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  • Orginal Article
    Yue LU, Xingyu CHEN, Zhengxing WU, Junzhi YU, Li WEN
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1602-1619.

    Underwater robotic operation usually requires visual perception (e.g., object detection and tracking), but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual perception. In addition, detection continuity and stability are important for robotic perception, but the commonly used static accuracy based evaluation (i.e., average precision) is insufficient to reflect detector performance across time. In response to these two problems, we present a design for a novel robotic visual perception framework. First, we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection performance. As a result, although domain quality has an ignorable effect on within-domain detection accuracy, visual restoration is beneficial to detection in real sea scenarios by reducing the domain shift. Moreover, non-reference assessments are proposed for detection continuity and stability based on object tracklets. Further, online tracklet refinement is developed to improve the temporal performance of detectors. Finally, combined with visual restoration, an accurate and stable underwater robotic visual perception framework is established. Small-overlap suppression is proposed to extend video object detection (VID) methods to a single-object tracking task, leading to the flexibility to switch between detection and tracking. Extensive experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed approaches. The codes are available at

  • Orginal Article
    Yun TENG, Zhiyue LI, Jing HUANG, Guangyan ZHANG
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1646-1657.

    In-memory systems with erasure coding (EC) enabled are widely used to achieve high performance and data availability. However, as the scale of clusters grows, the server-level fail-slow problem is becoming increasingly frequent, which can create long tail latency. The influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC sub-operations. In this paper, we propose an EC-enabled in-memory storage system called ShortTail, which can achieve consistent performance and low latency for both reads and writes. First, ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow node. Second, ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow nodes. Finally, ShortTail posts an adaptive write strategy to reduce write amplification of small writes. We implement ShortTail on top of Memcached and compare it with two baseline systems. The experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%; it also brings significant improvements in the median latency and average latency.

  • Orginal Article
    Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1714-1732.

    The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.

  • Orginal Article
    Mingtian SHAO, Kai LU, Wanqing CHI, Ruibo WANG, Yiqin DAI, Wenzhe ZHANG
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1631-1645.

    High-performance computing (HPC) systems are about to reach a new height: exascale. Application deployment is becoming an increasingly prominent problem. Container technology solves the problems of encapsulation and migration of applications and their execution environment. However, the container image is too large, and deploying the image to a large number of compute nodes is time-consuming. Although the peer-to-peer (P2P) approach brings higher transmission efficiency, it introduces larger network load. All of these issues lead to high startup latency of the application. To solve these problems, we propose the topology-aware execution environment service (TEES) for fast and agile application deployment on HPC systems. TEES creates a more lightweight execution environment for users, and uses a more efficient topology-aware P2P approach to reduce deployment time. Combined with a split-step transport and launch-in-advance mechanism, TEES reduces application startup latency. In the Tianhe HPC system, TEES realizes the deployment and startup of a typical application on 17 560 compute nodes within 3 s. Compared to container-based application deployment, the speed is increased by 12-fold, and the network load is reduced by 85%.

  • Review
    Mingxuan BU, Yue WANG, Lei YIN, Zhouyu TONG, Yiqiang ZHANG, Deren YANG, Xiaodong PI
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1579-1601.

    To meet a growing demand for information processing, brain-inspired neuromorphic devices have been intensively studied in recent years. As an important type of neuromorphic device, synaptic devices have attracted strong attention. Among all the kinds of materials explored for the fabrication of synaptic devices, semiconductor nanocrystals (NCs) have become one of the preferred choices due to their excellent electronic and optical properties. In this review, we first introduce the research background of synaptic devices based on semiconductor NCs and briefly present the basic properties of semiconductor NCs. Recent developments in the field of synaptic devices based on semiconductor NCs are then discussed according to the materials employed in the active layers of the devices. Finally, we discuss existing problems and challenges of synaptic devices based on semiconductor NCs.

  • Orginal Article
    Hongbin ZHANG, Quan CHEN, Weiwen ZHANG
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1620-1630.

    Entity linking (EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the global model, but ignore latent semantic information in the local model and the acquisition of effective entity type information. In this paper, we propose two adaptive features, in which the first adaptive feature enables the local and global models to capture latent information, and the second adaptive feature describes effective information for entity type embeddings. These adaptive features can work together naturally to handle some uncertain entity type information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed adaptive features, which are based on their own diverse contexts, can capture information that is conducive for EL.

  • Orginal Article
    Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1673-1683.

