The objective of this document is to describe the design and implementation of an intelligent biomedical system. The system consists of an array of multiple sensors. The sensors are connected to the Arduino Uno device...The objective of this document is to describe the design and implementation of an intelligent biomedical system. The system consists of an array of multiple sensors. The sensors are connected to the Arduino Uno device to allow data capture and processing. The captured signals from the sensors are then digitized by the microcontroller. The data from the microcontroller are accessible via two different portals: the LCD display and the LabVIEW graphical user interface. The microcontroller is also equipped with a GSM module to send the alert message to the user upon occurrence of events that simulate abnormal activity in the biological parameters of the user.展开更多
为提高无人机避障的灵活性和可靠性,提出了一种基于LGMD(Lobula Giant Movement Detector)的无人机避障方法,通过将视场分割为上、下、左、右4个方位,形成4个方位竞争的LGMD(C-LGMD),并利用Matlab软件进行算法实现和视频仿真分析,最后...为提高无人机避障的灵活性和可靠性,提出了一种基于LGMD(Lobula Giant Movement Detector)的无人机避障方法,通过将视场分割为上、下、左、右4个方位,形成4个方位竞争的LGMD(C-LGMD),并利用Matlab软件进行算法实现和视频仿真分析,最后将算法移植到无人机硬件系统,开展悬停测试和实时飞行实验研究。由视频仿真分析和悬停测试结果表明,该算法能有效分辨来自不同方位的障碍物,具有较好的避障性能和鲁棒性;在实时飞行测试中,无人机在室内环境中可实现三维空间有效避障,验证了该算法的可靠性。研究结果为进一步探索无人机高效、可靠避障提供参考依据。展开更多
文摘The objective of this document is to describe the design and implementation of an intelligent biomedical system. The system consists of an array of multiple sensors. The sensors are connected to the Arduino Uno device to allow data capture and processing. The captured signals from the sensors are then digitized by the microcontroller. The data from the microcontroller are accessible via two different portals: the LCD display and the LabVIEW graphical user interface. The microcontroller is also equipped with a GSM module to send the alert message to the user upon occurrence of events that simulate abnormal activity in the biological parameters of the user.
文摘为提高无人机避障的灵活性和可靠性,提出了一种基于LGMD(Lobula Giant Movement Detector)的无人机避障方法,通过将视场分割为上、下、左、右4个方位,形成4个方位竞争的LGMD(C-LGMD),并利用Matlab软件进行算法实现和视频仿真分析,最后将算法移植到无人机硬件系统,开展悬停测试和实时飞行实验研究。由视频仿真分析和悬停测试结果表明,该算法能有效分辨来自不同方位的障碍物,具有较好的避障性能和鲁棒性;在实时飞行测试中,无人机在室内环境中可实现三维空间有效避障,验证了该算法的可靠性。研究结果为进一步探索无人机高效、可靠避障提供参考依据。
