Abstract:Top-emitting organic light-emitting diodes (TEOLEDs) have attracted extensive attention for their high brightness and flat-panel display. However, the efficiency roll-off at high brightness is the issue that needs to be resolved for further practical applications using TEOLED devices. Herein, a serial of high-efficiency tandem TEOLED introducing a fullerene/zinc-phthalocyanine organic semiconductor heterojunction as a charge generation layer is demonstrated. With unique photovoltaic properties, the charge generation layer can absorb part of the photons emitted by the emission layer (Ir(ppy)3) and generate electrons and holes. By optimizing the thickness of the charge generation layer, the pure green electroluminescent TEOLED device manufactured has a high brightness of 156 000 cd/cm2 and a maximum current efficiency of 86 cd/A. Importantly, relying on the energy between the photovoltaic and the microcavity effects, only 1.5% of the efficiency roll-off is obtained at 1 000—10 000 cd/cm2. Introducing fullerene/zinc-phthalocyanine as the charge-generating layer provides a promising alternative for developing high-efficiency tandem TEOLED devices.

Abstract:This paper is devoted to introduce a novel complex fifth-order memristive circuit system and its applications in synchronous stability and weak signal detection. Firstly, the typical dynamical behaviors of the memristive system are discussed by chaotic phase portrait, complexity analysis, one-parameter bifurcation and Lyapunov exponent spectrum. Secondly, the adaptive control method is applied to realize the synchronization between the drive memristive system (DMS) and the response memristive system (RMS). The results indicate that the synchronization method has strong robustness and anti-interference ability. Thirdly, the weak signal detection of the novel five-dimensional memristive system is realized by using the extreme sensitivity of chaotic system to initial values. Finally, the fifth-order memristive circuit is designed by using basic electronic elements and simulated by Multisim software. And the anti-interference ability and sensitivity of the fifth-order memristive circuit are further verified by adding different weak disturbance signals at different positions of the circuit.

Abstract:In this paper, an effective method to garner sub-wavelength longitudinally polarized multi-segment optical needle sequence by using a specially designed hybrid filter (HF) in a high numerical aperture (NA) objective focusing system is proposed. The HF is coupled by a binary phase transmission function and a multi-segment modulation function, and the binary phase filter is designed by the particle swarm optimization (PSO) algorithm and acts on the radially polarized Bessel Gaussian (RPBG) beam to obtain a longitudinally polarized optical needle with long depth of focus (DOF, 6λ) and a sub-wavelength transverse spot size (0.430λ). The optical needle is with high uniformity of 98% and high beam quality of 96%, and the negligible sidelobe is 15%. On this basis, the multi-segment optical needle sequence with tunable spacing or number can be realized by the multi-segment modulation function. It is found that the HF makes the generation of multi-segment optical needle sequence more flexible and reliable. This research has broad application prospects in material processing, particle acceleration, particle capture and other fields.

Abstract:To solve the problem that the conventional detection technique of alpha radiation sources is unable to achieve remote detection and not suitable for detecting alpha radiation sources with complex morphology, remote detection of alpha radiation sources by measuring the fluorescence induced by the interaction between alpha particles and nitrogen molecules is investigated in this paper. In dark conditions, nitrogen fluorescence is collected by the optical system with an aperture of 50 mm and detected by a photomultiplier tube working in the photon counting mode. The alpha radiation source with total activity of 7.3 kBq and unit area activity of 49 Bq/cm2 can be detected within 30 s at a distance of 0.6 m. The detection limit of the alpha radiation source detection system proposed in this paper ranks among the top in the related research fields.

Abstract:The effects of different preheating and annealing temperatures on the surface morphology, microstructure, and optical properties of Cu2ZnSnS4 (CZTS) thin films are investigated by controlling the preheating and annealing temperatures. The prepared thin films were characterized using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), and ultra-violet-visible (UV-Vis) spectroscopy techniques. XRD and Raman spectroscopy showed that a Kesterite structure with a selective orientation along the (112) peak was generated, and the thin films produced at a preheating temperature of 300 °C and annealing temperature of 570 °C had fewer secondary phases, which was beneficial for improving the performance of the solar cells. SEM confirms that the crystallite size increases and then decreases as the temperature increases, and the largest and most uniform crystallite size with the smoothest surface is generated at the above preheating and annealing temperatures. UV-Vis measurements show that the thin films generated at the above temperature have the lowest transmittance and the lowest optical band gap value of 1.46 eV, which is close to the optimal band gap value for solar cells and is suitable as an absorber layer material.

