Accurate traffic prediction is crucial for managing and making decisions in urban traffic systems. Recent studies have shown outstanding performance in traffic prediction by using methods that employ graph adjacency matrices predefined by heuristic rules or learn graph structures through trainable parameters. These studies tend to focus on interactions between nodes based on macroscopic traffic node attributes, overlooking the profound impact of individual behaviors and decisions of microscopic traffic participants on the transportation system. This valuable microscopic information has not been utilized to guide graph structure learning. In this paper, we proposed a spatio-temporal model with the capability of multi-scale adaptive graph structure learning, named Adap-STWT. Initially, an optimal graph structure learning module is constructed for dynamic traffic systems, encompassing multiple scales from microscopic traffic participants to macroscopic node attributes. Subsequently, in conjunction with this optimal graph structure, an optimized spatio-temporal prediction module is designed to facilitate multi-step traffic flow prediction. Ultimately, iterative optimization of the two modules is achieved through an alternating training approach. Notably, owing to the limited availability of large-scale public traffic datasets containing trajectory information, evaluations are conducted on two relatively small public datasets. The results underscore the potential of graph structure learning based on microscopic trajectories for traffic flow prediction. The source code is publicly available athttps://github.com/caisj11/Adap-STWT.git

Reconfigurable antennas can provide superior performance over legacy antennas, since they can dynamically reshape the channels by configuring the optimal operation mode. In this paper, we focus on the effectiveness of applying reconfigurable antennas in an industrial multi-user MIMO system. Firstly, we propose a new reconfigurable antenna structure that can realize dual-polarization independent control of operation modes. Then, we capture the relationship between the cell throughput performance with the operation modes of the reconfigurable antennas, based on which a mode selection problem is formulated. Additionally, we propose an efficient downlink transmission scheme for the reconfigurable MIMO system and design a near-optimal but low-complexity joint mode selection and user scheduling algorithm. Numerical results validate the superiority of reconfigurable MIMO systems over legacy MIMO systems, and also demonstrate the effectiveness of the proposed algorithm.

Traditional macrocell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macrocells. Given the massive deployment of small-cells in a macrocell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macrocell. Compared to the macrocell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are nonuniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1% performance loss in our simulations) in polynomial time.

WiFi offloading helps serve ever-increasing data traffic in cellular networks and mitigate the network congestion. Yet it can only apply to cellular users within WiFi coverage. Recently, delayed WiFi offloading is proposed to exploit users' mobility to purposely travel to WiFi coverage for data offloading. Its successful implementation depends on users' willingness to delay ongoing cellular data services until entering WiFi coverage. This paper proposes an incentive mechanism to allow a network operator to optimally reward his users to participate in delayed WiFi offloading, so as to reduce the network congestion. We formulate the design problem as a two-stage Stackelberg game: in Stage I, the operator announces a uniform reward to users to delay their existing cellular services; and in Stage II, each user decides to join the delayed offloading or not, depending on the reward, the network congestion, and his estimation of waiting cost for WiFi connection. The operator and users may or may not know all users' mobility and waiting cost information; thus, we propose optimal reward mechanisms under various information availability scenarios. Interestingly, we show that the optimal reward does not always increase with the cellular traffic load, as the increased network congestion can also help motivate users to switch to WiFi networks.

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

This paper presents a novel parameterized language for modeling architecture components and structures. Aiming at the capability and flexibility of describing various information of a large set of components, this language integrates the power of parameterized 3D modeling and easy-to-use 2D interfaces. This language is used in a 3D architecture design software - Easy Structure. We present the design objectives and the language syntax structure, and illustrate its practical applications used in staircase modeling.

Merged characters are the major cause of recognition errors. We classify the merging relationship between two involved characters into three types: "linear," "nonlinear," and "overlapped." Most segmentation methods handle the first type well, however, their capabilities of handling the other two types are limited. The weakness of handling the nonlinear and overlapped types results from character segmentation by linear, usually vertical, cuts assumed in these methods. This paper proposes a novel merged character segmentation and recognition method based on forepart prediction, necessity-sufficiency matching and character-adaptive masking. This method utilizes the information obtained from the forepart of merged characters to predict candidates for the leftmost character, and then applies character-adaptive masking and character recognition to verifying the prediction. Therefore, the arbitrary-shaped cutting path will follow the right shape of the leftmost character so as to preserve the shape of the next character. This method handles the first two types well and greatly improves the segmentation accuracy of the overlapped type. The experimental results and the performance comparisons with other methods demonstrate the effectiveness of the proposed method.

Arc segmentation plays an important role in the process of graphics recognition from scanned images. The GREC arc segmentation contest shows there is a lot of room for improvement in this area. This paper proposes a multiresolution arc segmentation method based on our previous seeded circular tracking algorithm which largely depends on the OOPSV model. The newly-introduced multiresolution paradigm can handle arcs/circles with large radii well. We describe new approaches for arc seed detection, arc localization, and arc verification, making the proposed method self-contained and more efficient. Moreover, this paper also brings major improvement to the dynamic adjustment algorithm of circular tracking to make it more robust. A systematic performance evaluation of the proposed method has been conducted using the third-party evaluation tool and test images obtained from the GREC arc segmentation contests. The overall performance over various arc angles, arc lengths, line thickness, noises, arc-arc intersections, and arc-line intersections has been measured. The experimental results and time complexity analyses on real scanned images are also reported and compared with other approaches. The evaluation result demonstrates the stable performance and the significant improvement on processing large arcs/circles of the MAS method.

Existing vectorization systems for engineering drawings usually take a two-phase workflow: convert a raster image to raw vectors and recognize graphic objects from the raw vectors. The first phase usually separates aground truth graphic object that intersects or touches other graphic objects into several parts, thus, the second phase faces the difficulty of searching for and merging raw vectors belonging to the same object. These operations slow down vectorization and degrade the recognition quality. Imitating the way humans read engineering drawings, we propose an efficient one-phase object-oriented vectorization model that recognizes each class of graphic objects from their natural characteristics. Each ground truth graphic object is recognized directly in its entirety at the pixel level. The raster image is progressively simplified by erasing recognized graphic objects to eliminate their interference with subsequent recognition. To evaluate the performance of the proposed model, we present experimental results on real-life drawings and quantitative analysis using third party protocols. The evaluation results show significant improvement in speed and recognition rate.

This paper presents a novel approach for recognizing and interpreting dimensions in engineering drawings. It starts by detecting potential dimension frames, each comprising only the line and text components of a dimension, then verifies them by detecting the dimension symbols. By removing the prerequisite of symbol recognition from detection of dimension sets, our method is capable of handling low quality drawings. We also propose a reconstruction algorithm for rebuilding the drawing entities based on the recognized dimension annotations. A coordinate grid structure is introduced to represent and analyze two-dimensional spatial constraints between entities; this simplifies and unifies the process of rectifying deviations of entity dimensions induced during scanning and vectorization.

In this paper, an efficient global algorithm for vectorizing line drawings is presented. It first extracts a seed segment of a graphic entity from a raster image to obtain its direction and width, then tracks the pixels under the guidance of the direction so that the tracking can track through functions and is not affected by noise and degradation of image quality. Thus, an entity will be vectorized in one step without postprocessing. The relations among lines are also used to realize the continuous vectorization of a line net. The speed and quality of vectorization are greatly improved with this algorithm. The performance evaluation is carried out both by theoretical analysis and by experiments. Comparisons with other vectorization algorithms are also made.