Pedestrian detection is an essential but challenging task in computer vision, especially in crowded scenes due to heavy intra-class occlusion. In human visual system, head information can be used to locate pedestrian in a crowd because it is more stable and less likely to be occluded. Inspired by this clue, we propose a dual-task detector which detects head and human body simultaneously. Concretely, we estimate human body candidates from head regions with statistical head-body ratio. A head-body alignment map is proposed to perform relational learning between human bodies and heads based on their inherent correlation. We leverage the head information as a strict detection criterion to suppress common false positives of pedestrian detection via a novel pull-push loss. We validate the effectiveness of the proposed method on the CrowdHuman and CityPersons benchmarks. Experimental results demonstrate that the proposed method achieves impressive performance in detecting heavy-occluded pedestrians with little additional computation cost.

Towards involving the convolutional neural networks into the object detection field, many computer vision tasks have achieved favorable successes. In order to adapt targets with various scales, deep feature pyramid is widely used, since the traditional object detection methods detect different objects in Gaussian image pyramid. However, due to the mismatching between the anchors and the feature distributions of targets, the accurate detection for targets with various scales is still a challenge. Considering the differences between the theoretical receptive field and effective receptive field, we propose a novel anchor generation method, which takes the effective receptive field as the standard. The proposed method is evaluated on the PASCAL VOC dataset and shows the favorable results.

Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

In the 5G era, centralized mobility management raises the issue of traffic concentration on the mobility anchor. Distributed mobility management is expected to be a solution for this issue, as it moves mobility anchor functions to multiple edge routers. However, it incurs path stretch and redundant traffic on the backhaul links. Although these issues were not considered important in the 3G/4G era, they are expected to be a serious problem in the 5G era. In this paper, we design a routing-based mobility management mechanism to address the above problems. The mechanism integrates distributed routing with Bloom Filters and an anchor-less scheme where edge routers work as mobility anchors. Simulations show that the proposed mechanism achieves a good balance between redundant traffic on the backhaul links and routing overhead.

Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.

WSNs (Wireless Sensor Networks) are becoming more widely used in various fields, and localization is a crucial and essential issue for sensor network applications. In this letter, we propose a low-complexity localization mechanism for WSNs that operate in 3D (three-dimensional) space. The basic idea is to use aerial vehicles that are deliberately equipped with anchor nodes. These anchors periodically broadcast beacon signals containing their current locations, and unknown nodes receive these signals as soon as the anchors enter their communication range. We estimate the locations of the unknown nodes based on the proposed scheme that transforms the 3D problem into 2D computations to reduce the complexity of 3D localization. Simulated results show that our approach is an effective scheme for 3D self-positioning in WSNs.

For the deployment of sensor networks, the sensor localization, which finds the position of sensor nodes, is very important. Most previous localization schemes generally use the GPS signal for the sensor localization. However, the GPS signal is unavailable when there is an obstacle between the sensor nodes and satellites. Therefore, in this paper, we propose a new localization scheme which does not use the GPS signal. The proposed scheme localizes the sensors by using three mobile anchors. Because the three mobile anchors collaboratively move by themselves, it is self-localizable and can be adopted even when the sensors are randomly and sparsely deployed in the target field.

In this letter, we propose an improved anchor shot detection (ASD) method in order to effectively retrieve anchor shots from news video. The face location and dissimilarity of icon region are used to reduce false alarms in the proposed method. According to the results of the experiment on several types of news video, the proposed method obtained high anchor detection results compared with previous methods.

In this paper, we present a Double-Anchoring Based Tone Mapping (DABTM) algorithm for displaying high dynamic range (HDR) images. First, two anchoring values are obtained using the double-anchoring theory. Second, we use the two values to formulate the compressing operator, which can achieve the aim of tone mapping directly. A new method based on accelerated K-means for the decomposition of HDR images into groups (frameworks) is proposed. Most importantly, a group of piecewise-overlap linear functions is put forward to define the belongingness of pixels to their locating frameworks. Experiments show that our algorithm is capable of achieving dynamic range compression, while preserving fine details and avoiding common artifacts such as gradient reversals, halos, or loss of local contrast.

The self-organized microwrinkles can serve as a surface alignment layer to align nematic liquid crystals, which is primarily based on the groove mechanism. The azimuthal anchoring energy is discussed and estimated from the groove topography and the actual twist angle in the twisted nematic cell.

Because network diagrams drawn using the spring embedder are not easy to read, this paper proposes the use of "anchored maps" in which some nodes are fixed as anchors. The readability of network diagrams is discussed, anchored maps are proposed, and a method for drawing anchored maps is explained. The method uses indices to decide the orders of anchors because those orders markedly affect the readability of the network diagrams. Examples showing the effectiveness of the anchored maps are also shown.