Abstract:When dehazing underwater images, the patch-by-patch dark channel prior (DCP) method is frequently used. After the DCP-based processing, there are still some drawbacks, such as patch artifacts, and these artifacts will seriously affect the subjective quality of some challenging images. To remove the patch artifacts from the DCP-guided enhancement mechanism, this paper proposes a coordinated underwater dark channel prior (CUDCP) method. The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations, and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse images. The requirement for color restoration is then assessed by an evaluation criterion, and the color restoration is carried out by using the compensated gray world (CGW) theory, which further coordinates the intensity of various red-green-blue channels. Our method next applies a patch-division average filter in accordance with the sub-patch classification. On the typical dataset, the enhanced images of our CUDCP method have higher average underwater image quality measure (UIQM) scores (about 2.274 8) when compared with the original images and those of some state-of-the-art enhancement methods, while the computational cost of CUDCP (about 88.618 8 s) is slightly higher than that of the original DCP (about 87.493 8 s). The experimental results demonstrate that in comparison to state-of-the-art enhancement methods, the proposed method can significantly reduce patch artifacts in challenging image enhancement, while maintaining the objective quality of such underwater images, and also enhancing their subjective quality at a reasonable computational cost.

Abstract:When the aircraft is moving at high speed in the atmosphere, aero-optical imaging deviation will appear due to the influence of aero-optical effect. In order to achieve real-time compensation during the flight of the aircraft, it is necessary to analyze and predict the obtained imaging deviation data. In order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum, in this paper, an improved sparrow search algorithm optimized extreme learning machine (ISSA-ELM) neural network model is proposed to predict the aero-optical imagine deviation. Finally, the performance of ISSA-ELM, ELM neural network and SSA-ELM neural network was tested. The results showed that compared with ELM and SSA-ELM algorithms, the convergence speed of ISSA-ELM was significantly enhanced, and the accuracy of data prediction was also significantly improved.

Abstract:Hyperspectral image (HSI) restoration has been widely used to improve the quality of HSI. HSIs are often impacted by various degradations, such as noise and deadlines, which have a bad visual effect and influence the subsequent applications. For HSIs with missing data, most tensor regularized methods cannot complete missing data and restore it. We propose a spatial-spectral consistency regularized low-rank tensor completion (SSC-LRTC) model for removing noise and recovering HSI data, in which an SSC regularization is proposed considering the images of different bands are different from each other. Then, the proposed method is solved by a convergent multi-block alternating direction method of multipliers (ADMM) algorithm, and convergence of the solution is proved. The superiority of the proposed model on HSI restoration is demonstrated by experiments on removing various noises and deadlines.

Abstract:Over the past several decades, micro-expression recognition (MER) has become a growing concern for scientific community. As the filming conditions vary from database to database, previous single-domain MER methods generally exhibit severe performance drop when applied to another database. To deal with this pressing problem, in this paper, a sample-aware and feature refinement network (SFR-Net) is proposed, which combines domain adaptation with deep metric learning to extract intrinsic features of micro-expressions for accurate recognition. With the help of decoders, siamese networks increasingly refine shared features relevant to emotions while exclusive features irrelevant to emotions are gradually obtained by private networks. In order to achieve promising performance, we further design sample-aware loss to constrain the feature distribution in the high-dimensional feature space. Experimental results show the proposed algorithm can effectively mitigate the diversity among different micro-expression databases, and achieve better generalization performance compared with state-of-the-art methods.

Abstract:This paper introduces an approximate nuclear norm based matrix regression projection (ANMRP) model, an adaptive graph embedding method, for feature extraction of hyperspectral images. The ANMRP utilizes an approximate NMR model to construct an adaptive neighborhood map between samples. The globally optimal weight matrix is obtained by optimizing the approximate NMR model using fast alternating direction method of multipliers (ADMM). The optimal projection matrix is then determined by maximizing the ratio of the local scatter matrix to the total scatter matrix, allowing for the extraction of discriminative features. Experimental results demonstrate the effectiveness of ANMRP compared to related methods.