Galvanic coupling effect on corrosion behavior of Al alloy-matrix composites

Galvanic coupling effect on the corrosion of SiC-reinforced aluminum alloy-matrix composites was investigated in a sodium chloride solution. The potentiodynamic polarization measurement indicated that pitting potentials of metal matrix composites (MMCs) and AA2124 matrix alloy were similar, and pitting potential of MMCs was almost same as corrosion potential, while pitting susceptibility of MMCs was higher than that of AA2124 alloy. Galvanic current by formation of galvanic couple between SiCw and matrix reveals very low value because of large cathodic polarization of SiC. However, by increasing potential of matrix to pitting potential by this galvanic couple and thus, forming pits easily at the weak passive film near SiC reinforcing phase preferentially, it is concluded that pitting susceptibility of MMCs increases highly than AA2124 alloy of matrix composition.     

Corrosion resistance of austenitic stainless steel separator for molten carbonate fuel cell

STS310S and SC-STS310S (simultaneously co-deposited chromium and aluminum onto 310S austenitic stainless steel substrate by pack-cementation process) were used as separator materials on the cathode side of a molten carbonate fuel cell. With the STS310S, corrosion proceeded via three steps; a formation step of unstable corrosion product, a protection step against corrosion until breakaway, and an advance step of corrosion after breakaway. The final corrosion product was LiFeO₂ and the loss of mass was 6.5 mg/cm² after a corrosion test of 480 hr at 650°C. The SC-STS310S showed more effective corrosion resistance, however, than did common STS310S. There was especially no corrosion loss on the SC-STS310S after the 480 hr corrosion test. It is anticipated that it will be very useful as an alternative separator on the cathode side off the MCFC in the future.     

Interference impacts on 60 ghz real-time online video streaming in wireless smart tv platforms

In this paper we provide a method for computing and estimating the impact of interference on real-time online 1080p@30Hz and 1080p@60Hz high-definition video streaming in 60 GHz wireless smart TV platforms. The analysis involves two different interference scenarios: 1) downlink interference from deployed 60 GHz access points to the associated mobile ad-hoc devices, and 2) uplink interference from randomly deployed 60 GHz ad-hoc mobile devices to their associated access points. With these interference scenarios, the interference impact on the quality of main 1080p@30Hz and 1080p@60Hz wireless high-definition video streaming with various simulation settings are measured and estimated in terms of signal-to-interference-plus-noise ratio.     

A novel part-based approach to mean-shift algorithm for visual tracking

Visual tracking is one of the most important problems considered in computer vision. To improve the performance of the visual tracking, a part-based approach will be a good solution. In this paper, a novel method of visual tracking algorithm named part-based mean-shift (PBMS) algorithm is presented. In the proposed PBMS, unlike the standard mean-shift (MS), the target object is divided into multiple parts and the target is tracked by tracking each individual part and combining the results. For the part-based visual tracking, the objective function in the MS is modified such that the target object is represented as a combination of the parts and iterative optimization solution is presented. Further, the proposed PBMS provides a systematic and analytic way to determine the scale of the bounding box for the target from the perspective of the objective function optimization. Simulation is conducted with several benchmark problems and the result shows that the proposed PBMS outperforms the standard MS.

     

Note: Interpolation for evaluation of a two-dimensional spatial profile of plasma densities at low gas pressures

An interpolation algorithm for the evaluation of the spatial profile of plasma densities in a cylindrical reactor was developed for low gas pressures. The algorithm is based on a collisionless two-dimensional fluid model. Contrary to the collisional case, i.e., diffusion fluid model, the fitting algorithm depends on the aspect ratio of the cylindrical reactor. The spatial density profile of the collisionless fitting algorithm is presented in two-dimensional images and compared with the results of the diffusion fluid model.     

A high performance parallel DCT with OpenCL on heterogeneous computing environment

A noteworthy thing in desktop PCs is that they can provide a great opportunity to increase the performance of processing multimedia data by exploiting task- and data-parallelism with multi-core CPU and many-core GPU. This paper presents a high performance parallel implementation of 2D DCT on this heterogeneous computing environment. For this purpose, Intel TBB (threading building blocks) and OpenCL (Open Compute Language) are utilized for task- and data-parallelism, respectively. The simulation result shows that the parallel DCT implementations far the serial ones in processing speed. Especially, OpenCL implementation shows a linear speedup, a typical SIMD characteristic as the increase of 2D data sets.     

A finite memory filtering for end-to-end available bandwidth estimation

In this paper, a finite memory filter is proposed to estimate the available bandwidth through real-time tracking unknown parameters of the sloping straight line while removing undesired system and measurement noises. The finite memory filter is developed under a weighted least square criterion using only the most recent finite probe-packet measurements on the window. The proposed finite memory filtering based available bandwidth estimate is shown to have several inherent properties such as unbiasedness, deadbeat, and robustness. A guideline for choosing appropriate window length is described as it can significantly affect the estimation performance. Finally, computer simulations show that the proposed finite memory filtering based approach can be comparable with the Kalman filtering based approach with infinite memory structure for constantly or slowly changing available bandwidth and outperform that for dynamically changing available bandwidth.     

Color segmentation robust to brightness variations by using B-spline curve modeling

Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. However, color segmentation suffers from color variations incurred by irregular illumination changes. We propose a reliable color modeling approach in hue–saturation–intensity (HSI) color space while considering intensity information by adopting the B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to intensity. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in the HS (hue–saturation) plane. The statistical characteristics contain the mean and standard deviation of hue and saturation with respect to intensity. They are mathematically expressed as four bar graphs. In order to fit the bar graphs to continuous curves, we use B-spline curve fitting procedure. From several experimental results, we verify that the proposed algorithm is successfully applied to color segmentation under various illumination conditions.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.