Set Of Anchors Research Articles

PROBABILISTIC ANALYSIS OF ANCHOR (DOWEL) PULL-OUT CAPACITY IN AERATED CONCRETE

Aerated concrete structures are widely used in construction as load-bearing or filler structures. The article describes the probabilistic approach to estimation of anchor (dowel) pull-out capacity. The article also presents the experimental studies of control samples from aerated concrete and diagrams “stress – strain”. The maximum permissible load values are obtained for the given batch of aerated concrete blocks based on the requirements of ensuring the specified reliability (probability of no-failure). Permissible load on the anchor (dowel) will also depend on the number of anchors in case of set of anchors as a conditional sequential mechanical system in terms of reliability theory. The design equations is given the reliability analysis of anchor (dowel) on pull-out force with limited statistical data and small number of control samples for using probabilistic and statistical approaches.

Network Topology Mapping From Partial Virtual Coordinates and Graph Geodesics

For many important network types, physical coordinate systems and physical distances are either difficult to discern or inapplicable. Accordingly, coordinate systems and characterizations based on hop-distance measurements, such as Topology Preserving Maps (TPMs) and Virtual-Coordinate (VC) systems are attractive alternatives to geographic coordinates for many network algorithms. We present an approach to recover geometric and topological properties of a network with a small set of distance measurements. The approach is a combination of shortest path (often called geodesic) recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. Results for sensor networks embedded in 2-D and 3-D spaces as well as for social networks indicate that the method can accurately capture the network connectivity with a small set of measurements. TPM generation can now be based on various context appropriate measurements or VC systems instead of distances to a set of global anchors. The proposed method is a significant generalization that allows the topology to be extracted from a random set of graph shortest paths, making it applicable in contexts such as social networks where VC generation may not be possible.

ECLES: A general method for local editing of parameters with linear constraints

SummaryWe describe ECLES (Editing by Constrained LEast Squares), a general method for interactive local editing of objects that are defined by a list of parameters subject to a set of linear or affine constraints. The method is intended for situations where each edit action affects only a small set of the parameters; some of which (the “anchors”) are to be set to new given values, whereas the rest (the “derived” ones) are to be modified so as to preserve the constraints. We use exact integer arithmetic in order to reliably determine solvability, to detect and eliminate redundancies in the set of constraints, and to ensure that the solution exactly satisfies the constraints. We also use constrained least squares to choose a suitable solution when the constraints allow multiple solutions. Unlike the usual finite element approach, the method allows editing of any set of anchors with any sufficiently large set of derived parameters. As an example of application, we show how the method can be used to edit smooth ( ) deformations of geometric mesh models.

Mobile anchor assisted localization and path-planning techniques in wireless sensor networks: Challenges and Solutions

Localization is one of the most significant technique in wireless sensor networks (WSNs). The location information of nodes can be provided by Global Positioning System (GPS), but the cost is huge if every node equips GPS. This problem can be solved by deploying a set of mobile anchors which can exchange information with other nodes that estimating their locations. Planning an adaptive path can reduce the cost by using smaller number of mobile anchors and estimate the location of other nodes precisely in WSNs. In recent years, many path planning algorithms have been proposed. In this paper, a comprehensive survey is presented to point out key problems and challenges of mobile anchors. We compare the differences among the algorithms. Also, we make a discussion for future research and show new challenges in this field.

Composite strands for prestressed concrete: State-of-the-practice and experimental investigation into mild prestressing with GFRP

An overview of available solutions and historic experiences in the state-of-the-practice of prestressing technologies with non-conventional tendons is presented to determine whether mild prestressing with Glass Fiber Reinforced Polymer (GFRP) strands may represent a solution for some of the existing challenges. The experimental portion of this project explores the potential of GFRP strands coupled with traditional steel anchors as an efficient, easy-to-deploy, and non-corrosive pre-tensioning solution in applications that require limited levels of prestress. The strands are composed of seven twisted wires and have a nominal diameter of 15.2 mm. This study on GFRP strand prototypes covers: a) mechanical and geometrical characterization; b) jacking strength of the coupled strand-anchor under instantaneous and sustained load; c) load loss during anchor setting using various pulling techniques; and, d) some flexural properties and coilability. Results should only be considered indicative of the potential of the technology given also the limited material availability. Experimental outcomes are evaluated with reference to information available in archival literature with particular reference to GFRP reinforcing bars.

