This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown correspondences are handled via mixture models. Adopting a maximum likelihood principle, we introduce an innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm. The algorithm allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case. We analyze in detail the associated consequences in terms of estimation of the registration parameters, and propose an optimal method for estimating the rotational and translational parameters based on semidefinite positive relaxation. We extend rigid registration to articulated registration. Robustness is ensured by detecting and rejecting outliers through the addition of a uniform component to the Gaussian mixture model at hand. We provide an in-depth analysis of our method and compare it both theoretically and experimentally with other robust methods for point registration.

Spatiotemporal information of the vehicles on a bridge is important evidence for reflecting the stress state and traffic density of the bridge. A methodology for obtaining the information is proposed based on computer vision technology, which contains the detection by Faster region‐based convolutional neural network (Faster R‐CNN), multiple object tracking, and image calibration. For minimizing the detection time, the ZF (Zeiler & Fergus) model with five convolutional layers is selected as the shared part between Region Proposal Network and Fast R‐CNN in Faster R‐CNN. An image data set including 1,694 images is established about eight types of vehicles for training Faster R‐CNN. Combined with the detection of each frame of the video, the methods of multiple object tracking and image calibration are developed for acquiring the vehicle parameters, including the length, number of axles, speed, and the lane that the vehicle is in. The method of tracking is mainly based on the judgment of the distances between the vehicle bounding boxes in virtual detection region. As for image calibration, it is based on the moving standard vehicles whose lengths are known, which can be regarded as the 3D template to calculate the vehicle parameters. After acquiring the vehicles' parameters, the spatiotemporal information of the vehicles can be obtained. The proposed system has a frame rate of 16 fps and only needs two cameras as the input device. The system is successfully applied on a double tower cable‐stayed bridge, and the identification accuracies of the types and number of axles are about 90 and 73% in the virtual detection region, and the speed errors of most vehicles are less than 6%.

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Short-term traffic flow prediction is fundamental for the intelligent transportation system and is proved to be a challenge. This paper proposed a hybrid strategy that is general and can make use of a large number of underlying machine learning or time-series prediction models to capture the complex patterns beneath the traffic flow. With the strategy, four different combinations were implemented. To consider the spatial features of traffic phenomenon, several different state vectors including different observations were built. The performance of the proposed strategy was investigated using the traffic flow measurements from the Traffic Operation and Safety Laboratory in Wisconsin, USA. The results show the overall performance of hybrid strategy is better than a single model. Also, incorporating observations from adjacent junctions can improve prediction accuracy.

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Two‐dimensional transfer functions are an effective and well‐accepted tool in volume classification. The design of them mostly depends on the user's experience and thus remains a challenge. Therefore, we present an approach in this paper to automate the transfer function design based on 2D density plots. By exploiting their smoothness, we adopted the Morse theory to automatically decompose the feature space into a set of valley cells. We design a simplification process based on cell separability to eliminate cells which are mainly caused by noise in the original volume data. Boundary persistence is first introduced to measure the separability between adjacent cells and to suitably merge them. Afterward, a reasonable classification result is achieved where each cell represents a potential feature in the volume data. This classification procedure is automatic and facilitates an arbitrary number and shape of features in the feature space. The opacity of each feature is determined by its persistence and size. To further incorporate the user's prior knowledge, a hierarchical feature representation is created by successive merging of the cells. With this representation, the user is allowed to merge or split features of interest and set opacity and color freely. Experiments on various volumetric data sets demonstrate the effectiveness and usefulness of our approach in transfer function generation.

Although Unmanned Aerial Vehicles (UAV) are usually deploy outdoors, there is increasing interest in applying UAVs for indoor applications. It is a highly attractive and challenging task to precisely localize a UAV in an indoor environment where Global Positioning System (GPS) service is absent. In this paper, we present RFUAV, a Radio-frequency Identification (RFID) enhanced UAV system that provides a precise 6 degrees of freedom (6-DoF) pose for UAVs. With RFUAV, three or more ultra high frequency (UHF) RFID tags are attached to the UAV and are interrogated by a Commercial Off-The-Shelf (COTS) RFID reader with multiple antennas. Based on phase measurements of the RFID tag responses, the RFID tracker of RFUAV, a Bayesian filter-based algorithm, was employed to track the position of the tags in a global coordinate system. The pose estimator of RFUAV computes the 6-DoF pose of the UAV from tag positions. We tested the performance of RFUAV in a representative, structure-rich, indoor environment, where 0.04 m position error and 2.5 degrees orientation error were achieved. INDEX TERMS Radio-frequency identification (RFID), six degrees of freedom (6-DoF), indoor localization, unmanned aerial vehicle (UAV).

Embraced for decision-making, resilience has evolved as a meaningful term in areas such as ecology, the economy and society. After a policy of grassland contracts was implemented on the Qinghai-Tibetan Plateau, two grassland management patterns evolved: the multi-household management pattern (MMP) and the singlehousehold management pattern (SMP). Within a resilience-driven perspective, this study compared the outcomes of these grassland management patterns by measuring their effects on the resilience of grazing, ecological, economic and social systems. Resilience indicators for each of the four systems were: grazing system (grazing space, transhumance, water source and reproduction); ecological system (vegetation including cover, biomass, species richness and soil properties including pH, organic carbon, total nitrogen and total phosphorus); economic system (income, expenditure and infrastructure) and the social system (health, assistance, social relations, cultural inheritance and institutional arrangements). In order to provide a social-ecological resilience framework for the two grassland management patterns, a decision support tool was applied to approximately gauge the resilience of each indicator. The results showed that each of the four systems under the MMP had a greater degree of resilience than the SMP, and that the overall resilience of the MMP was estimated at 5.8 units compared to about −5.8 units for the SMP. The relative success of the MMP was seen to rest largely on the maintenance of traditional management practices, social networks, trust and the low cost and high efficiency of informal institutions, which acted to reduce the risk of unsustainable development of ecological and social systems. The important take-home lesson from this study is that contracting of grasslands to private entities on the Qinghai-Tibetan Plateau, and in the rest of the world where similar land management practices exist, must be undertaken with caution.