KEYWORDS: Phased array optics, Spatial light modulators, Optical simulations, Phase modulation, Modulation, Beam steering, Near field optics, Monte Carlo methods, Computer simulations, Free space
Optical phased array provides a promising platform for multi-beam steering in optical bands. Here, we propose and demonstrate an effective multi-beam steering scheme employing a phase-only spatial light modulator. The beam orientations and beam amplitudes are both programmable in our proposal. With Huygens-Fresnel simulation, the fidelity value of ~0.998 and ~0.999 are evaluated corresponding to the case of random beam orientations and random beam amplitude ratios, respectively. Furthermore, up to 101-beam steering is experimentally observed, which is much higher than existed reports.
UAV (Unmanned Aerial Vehicle) swarm search has the advantages of flexible deployment, no casualties, and high cost-effectiveness. It has become a force that cannot be ignored in the battlefield. Aiming at the task planning problem in the UAV swarm search, this paper treats each UAV as a subsystem based on the self-organization idea, and proposes a search algorithm based on the IAPF (Improved Artificial Potential Field ). First, in order to improve search efficiency and reduce computational complexity, a new type of target attraction field function was constructed. Subsequently, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Finally, a collaborative search process based on the direction standard deviation of the artificial potential field was designed. The simulation results show that compared with the scanning search and the HAPF-ACO (Hybrid Artificial Potential Field and Ant Colony Optimization) algorithm, this method can significantly improve the target discovery rate while achieving similar task area coverage. At the same time, the disturbance experiment proves that the method in this paper is robust in the case of some UAV failures.
With the rapid increase of the amount of available medical images, computer-based automatic medical image analysis is becoming a promising and prosperous research area, among which, automatic segmentation of medical image is more and more important for its necessity in the clinical diagnosis. In recent years, quite a number of semantic segmentation methods based on deep convolution neural networks (CNNs) have been proposed and shown much better performance than those conventional methods. However, most of them are designed for 2D images based on 2D CNN. In nowadays, more and more medical images are three dimensional. The 3D CNN-based methods are good at dealing with 3D images, but more computationally expensive and require more training samples. Training a 3D CNN model from scratch is much difficult when the training dataset is relatively small. To leverage full information of 3D medical image data and reduce the difficulty of training at the same time, a new end-to-end pseudo-3D fully convolutional densenet model for 3D brain tumor segmentation is presented in this paper. The new method decomposes one 3D convolution into two correlated 2D convolutions to reduce the number of parameters to be tuned. It pre-trains a 2D model on large 2D image datasets first, then adapts it on the relatively small 3D brain tumor dataset to implement 3D brain tumor segmentation. Experiment results show that the new model achieves comparable or even better performance than some well-known networks (e.g. Vnet, 3D-Unet), while reduces the training complexity obviously.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.