The Journal of China Universities of Posts and Telecommunications

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Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

Yang Chao, Li Yimin, Li Tong, Xu Siya, Qi Jun, Zhang Yu

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 54-68. DOI: 10.19682/j.cnki.1005-8885.2021.1018

Abstract （100）

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With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.

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HQD-RRT*: a high-quality path planner for mobile robot in dynamic environment

Li Qinghua, Wang Jiahui, Li Haiming, Feng Chao

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 69-80. DOI: 10.19682/j.cnki.1005-8885.2022.1007

Abstract （142）

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Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree ( RRT* ) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree ( HQD-RRT* ) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree ( RRT* -SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic

environment can be achieved in this paper. HQD-RRT* algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT* in avoiding unknown obstacles.

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Quantum classifier with parameterized quantum circuit based on the isolated quantum system

Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin

The Journal of China Universities of Posts and Telecommunications 2022, 29 (4): 21-31. DOI: 10.19682/j.cnki.1005-8885.2022.2016

Abstract （107）

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It is a critical challenge for quantum machine learning to classify the datasets accurately. This article develops a quantum classifier based on the isolated quantum system (QC-IQS) to classify nonlinear and multidimensional datasets. First, a model of QC-IQS is presented by creating parameterized quantum circuits (PQCs) based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system. Then, a parameterized quantum classification algorithm (QCA) is designed to calculate the classification results by updating the loss function until it converges. Finally, the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets. The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data. Moreover, QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets. It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.

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Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

Zhang Miao, Wang Zixian, Yan Danfeng

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 92-104. DOI: 10.19682/j.cnki.1005-8885.2022.1012

Abstract （69）

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Simultaneous localization and mapping (SLAM) technology becomes more and more important in robot localization. The purpose of this paper is to improve the robustness of visual features to lighting changes and increase the recall rate of map re-localization under different lighting environments by optimizing the image transformation model. An image transformation method based on matches and photometric error (name the method as MPT) is proposed in this paper, and it is seamlessly integrated into the pre-processing stage of the feature-based visual SLAM framework. The results of the experiment show that the MPT method has a better matching effect on different visual features. In addition, the image transformation module encapsulated by a robot operating system (ROS) can be used with multiple visual SLAM systems and improve its re-localization effect under different lighting environments.

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User selection based on user-union and relative entropy in mobile crowdsensing

Shao Zihao, Qu Tianguang, Wang Huiqiang, Zou Yifan, Lv Hongwu

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 34-42. DOI: 10.19682/j.cnki.1005-8885.2022.1001

Abstract （73）

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A critical issue in mobile crowdsensing (MCS) involves selecting appropriate users from a number of participants to guarantee the completion of a sensing task. Users may upload unnecessary data to the sensing platform, leading to redundancy and low user selection efficiency. Furthermore, using exact values to evaluate the quality of the user-union will further reduce selection accuracy when users form a union. This paper proposes a user selection method based on user-union and relative entropy in MCS. More specifically, a user-union matching scheme based on similarity calculation is constructed to achieve user-union and reduce data redundancy effectively. Then, considering the interval-valued influence, a user-union selection strategy with the lowest relative entropy is proposed. Extensive testing was conducted to investigate the impact of various parameters on user selection. The results obtained are encouraging and provide essential insights into the different aspects impacting the data

redundancy and interval-valued estimation of MCS user selection.

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Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

Jia Wei, Gong Chao

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 1-14. DOI: 10.19682/j.cnki.1005-8885.2022.1014

Abstract （164）

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In this paper, the performance of you only look once ( YOLO) series detectors on Chinese license plate recognition (LPR) in the real intelligent transportation system (ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition ( ALPR ) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection (LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1 (HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.

