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Special Topic on Artificial Intelligence of Things

  • Special Topic on Artificial Intelligence of Things

    The recent trends of the Internet of Things (IoT), Artificial intelligence (AI), and communication technologies are gradually paving the evolution of the IoT into the Artificial Intelligence of Things (AIoT). AIoT contains many aspects of knowledge, such as big data, cloud computing, machine learning and so on. AIoT offers a wide range of applications in industry, agriculture and other fields. However, there are many challenges potentially impede the further development of the AIoT. This Special Issue aims to bring together researchers and practitioners to discuss advanced academic and industrial research results related to AIoT.

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    1. Random mating mayfly algorithm for RFID network planning
    谢孝德 郑嘉利 林子涵 何思怡 冯敏瑜
    中国邮电高校学报(英文版)    2022, 29 (5): 40-50.   DOI: 10.19682/j.cnki.1005-8885.2022.0010
    摘要253)      PDF(pc) (4520KB)(31)    收藏
    In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.
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    2. RFID network planning based on improved brain storm optimization algorithm
    林子涵 郑嘉利 谢孝德 冯敏瑜 何思怡
    中国邮电高校学报(英文版)    2022, 29 (5): 30-39.   DOI: 10.19682/j.cnki.1005-8885.2022.0008
    摘要189)      PDF(pc) (1408KB)(43)    收藏
    In order to improve the service quality of radio frequency identification (RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization (BSO) algorithm, was proposed to solve the problem of RFID network planning (RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm's tag coverage was increased by 9.62% over the Cuckoo search (CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.
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    3. Saliency guided self-attention network for pedestrian attribute recognition in surveillance scenarios
    李娜 武阳阳 刘颖 李大湘 高嘉乐
    中国邮电高校学报(英文版)    2022, 29 (5): 21-29.   DOI: 10.19682/j.cnki.1005-8885.2022.0007
    摘要194)      PDF(pc) (1938KB)(36)    收藏
    Pedestrian attribute recognition is often considered as a multi-label image classification task. In order to make full use of attribute-related location information, a saliency guided sel-attention network ( SGSA-Net) was proposed to weakly supervise attribute localization, without annotations of attribute-related regions. Saliency priors were integrated into the spatial attention module ( SAM ). Meanwhile,channel-wise attention and spatial attention were introduced into the network. Moreover, a weighted binary cross-entropy loss ( WCEL) function was employed to handle the imbalance of training data. Extensive experiments on richly annotated pedestrian ( RAP) and pedestrian attribute ( PETA) datasets demonstrated that SGSA-Net outperformed other state-of-the-art methods.

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    4. Vehicle-following system based on deep reinforcement learning in marine scene
    张新 娄皓然 蒋励 肖前浩 蔡著文
    中国邮电高校学报(英文版)    2022, 29 (5): 10-20.   DOI: 10.19682/j.cnki.1005-8885.2022.0025
    摘要253)      PDF(pc) (5117KB)(27)    收藏
    In order to solve the problems that the feature data type are not rich enough in the data collection process about the vehicle-following task in marine scene which results in a long model convergence time and high training difficulty, a two-stage vehicle-following system was proposed. Firstly, semantic segmentation model predicts the number of pixels of the followed target, then the number of pixels of the followed target is mapped to the position feature. Secondly, deep reinforcement learning algorithm enables the control equipment to make decision action, to ensure that two moving objects remain within the safe distance. The experimental results show that the two-stage vehicle-following system has a 40% faster convergence rate than the model without position feature, and the following stability is significantly improved by adding the position feature.
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    5. Design of high parallel CNN accelerator based on FPGA for AIoT
    林志坚 高学伟 陈小培 祝志鹏 杜小勇 陈平平
    中国邮电高校学报(英文版)    2022, 29 (5): 1-9.   DOI: 10.19682/j.cnki.1005-8885.2022.0026
    摘要475)      PDF(pc) (3802KB)(58)    收藏

    To tackle the challenge of applying convolutional neural network (CNN) in field-programmable gate array (FPGA) due to its computational complexity, a high-performance CNN hardware accelerator based on Verilog hardware description language was designed, which utilizes a pipeline architecture with three parallel dimensions including input channels, output channels, and convolution kernels. Firstly, two multiply-and-accumulate (MAC) operations were packed into one digital signal processing (DSP) block of FPGA to double the computation rate of the CNN accelerator. Secondly, strategies of feature map block partitioning and special memory arrangement were proposed to optimize the total amount of off-chip access memory and reduce the pressure on FPGA bandwidth. Finally, an efficient computational array combining multiplicative-additive tree and Winograd fast convolution algorithm was designed to balance hardware resource consumption and computational performance. The high parallel CNN accelerator was deployed in ZU3EG of Alinx, using the YOLOv3-tiny algorithm as the test object. The average computing performance of the CNN accelerator is 127.5 giga operations per second (GOPS). The experimental results show that the hardware architecture effectively improves the computational power of CNN and provides better performance compared with other existing schemes in terms of power consumption and the efficiency of DSPs and block random access memory (BRAMs).

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