    This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a deep Q-learning network (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

  • Orginal Article
    Zhanyi HU, Yingjun QIAO, Xingyu LI, Jin HUANG, Yifan JIA, Zhihua ZHONG
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1700-1713.

    Platoon control is widely studied for coordinating connected and automated vehicles (CAVs) on highways due to its potential for improving traffic throughput and road safety. Inspired by platoon control, the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning. Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems. Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets. As a result, unnecessary transmission needs to be avoided to establish a reliable wireless network. To this end, an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty. The uniform boundedness, uniform ultimate boundedness, and string stability of the closed-loop system are analytically proved. As for the triggering condition, the uncertainty of the boundary information is considered, so that the threshold can be estimated more reasonably. Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation. The threshold affects the tracking ability and communication burden, and hence an optimization framework for choosing the threshold is worth exploring in future research.

  • Orginal Article
    Jiaxin ZHANG, Meiqin LIU, Senlin ZHANG, Ronghao ZHENG
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1658-1672.

    This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic. The problem is formulated as a variant of the orienteering problem. Based on the genetic algorithm (GA), we propose the greedy strategy based GA (GGA) which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization, and use a differential evolution planner for providing the deterministic local path cost. The uncertainty of the local path cost comes from unpredictable obstacles, measurement error, and trajectory tracking error. To improve the robustness of the planner in an uncertain environment, a sampling strategy for path evaluation is designed, and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths. Monte Carlo simulations are used to verify the superiority and effectiveness of the planner. The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6% in terms of total profit, and the sampling-based GGA route planner (S-GGARP) improves the average profit by 5.5% compared to the GGA route planner (GGARP).

  • Orginal Article
    Zhiqian LIU, Xuyang LOU, Jiajia JIA
    Frontiers of Information Technology & Electronic Engineering, 2022, 23(11): 1684-1699.

    This paper investigates the problem of dynamic output-feedback control for a class of Lipschitz nonlinear systems. First, a continuous-time controller is constructed and sufficient conditions for stability of the nonlinear systems are presented. Then, a novel event-triggered mechanism is proposed for the Lipschitz nonlinear systems in which new event-triggered conditions are introduced. Consequently, a closed-loop hybrid system is obtained using the event-triggered control strategy. Sufficient conditions for stability of the closed-loop system are established in the framework of hybrid systems. In addition, an upper bound of a minimum inter-event interval is provided to avoid the Zeno phenomenon. Finally, numerical examples of a neural network system and a genetic regulatory network system are provided to verify the theoretical results and to show the superiority of the proposed method.

  • Orginal Article
    Ping ZHANG, Xiaodong XU, Chen DONG, Kai NIU, Haotai LIANG, Zijian LIANG, Xiaoqi QIN, Mengying SUN, Hao CHEN, Nan MA, Wenjun XU, Guangyu WANG, Xiaofeng TAO
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 801-812.

    In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

  • Orginal Article
    Yifeng LI, Lan WANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 906-915.

    In this paper, the problem of controllability of Boolean control networks (BCNs) with multiple time delays in both states and controls is investigated. First, the controllability problem of BCNs with multiple time delays in controls is considered. For this controllability problem, a controllability matrix is constructed by defining a new product of matrices, based on which a necessary and sufficient controllability condition is obtained. Then, the controllability of BCNs with multiple time delays in states is studied by giving a necessary and sufficient condition. Subsequently, based on these results, a controllability matrix for BCNs with multiple time delays in both states and controls is proposed that provides a concise controllability condition. Finally, two examples are given to illustrate the main results.

  • Review
    Syed Agha Hassnain MOHSAN, Haoze QIAN, Hussain AMJAD
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 767-800.

    Ground-breaking optical wireless power transfer (OWPT) techniques have gained significant attention from both academia and industry in recent decades. Powering remote systems through laser diodes (LDs) to either operate devices or recharge batteries offers several benefits. Remote LDs can remove the burden of carrying extra batteries and can reduce mission time by removing battery swap-time and charging. Apart from its appealing benefits, laser power transfer (LPT) is still a challenging task due to its low transfer efficiency. In this paper, we discuss the necessity and feasibility of OWPT and discuss several projects, working principle, system design, and components. In addition, we show that OWPT is an essential element to supply power to Internet-of-Things (IoT) terminals. We also highlight the impacts of dynamic OWPT. We outline several OWPT techniques including optical beamforming, distributed laser charging (DLC), adaptive-DLC (ADLC), simultaneous lightwave information and power transfer (SLIPT), Thing-to-Thing (T2T) OWPT, and high intensity laser power beaming (HILPB). We also deal with laser selection, hazard analysis, and received photovoltaic (PV) cell selection for OWPT systems. Finally, we discuss a range of open challenges and counter measures. We believe that this review will be helpful in integrating research and eliminating technical uncertainties, thereby promoting progress and innovation in the development of OWPT technologies.