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Secrecy Energy Efficiency Maximization for UAV-Enabled Multi-Hop Mobile Relay System

Miao Jiansong, Li Hairui, Zheng Ziyuan, Wang Chu, Zhao Zhenmin

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 81-91. DOI: 10.19682/j.cnki.1005-8885.2022.1002

Abstract （132）

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To deal with the secrecy issues and energy efficiency issues in the unmanned aerial vehicles ( UAVs) assisted communication systems, an UAV-enabled multi-hop mobile relay system is studied in an urban environment. Multiple rotary-wing UAVs with energy budget considerations are employed as relays to forward confidential information between two ground nodes in the presence of multiple passive eavesdroppers. The system secrecy energy efficiency ( SEE), defined by the ratio of minimum achievable secrecy rate ( SR) to total propulsion energy consumption (PEC), is maximized via jointly optimizing the trajectory and transmit power of each UAV relay. To solve the formulated non-convex fractional optimization problem subject to mobility, transmit power and information-causality constraints, an effective iterative algorithm is proposed by applying the updated-rate-assisted block coordinate decent method, successive convex approximation (SCA) technique and Dinkelbach method. Simulation

results demonstrate the effectiveness of the proposed joint trajectory design and power control scheme.

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Lighting control with Myo armband based on customized classifier

Jiang Yujian, Yang Xue, Zhang Junming, Song Yang

The Journal of China Universities of Posts and Telecommunications 2022, 29 (4): 106-116. DOI: 10.19682/j.cnki.1005-8885.2022.2023

Abstract （89）

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This paper focuses on gesture recognition and interactive lighting control. The collection of gesture data adopts the Myo armband to obtain surface electromyography (sEMG). Considering that many factors affect sEMG, a customized classifier based on user calibration data is used for gesture recognition. In this paper, machine learning classifiers k-nearest neighbor (KNN), support vector machines (SVM), and naive Bayesian (NB) classifier, which can be used in small sample sets, are selected to classify four gesture actions. The performance of the three classifiers under different training parameters, different input features, including root mean square (RMS), mean absolute value (MAV), waveform length (WL), slope sign change (SSC) number, zero crossing (ZC) number, and variance (VAR) are tested, and different input channels are also tested. Experimental results show that: The NB classifier, which assumes that the prior probability of features is polynomial distribution, has the best performance, reaching more than 95% accuracy. Finally, an interactive stage lighting control system based on Myo armband gesture recognition is implemented.

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Multi-level fusion with deep neural networks for multimodal sentiment classification

Zhang Guangwei, Zhao Bing, Li Ruifan

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 25-33. DOI: 10.19682/j.cnki.1005-8885.2022.1013

Abstract （124）

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The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in visual and textual modalities. However, most existing methods treat various levels of features independently without having effective method for feature fusion. In this paper, we propose a multi-level fusion classification (MFC) model to predict the sentiment polarity based on the fusing features from different levels by exploiting the dependency among them. The proposed architecture leverages convolutional neural networks ( CNNs) with multiple layers to extract levels of features in image and text modalities. Considering the dependencies within the low-level and high-level features, a bi-directional (Bi) recurrent neural network (RNN) is adopted to integrate the learned features from different layers in CNNs. In addition, a conflict detection module is incorporated to address the conflict between modalities. Experiments on the Flickr dataset demonstrate that the MFC method achieves comparable performance compared with strong baseline methods.

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Design of high parallel CNN accelerator based on FPGA for AIoT

Lin Zhijian Gao Xuewei Chen Xiaopei Zhu Zhipeng Du Xiaoyong Chen Pingping

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 1-9. DOI: 10.19682/j.cnki.1005-8885.2022.0026

Abstract （264）

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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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Research on emotional space for movie and TV drama videos

Li Yujie Zhang Jingjing Jiang Wei Wang Chunxiao

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 73-82. DOI: 10.19682/j.cnki.1005-8885.2022.0006

Abstract （83）

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Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding (t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.

