Factors associated with serum magnesium and vascular stiffness in maintenance hemodialysis patients

Introduction : We evaluated the associated factors of serum magnesium in patients on maintenance hemodialysis (MHD). Furthermore, we evaluated the relationship between low serum magnesium and arteriosclerosis in these patients. Methods : In 129 patients on MHD, we evaluated the blood levels of magnesium, brachial‐ankle pulse wave velocity (ba‐PWV), ankle‐brachial index (ABI), and intima‐media thickness of the common carotid artery (IMT). Findings : In MHD patients, the serum level of magnesium was significantly correlated with age, calcium, TNF‐α, albumin, and ba‐PWV but not with ABI or IMT. In the multiple regression analysis, albumin (P = 0.0001, β = 0.31) and calcium (P = 0.029, β = 0.18) were selected as significant predictors of the magnesium level in MHD patients. Furthermore, the serum level of magnesium, as well as systolic blood pressure (P = 0.0001, β = 0.32) and age (P = 0.005, β = 0.25), were selected as significant (P = 0.012, β = ?0.22) predictors of ba‐PWV in MHD patients. Discussion : In MHD patients, the serum magnesium level was associated with the serum levels of calcium and albumin. Furthermore, a low serum magnesium level in MHD patients was associated with the index of vascular stiffness.     

Automated 360° profilometry of a three-dimensional diffuse object and its reconstruction by use of the shading model

The 360° profilometry of a three-dimensional (3-D) diffuse object by use of the light intersection and its image reconstruction by surface shading are presented. The lack of data in one direction, which was due to occlusion, was compensated by the projection of two lines of light from different directions. Some experiments to profile objects and their reconstruction by computer are shown. The entire surface model was constructed, and a real shading image was obtained by means of computer graphics.     

Soft magnetic properties of NiFeTa thin films for biasing film applications in magnetoresistive elements

The effects of Ta addition on the magnetic properties of permalloy thin films have been investigated. The alloy compositions on a weight basis are (Ni₈₁Fe₁₉)_1-x Ta _x with 0 x 0.105, and the films are sputtered onto a glass substrate at between room temperature and 300 C. The saturation magnetization and anisotropic magnetic field decrease with increasing Ta content. The saturation magnetization is 0.75 T at 5 wt % Ta. The coercivity remains constant at 125 Am^-1. The electrical resistivity increases linearly with increasing Ta content, then saturates at approximately 7.5 wt% Ta. The saturation resistivity is approximately 1.00 m. The magnetoresistivity ratio (/) decreases with increasing Ta content, mainly due to increased electrical resistivity (). The magnetostriction changes from negative to positive with increasing Ta content and reaches nearly zero at 2 wt% Ta. The NiFeTa films containing 5–6 wt% Ta have potential for use as the soft-biasing film in magnetoresistive elements.     

Use of solid-electrolyte Galvanic cells to determine the activity of CaO in the CaO-ZrO2 system and the standard Gibbs free energies of formation of CaZrO3 from CaO and ZrO2

The activity of CaO in the CaO-ZrO₂ system has been measured at 1572 to 1877 K with a Galvanic cell composed of 4CaO ? P₂O₅ as the solid electrolyte. The activity ZrO₂ was calculated from the activity of CaO by integrating the Gibbs-Duhem relation. From the activities of CaO and ZrO₂, the standard Gibbs free energy of formation of CaO ? ZrO₂ was determined as follows: ΔG _f ⁰ /J mol^?1 = -25,200 (±150) - 17.58 (±0.085)T (1633 to 1873 K)     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

A reduction method of dynamic false contour utilizing the frame‐rate‐control method with suppression of the side effect on PDPs

Abstract— The plasma‐display panel (PDP) is a type of flat‐panel display that can display a high‐quality image. However, when moving images are displayed, annoying disturbances such as false contour noise occurs. This noise is called dynamic false contour (DFC). To achieve a higher‐quality image, DFC has to be reduced. Therefore, a new method to reduce DFC is proposed. To find a way to reduce DFC, a new evaluation value for it has been defined: the evaluation value of dynamic false contour (EVDFC). This value is defined on the basis of a person's subjective evaluation. By applying this value, the cause that generates DFC can be identified. On the basis of these studies, a new method for reducing DFC by applying frame‐rate control (FRC) with suppression of the side effect is proposed. This improved method can be used to provide high‐quality images.     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates

In this paper, we propose a method for pose-invariant facial expression recognition from monocular video sequences. The advantage of our method is that, unlike existing methods, our method uses a simple model, called the variable-intensity template, for describing different facial expressions. This makes it possible to prepare a model for each person with very little time and effort. Variable-intensity templates describe how the intensities of multiple points, defined in the vicinity of facial parts, vary with different facial expressions. By using this model in the framework of a particle filter, our method is capable of estimating facial poses and expressions simultaneously. Experiments demonstrate the effectiveness of our method. A recognition rate of over 90% is achieved for all facial orientations, horizontal, vertical, and in-plane, in the range of ±40 degrees, ±20 degrees, and ±40 degrees from the frontal view, respectively. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.     

Performance of SOR methods on modern vector and scalar processors

The building-cube method (BCM) is a new generation algorithm for CFD simulations. The basic idea of BCM is to simplify the algorithm in all stages of flow computation to achieve large-scale simulations. Calculation of a pressure field using the Successive Over Relaxation (SOR) method consumes most of the total execution time required for BCM. In this paper, effective implementations on modern vector and scalar processors are investigated. NEC SX-9 and Intel Nehalem-EX are the latest vector and scalar processors. Those processors have much higher peak performances than their previous-generation processors. However, their memory bandwidth improvement cannot catch up with the performance improvement of processors. This is the so-called memory wall problem. In our paper, we discuss optimization techniques for implementation of the SOR method based on architectural characteristics of these modern processors, and evaluate their effects on the sustained performances of these processors for BCM.     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.