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IEEE 6th International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Yu Xiuwu Zhou Lixing Zhang Feng Fan Feisheng Univ South China Environm Protect & Safety Engn Inst Hengyang 421001 Hunan Peoples R China

ISBN: (纸本)9781509019977

Aiming at the problem of high cost of cable layout and poor maintenance in traditional radioactive pollution monitoring for uranium tailings, considering banded dam and little node connectivity, a weight optimized centroid localization algorithm is proposed. Under different index, by comparing the positioning accuracy and the influence of the external disturbance, new algorithm changes the index of weight and measured distance -1 to -1.5, resulting in an accuracy improvement in long distance. Simulation demonstrated that compared with centroid localization algorithm and maximum likelihood estimation, average positioning error of the new algorithm was reduced by 37.5% and 30.7%, and the maximum positioning error was reduced by 1/5 and by 2/5. With high reliability and stability, the new algorithm can meet the requirements of position in radioactive pollution monitoring for uranium tailings.

关键词： WSN centroid algorithm Weighe optimized uranium tailings monitoring localization

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New Location algorithm with Primary User Based on Scatter Information in Cognitive Radio

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期119卷 1845-1867页

作者： Ye, Dan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan

In cognitive radio network, if cognitive user conflicts with primary user during the spectrum sensing, it should concede avoiding the effect of communication with primary user. The paper initiates a new sensing methodology using scatter information around mobile users to locate primary user. The multi-group TOA (Time of Arrival) and AOA (Angle of Arrival) provided by two base stations are measured based on the single reflect statistics channel model under NLOS (Non-line-of-sight) environment. An innovative location algorithm, proposed in this paper, which combines neural network with the weighted centroid algorithm to achieve primary user's location. The proposed algorithm can make great improvement on the location accuracy by effectively mitigating NLOS error in cognitive radio networks. Another contribution of the proposed algorithm is the benefit to the cognitive performance, which can be characterized by the probability of spectrum coexistence. The results under different simulation circumstances illustrate that the current location algorithm improves detection performance and location performance to obtain more exact probability of spectrum coexistence.

关键词： Cognitive radio Wireless location Scatter Neural network centroid algorithm

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Human flow recognition using deep networks and vision methods

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104346-104346页

作者： Zimoch, Mateusz Markowska-Kaczmar, Urszula Wroclaw Univ Sci & Technol Dept Computat Intelligence Wyb Wyspianskiego 27 PL-50370 Wroclaw Poland

The paper focuses on developing the system for people flow recognition based on many video camera images. The project arose based on the real need - its application in the city malls to find the most visited places and support customers' campaigns. The potential application in other domains is possible (public buildings, airports). In contrast to the existing solutions, our approach involves on-edge image analysis to decrease the risk of data loss and the system cost. In the project, we design the whole processing pipeline composed of the component modules responsible for detecting, tracking, and reidentifying (shuffling) people. Our experimental platform enabled us to compare multiple method variants for each module. Based on extensive experimental research, the final solution uses the pretrained SSD method in the detection module. The centroid algorithm applied to displacement vectors combined with the Siamese network is the basis for the object tracking module. The best model to solve the reidentification task is the Resnet50. For the Market 1501 dataset, it achieved Rank-1 efficiency of 84.6%. The system gives a visualization of the main paths of people's movements in the form of a heat map and assigns the direction where people most often look. In the experimental study, we assessed the system's effectiveness and time efficiency, and the current results give a perspective for its commercialization in the nearest future.

关键词： Human flow Object tracking Siamese network centroid algorithm

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An Improved centroid DV Hop Based algorithm

An Improved Centroid DV Hop Based Algorithm

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Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)

作者： Agarwal, Harshit Dwivedi, Ananya Kaur, Amanpreet Jaypee Inst Informat Technol Dept CSE & IT Noida India

ISBN: (纸本)9781509039784

To reduce the positioning error in localization algorithms, an improved DV Hop based localization algorithm is proposed which takes the influence of closest anchor. Firstly, the smallest number of hops is computed for all nodes from every anchor. Then, distance of every unknown node from each anchor node is determined. Then highly accurate 2D Hyperbolic algorithm is applied to determine coordinates of all unknown nodes. In last step, every unknown node applies centroid method which uses impact of nearest anchor to get final coordinates. The proposed algorithm is implemented and simulated in Matlab and it is observed that it outperforms original algorithm in terms of localization error when varying anchor nodes, communication range, unknown nodes.