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FS-LSTM: sales forecasting in e-commerce on feature selection

Zhang Han Jing Yinji Zhao Yongli

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 92-98. DOI: 10.19682/j.cnki.1005-8885.2022.0018

Abstract （85）

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There are many studies on sales forecasting in e-commerce, most of which focus on how to forecast sales volume with related e-commerce operation data. In this paper, a deep learning method named FS-LSTM was proposed, which combines long short-term memory (LSTM) and feature selection mechanism to forecast the sales volume. The indicators with most contributions by the extreme gradient boosting (XGBoost) model are selected as the input features of LSTM model. FS-LSTM method can get less mean average error (MAE) and mean squared error (MSE) in the forecasting of e-commerce sales volume, comparing with the LSTM model without feature selection. The results show that the FS-LSTM can improve the performance of original LSTM for forecasting the sales volume.

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Quantum algorithm for soft margin support vector machine with hinge loss function

Liu Hailing, Zhang Jie, Qin Sujuan, Gao Fei

The Journal of China Universities of Posts and Telecommunications 2022, 29 (4): 32-41. DOI: 10.19682/j.cnki.1005-8885.2022.2017

Abstract （116）

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Soft margin support vector machine (SVM) with hinge loss function is an important classification algorithm, which has been widely used in image recognition, text classification and so on. However, solving soft margin SVM with hinge loss function generally entails the sub-gradient projection algorithm, which is very time-consuming when processing big training data set. To achieve it, an efficient quantum algorithm is proposed. Specifically, this algorithm implements the key task of the sub-gradient projection algorithm to obtain the classical sub-gradients in each iteration, which is mainly based on quantum amplitude estimation and amplification algorithm and the controlled rotation operator. Compared with its classical counterpart, this algorithm has a quadratic speedup on the number of training data points. It is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state form. This enables the algorithm to classify new data at little cost when the optimal model parameters are determined.

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Adaptive learning path recommendation model for examination-oriented education

Wang Jian, Qiao Kuoyuan, Yuan Yanlei, Liu Xiaole, Yang Jian

The Journal of China Universities of Posts and Telecommunications 2022, 29 (4): 77-88. DOI: 10.19682/j.cnki.1005-8885.2022.2021

Abstract （101）

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Adaptive learning paths provide individual learning objectives that best match a learner's characteristics. This is especially helpful when learners need to balance limited available learning time and multiple learning objectives. The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest. However, most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions. There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible, which is a typical scenario in exanimation-oriented education systems. This study aims to solve this problem by introducing a new approach that builds on existing methods. First, the eight properties in Gardner's multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects (LOs) and learners, thereby improving recommendation accuracy rates. Then, a novel adaptive learning path recommendation model is presented where viable knowledge topologies, knowledge bases and the previously-established properties relating to a learner's ability are combined by Dempster-Shafer (D-S) evidence theory. A series of practical experiments were performed to assess the approach's adaptability, the appropriateness of the selected evidence and the effectiveness of the recommendations. In the results, it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

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Compact multilayer dual-band bandpass filter design using stepped impedance resonators

Han Yunan Liang Peiyun Lin Yang Cheng Chunyue Yao Yuchen Jin Ming Dai Lin

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 99-104. DOI: 10.19682/j.cnki.1005-8885.2022.0016

Abstract （82）

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Compact dual-band bandpass filter (BPF) for the 5th generation mobile communication technology (5G) radio frequency (RF) front-end applications was presented based on multilayer stepped impedance resonators (SIRs). The multilayer dual-band SIR BPF can achieve high selectivity and four transmission zeros (TZs) near the passband edges by the quarter-wavelength tri-section SIRs. The multilayer dual-band SIR BPF is fabricated on a 3-layer FR-4 substrate with a compact dimension of 5.5 mm ×5.0 mm ×1.2 mm. The measured two passbands of the multilayer dual-band SIR BPF are 3.3 GHz -3.5 GHz and 4.8 GHz -5.0 GHz with insertion loss (IL) less than 2 dB respectively. Both measured and simulated results suggest that it is a possible candidate for the application of 5G RF front-end at sub-6 GHz frequency band.

