Evaluation of TEM samples of an Mg-Al alloy prepared using FIB milling at the operating voltages of 10 kV and 40 kV

Transmission electron microscopy (TEM) samples of an Mg-Al alloy has been prepared using a Ga-focused ion beam (FIB) milling at two different operating voltages of 10 kV and 40 kV to investigate the influence of the FIB energy on the sample quality. The fine structures of the samples have been studied using a high resolution TEM, and the concentration of the implanted Ga was analysed using an energy dispersive X-ray (EDX) analysis. The result of the TEM observation revealed that point defects were introduced to the sample finally milled at 40 kV but not at 10 kV. However, crystal lattice images and electron diffraction patterns were clearly observed on both the samples. The typical influence of the FIB energy was indicated in the elemental analysis. The relative Ga concentration in the thin sample finally milled at 10 kV was 1.0-2.0 at% that is less than half of 4.0-6.0 at% of the Ga concentration in the sample finally milled at 40 kV. A comparison between the experimental results of the Ga concentration measurement with simulation was also discussed.     

Trading variance reduction with unbiasedness: the regularized subspace information criterion for robust model selection in kernel regression

A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results.     

Speech recognition using pulse-coupled neural networks with a radial basis function

We have developed a novel pulse-coupled neural network (PCNN) for speech recognition. One of the advantages of the PCNN is in its biologically based neural dynamic structure using feedback connections. To recall the memorized pattern, a radial basis function (RBF) is incorporated into the proposed PCNN. Simulation results show that the PCNN with a RBF can be useful for phoneme recognition. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Diagnosis of right ventricular infarction: experimental study through the use of body surface isopotential maps

This investigation was designed to diagnose right ventricular infarction, which is difficult to diagnose by the standard twelve-lead ECG, through the use of body surface isopotential maps which have significant diagnostic information. Right ventricular infarction was experimentally caused by ligation of the canine right coronary artery. Each dog had a series of maps recorded before and a week after experimentally-induced myocardial infarction. The common features of maps in right ventricular infarction are: 1. In the early stage of the ventricular depolarization the negative area occupies a comparatively large part of the right anterior chest surface, and in the middle stage, the larger part of the right anterior surface is also occupied by the negative area. 2. A minimum appears on the right anterior chest surface in the early stage. The delayed excitation resulting from intraventricular conduction disturbance caused by infarction, as verified by the epicardial isochronic map, is also well represented by the body surface isopotential map. In conclusion, through the use of body surface isopotential maps, it is much easier to diagnose right ventricular infarction and intraventricular conduction disturbance caused by infarction even in cases in which the standard twelve-lead ECG does not show the abnormalities clearly.     

Low temperature deposition of hexagonal BN films by chemical vapour deposition

Transparent hexagonal BN films were deposited onto copper substrates from the reactant gas BCl₃-NH₃-H₂ at temperatures in the range 250–700°C. The lowest deposition temperature of the films was about 250°C. The films deposited at temperatures below 450°C were unstable in moist atmosphere and devitrified; a 20%–30% decrease in weight was observed when these films were heated above 600°C in an argon atmosphere. In contrast, the films deposited at temperatures above 600°C were very stable, decreased in weight by 1%–2% on heating and were stable in air at temperatures below 750°C.     

Subspace information criterion for model selection

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this article, we propose a new criterion for model selection, the subspace information criterion (SIC), which is a generalization of Mallows's C(L). It is assumed that the learning target function belongs to a specified functional Hilbert space and the generalization error is defined as the Hilbert space squared norm of the difference between the learning result function and target function. SIC gives an unbiased estimate of the generalization error so defined. SIC assumes the availability of an unbiased estimate of the target function and the noise covariance matrix, which are generally unknown. A practical calculation method of SIC for least-mean-squares learning is provided under the assumption that the dimension of the Hilbert space is less than the number of training examples. Finally, computer simulations in two examples show that SIC works well even when the number of training examples is small.     

Mask design compensation for sloped sidewall structures fabricated by X-ray lithography

We have investigated and report in this paper the factors influencing the deformation caused by the dependence between the absorbed X-ray energy on the resist and the shape of the absorber on the X-ray mask. Based on the measurement of errors that occurred during the transferring process between the 2-D shape of mask pattern and the resulting wall of the fabricated 3-D structure, we have developed newly useful graphical data on the absorbed X-ray energy, dosage, and shape of a microstructure. As a result, it is being reported as a method for compensation for the deformed shape after the fabrication of a quadruplets-microneedle. We have considered a number of factors affecting the deformation and finally realized that the effect of a dose–depth nonlinear curve is the most possible cause. Without the compensation of the mask design, we could observe the deformed shapes of the sloped sidewall on the exposed structures. Polymethylmethacrylate microneedle structures fabricated by X-ray lithography with an additional plane-pattern to cross-section transfers technique are directly influenced by the absorber on the X-ray mask pattern. The sidewall of the microneedle was improved by changing the mask pattern from a double right-triangular pattern to a double semi-circular pattern, modeled by comparing the results from a mask-pattern and the actual structure.     

Real-time exploration of a multi-robot rescue system in disaster areas

A system procedure is proposed for a multi-robot rescue system that performs real-time exploration over disaster areas. Real-time exploration means that every robot exploring the area always has a communication path to human operators standing by at a base station and that the communication path is configured by ad hoc wireless networking. Real-time exploration is essential in multi-robot systems for USAR (urban search and rescue) because operators must communicate with every robot to support the victim detection process and ad hoc networking is suitable to configure a communication path among obstacles. The proposed system procedure consists of the autonomous classification of robots into search and relay types and behavior algorithms for each class of robot. Search robots explore the areas and relay robots act as relay terminals between search robots and the base station. The rule of the classification and the behavior algorithm refer to the forwarding table of each robot constructed for ad hoc networking. The table construction is based on DSDV (destination-sequenced distance vector) routing that informs each robot of its topological position in the network and other essentials. Computer simulations are executed with a specific exploration strategy of search robots. The results show that a multi-robot rescue system can perform real-time exploration with the proposed system procedure and reduce exploration time in comparison with the case where the proposed scheme is not adopted.