  • Orginal Article
    Ziyang XING, Hui QI, Xiaoqiang DI, Jinyao LIU, Rui XU, Jing CHEN, Ligang CONG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 844-858.

    With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.

  • Orginal Article
    Gengyu GE, Yi ZHANG, Wei WANG, Lihe HU, Yang WANG, Qin JIANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 876-889.

    Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement. In an indoor environment, the current mainstream localization scheme uses two-dimensional (2D) laser light detection and ranging (LiDAR) to build an occupancy grid map with simultaneous localization and mapping (SLAM) technology; it then locates the robot based on the known grid map. However, such solutions work effectively only in those areas with salient geometrical features. For areas with repeated, symmetrical, or similar structures, such as a long corridor, the conventional particle filtering method will fail. To solve this crucial problem, this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor. First, the mobile robot is remote-controlled to move from the starting position to the end along a middle line. In the moving process, a grid map is built using the laser-based SLAM method. At the same time, a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy. The keyframes are associated with the robot’s poses through timestamps. Second, a moving strategy is proposed, based on the extracted range features of the laser scans, to decide on an initial rough position. This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose. Third, the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results show that our method is effective and robust for global localization. The localization success rate reaches 98.8% while the average moving distance is only 0.31 m. In addition, the method works well when the mobile robot is kidnapped to another position in the corridor.

  • Correspondence
    Liangjie QIU, Xiuping LI, Zihang QI, Wenyu ZHAO, Yuhan HUANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 927-934.
  • Orginal Article
    Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 813-827.

    In this paper, an efficient image encryption scheme based on a novel mixed linear–nonlinear coupled map lattice (NMLNCML) system and DNA operations is presented. The proposed NMLNCML system strengthens the chaotic characteristics of the system, and is applicable for image encryption. The main advantages of the proposed method are embodied in its extensive key space; high sensitivity to secret keys; great resistance to chosen-plaintext attack, statistical attack, and differential attack; and good robustness to noise and data loss. Our image cryptosystem adopts the architecture of scrambling, compression, and diffusion. First, a plain image is transformed to a sparsity coefficient matrix by discrete wavelet transform, and plaintext-related Arnold scrambling is performed on the coefficient matrix. Then, semi-tensor product (STP) compressive sensing is employed to compress and encrypt the coefficient matrix. Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit XOR operation. The NMLNCML system is applied to generate chaotic elements in the STP measurement matrix of compressive sensing and the pseudo-random sequence in DNA operations. An SHA-384 function is used to produce plaintext secret keys and thus makes the proposed encryption algorithm highly sensitive to the original image. Simulation results and performance analyses verify the security and effectiveness of our scheme.

  • Orginal Article
    Jingfa LIU, Zhen WANG, Guo ZHONG, Zhihe YANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 859-875.

    To solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in traditional focused crawling methods, in this paper, we propose a novel focused crawler using an improved tabu search algorithm with domain ontology and host information (FCITS_OH), where a domain ontology is constructed by formal concept analysis to describe topics at the semantic and knowledge levels. To avoid crawling visited hyperlinks and expand the search range, we present an improved tabu search (ITS) algorithm and the strategy of host information memory. In addition, a comprehensive priority evaluation method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks. Experimental results on both tourism and rainstorm disaster domains show that the proposed focused crawlers overmatch the traditional focused crawlers for different performance metrics.

  • Orginal Article
    Zhen FANG, Jihua ZHANG, Libin GAO, Hongwei CHEN, Wenlei LI, Tianpeng LIANG, Xudong CAI, Xingzhou CAI, Weicong JIA, Huan GUO, Yong LI
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 916-926.