关键词： Wireless Sensor Network range free DV Hop Localization 2D Hyperbolic centroid algorithm

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centroid Location Technology Based on Fuzzy Clustering and Data Consistency 4th

Centroid Location Technology Based on Fuzzy Clustering and D...

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4th International Conference on Cloud Computing and Security (ICCCS)

作者： Xue, Shanliang Li, Mengying Yang, Peiru Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783030000189;9783030000172

RSSI technology has no additional hardware support, low energy consumption and low cost, but it has poor adaptability in different environments which would result in large errors when mapping RSSI signal to the measurement distance between nodes directly. In order to improve localization accuracy of Wireless Sensor Network, we propose a centroid Localization based on Fuzzy Clustering and Data Consistency. Firstly, the measurement distance is preprocessed, and the anchor node with the largest received signal strength is found as the reference node to eliminate the measurement error within communication range of unknown nodes. Secondly, Fuzzy Clustering and Data Consistency are used to remove the coarse error. Finally, the improved Weighted centroid algorithm is used to locate unknown nodes. The simulation results show that the FCDC-CL algorithms average localization error is approximately 9.4% and the error is significantly reduced compared with the traditional WCL algorithm.

关键词： Wireless sensor network Node localization RSSI centroid algorithm Fuzzy clustering Data consistency

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Accurate centroid determination for evaluating the modulation transfer function with a circular edge in CT images

Accurate centroid determination for evaluating the modulatio...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Zhang, Guozhi Stratis, Andreas Marshall, Nicholas Bosmans, Hilde Univ Hosp Leuven B-3000 Leuven Belgium

ISBN: (数字)9781510616363

ISBN: (纸本)9781510616363

The in-plane modulation transfer function (MTF) for multi-slice computed tomography (CT) can be found by scanning a phantom with cylindrical contrast inserts and making use of the circular edges presented in reconstructed axial images. Pixel data across the edge are used to establish an edge spread function, which is then used to obtain the line spread function and finally the MTF. A crucial step in this approach is to accurately locate the centroid of the circular region. Since the ESF is usually established in subpixel scale, slight deviation of the centroid may result in large errors. It has been a common practice to apply a preset threshold and calculate the center of mass in the binary output on each individual slice. It has also been suggested to locate the centroid on each slice by maximizing the sum of pixel values lying under a predefined template. In this paper, we propose a new algorithm based on registering the entire cylindrical object in 3D space. In a test on a high-noise low-contrast edge, both the threshold and the maximization algorithm showed scattered distribution of centroids across consecutive slices, resulting in underestimation of the MTF up to 10% at intermediate frequencies. In comparison, the method based on 3D registration has been found more robust to noise and the centroid locations are more consistent in the longitudinal direction. It is therefore recommended to use the proposed algorithm for centroid determination in evaluating the MTF with a circular edge in CT images.

关键词： Computed tomography image quality modulation transfer function centroid algorithm

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A Multi-Object Oriented Iterative Closest Point algorithm in Augmented Reality 8

A Multi-Object Oriented Iterative Closest Point Algorithm in...

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Conference on Advances in Display Technologies VIII

作者： Wang, Zhenhao Zhao, Yan Wang, Shigang Jilin Univ Sch Commun Engn 5372 Nanhu Rd Changchun 130012 Jilin Peoples R China

ISBN: (数字)9781510615984

ISBN: (纸本)9781510615984;9781510615977

The three-dimensional point cloud registration is a key technology of augmented reality and three-dimensional reconstruction. Also the Interactive Closest Point (ICP) is one of the most widely used algorithms of three-dimensional point cloud registration. However, the current ICP algorithms often come with an inaccurate initial value of the rotation matrix and the translation vector and cannot be used in scenarios with multiple objects. Therefore, we propose an ICP algorithm for multiple objects, which has an accurate initial rotation matrix and a translation vector. Firstly, the point cloud segmentation is applied to get multiple objects based on the Pass-through Filter and the Normal Estimation algorithms. The main spatial features involved are geometry and texture features. Secondly, the centroid of the regular point cloud data model such as cups, desks, etc. of each segmented point cloud, is calculated. Moreover, the singular value decomposition algorithm is used to obtain the rotation matrix of each point cloud model respectively. Finally, the translation vector of each point cloud model is obtained by combining the centroid and the rotation matrix. Experimental results show that the proposed method solves the problem of the inaccurate initial position and can be used for three-dimensional point cloud registration with multiple objects being compared with the existed ICP algorithms. At the same time, the ICP registration efficiency of a three-dimensional point cloud with single object using the proposed method is also improved about 5 percent.

