为了解决GPS(Global Position System)定位精度不够高,无法满足无人机完成货物搬运任务的问题,设计并实现了一种以TMS320DM6437为平台的机载视觉探测与目标定位系统,由机载子系统和地面监视子系统组成;机载视觉信息处理子系统利用圆的...为了解决GPS(Global Position System)定位精度不够高,无法满足无人机完成货物搬运任务的问题,设计并实现了一种以TMS320DM6437为平台的机载视觉探测与目标定位系统,由机载子系统和地面监视子系统组成;机载视觉信息处理子系统利用圆的几何特性改进了经典的Hough变换圆检测算法,在保证精度的同时大大降低了计算量,提高了算法运行效率;同时设计并实现了区分多个相同目标的方案与算法,其冗余机制增强了方案的可靠性;最后搭建了静目标定位测试平台和动目标定位跟踪机载测试系统,验证了系统的可靠性、鲁棒性、实时性和探测精度。展开更多
In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compres...In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).展开更多
The complexity of biological samples determines that the detection of a single biomolecule is unable to satisfy actual needs. Moreover, the "false positives" results caused by a single biomolecule detections...The complexity of biological samples determines that the detection of a single biomolecule is unable to satisfy actual needs. Moreover, the "false positives" results caused by a single biomolecule detections easily leads to erroneous clinical diagnosis and treatment. Thus, it is important for the homogenous quantification of multiple biomolecules in not only basic research but also practical application. As a consequent, a large number of literatures have been exploited to monitor multiple biomolecules in homogenous solution, enabling facilitating the development of the disease diagnosis, treatment as well as drug discovery. One-dimensional nanomaterials and two-dimensional nanomaterials have special physical and chemical properties, such as good electrochemical properties, stable structure, large specific surface area, and biocompatibility, which are widely used in electrochemical and fluorescent detection of biomolecules. This tutorial review highlights the recent development for the detection of multiple biomolecules by using nanomaterials including one-dimensional materials(1DMs) as well as twodimensional materials(2DMs).展开更多
In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise d...In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.展开更多
文摘为了解决GPS(Global Position System)定位精度不够高,无法满足无人机完成货物搬运任务的问题,设计并实现了一种以TMS320DM6437为平台的机载视觉探测与目标定位系统,由机载子系统和地面监视子系统组成;机载视觉信息处理子系统利用圆的几何特性改进了经典的Hough变换圆检测算法,在保证精度的同时大大降低了计算量,提高了算法运行效率;同时设计并实现了区分多个相同目标的方案与算法,其冗余机制增强了方案的可靠性;最后搭建了静目标定位测试平台和动目标定位跟踪机载测试系统,验证了系统的可靠性、鲁棒性、实时性和探测精度。
文摘In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).
基金supported by the National Natural Science Foundation of China (Nos. 21525523, 21722507, 21574048, 21874121)the National Basic Research Program of China (973 Program, No. 2015CB932600)+1 种基金the National Key R&D Program of China (Nos. 2017YFA020800, 2016YFF0100800)Natural Science Foundation of Zhejiang Province of China (No. LY18B050002)
文摘The complexity of biological samples determines that the detection of a single biomolecule is unable to satisfy actual needs. Moreover, the "false positives" results caused by a single biomolecule detections easily leads to erroneous clinical diagnosis and treatment. Thus, it is important for the homogenous quantification of multiple biomolecules in not only basic research but also practical application. As a consequent, a large number of literatures have been exploited to monitor multiple biomolecules in homogenous solution, enabling facilitating the development of the disease diagnosis, treatment as well as drug discovery. One-dimensional nanomaterials and two-dimensional nanomaterials have special physical and chemical properties, such as good electrochemical properties, stable structure, large specific surface area, and biocompatibility, which are widely used in electrochemical and fluorescent detection of biomolecules. This tutorial review highlights the recent development for the detection of multiple biomolecules by using nanomaterials including one-dimensional materials(1DMs) as well as twodimensional materials(2DMs).
基金supported by the Innovation Project of Science and Technology Commission of the Central Military Commission,China(No.19-HXXX-01-ZD-006-XXX-XX)。
文摘In this paper,a novel multi-frame track-before-detect algorithm is proposed,which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase.A criterion of track extrapolation is used to construct state transition set,root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space,then measurement plots of multi-frame are divided into several clusters,and finally multi-frame track-before-detect algorithm is implemented in each cluster.The computational complexity can be reduced by employing the proposed algorithm.Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.
