Densification of silicon nitride ceramics under sinter-HIP conditions

The influence of several sinter-HIP variables on the densification behaviour of silicon nitride-based ceramics has been investigated. The processing conditions were studied for Si₃N₄ powder mixtures containing controlled amounts of Y₂O₃+Al₂O₃ or Y₂O₃+MgO. The specimens were subjected to sinter-HIP cycles under argon or nitrogen atmospheres at various temperatures and pressures. The final density of the powder compacts exhibited a strong dependence not only on the applied pressure, the composition and the processing temperature, but also on the pressurization rate and the initial pressurization time. The microstructural changes induced by the application of high pressure were followed by transmission electron microscopy. On examination by TEM, large concentrations of dislocations, generated inside some-Si₃N₄ grains, were observed. Characterization of these dislocations showed thatb=0001 is their most frequently found Burgers vector. Also, two relaxation mechanisms, tending to release the stored energy of deformation in the-Si₃N₄ grains, namely grain penetration (a form of strain-induced boundary migration) and grain fragmentation (the formation of subgrains due to rearrangement of dislocations into low energy configurations), have been identified. The intergranular phases formed were characterized by energy dispersive X-ray spectroscopic analysis, X-ray diffraction and electron diffraction. The influence of different sinter-HIP cycles on the transformation of silicon nitride without additives was also investigated by X-ray diffractometry.     

Deploying deep learning approaches to left ventricular non-compaction measurement

The Journal of Supercomputing - Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer...     

A comparison study of wavelet transforms for the visualization of differentially methylated regions in DNA samples

The Journal of Supercomputing - DNA methylation analysis has become an important topic in the study of human health. DNA methylation analysis requires not only a specific treatment of DNA samples...     

Observing Inequality: Can Ergonomic Observations Help Interventions Transform the Role of Gender in Work Activity?

Computer Supported Cooperative Work (CSCW) - Work activity ergonomics (sometimes called francophone ergonomics) draws heavily on observation in order to support transformation of work to arrive at...     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Does maintainability relate to the energy consumption of software? A case study

Software Quality Journal - Energy consumption of software has been becoming increasingly significant, since it can vary according to how the software has been developed. In recent years, developers...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.