Detecting multi-oriented text with corner-based region proposals

Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn.

Orientation Robust Object Detection in Aerial Images Based on R-NMS

Object detection in aerial images is a challenging task which plays an important role in many fields, such as intelligent traffic management, fishery management and so on. Different from object detection in natural images, the orientation of objects in aerial images is arbitrary. The axis-aligned bounding box detection, which is always used in traditional object detection methods, will cover a lot of redundant information and deteriorate the detection results when it is used to locate the object in aerial images. Therefore, traditional object detection methods are no longer applicable for aerial images. In order to promote the object detection performance in aerial images, we propose a novel orientation robust object detection model based on rotated non-maximum suppression (R-NMS). In addition, we adjust the anchor setting according to the diversity shapes of the aerial objects to enhance the performance of the model. Our model is tested on the public DOTA dataset, and the mAP is 16.31% higher than the baseline, indicating that our method is very effective and competitive in the object detection of aerial image.

MICRO-RADAR AND UWB AIDED UAV NAVIGATION IN GNSS DENIED ENVIRONMENT

Abstract. During the last decade, the number of applications of UAVs has continuously increased, making the global UAV market one of those with the highest rate of growth. The worldwide increasing usage of UAVs is causing an ever-growing demand for efficient solutions in order to make them usable in every kind of working condition. In fact, nowadays the main restriction to the usage of UAVs is probably the need of reliable position estimates provided by using the Global Navigation Satellite System (GNSS): since UAVs mostly rely on the integration of GNSS/Inertial Navigation System (INS) to properly fulfil their tasks, they face a major challenge while navigating in GNSS denied environments. The goal of this paper is that of investigating possible strategies to reduce such main restriction to UAV usage, i.e. enabling flights in GNSS denied environment by providing position estimates with accuracy quite comparable to that of standard GNSS receivers currently mounted on commercialized drones. To be more specific, this paper proposes the combined use of a novel Frequency Modulated Continuous Wave (FMCW) Radar, a set of Ultra-Wideband (UWB) devices, and Inertial Measurement Unit (IMU) measurements in order to compensate for the unavailability of the GNSS signal units. A 24-GHz micro FMCW radar and a UWB device have been attached to a quadcopter during the flight. The radar receives the reflections from ground scatterers, whereas the UWB system provides range measurements between a UWB rover mounted on the UAV and a set of UWB anchors distributed along the flying area.

Using Acoustic Signal and Image to Achieve Accurate Indoor Localization.

Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m () relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.

UNLOC: Optimal Unfolding Localization from Noisy Distance Data

Target localization is an important problem in signal processing and sensor networks, with many application areas including security, consumer electronics, and health monitoring. In this paper, we formulate target localization as an inverse problem: given the locations of a set of anchors and noisy distance measures to a target, the localization problem is to estimate the unknown location of the target. We propose to solve the localization problem using an unfolding-based optimization. We show that the corresponding stress optimization, despite being a nonlinear problem, yields a global optimum that can be approximated using an efficient iterative algorithm. We term our computational approach as UNLOC, unfolding-based localization, and benchmark its effectiveness on both synthetic data and lab-generated experimental data. The proposed localization technique generally produces accurate target location, and the quality of localization can be further improved by an appropriate choice of weights in the objective function of the optimization problem.

Flexible Live‐Wire: Image Segmentation with Floating Anchors

AbstractWe introduce Flexible Live‐Wire, a generalization of the Live‐Wire interactive segmentation technique with floating anchors. In our approach, the user input for Live‐Wire is no longer limited to the setting of pixel‐level anchor nodes, but can use more general anchor sets. These sets can be of any dimension, size, or connectedness. The generality of the approach allows the design of a number of user interactions while providing the same functionality as the traditional Live‐Wire. In particular, we experiment with this new flexibility by designing four novel Live‐Wire interactions based on specific primitives: paint, pinch, probable, and pick anchors. These interactions are only a subset of the possibilities enabled by our generalization. Moreover, we discuss the computational aspects of this approach and provide practical solutions to alleviate any additional overhead. Finally, we illustrate our approach and new interactions through several example segmentations.

