Optically and electrically invariant multi-color single InGaN/GaN nanowire light-emitting diodes on a silicon substrate under mechanical compression.

Recent years have seen immense advances in electroluminescent InGaN-based light-emitting diodes (LEDs) that may revolutionize lighting and display technologies. Driven by the need for the development of submicrometer-sized, multicolor light sources monolithically integrated on a single chip, it is necessary to accurately characterize the size-dependent electroluminescence (EL) properties of selective-area grown single InGaN-based nanowire (NW) LEDs. Moreover, InGaN-based planar LEDs generally undergo; external mechanical compression induced by the packaging process which could potentially degrade the emission efficiency this further motivates us to investigate the size-dependent EL properties of single InGaN-based NW LEDs on a Si substrate under external mechanical compression. In this work, we perform opto-electro-mechanical characterization of single InGaN/GaN NWs using a scanning electron microscopy (SEM)-based multi-physical characterization technique. We first tested the size-dependent EL properties of selective-area grown single InGaN/GaN NWs on a Si substrate with a high injection current density up to 12.99 kA cm-2. In addition, the effect of external mechanical compression on the EL properties of the single NWs was investigated. Stable EL properties (no degradation of EL peak intensity and no peak wavelength shift) and electrical characteristics have been observed by applying a 5 µN compressive force to single NWs with different diameters. The results confirm no degradation of the NW light output with the applied stress (up to 62.2 MPa) and demonstrate the superior optical and electrical robustness of single InGaN/GaN NW LEDs under mechanical compression.

LF-ACO: an effective formation path planning for multi-mobile robot.

Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem.

[An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network].

In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.