Display illumination with modulated directional backlight

Illumination is essential for modern life as colorful world is perceived by human visionary system. Display technology has been developing rapidly in recent decades, and the basic principle is related to the way that the image is illuminated and light is emanated. Traditional illumination is provided by different types of light sources, and the display image is visible in large viewing space until the emanating light decays to zero. This work proposes and demonstrates a novel illumination scheme for a display in which the displaying images are visible only in specific spatial regions. The directional backlight ensures the image propagating to specific direction while imaging visibility can be controlled to terminate abruptly at certain distance from the display screen while exerting no influence to nearby regions. The working principle for such an illumination scheme is the use of the modulated coherent directional backlight through an axicon lens. It is shown that the illumination scheme can robustly deliver carried image information to the designated viewing region. This new illumination scheme has many advantages over conventional illumination, including its usage for personal display, very lower energy consumption, as well as minimizing light hazard pollution.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Fabrication of Cu/graphite film/Cu sandwich composites with ultrahigh thermal conductivity for thermal management applications

Effective thermal management of electronic integrated devices with high powder density has become a serious issue, which requires materials with high thermal conductivity (TC). In order to solve the problem of weak bonding between graphite and Cu, a novel Cu/graphite film/Cu sandwich composite (Cu/GF/Cu composite) with ultrahigh TC was fabricated by electro-deposition. The micro-riveting structure was introduced to enhance the bonding strength between graphite film and deposited Cu layers by preparing a rectangular array of micro-holes on the graphite film before electro-deposition. TC and mechanical properties of the composites with different graphite volume fractions and current densities were investigated. The results showed that the TC enhancement generated by the micro-riveting structure for Cu/GF/Cu composites at low graphite content was more effective than that at high graphite content, and the strong texture orientation of deposited Cu resulted in high TC. Under the optimizing preparing condition, the highest in-plane TC reached 824.3 W·m⁻¹·K⁻¹, while the ultimate tensile strength of this composite was about four times higher than that of the graphite film.     

Highly active sites of low spin FeⅡN4species:The identification and the ORR performance

Over recent years,catalytic materials of Fe-N-C species have been recognized being active for oxygen reduction reaction(ORR).However,the identification of active site remains challenging as it generally involves a pyrolysis process and mixed components being obtained.Herein Fe₃C/C and Fe₂N/C samples were synthesized by temperature programmed reduction of Fe precursors in 15%CH₄/H₂and pure NH₃,respectively.By acid leaching of Fe₂N/C sample,only single sites of FeN₄species were presented,providing an ideal model for identification of catalytic functions of the single sites of FeN₄in ORR.A correlation was conducted between the concentration of Fe^ⅡN₄in low spin state by Mossbauer spectra and the kinetic current density at 0.8 V in alkaline media,and such a structure-performance correlation assures the catalytic roles of low spin Fe^ⅡN₄ species as highly active sites for the ORR.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.