Deep detection network for real-life traffic sign in vehicular networks

The challenge for real-life traffic sign detection lies in recognizing small targets in a large and complex background, making state-of-the-art general object detection methods not work well in both detection speed and precision. The existing deep learning models for traffic signs detection fail to use the fixed feature of the targets. This paper proposes a novel end-to-end deep network that extracts region proposals by a two-stages adjusting strategy. Firstly, we introduce an AN (Attention Network) to Faster-RCNN for finding all potential RoIs (Regions of Interest) and roughly classifying them into three categories according to colour feature of the traffic signs. Then the FRPN (Fine Region Proposal Network) produces the final region proposals from a set of anchors per feature map location extracted by the AN. We also modify the model by (1) adding a deconvolutional structure to convolutional layers to fit the small size of targets, and (2) replacing the classifier with three softmax corresponding to three coarse categories obtained by the AN. Our method is evaluated on two publicly available traffic sign benchmarks which are collected in real road condition. The experiments show our method generates only 1/14 of the anchors generated by Faster-R-CNN so the detection speed is increased by about 2 fps with ZF-Net and it reaches an average mAP of 80.31% and 94.95% in two benchmarks, 9.69% and 7.88% higher than Faster-R-CNN with VGG16, respectively.

FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization

Knowledge graphs have received intensive research interests. When the labels of most nodes or datapoints are missing, anchor graph and hierarchical anchor graph models can be employed. With an anchor graph or hierarchical anchor graph, we only need to optimize the labels of the coarsest anchors, and the labels of datapoints can be inferred from these anchors in a coarse-to-fine manner. The complexity of optimization is therefore reduced to a cubic cost with respect to the number of the coarsest anchors. However, to obtain a high accuracy when a data distribution is complex, the scale of this anchor set still needs to be large, which thus inevitably incurs an expensive computational burden. As such, a challenge in scaling up these models is how to efficiently estimate the labels of these anchors while keeping classification performance. To address this problem, we propose a novel approach that adds an anchor label predictor in the conventional anchor graph and hierarchical anchor graph models. In the proposed approach, the labels of the coarsest anchors are not directly optimized, and instead, we learn a label predictor which estimates the labels of these anchors with their spectral representations. The predictor is optimized with a regularization on all datapoints based on a hierarchical anchor graph, and we show that its solution only involves the inversion of a small-size matrix. Built upon the anchor hierarchy, we design a sparse intra-layer adjacency matrix over these anchors, which can simultaneously accelerate spectral embedding and enhance effectiveness. Our approach is named Faster Learning on Anchor Graph (FLAG) as it improves conventional anchor-graph-based methods in terms of efficiency. Experiments on a variety of publicly available datasets with sizes varying from thousands to millions of samples demonstrate the effectiveness of our approach.

Random walks on uniform and non-uniform combs and brushes

We consider random walks on comb- and brush-like graphs consisting of a base (of fractal dimension D) decorated with attached side-groups. The graphs are also characterized by the fractal dimension Da of a set of anchor points where side-groups are attached to the base. Two types of graphs are considered. Graphs of the first type are uniform in the sense that anchor points are distributed periodically over the base, and thus form a subset of the base with dimension . Graphs of the second type are decorated with side-groups in a regular yet non-uniform way: the set of anchor points has a fractal dimension smaller than that of the base, . For uniform graphs, a qualitative method for evaluating the sub-diffusion exponent suggested by Forte et al for combs () is extended for brushes () and numerically tested for the Sierpinski brush (with the base and anchor set built on the same Sierpinski gasket). As an example of nonuniform graphs we consider the Cantor comb composed of a one-dimensional base and side-groups, the latter attached to the former at anchor points forming the Cantor set. A peculiar feature of this and other nonuniform systems is a long-lived regime of super-diffusive transport when side-groups are of a finite size.

Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy

Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. However, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue, we propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and we demonstrate that the optimization process only involves a small-size reduced Laplacian matrix. We also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task.

New path planning model for mobile anchor-assisted localization in wireless sensor networks

As event detection is one of the main purposes of using wireless sensor networks (WSNs), the nodes location is essential to determine the location of that event when it occurs. Many localization models have been proposed in the literature. One of the solutions is to deploy a set of location-aware nodes, called anchors, to exchange information with the other nodes in order to help estimate their own location. Another promising proposal involves replacing these sets of anchors with only one mobile anchor. While this proposal seems to provide favorable results, it brings new challenges. The main challenge is to find an optimal path for the mobile anchor to follow while taking into account the need to provide highly accurate data and more localizable nodes in less time and with less energy. In this paper, we introduced a new static path planning model for mobile anchor-assisted localization in WSNs. Our proposed model guarantees that all nodes are able to receive the localization information, thus, estimate their own location with higher localization accuracy in comparison to similar static models. Moreover, this model overcomes the problem of collinearity and takes into account the metrics of precision and energy consumption as well as accuracy, localization ratio and the path length of the mobile anchor.

Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval.

Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.

Investigating Separate and Concurrent Approaches for Item Parameter Drift in 3PL Item Response Theory Equating

This study examines separate and concurrent approaches to combine the detection of item parameter drift (IPD) and the estimation of scale transformation coefficients in the context of the common item nonequivalent groups design with the three-parameter item response theory equating. The study uses real and synthetic data sets to compare the two approaches based on IPD flagging rates, type I error and power rates, and recovery of scale transformation coefficients. Results indicate that the two approaches render similar outcomes with stable anchor sets. However, they can produce dissimilar results with unstable anchor sets because of differences in the performance of their IPD components. Further, the findings of this study caution about working backward from equated cut scores to motivate the selection of an anchor set.

Local structure learning in high resolution remote sensing image retrieval

High resolution remote sensing image captured by the satellites or the aircraft is of great help for military and civilian applications. In recent years, with an increasing amount of high resolution remote sensing images, it becomes more and more urgent to find a way to retrieve them. In this case, a few methods based on the statistical information of the local features are proposed, which have achieved good performances. However, most of the methods do not take the topological structure of the features into account. In this paper, we propose a new method to represent these images, by taking the structural information into consideration. The main contributions of this paper include: (1) mapping the features into a manifold space by a Lipschitz smooth function to enhance the representation ability of the features; (2) training an anchor set with several regularization constrains to get the intrinsic manifold structure. In the experiments, the method is applied to two challenging remote sensing image datasets: UC Merced land use dataset and Sydney dataset. Compared to the state-of-the-art approaches, the proposed method can achieve a more robust and commendable performance.

Unilateral sacrospinous ligament fixation (USLF) with a mesh stabilizing anchor set: clinical outcome and impact on quality of life

Genital prolapse is one of the most significant problems which lowers the quality of life measures of middle and older aged women. A continuously growing number of women are being operated due to this indication. Objective: This study intends to asses the clinical outcome and the impact on quality of life of uterine sacrospinous ligament fixation (USLF) conducted with a mesh stabilizing anchor set in the present clinic following vaginal hysterectomy. Materials and Methods: Twenty-one patients, diagnosed with genital prolapse and for whom vaginal hysterectomy and (USLF) with the Surelift nesh stabilizing anchor set were performed from April 2010 to June 2013, were assesed in this study. Posterior colporrhaphy was performed in all cases, as well. The cuff level was used to asses the anatomical recovery one year following the surgery. Postoperative relaxation of the vaginal cuff line below the hymenal level was defined as failure. Quality of life (P-QOL) questionarries validated for Turkish women were used preoperatively and on their first year to asses patient satisfaction. Clinical outcome and impact on quality of life were analyzed in all these cases by using t-test for paired samples. Results: The mean age of the patients was 67.4 (min-max:43-84) years; mean parity 5.4 (min-max: 2-13). The mean operation time was 56 ± 12 minutes. The mean postoperative follow-up period was 21.4 months. Preoperative mild bleeding (two), postoperative severe pain (three), and micturition problems (one) were found. Therapeutic results and patint satisfaction were evaluated in the 12th month postoperavely: In 18 / 21 (85%) patients, the cuff was located above the hymenal ring. P-QOL scores validated for Turkish women were 52.5 ± 12.9 preoperatively and 11.08 ± 7.9 postoperatively (t-test for paired samples revealed a significant difference; (p = 0.04). Conclusion: The treatment of genital prolapse through the abdominal route includes the sacrocolpopexy operation with or without hysterectomy. This method, most of the time, requires a laparotomy if not performed by a specifically trained laparoscopist. It has a longer operation time and mesh erosions are feared complications compared to vaginal route. In sacrospinous fixation cases added to vaginal hysterectomy, operation times are shorter and especially preferable in patients where medical problems coexist. Operative success and patients’ satisfaction seems to be provided by this technique.