关键词： interactive closest point rotation matrix translation vector singular value decomposition centroid algorithm direct filter normal estimation spatial feature geometry feature texture feature point cloud segmentation augmented reality

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Feed Forward Neural Network-Based Sensor Node Localization in Internet of Things

Feed Forward Neural Network-Based Sensor Node Localization i...

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International Conference on Computing, Analytics and Networking (ICCAN)

作者： Kumar, Ajay Jain, V. K. Mody Univ Sci & Technol Dept CSE Coll Engn & Technol Lakshmangarh India

ISBN: (纸本)9789811078712;9789811078705

Internet of Things and wireless sensor networks have attracted worldwide researchers because of their various applications in different fields. Specifically, in IoT, knowledge of location is a very critical issue which deals with identifying the position of deployed node in the sensor network. It is extremely advantageous to propose scalable, cost efficient, and proficient localization procedure for IoT. This paper provides a sensor positioning algorithm named centroid algorithm which is a range-free position identifying scheme. The centroid of the polygon is used to compute the coordinates of estimate position to get better node location precision and further neural networks such as feed forward have been implemented to improve the accuracy. A comparison of centroid algorithm and feed forward neural network-based localization has been done and found that the neural network promises better results for higher localization accuracy.

关键词： centroid algorithm Global positioning system Neural networks Internet of Things Wireless sensor networks

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centroids computation and point spread function analysis for reverse Hartmann test

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OPTICS COMMUNICATIONS 2017年 387卷 328-337页

作者： Zhao, Zhu Hui, Mei Liu, Ming Dong, Liquan Kong, Linqqin Zhao, Yuejin Beijing Inst Technol Sch Optoelect Beijing Key Lab Precis Optoelect Measurement Inst Beijing 100081 Peoples R China

This paper studies the point spread function (PSF) and centroids computation methods to improve the performance of reverse Hartmann test (RHT) in poor conditions, such as defocus, background noise, etc. In the RHT, we evaluate the PSF in terms of Lommel function and classify it as circle of confusion (CoC) instead of Airy disk. Approximation of a CoC spot with Gaussian or super-Gaussian profile to identify its centroid forms the basis of centroids algorithm. It is also effective for fringe pattern while the segmental fringe is served as a 'spot' with an infinite diameter in one direction. RHT experiments are conducted to test the fitting effects and centroiding performances of the methods with Gaussian and super-Gaussian approximations. The fitting results show that the super-Gaussian obtains more reasonable fitting effects. The super-Gauss orders are only slightly larger than 2 means that the CoC has a similar profile with Airy disk in certain conditions. The results of centroids computation demonstrate that when the signal to noise ratio (SNR) is falling, the centroid computed by super-Gaussian method has a less shift and the shift grows at a slower pace. It implies that the super-Gaussian has a better anti-noise capability in centroid computation.

关键词： Point spread function Circle of confusion centroid algorithm Gaussian and super-Gaussian approximations Reverse Hartmann test

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Research of the Wavefront Detection Method based on the Scanning Pentaprism 2

Research of the Wavefront Detection Method based on the Scan...

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2nd International Conference on Photonics and Optical Engineering (icPOE)

作者： Zhu Xuelianga Zhao Huiying Dong Longchao Tian Ailing Yuan Daocheng Ban Xinxing Liu Bingcai Tian Yanan Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Fourth Mil Med Univ Shaanxi Prov Key Lab Thin Films Technol & Opt Tes 4 Jinhua North Rd Xian 710032 Shaanxi Peoples R China

ISBN: (纸本)9781510610132;9781510610149

With the rapid developing of science and technology, large aperture optical system plays an important role in the high-tech fields including space optics, astronomical optics, inertial confinement fusion, the detecting and recognizing of space target. However, the problems of the wavefront sensing about large aperture optical system has been totally solved because of the equipment expenses and long manufacturing periods. In order to test the large aperture elements in optical system with cheaper costs and higher resolution, more and more attentions are paid into the wavefront sensing of large aperture optical systems. The scanning pentaprism system is introduced to divide the wavefront of the interferometer into a series of sub-wavefront, and the relative positions of the spot centroid accroding to every sub-wavefront are recorded on the CCD camera. The normal directions of every sub-wavefront are obtained to reconstruct the tested wavefront. Experimental results are accord with the interferometer measure results. The feasibility of the pentaprism scanning method has been validated. Finally the influences of measurement apparatus and environment on the measuring precision is discussed. Which is useful to expand the measuring range to keep high spatial resolution and reduce cost.

关键词： Optical testing Collimated wavefront Pentaprism scanning centroid algorithm

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