    This work presents a novel design of Ka-band (33 GHz) filtering packaging antenna (FPA) that features broadband and great filtering response, and is based on glass packaging material and through-glass via (TGV) technologies. Compared to traditional packaging materials (printed circuit board, low temperature co-fired ceramic, Si, etc.), TGVs are more suitable for miniaturization (millimeter-wave three-dimensional (3D) packaging devices) and have superior microwave performance. Glass substrate can realize 3D high-density interconnection through bonding technology, while the coefficient of thermal expansion (CTE) matches that of silicon. Furthermore, the stacking of glass substrate enables high-density interconnections and is compatible with micro-electro-mechanical system technology. The proposed antenna radiation patch is composed of a patch antenna and a bandpass filter (BPF) whose reflection coefficients are almost complementary. The BPF unit has three pairs of λg/4 slots (defect microstrip structure, DMS) and two λg/2 U-shaped slots (defect ground structure, DGS). The proposed antenna achieves large bandwidth and high radiation efficiency, which may be related to the stacking of glass substrate and TGV feed. In addition, the introduction of four radiation nulls can effectively improve the suppression level in the stopband. To demonstrate the performance of the proposed design, a 33-GHz broadband filtering antenna is optimized, debugged, and measured. The antenna could achieve |S11|<-10 dB in 29.4-36.4 GHz, and yield an impedance matching bandwidth up to 21.2%, with the stopband suppression level at higher than 16.5 dB. The measurement results of the proposed antenna are a realized gain of ~6.5 dBi and radiation efficiency of ~89%.

  • Orginal Article
    Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 828-843.

    Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes. However, the distortion of objects during data acquisition and representation is seldom considered. In this paper, we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations. Based on this, a feature fusion framework is proposed to fully use the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data. Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline. In addition, the fusion strategies can be easily integrated into other detectors, such as the You Only Look Once (YOLO) series. The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments. Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.

  • Orginal Article
    Huanpei LYU, Libin ZHANG, Dapeng TAN, Fang XU
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 890-905.

    Low-voltage electrical apparatuses (LVEAs) have many workpieces and intricate geometric structures, and the assembly process is rigid and labor-intensive, and has little balance. The assembly process cannot readily adapt to changes in assembly situations. To address these issues, a collaborative assembly is proposed. Based on the requirements of collaborative assembly, a colored Petri net (CPN) model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly. Also, an artificial potential field based planning algorithm (AFPA) is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs. Then an adaptive quantum genetic algorithm (AQGA) is developed to optimize the assembly process. Lastly, taking a two-pole circuit-breaker controller with leakage protection (TPCLP) as an assembly instance, comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly. The distribution of resources can also be optimized in the assembly. The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.

  • Orginal Article
    Fatma KHALLAF, Walid EL-SHAFAI, El-Sayed M. EL-RABAIE, Naglaa F. SOLIMAN, Fathi E. Abd EL-SAMIE
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1045-1061.

    Quaternion algebra has been used to apply the fractional Fourier transform (FrFT) to color images in a comprehensive approach. However, the discrete fractional random transform (DFRNT) with adequate basic randomness remains to be examined. This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform (DQFrFT) watermarking and three-dimensional chaotic logistic map (3D-CLM) encryption. First, we describe quaternion DFRNT (QDFRNT), which generalizes DFRNT to handle quaternion signals effectively, and then use QDFRNT to perform color medical image adaptive watermarking. To efficiently evaluate QDFRNT, this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal. Moreover, it uses the human vision system’s (HVS) masking qualities of edge, texture, and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine (SVM) techniques. The limitations of watermark embedding are also explained to conserve watermarking energy. Second, 3D-CLM encryption is employed to improve the system’s security and efficiency, allowing it to be used as a multistage privacy system. The proposed security system is effective against many types of channel noise attacks, according to simulation results.

  • Orginal Article
    Bimal Jeet GOTEEA, Qianjun ZHANG, Wei DONG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1080-1092.

    This paper presents a precision centimeter-range positioner based on a Lorentz force actuator using flexure guides. An additional digital-to-analog converter and an operational amplifier (op amp) circuit together with a suitable controller are used to enhance the positioning accuracy to the nanometer level. First, a suitable coil is designed for the actuator based on the stiffness of the flexure guide model. The flexure mechanism and actuator performance are then verified with finite element analysis. Based on these, a means to enhance the positioning performance electronically is presented together with the control scheme. Finally, a prototype is fabricated, and the performance is evaluated. This positioner features a range of 10 mm with a resolution of 10 nm. The proposed scheme can be extended to other systems.

  • Orginal Article
    Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1028-1044.

    The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images, we propose a recurrent network based on filter-cluster attention (FCA), the main body of which consists of three units: difference concern, gate recurrent, and iterative residual. The network performs multi-stage recursive learning on low-light images, and then extracts deeper feature information. To compute more accurate dependence, we design a novel FCA that focuses on the saliency of feature channels. FCA and self-attention are used to highlight the low-light regions and important channels of the feature. We also design a dense connection pyramid (DenCP) to extract the color features of the low-light inversion image, to compensate for the loss of the image’s color information. Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

  • Orginal Article
    Juan FANG, Sheng LIN, Huijing YANG, Yixiang XU, Xing SU
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 994-1006.

