在多应用的复杂恶劣环境,通常数据传输的耗能对电池供电的传感云网络中无线传感器工作寿命的影响一直是研究热点问题。因此,针对传感云网络多源数据提出一种低时延多径协同传输方法。构建包含基站、中继节点和目的节点的两跳有损网络,...在多应用的复杂恶劣环境,通常数据传输的耗能对电池供电的传感云网络中无线传感器工作寿命的影响一直是研究热点问题。因此,针对传感云网络多源数据提出一种低时延多径协同传输方法。构建包含基站、中继节点和目的节点的两跳有损网络,根据选取原则确定合适的目的节点和中继节点。然后,基于随机线性网络编码(Random Linear Network Coding,RLNC)的用户数据报协议(User Datagram Protocol,UDP)实现多源数据的低时延传输。最后,在此基础上,选择一种低能耗的最佳协同传输策略,在保证无线传感云网络吞吐量最大化的前提下,完成多源数据高效的协同传输。结果表明,所提方法数据递交率不低于99%,数据传输能耗最高为51 k Wh,数据递交时延为15.5 ms,丢包率为0,具有较强的多源数据传输性能。展开更多
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional(2D) point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional(2D) point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.展开更多
物联网(Internet of Things,IoT)的迅速发展为工业、医疗和环境应用的科技进步做出了重要贡献。针对旋转机械运行时需要定期监测机器设备,及早发现设备故障,从而实现高效的过程控制及提升工业自动化效益,文中在基于工业无线传感器网络(I...物联网(Internet of Things,IoT)的迅速发展为工业、医疗和环境应用的科技进步做出了重要贡献。针对旋转机械运行时需要定期监测机器设备,及早发现设备故障,从而实现高效的过程控制及提升工业自动化效益,文中在基于工业无线传感器网络(Industrial Wireless Sensor Networks,IWSNs)和物联网服务的云平台上设计了一种旋转机械故障监测系统。该系统由工业无线传感器网络部署、用于诊断机械故障的监测站以及基于物联网的云平台构成。展开更多
Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Impe...Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.展开更多
摘要在多应用的复杂恶劣环境,通常数据传输的耗能对电池供电的传感云网络中无线传感器工作寿命的影响一直是研究热点问题。因此,针对传感云网络多源数据提出一种低时延多径协同传输方法。构建包含基站、中继节点和目的节点的两跳有损网络,根据选取原则确定合适的目的节点和中继节点。然后,基于随机线性网络编码(Random Linear Network Coding,RLNC)的用户数据报协议(User Datagram Protocol,UDP)实现多源数据的低时延传输。最后,在此基础上,选择一种低能耗的最佳协同传输策略,在保证无线传感云网络吞吐量最大化的前提下,完成多源数据高效的协同传输。结果表明,所提方法数据递交率不低于99%,数据传输能耗最高为51 k Wh,数据递交时延为15.5 ms,丢包率为0,具有较强的多源数据传输性能。
摘要For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional(2D) point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
摘要Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.