Matrix Manipulation of Directly-Synthesized PbS Quantum Dot Inks Enabled by Coordination Engineering

The direct-synthesis of conductive PbS quantum dot (QD) ink is facile, scalable, and low-cost, boosting the future commercialization of optoelectronics based on colloidal QDs. However, manipulating the QD matrix structures still is a challenge, which limits the corresponding QD solar cell performance. Here, for the first time a coordination-engineering strategy to finely adjust the matrix thickness around the QDs is presented, in which halogen salts are introduced into the reaction to convert the excessive insulating lead iodide into soluble iodoplumbate species. As a result, the obtained QD film exhibits shrunk insulating shells, leading to higher charge carrier transport and superior surface passivation compared to the control devices. A significantly improved power-conversion efficiency from 10.52% to 12.12% can be achieved after the matrix engineering. Therefore, the work shows high significance in promoting the practical application of directly synthesized PbS QD inks in large-area low-cost optoelectronic devices.     

光谱编码计算关联成像技术研究

现有的多光谱成像技术通常采用光学分光的方式，使用多个探测器对成像场景的光谱图像进行采集，导致现有成像系统复杂，数据量大、效率低。针对现有技术的不足，提出基于正交调制模式的光谱编码计算关联成像技术。通过正交光谱编码矩阵融合Hadamard基图案构造投影散斑对宽带光源进行调制，单像素探测器收集成像物体与调制光源相互作用后的反射信号；应用演化压缩技术复原成像物体的混叠光谱图像；利用编码矩阵的正交性质解码出欠采样的光谱分量图像，对分离出的图像应用组稀疏压缩感知算法重构全采样的光谱分量图像，最后融合出成像物体的多光谱图像。通过数值模拟与实验两方面验证了所提方法的高效性。所提的技术简化了多光谱关联成像系统，降低了数据量。光谱编码方法可以扩展到更多的光谱通道，也可以应用在偏振关联成像、信息加密等领域。     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Highly oriented α-Al2O3 transparent ceramics shaped by shear force

Alumina platelets were arranged horizontally in submicron alumina particles by shear force in the flow of slurries during casting. The obtained alumina green bodies with platelets were pressureless-sintered in vacuum, producing ceramics with thoroughly oriented grains and high transmittance. The effects of sintering parameters on the densification, microstructure evolution, and orientation degree of alumina ceramics were investigated and discussed. The results showed that the densification, grain size, orientation degree, and in-line transmittance were increased with increasing sintering temperature. The enhancement of orientation degree was mainly coherent with grain growth. The grain-oriented samples exhibited a much higher in-line transmittance (at 600 nm) of 61 % than that of the grain random sample (29 %). Moreover, the transmission remained a high level in the ultraviolet range (<300 nm).     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Morphology-controlled fabrication of nanostructured WO3 thin films by magnetron sputtering with glancing angle deposition for enhanced efficiency photo-electrochemical water splitting

Herein, the tungsten trioxide (WO₃) nanostructure thin films with different morphologies are firstly fabricated by magnetron sputtering with glancing angle deposition technique (MS-GLAD), followed by the post annealed treatment process in air ambient for 2 h. It is demonstrated that the geometry of MS-GLAD setup, mainly substrate position, played a crucial role in determining the morphology, crystallinity, optical transmittance, and photo-electrochemical (PEC) performance of the WO₃ nanostructured thin film. With the different substrate positions in the MS-GLAD system, the WO₃ nanorod film layer could be precisely changed to combine an underlying dense layer with a nanorod layer and then nanocolumnar film. Moreover, the prepared samples' chemical composition and work function are studied by X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS), respectively. The combining WO₃ nanostructure produced high PEC efficiency compared to the single layer of the WO₃ nanorods sample and the dense WO₃ thin film sample. Thus, morphology-controlled nanostructure film based on the MS-GLAD technique in our study provides a simple approach to enhance the photo-anode for PEC water splitting application.