Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.
Semantic segmentation of high-resolution aerial images is a concerning issue of remote sensing applications. To address the issues of intra-class heterogeneity and inter-class homogeneity, a novel end-to-end semantic segmentation network, namely Context and Semantic Enhanced High-Resolution Network (CSE-HRNet), is proposed in this paper. Two procedures are considered comprehensively, which are multi-scale contextual feature extractor and multi-level semantic feature producer. Nested Dilated Residual Block (NDRB) is designed firstly, which could enhance the representational power of multi-scale contexts and tackle the issue of intra-class heterogeneity. The pyramidal feature hierarchy is introduced secondly, by which multi-level feature fusions could be utilized to enlarge inter-class semantic differences. Experimental results verify that, based on the Potsdam and Vaihingen benchmarks, the proposed CSE-HRNet can achieve competitive performance compared with other state-of-the-art methods. INDEX TERMS Semantic segmentation, image analysis, machine learning, remote sensing image.
To utilize the benefits of cellular systems, wireless machine-to-machine (M2M) communications over cellular systems are being widely considered. In order to support efficient spectrum sharing between M2M devices and normal mobile users, in the paper, we propose a multilayer orthogonal beamforming (MOBF) scheme for M2M communications over orthogonal frequency division multiple access (OFDMA-) based cellular systems. Using MOBF, each subcarrier in OFDMA systems could be efficiently reused by both normal mobile users and machine-type devices which are organized into multiple virtual layers. The users located in higher layers (e.g., mobile users) are not to be interfered by those in lower layers (e.g., machine devices). To improve the performance, the orthogonal deficiency (OD-) based user selection is carried out, where the intralayer fairness and quasimaximal performance can be guaranteed, simultaneously. Moreover, the signal-to-interference plus noise ratio (SINR) is investigated to measure the performance lower bound of different layers. It is demonstrated by both theoretical and numerical results that the proposed approach provides a stable SINR performance for each layer, that is, the interference free ability from lower level layers.
In order to support a 4-Dimensional Trajectory (4DT) based Air Traffic Services (ATS), aircraft should update trajectory data back to a ground control center with aeronautical short messages (ASM). An efficient transmission mechanism for 4DT-ASM is developed in this study. The proposed approach firstly divides aircraft into groups by spatial diversity. Then, if one airborne station (AS) requests resource for sending ASM, the control will allocate the corresponding radio resources to all group members including this AS. Thanks to the space division multiple access (SDMA), all aircraft in the same group could transmit ASM randomly without causing any collision. The capacity of 4DT-ASM hereby will be improved and the request time delay of other AS nodes in this group can be saved, i.e., the real-time performance enhancement. Furthermore, a low complex selection algorithm for grouping aircraft is detailed analyzed, with which the control could group aircraft under SINR constraints. Simulation results prove that SDMA based collision free random access (CFRA) can improve the spectrum efficiency and reduce average time delay remarkably.
Semantic segmentation of high-resolution aerial images is of paramount importance in a wide range of remote sensing applications. The ever-increasing spatial resolution of aerial imagery brings about two specific challenges that incur labelling ambiguities: intra-class heterogeneity and inter-class homogeneity. To address these two challenges, a novel end-to-end semantic segmentation network for high-resolution aerial imagery, namely Context and Semantic Enhanced UNet (CSE-UNet), is proposed in this paper. Specifically, we exploit multi-level Receptive Field Block (RFB) based skip pathways to enhance the representational power of multi-scale contextual information, and therefore tackle the issue of intra-class heterogeneity. To solve the inter-class homogeneity problem, we propose a dual-path encoder where an auxiliary multi-kernel based feature encoding path is embed to produce strong semantic features at all levels to enlarge the inter-class differences. Experimental results shows that our proposed CSE-UNet achieves competitive performance and outperforms UNet and several other deep networks on the ISPRS Potsdam and Vaihingen datasets.
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