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Nearly universal and efficient quantum secure multi-party computation protocol

Han Yushan, Che Bichen, Liu Jiali, Dou Zhao, Di Junyu

The Journal of China Universities of Posts and Telecommunications 2022, 29 (4): 51-68. DOI: 10.19682/j.cnki.1005-8885.2022.2019

Abstract （60）

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Universality is an important property in software and hardware design. This paper concentrates on the universality of quantum secure multi-party computation (SMC) protocol. First of all, an in-depth study of universality has been onducted, and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger (GHZ)-like state and stabilizer formalism. The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction, and the steps are simple and effective. Secondly, three quantum SMC protocols based on the proposed universal protocol: Quantum private comparison (QPC) protocol, quantum millionaire (QM) protocol, and quantum multi-party summation (QMS) protocol are presented. These protocols are given as examples to explain universality. Thirdly, analyses of the example protocols are shown. Concretely, the correctness, fairness, and efficiency are confirmed. And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks. Finally, the experimental results of the example protocols on the International Business Machines (IBM) quantum platform are consistent with the theoretical results. Our research indicates that our protocol is universal to a certain degree and easy to perform.

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User Friendly Preferential Private Recommendation

Yang Jingjing, Guo Yuchun, Feng Tingting, Chen Yishuai

The Journal of China Universities of Posts and Telecommunications 2022, 29 (3): 43-53. DOI: 10.19682/j.cnki.1005-8885.2022.1006

Abstract （72）

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To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.

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RFID network planning based on improved brain storm optimization algorithm

Lin Zihan, Zheng Jiali, Xie Xiaode, Feng Minyu, He Siyi

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 30-39. DOI: 10.19682/j.cnki.1005-8885.2022.0008

Abstract （82）

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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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Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

Chao Hao Lian Weifang Liu Yongli

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 62-72. DOI: 10.19682/j.cnki.1005-8885.2022.0017

Abstract （90）

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The research of emotion recognition based on electroencephalogram (EEG) signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals, which may contain important characteristics related to emotional states. Aiming at the above defects, a spatiotemporal emotion recognition method based on a 3-dimensional (3D) time-frequency domain feature matrix was proposed. Specifically, the extracted time-frequency domain EEG features are first expressed as a 3D matrix format according to the actual position of the cerebral cortex. Then, the input 3D matrix is processed successively by multivariate convolutional neural network (MVCNN) and long short-term memory (LSTM) to classify the emotional state. Spatiotemporal emotion recognition method is evaluated on the DEAP data set, and achieved accuracy of 87.58% and 88.50% on arousal and valence dimensions respectively in binary classification tasks, as well as obtained accuracy of 84.58% in four class classification tasks. The experimental results show that 3D matrix representation can represent emotional information more reasonably than two-dimensional (2D). In addition, MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

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TSR: algorithm of image hole-filling based on three-step repairing

Li Fucheng Deng Junyong Zhu Yun Luo Jiaying Ren Han

The Journal of China Universities of Posts and Telecommunications 2022, 29 (5): 83-91. DOI: 10.19682/j.cnki.1005-8885.2022.0005

Abstract （58）

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In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing (TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave transform ( NSST), which will generate high-/ low-frequency sub-images with different resolutions. Then the improved Criminisi algorithm was used to repair the texture information in the high-frequency sub-images, while the improved curvature driven diffusion (CDD) algorithm was used to repair the low-frequency sub-images with the image structure information. Finally, the repaired parts of high-frequency and low-frequency sub-images are synthesized to obtain the final image through inverse NSST. Experiments show that the peak signal-to-noise ratio (PSNR) of the TSR algorithm is improved by an average of 2 - 3 dB and 1 - 2 dB compared with the Criminisi algorithm and the nearest neighbor interpolation (NNI) algorithm, respectively.

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