This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high di...This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.展开更多
针对认知无线Mesh网络传统的多约束QoS组播路由算法一贯的进行随机初始化种群这一问题,在没有增加智能算法的复杂度的同时,首次将武器-目标分配问题(weapon to target allocation,WTA)应用在群智能算法对初始种群的优化上,基于蚁群算法...针对认知无线Mesh网络传统的多约束QoS组播路由算法一贯的进行随机初始化种群这一问题,在没有增加智能算法的复杂度的同时,首次将武器-目标分配问题(weapon to target allocation,WTA)应用在群智能算法对初始种群的优化上,基于蚁群算法,将集火射击、分火射击和混合射击的思想加入到对初始种群的设计上,提出一种基于WTA的QoS组播路由优化算法。其目标是满足无线组播业务的QoS约束且不增加算法复杂度的同时,结合蚁群的强鲁棒性和并行性等性能优势。经过实验验证,在网络开销和时延等方面的指标具有很好改善。展开更多
针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strengt...针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strength Indicator,RSSI)进行高斯-卡尔曼融合滤波,提高了RSSI值的准确性;然后利用生存时间(Time To Live,TTL)对中继节点进行约束,提高了数据传输的有效性;最后利用半径弥补法与Bellman-Ford融合迭代方案对生成的距离矩阵进行修正,减小了测距误差。实验结果表明,所设计的系统可有效完成蓝牙标签信息更新以及位置展示,平均定位精度达到了0.94 m。本系统具有成本低、工程实施方便的优点,有一定的应用价值和发展前景。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61375030)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20090149)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province,China(Grant No.08KJD520019)
文摘This paper presents one novel spatial geometric constraints histogram descriptors （SGCHD） based on curvature mesh graph for automatic three-dimensional （3D） pollen particles recognition. In order to reduce high dimensionality and noise disturbance arising from the abnormal record approach under microscopy, the separated surface curvature voxels are ex- tracted as primitive features to represent the original 3D pollen particles, which can also greatly reduce the computation time for later feature extraction process. Due to the good invariance to pollen rotation and scaling transformation, the spatial geometric constraints vectors are calculated to describe the spatial position correlations of the curvature voxels on the 3D curvature mesh graph. For exact similarity evaluation purpose, the bidirectional histogram algorithm is applied to the spatial geometric constraints vectors to obtain the statistical histogram descriptors with fixed dimensionality, which is invariant to the number and the starting position of the curvature voxels. Our experimental results compared with the traditional methods validate the argument that the presented descriptors are invariant to different pollen particles geometric transformations （such as posing change and spatial rotation）, and high recognition precision and speed can be obtained simultaneously.
文摘针对认知无线Mesh网络传统的多约束QoS组播路由算法一贯的进行随机初始化种群这一问题,在没有增加智能算法的复杂度的同时,首次将武器-目标分配问题(weapon to target allocation,WTA)应用在群智能算法对初始种群的优化上,基于蚁群算法,将集火射击、分火射击和混合射击的思想加入到对初始种群的设计上,提出一种基于WTA的QoS组播路由优化算法。其目标是满足无线组播业务的QoS约束且不增加算法复杂度的同时,结合蚁群的强鲁棒性和并行性等性能优势。经过实验验证,在网络开销和时延等方面的指标具有很好改善。
文摘针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strength Indicator,RSSI)进行高斯-卡尔曼融合滤波,提高了RSSI值的准确性;然后利用生存时间(Time To Live,TTL)对中继节点进行约束,提高了数据传输的有效性;最后利用半径弥补法与Bellman-Ford融合迭代方案对生成的距离矩阵进行修正,减小了测距误差。实验结果表明,所设计的系统可有效完成蓝牙标签信息更新以及位置展示,平均定位精度达到了0.94 m。本系统具有成本低、工程实施方便的优点,有一定的应用价值和发展前景。
