Native-oxidized InAlAs blocking layer buried heterostructure InGaAsP-InP MQW laser for high-temperature operation

A novel idea of InAlAs native oxide utilized to replace the p-n-p-n thyristor blocking layer and improve the high-temperature performance of buried heterostructure InGaAsP-InP laser is first proposed and demonstrated. A characteristic temperature (T-0) of 50 K is achieved from an InA1As native oxide buried heterostructure (NOBH) InGaAsP-InP multiquantum-well laser with 1.5-mu m-wide diode leakage passage path. The threshold current and slope efficiency of NOBH laser changes from 5.6 mA, 0.23 mW/mA to 28 mA, 0.11 mW/mA with the operating temperature changing from 20 degrees C to 100 degrees C. It is comparable to conventional p-n reverse biased junction BH laser with minimized diode leakage current, and is much better than the buried ridge strip with proton implanted laterally confinement laser.

Buried heterostructure InGaAsP/InP strain-compensated multiple quantum well laser with a native-oxidized InAlAs current blocking layer

An InAlAs native oxide is used to replace the p-n reverse-biased junction in a conventional buried heterostructure InP-based laser. This technique reduces the number of regrowth steps and eliminates leakage current under high-temperature operation. The InAlAs native oxide buried heterostructure (NOBH) laser with strain-compensated InGaAsP/InP multiple quantum well active layers has a threshold current of 5.6 mA, a slope efficiency of 0.23 mW/mA, and a linear power up to 22.5 mW with a HR-coated facet. It exhibits single transverse mode with lasing wavelength at 1.532 mu m. A characteristic temperature (T-0) of 50 K is obtained from the NOBH laser with a nonoptimized oxide layer width. (C) 1998 American Institute of Physics. [S0003-6951(98)01352-7].

A VSLMS Style Tap-length Learning Algorithm for Structure Adaptation

Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller., lower power consumption and higher computational complexity output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style Variable Tap-Length Algorithm (also called Fractional Tap-Length Algorithm or FT Algorithm) proposed by Y.Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called 'VSLMS' (Variable Step-size LMS) Style Tap-Length Learning Algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.

Two-dimensional photonic crystals with a large photonic band gap designed by a rapid genetic algorithm

We used Plane Wave Expansion Method and a Rapid Genetic Algorithm to design two-dimensional photonic crystals with a large absolute band gap. A filling fraction controlling operator and Fourier transform data storage mechanism had been integrated into the genetic operators to get desired photonic crystals effectively and efficiently. Starting from randomly generated photonic crystals, the proposed RGA evolved toward the best objectives and yielded a square lattice photonic crystal with the band gap (defined as the gap to mid-gap ratio) as large as 13.25%. Furthermore, the evolutionary objective was modified and resulted in a satisfactory PC for better application to slab system.

A face detection algorithm based on high dimensional space geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.

An algorithm of analysis tools on points distribution in high dimension space: The distance of a point and an infinite sub-space

This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.

A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space

A novel geometric algorithm for blind image restoration is proposed in this paper, based on High-Dimensional Space Geometrical Informatics (HDSGI) theory. In this algorithm every image is considered as a point, and the location relationship of the points in high-dimensional space, i.e. the intrinsic relationship of images is analyzed. Then geometric technique of "blurring-blurring-deblurring" is adopted to get the deblurring images. Comparing with other existing algorithms like Wiener filter, super resolution image restoration etc., the experimental results show that the proposed algorithm could not only obtain better details of images but also reduces the computational complexity with less computing time. The novel algorithm probably shows a new direction for blind image restoration with promising perspective of applications.

An Enhanced Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) algorithm is often used for finding optimal solution, but it easily entraps into the local extremum in later evolution period. Based on improved chaos searching strategy, an enhanced particle swarm optimization algorithm is proposed in this study. When particles get into the local extremum, they are activated by chaos search strategy, where the chaos search area is controlled in the neighborhood of current optimal solution by reducing search area of variables. The new algorithm not only gets rid of the local extremum effectively but also enhances the precision of convergence significantly. Experiment results show that the proposed algorithm is better than standard PSO algorithm in both precision and stability.