A Digitally Controlled Stimulator for Neurophysiological Applications

A stimulator circuit is presented which is capable of generating pulse train waveforms suitable for neurophysiological experiments are available, as well as special test conditions such as dishabituation. Digital and linear integrated circuits are used to provide precise control over the stimulus parameters.     

Preparation of Pt supported on mesoporous carbons for the reduction of oxygen in polymer electrolyte membrane fuel cell (PEMFC)

Porous carbon materials (SM-C, HS-C and TM-C) were prepared using commercial colloidal silicas (SM-30, HS-40 and TM-50) and a resorcinol-formaldehyde resin as a removable template and a carbon precursor, respectively. All of the prepared carbons had high surface areas with narrow pore size distributions. In particular, the pore diameter of the carbons could be controlled over a range of mesopore size by the use of an appropriate silica employed as a template. Mesoporous carbon templated using TM-50 had the largest pore size, while that for SM-C, was the smallest. Pt nanoparticles were supported on these mesoporous carbons for use as a catalyst in a polymer electrolyte membrane fuel cell (PEMFC). The crystallite size of the Pt catalyst was found to be closely related to the properties of the corresponding carbon support. A carbon support with a large pore size and a high surface roughness was found to favor the dispersion of Pt crystallite. In a single cell test, the Pt catalysts supported on mesoporous carbons exhibited higher cell performance than that on activated carbon. In particular, the Pt/TM-C catalyst showed the best cell performance among the catalysts tested. In addition to the high surface area of the active metal, the large pore size of the Pt/TM-C appears to have positive effect on the distribution of ionomer, resulting in facile formation of a triple-phase boundary.     

The preparation and characterization of porous carbons for hydrogen storage

Porous carbon materials with a cylindrical pore structure were prepared using ordered mesoporous silica as a removable template. To investigate the effect of the structural and textural properties of the products on hydrogen adsorption capacity, different carbon precursors and synthetic methods were used in their preparation. All of the carbon materials prepared showed a well-defined pore structure with a high surface area irrespective of the carbon precursor used in the preparation. Hydrogen adsorption tests indicated that the capacity of the materials for hydrogen adsorption was highly dependent on total surface area and the pore structure. Based on the N₂ sorption results, the total surface area was directly correlated with the hydrogen adsorption capacity.     

Time-accurate Navier-Stokes simulation of vortex convection using an unstructured dynamic mesh procedure

A two-dimensional Navier-Stokes flow solver is developed for the simulation of unsteady flows on unstructured adaptive meshes. The solver is based on a second-order accurate implicit time integration using a point Gauss-Seidel relaxation scheme and a dual time-step subiteration. A vertex-centered, finite-volume discretization is used in conjunction with Roe’s flux-difference splitting. The Spalart-Allmaras one equation model is employed for the simulation of turbulence. An unsteady solution-adaptive dynamic mesh scheme is used by adding and deleting mesh points to take account of spatial and temporal variations of the flowfield. Unsteady viscous flow for a traveling vortex in a free stream is simulated to validate the accuracy of the dynamic mesh adaptation procedure. Flow around a circular cylinder and two blade-vortex interaction problems are investigated for demonstration of the present method. Computed results show good agreement with existing experimental and computational results. It was found that unsteady time-accurate viscous flows can be accurately simulated using the present unstructured dynamic mesh adaptation procedure.     

Face recognition using fuzzy Integral and wavelet decomposition method

In this paper, we develop a method for recognizing face images by combining wavelet decomposition, Fisherface method, and fuzzy integral. The proposed approach is comprised of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. As a result of this decomposition, we obtain four subimages (namely approximation, horizontal, vertical, and diagonal detailed images). The second stage of the approach concerns the application of the Fisherface method to these four decompositions. The choice of the Fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The two last phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet type of fuzzy integral are considered as the aggregation method. In the experiments we use n-fold cross-validation to assure high consistency of the produced classification outcomes. The experimental results obtained for the Chungbuk National University (CNU) and Yale University face databases reveal that the approach presented in this paper yields better classification performance in comparison to the results obtained by other classifiers.     

Computational models of a nano probe tip for static behaviors

It is difficult to predict the measurement bias arising from the compliance of the atomic force microscope (AFM) probe. The issue becomes particularly important in this situation where nanometer uncertainties are sought for measurements with dimensional probes composed of flexible carbon nanotubes mounted on AFM cantilevers. We have developed a finite element model for simulating the mechanical behavior of AFM cantilevers with carbon nanotubes attached. Spring constants of both the nanotube and cantilever in two directions are calculated using the finite element method with known Young's moduli of both silicon and multiwall nanotube as input data. Compliance of the nanotube-attached AFM probe tip may be calculated from the set of spring constants. This paper presents static models that together provide a basis to estimate uncertainties in linewidth measurement using nanotubes. In particular, the interaction between a multiwall nanotube tip and a silicon sample is modeled using the Lennard-Jones theory. Snap-in and snap-out of the probe tip in a scanning mode are calculated by integrating the compliance of the probe and the sample-tip interacting force model. Cantilever and probe tip deflections and points of contact are derived for both horizontal scanning of a plateau and vertically scanning of a wall. The finite element method and the Lennard-Jones model provide a means to analyze the interaction of the probe and sample and measurement uncertainty, including actual deflection and the gap between the probe tip and the measured sample surface.     

Two additional cases of metformin‐associated encephalopathy in patients with end‐stage renal disease undergoing hemodialysis

We report on two additional cases of metformin‐associated encephalopathy in patients with end‐stage renal disease (ESRD) undergoing hemodialysis. Two patients were seen at our hospital with abnormal neurological signs and symptoms. Magnetic resonance imaging (MRI) revealed the same pattern of high signal intensity in both basal ganglia in T2‐weighted images in the two patients. The two patients had started taking metformin 5 and 6 weeks earlier at the same dose of 1000 mg per day. Metformin was immediately stopped, and regular hemodialysis was conducted. Their signs and symptoms resolved completely after these measures. The high signal intensity in both ganglia in T2‐weighted MRI also disappeared. We should suspect metformin‐induced encephalopathy and withdraw the drug when presented with diabetic patients with chronic kidney disease and neurological signs and symptoms of unknown cause.     

Effects of Annealing Temperature on Microstructure and Tensile Properties in Ferritic Lightweight Steels

An investigation was conducted into the effects of annealing temperature on microstructure and tensile properties of ferritic lightweight steels. Two steels were fabricated by varying the C content, and were annealed at 573 K to 1173 K (300 °C to 900 °C) for 1 hour. According to the microstructural analysis results, κ-carbides were formed at about 973 K (700 °C), which was confirmed by equilibrium phase diagrams calculated from a THERMO-CALC program. In the steel containing low carbon content, needle-shaped κ-carbides were homogeneously dispersed in the ferrite matrix, whereas bulky band-shaped martensites were distributed in the steel containing high carbon content. In the 973 K (700 °C)-annealed specimen of the steel containing high carbon content, deformation bands were formed throughout the specimen, while fine carbides were sufficiently deformed inside the deformation bands, thereby resulting in the greatest level of strength and ductility. These results indicated that the appropriate annealing treatment of steel containing high carbon content was useful for the improvement of both strength and ductility over steel containing low carbon content.     

Generalized mean for feature extraction in one-class classification problems

Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a generalized mean. A novel method is also presented to effectively maximize the objective function. The experimental results show that the proposed method provides better discriminative features than the BDA and its variants.     

A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.