechanism of Mild Steel in Hydrochloric Acid by a Triazole Derivative: A Combined Experimental and Theoretical Approach

Protection of Metals and Physical Chemistry of Surfaces - The effect of an heterocycle triazole, namely (1-(4-methoxyphenyl)-1H-1,2,3-triazol-4-yl)methanol (MTM) on the corrosion of mild steel in...     

An Overview on the Performance of 1,2,3-Triazole Derivatives as Corrosion Inhibitors for Metal Surfaces

This review accounts for the most recent and significant research results from the literature on the design and synthesis of 1,2,3-triazole compounds and their usefulness as molecular well-defined corrosion inhibitors for steels, copper, iron, aluminum, and their alloys in several aggressive media. Of particular interest are the 1,4-disubstituted 1,2,3-triazole derivatives prepared in a regioselective manner under copper-catalyzed azide-alkyne cycloaddition (CuAAC) click reactions. They are easily and straightforwardly prepared compounds, non-toxic, environmentally friendly, and stable products to the hydrolysis under acidic conditions. Moreover, they have shown a good efficiency as corrosion inhibitors for metals and their alloys in different acidic media. The inhibition efficiencies (IEs) are evaluated from electrochemical impedance spectroscopy (EIS) parameters with different concentrations and environmental conditions. Mechanistic aspects of the 1,2,3-triazoles mediated corrosion inhibition in metals and metal alloy materials are also overviewed.     

Using augmented reality technology in storytelling activities: examining elementary students’ narrative skill and creativity

The aim of the study was to examine the effects of augmented reality technology on stories in terms of narrative skill, story length and creativity and also to examine correlations between these variables. Posttest-only design with a nonequivalent group model was used. In this study, the sample consisted of 100 fifth-grade elementary students, comprising 46 boys and 54 girls. Purposive and convenience sampling methods were applied. For purposive sampling, the group’s ages, education levels, and experiences in storytelling activities were gathered, and for convenience sampling, easy access to schools was considered. As data collection tools, a suitable narrative scale was used which was found in the literature and creative story form was developed by the researcher. According to the findings, mean scores for all variables for the experimental group were higher than those for the control group. Also, a statistically significant mean difference was found between the experimental and control groups with regard to narrative skill, length of stories, and creativity in stories. In fact, a positive correlation was found between all variables. It is important to recognize when a technology is found to contribute positively to narrative skill and creativity in telling stories, and to ensure this technology is used. Determining correlation between these variables may provide a contribution to studies about evaluating the effect of the new technologies.