A neural network with asymmetric basis functions for feature extraction of ECG P waves.

In this work a simple neural network with asymmetric basis functions is proposed as a feature extractor for P waves in electrocardiographic signals (ECG). The neural network is trained using the classical backward-error-propagation algorithm. The performance of the proposed network was tested using actual ECG signals and compared with other types of neural feature extractors.     

Diverse nearest neighbors queries using linear skylines

k-nearest neighbor (k-NN) queries are well-known and widely used in a plethora of applications. However, in the original definition of k-NN queries there is no concern regarding diversity of the answer set with respect to the user’s interests. For instance, travelers may be looking for touristic sites that are close to where they are, but that would also lead them to see different parts of the city. Likewise, if one is looking for restaurants close by, it may be more interesting to learn about restaurants of different categories or ethnicities which are nonetheless relatively close. The interesting novel aspect of this type of query is that there are two competing criteria to be optimized: closeness and diversity. We propose two approaches that leverage the notion of linear skyline queries in order to find the k diverse nearest neighbors within a radius r from a given query point, or (k, r)-DNNs for short. Our proposed approaches return a relatively small set containing all optimal solutions for any linear combination of the weights a user could give to the two competing criteria, and we consider three different notions of diversity: spatial, categorical and angular. Our experiments, varying a number of parameters and exploring synthetic and real datasets, in both Euclidean space and road networks, respectively, show that our approaches are several orders of magnitude faster than a straightforward approach.     

Brazing Al2O3 to sintered Fe-Ni-Co alloys

Active metal brazing has been widely used to join ceramics to metals, as sound joints are usually achieved in a single step process without special equipment. However, residual stresses may be a potential problem especially upon joining ceramics to alloys with relatively high thermal expansion coefficients. This work investigates the brazing behavior of Alumina (Al₂O₃) to a sintered Fe-Ni-Co alloy, specially designed to match its coefficient of thermal expansion to that of the ceramic counterpart. The results indicate the presence of an interfacial zone whose microstructure depends on the filler alloy employed. A relationship was established between the microstructure of the interface and the flexural strength of the joints.     

Additive spectral method for fuzzy cluster analysis of similarity data including community structure and affinity matrices

An additive spectral method for fuzzy clustering is proposed. The method operates on a clustering model which is an extension of the spectral decomposition of a square matrix. The computation proceeds by extracting clusters one by one, which makes the spectral approach quite natural. The iterative extraction of clusters, also, allows us to draw several stopping rules to the procedure. This applies to several relational data types differently normalized: network structure data (the first eigenvector subtracted), affinity between multidimensional vectors (the pseudo-inverse Laplacian transformation), and conventional relational data including in-house data of similarity between research topics according to working of a research center. The method is experimentally compared with several classic and recent techniques and shown to be competitive.     

Extracting Stray Magnetic Fields from Thin Ferromagnetic Layers in Hybrid Superconducting/Ferromagnetic Heterostructures

Journal of Superconductivity and Novel Magnetism - Hybrid Nb(20)/Cu(5)/Py(2), Nb(20)/Cu(5)/Co(40) and Nb(20)/Cu(d $$_{Cu}$$ )/Py(2)/Cu(5)/Co(40) heterostructures (values in nanometers and d...     

Interconnection between the solidification and precipitation hardening processes of an AlSiCu alloy

Horizontal directional solidification and precipitation hardening experiments were performed with the Al5.5Si3Cu alloy. Solidification thermal and microstructural parameters such as growth and cooling rates (V_L and T_R, respectively) and the secondary dendrite arm spacing (λ₂) were determined experimentally. As-solidified samples were selected and submitted to the T6-heat treatment (T6-HT). The T6-HT was carried out considering two solution times: 8 and 5?h at 490?±?2°C, followed by quenching in warm water (60?±?2°C), aging for 5?h at 155?±?2°C and air-cooling. Higher HV values were observed for the solution time of 5 h. As highlights of this work, the influence of the processing parameters (V_L, T_R and λ₂) on the distribution, morphology and size of the eutectic Si particles has been investigated.     

Influence of heat-treatment environment on Ni-ferrite nanoparticle formation from coconut water precursor

The kinetics of formation of NiFe₂O₄ nanoparticles were investigated where the nanoparticles were produced by the proteic-sol–gel method using coconut water followed by annealing in (i) air, (ii) air in the presence of boron nitride (BN), or (iii) nitrogen. The sample dried at 473 K for 5.5 h was composed of small disordered NiFe₂O₄ nanoparticles in a superparamagnetic state as determined from Mössbauer spectroscopy. In general, heat treatment at high temperature leads to a nanocomposite rich in NiFe₂O₄. In air, annealing at 1173 K for 8 h favored the formation of the magnetically ordered NiFe₂O₄ inverse spinel structure, with bulk characteristics and average crystal sizes of approximately 66 nm. In a nitrogen atmosphere and in compacted BN powder under air atmosphere, the NiFe₂O₄ spinel structure stabilized for temperatures up to 873 and 773 K, respectively, however, decomposition of the NiFe₂O₄ phase into other undesired structures was observed above 873 K.