Efficiency improvement of near-ultraviolet InGaN LEDs using patterned sapphire substrates

The use of conventional and patterned sapphire substrates (PSSs) to fabricate InGaN-based near-ultraviolet (410 nm) light-emitting diodes (LEDs) was demonstrated. The PSS was prepared using a periodic hole pattern (diameter: 3 /spl mu/m; spacing: 3 /spl mu/m) on the (0001) sapphire with different etching depths. From transmission-electron-microscopy and etch-pit-density studies, the PSS with an optimum pattern depth (D/sub h/=1.5 /spl mu/m) was confirmed to be an efficient way to reduce the thread dislocations in the GaN microstructure. It was found that the output power increased from 8.6 to 10.4 mW, corresponding to about 29% increases in the external quantum efficiency. However, the internal quantum efficiency (@ 20 mA) was about 36% and 38% for the conventional and PSS LEDs, respectively. The achieved improvement of the output power is not only due to the improvement of the internal quantum efficiency upon decreasing the dislocation density, but also due to the enhancement of the extraction efficiency using the PSS. Finally, better long-time reliability of the PSS LED performance was observed.     

Solving the bi-objective personnel assignment problem using particle swarm optimization

A particle swarm optimization (PSO) algorithm combined with the random-key (RK) encoding scheme (named as PSORK) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The main contribution of this work is to improve the f₁_f₂ heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f₁_f₂ heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f₁, and f₂ for the personnel assignment problem. In this paper, PSORK algorithm searches the solution of BOPAP space thoroughly. The experimental results show that the solution quality of BOPAP based on the proposed method is far better than that of the f₁_f₂ heuristic algorithm.     

Simulation of solid thermal explosion and liquid thermal explosion of dicumyl peroxide using calorimetric technique

Dicumyl peroxide (DCPO), is produced by cumene hydroperoxide (CHP) process, is utilized as an initiator for polymerization, a prevailing source of free radicals, a hardener, and a linking agent. DCPO has caused several thermal explosion and runaway reaction accidents in reaction and storage zone in Taiwan because of its unstable reactive property. Differential scanning calorimetry (DSC) was used to determine thermokinetic parameters including 700 J g^–1 of heat of decomposition (ΔH_d), 110 °C of exothermic onset temperature (T₀), 130 kJ mol^–1 of activation energy (E_a), etc., and to analyze the runaway behavior of DCPO in a reaction and storage zone. To evaluate thermal explosion of DCPO with storage equipment, solid thermal explosion (STE) and liquid thermal explosion (LTE) of thermal safety software (TSS) were applied to simulate storage tank under various environmental temperatures (T_e). T_e exceeding the T₀ of DCPO can be discovered as a liquid thermal explosion situation. DCPO was stored under room temperature without sunshine and was prohibited exceeding 67 °C of self-accelerating decomposition temperature (SADT) for a tank (radius = 1 m and height = 2 m). SADT of DCPO in a box (width, length and height = 1 m, respectively) was determined to be 60 °C. The TSS was employed to simulate the fundamental thermal explosion behavior in a large tank or a drum. Results from curve fitting demonstrated that, even at the earlier stage of the reaction in the experiments, ambient temperature could elicit exothermic reactions of DCPO. To curtail the extent of the risk, relevant hazard information is quite significant and must be provided in the manufacturing process.     

An expert system to classify microarray gene expression data using gene selection by decision tree

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.     

A wavelet-tree-based watermarking method using distance vector of binary cluster

This paper proposes a wavelet-tree-based watermarking method using distance vector of binary cluster for copyright protection. In the proposed method, wavelet trees are classified into two clusters using the distance vector to denote binary watermark bits. The two smallest wavelet coefficients in a wavelet tree are used to reduce distortion of a watermarked image. The distance vector, which is obtained from the two smallest coefficients of a wavelet tree, is quantized to decrease image distortion. The trees are classified into two clusters so that they exhibit a sufficiently large statistical difference based on the distance vector, which difference is then used for subsequent watermark extraction. We compare the statistical difference and the distance vector of a wavelet tree to decide which watermark bit is embedded in the embedding process. The experimental results show that the watermarked image looks visually identical to the original and the watermark can be effectively extracted upon image processing attacks.     

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.     

Multi-class support vector machine for classification of the ultrasonic images of supraspinatus

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods – texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum – are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.     

3D block-based medial axis transform and chessboard distance transform based on dominance

Traditionally, the block-based medial axis transform (BB-MAT) and the chessboard distance transform (CDT) were usually viewed as two completely different image computation problems, especially for three dimensional (3D) space. In fact, there exist some equivalent properties between them. The relationship between both of them is first derived and proved in this paper. One of the significant properties is that CDT for 3D binary image V is equal to BB-MAT for image V' where it denotes the inverse image of V. In a parallel algorithm, a cost is defined as the product of the time complexity and the number of processors used. The main contribution of this work is to reduce the costs of 3D BB-MAT and 3D CDT problems proposed by Wang [65]. Based on the reverse-dominance technique which is redefined from dominance concept, we achieve the computation of the 3D CDT problem by implementing the 3D BB-MAT algorithm first. For a 3D binary image of size N³, our parallel algorithm can be run in O(logN) time using N³ processors on the concurrent read exclusive write (CREW) parallel random access machine (PRAM) model to solve both 3D BB-MAT and 3D CDT problems, respectively. The presented results for the cost are reduced in comparison with those of Wang's. To the best of our knowledge, this work is the lowest costs for the 3D BB-MAT and 3D CDT algorithms known. In parallel algorithms, the running time can be divided into computation time and communication time. The experimental results of the running, communication and computation times for the different problem sizes are implemented in an HP Superdome with SMP/CC-NUMA (symmetric multiprocessor/cache coherent non-uniform memory access) architecture. We conclude that the parallel computer (i.e., SMP/CC-NUMA architecture or cluster system) is more suitable for solving problems with a large amount of input size.     

The effect of shoe sole tread groove depth on the friction coefficient with different tread groove widths, floors and contaminants

Slipping and falling are common phenomena in both workplaces and our daily activities. The risks associated with slipping and falling are related to the materials of footwear/floor, contamination condition, and geometric design of the sole. Shoe soles of various tread design are very common. Tread pattern of the shoe affects friction especially under liquid-contaminated conditions. Verification of the effects of tread groove depth is significant in assisting designers in designing proper footwear for workers exposed to slippery floor conditions. In this study, we measured the friction coefficients using the Neolite footwear pads on the terrazzo, steel, and vinyl floors under three liquid-contaminated conditions. A Brungraber Mark II slipmeter was used. The footwear pads had tread grooves with a width of either 3 or 9 mm. The depth of the tread grooves ranged from 1 to 5 mm. The results showed that tread groove depth affected the friction coefficients significantly. Higher friction values were recorded for footwear pads with deeper tread grooves on wet and water–detergent-contaminated floors. The averaged coefficient of friction (COF) gain per tread groove depth increase in millimeter under these two surface conditions ranged from 0.018 to 0.108, depending on the tread groove width, floor, and contaminant.