    When multiple central processing unit (CPU) cores and integrated graphics processing units (GPUs) share off-chip main memory, CPU and GPU applications compete for the critical memory resource. This causes serious resource competition and has a negative impact on the overall performance of the system. We describe the competition for shared-memory resources in a CPU-GPU heterogeneous multi-core architecture, and a shared-memory request scheduling strategy based on perceptual and predictive batch-processing is proposed. By sensing the CPU and GPU memory request conditions in the request buffer, the proposed scheduling strategy estimates the GPU latency tolerance and reduces mutual interference between CPU and GPU by processing CPU or GPU memory requests in batches. According to the simulation results, the scheduling strategy improves CPU performance by 8.53% and reduces mutual interference by 10.38% with low hardware complexity.

  • Orginal Article
    Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, Jiazhi XIA
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1007-1027.

    Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited data transformations fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations, the auto-generated transformations lack explainability concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization. We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study, respectively. A recommendation algorithm is designed to compute optimal transformations, which can reveal specified types of patterns and maintain explainability. We demonstrate the effectiveness of our approach through two cases and a user study.

  • Orginal Article
    Zhenhui FENG, Renbin XIAO
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1062-1079.

    We investigate a kind of vehicle routing problem with constraints (VRPC) in the car-sharing mobility environment, where the problem is based on user orders, and each order has a reservation time limit and two location point transitions, origin and destination. It is a typical extended vehicle routing problem (VRP) with both time and space constraints. We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user (or passenger) experience. To solve the scheduling model more accurately, a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer, and a spatiotemporal distance embedded hybrid ant colony algorithm (HACA-ST) is proposed. The algorithm can be divided into two stages. First, through spatiotemporal clustering, the spatiotemporal distance between users is the main measure used to classify customers in categories, which helps provide heuristic information for problem solving. Second, an improved ant colony algorithm (ACO) is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route. Computational analysis is carried out based on existing data sets and simulated urban instances. Compared with other heuristic algorithms, HACA-ST reduces the length of the shortest route by 2%–14% in benchmark instances. In VRPC testing instances, concerning the combined cost, HACA-ST has competitive cost compared to existing VRP-related algorithms. Finally, we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

  • Review
    Xiaoming CHEN, Zhaobin XU, Lin SHANG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 935-944.

    Satellite Internet of Things (IoT) is a promising way to provide seamless coverage to a massive number of devices all over the world, especially in remote areas not covered by cellular networks, e.g., forests, oceans, mountains, and deserts. In general, satellite IoT networks take low Earth orbit (LEO) satellites as access points, which solves the problem of wide coverage, but leads to many challenging issues. We first give an overview of satellite IoT, with an emphasis on revealing the characteristics of IoT services. Then, the challenging issues of satellite IoT, i.e., massive connectivity, wide coverage, high mobility, low power, and stringent delay, are analyzed in detail. Furthermore, the possible solutions to these challenges are provided. In particular, new massive access protocols and techniques are designed according to the characteristics and requirements of satellite IoT. Finally, we discuss several development trends of satellite IoT to stimulate and encourage further research in such a broad area.

  • Orginal Article
    Kaili QI, Minqing ZHANG, Fuqiang DI, Yongjun KONG
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1156-1168.

    To improve the embedding capacity of reversible data hiding in encrypted images (RDH-EI), a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit (MSB) prediction. First, according to the smoothness of the image, the image is partitioned into blocks based on adaptive quadtree partitioning, and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images. In the data embedding stage, the adaptive MSB prediction method proposed by Wang and He (2022) is improved by taking the upper-left pixel in the block as the target pixel, to predict other pixels to free up more embedding space. To the best of our knowledge, quadtree partitioning is first applied to RDH-EI. Simulation results show that the proposed method is reversible and separable, and that its average embedding capacity is improved. For gray images with a size of 512×512, the average embedding capacity is increased by 25565 bits. For all smooth images with improved embedding capacity, the average embedding capacity is increased by about 35530 bits.

  • Review
    Jie CHEN, Dandan WU, Ruiyun XIE
    Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1117-1142.

    Three technical problems should be solved urgently in cyberspace security: the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. Artificial intelligence (AI) algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace security. Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for cyberspace security applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in machine learning (ML), deep learning (DL), and some popular optimization algorithms, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.