A dynamic anchor-area (DAA) scheme is proposed to reduce the location management cost for PCS networks. Based on an Intra-LA location update (LU) scheme, the proposed DAA scheme utilizes a given timer value and a movement list. The DAA scheme considers a wider variety of mobile terminal's movement pattern than the previous intra-LA LU scheme. Simulations confirm that the proposed scheme offers a significant reduction in the location management cost.

Internet engineering task force (IETF) has proposed hierarchical mobile IPv6 (HMIPv6) in order to reduce a frequent location registration of a mobile node in mobile IPv6 (MIPv6). All traffics toward a mobile node must be transmitted through a MAP in HMIPv6. This brings unnecessary packet latency because of the increased processing cost of packet at the MAP. At this point, the processing cost of packet at the MAP is influenced by the packet arrival rate for a mobile node, cell mobility rate and the number of mobile nodes in MAP domain. In this paper, we analyze the MAP's performance considering the above elements. For this, we compare total cost of HMIPv6 with total cost of MIPv6 as MAP's capability after we define Markov chain model for performance analysis. Also, we define network's total profit as total cost of MIPv6 minus total cost of HMIPv6. Then, we can find optimal capability of MAP such that total profit has maximum value. Also, we use the blocking probability by the MAP's capability as performance estimation element. As a conclusion, we can observe both HMIPv6's performance by the MAP's capability and optimal capability of the MAP, and blocking probability form a relationship of trade off between them.

Recently, semantic text portion (STP) is getting popular in the field of Web mining. STP is a text portion in the original page which is semantically related to the anchor pointing to the target page. STPs may include the facts and the people's opinions about the target pages. STPs can be used for various upper-level applications such as automatic summarization and document categorization. In this paper, we concentrate on extracting STPs. We conduct a survey of STP to see the positions of STPs in original pages and find out HTML tags which can divide STPs from the other text portions in original pages. We then develop a method for extracting STPs based on the result of the survey. The experimental results show that our method achieves high performance.

In this paper, an algorithm for anchor frame detection in news video is proposed, which consists of four steps. First, the cumulative histogram method is used to detect shot boundaries in order to segment a news video into video shots. Second, skin color information is used to detect face regions in each video shot. Third, color information of upper body regions is used to extract anchor object. Then, a graph-theoretic cluster analysis algorithm is utilized to classify the news video into anchor-person shots and non-anchor shots. Experimental results have shown the effectiveness of the proposed algorithm.

Hierarchical Mobile IPv6 (HMIPv6) has been proposed to improve the performance capability of Mobile IPv6 at handover. In HMIPv6, local entities named Mobility Anchor Points (MAPs) are distributed throughout a network to localize the management of intra-domain mobility. In particular, multi-layered MAP has been proposed to improve performance. MAPs reduce the number of Binding Updates to the Home Agent and improve the communication quality at handover. These conventional methods that manage a multi-layered MAP cannot, however, select an appropriate MAP because they use the virtual mobility speed. As a result, they increase the signaling traffic in a multi-layered MAP. Moreover, they may cause the load to concentrate at a specific MAP. In this paper, we propose a location management method for Hierarchical Mobile IPv6 using the MN's mobile history. In this method, when a MN performs a handover, the Access Router calculates the area-covered rate of each upper MAP from the MN's mobile history and selects the MAP that best manages the MN in accordance with its rate. Thus, the proposed method reduces both the number of Binding Updates to the Home Agent and the signaling traffic because it reduces the frequency of changing the MAP. We evaluate the performance of the proposed method by simulation.

This paper proposes "Handover with Proactive Anchor Router Relocation and Data Buffering" to suppress packet loss and packet miss-ordering during handover. To prevent packet miss-ordering, anchor router is proactively relocated to the optimal position before the mobile terminal performs handover. And, to eliminate packet loss during handover, anchor router buffers the packet only during handover. Moreover, anchor router assigns sequential numbers to the buffered packets to eliminate duplicate packet reception. Simulation results show that our proposal eliminates packet miss-ordering and duplicate reception while preventing packet loss.

We have studied the electro-optical properties in Polymer Network Liquid Crystal Displays which are composed of a three-dimensional polymer network formed in continuous liquid crystal layers prepared by photo-polymerization induced phase separation processes. In view of the behavior in the orientation of the liquid crystal molecule at the polymers surface, it can be clarified that the electro-optical properties are strongly governed by the polymer. By a combination of two different characteristics of the polymers with respect to a temperature dependence, a variation in the driving voltage for a temperature change can be improved.