Frontiers of Information Technology & Electronic Engineering最新文献

The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant bandwidth and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.

Reversible data hiding in encrypted images (RDHEI) is essential for safeguarding sensitive information within the encrypted domain. In this study, we propose an intelligent pixel predictor based on a residual group block and a spatial attention module, showing superior pixel prediction performance compared to existing predictors. Additionally, we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space, outperforming single coding approaches. The image owner employs the presented intelligent predictor to forecast the original image, followed by encryption through additive secret sharing before conveying the encrypted image to data hiders. Subsequently, data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver. The receiver can extract secret data and recover the original image losslessly, with the processes of data extraction and image recovery being separable. Our innovative approach combines an intelligent predictor with additive secret sharing, achieving reversible data embedding and extraction while ensuring security and lossless recovery. Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity. For the Lena image, the number of prediction errors within the range of [−5, 5] is as high as 242 500 and our predictor achieves an embedding capacity of 4.39 bpp.

In graphic design, layout is a result of the interaction between the design elements in the foreground and background images. However, prevalent research focuses on enhancing the quality of layout generation algorithms, overlooking the interaction and controllability that are essential for designers when applying these methods in real-world situations. This paper proposes a user-centered layout design system, Iris, which provides designers with an interactive environment to expedite the workflow, and this environment encompasses the features of user-constraint specification, layout generation, custom editing, and final rendering. To satisfy the multiple constraints specified by designers, we introduce a novel generation model, multi-constraint LayoutVQ-VAE, for advancing layout generation under intra- and inter-domain constraints. Qualitative and quantitative experiments on our proposed model indicate that it outperforms or is comparable to prevalent state-of-the-art models in multiple aspects. User studies on Iris further demonstrate that the system significantly enhances design efficiency while achieving human-like layout designs.

As one of the essential tools for spatio–temporal traffic data mining, vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles. However, uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage. Therefore, one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy. We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model (IKV) based on the variational autoencoder (VAE) and an improved K-means algorithm. In the framework, the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server; the server uses the hidden variables for clustering analysis and delivers the analysis results to the client. The IKV’ workflow is as follows: first, we train the VAE with historical vehicle trajectory data (when VAE’s decoder can approximate the original data, the encoder is deployed to the edge computing device); second, the edge device transmits the hidden variables to the server; finally, clustering is performed using improved K-means, which prevents the leakage of the vehicle trajectory. IKV is compared to numerous clustering methods on three datasets. In the nine performance comparison experiments, IKV achieves optimal or sub-optimal performance in six of the experiments. Furthermore, in the nine sensitivity analysis experiments, IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations. These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments, such as carpooling tasks, but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

The integration of industrial Internet, cloud computing, and big data technology is changing the business and management mode of the industry chain. However, the industry chain is characterized by a wide range of fields, complex environment, and many factors, which creates a challenge for efficient integration and leveraging of industrial big data. Aiming at the integration of physical space and virtual space of the current industry chain, we propose an industry chain digital twin (DT) system framework for the industrial Internet. In addition, an industry chain information model based on a knowledge graph (KG) is proposed to integrate complex and heterogeneous industry chain data and extract industrial knowledge. First, the ontology of the industry chain is established, and an entity alignment method based on scientific and technological achievements is proposed. Second, the bidirectional encoder representations from Transformers (BERT) based multi-head selection model is proposed for joint entity–relation extraction of industry chain information. Third, a relation completion model based on a relational graph convolutional network (R-GCN) and a graph sample and aggregate network (GraphSAGE) is proposed which considers both semantic information and graph structure information of KG. Experimental results show that the performances of the proposed joint entity–relation extraction model and relation completion model are significantly better than those of the baselines. Finally, an industry chain information model is established based on the data of 18 industry chains in the field of basic machinery, which proves the feasibility of the proposed method.

The game of Tibetan Go faces the scarcity of expert knowledge and research literature. Therefore, we study the zero learning model of Tibetan Go under limited computing power resources and propose a novel scale-invariant U-Net style two-headed output lightweight network TibetanGoTinyNet. The lightweight convolutional neural networks and capsule structure are applied to the encoder and decoder of TibetanGoTinyNet to reduce computational burden and achieve better feature extraction results. Several autonomous self-attention mechanisms are integrated into TibetanGoTinyNet to capture the Tibetan Go board’s spatial and global information and select important channels. The training data are generated entirely from self-play games. TibetanGoTinyNet achieves 62%–78% winning rate against other four U-Net style models including Res-UNet, Res-UNet Attention, Ghost-UNet, and Ghost Capsule-UNet. It also achieves 75% winning rate in the ablation experiments on the attention mechanism with embedded positional information. The model saves about 33% of the training time with 45%–50% winning rate for different Monte-Carlo tree search (MCTS) simulation counts when migrated from 9 × 9 to 11 × 11 boards. Code for our model is available at https://github.com/paulzyy/TibetanGoTinyNet.

Ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection, navigable space detection, point cloud matching for localization, and registration for mapping. However, most works regard the ground as a plane without height information, which causes inaccurate manipulation in these applications. In this work, we propose GeeNet, a novel end-to-end, lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based representation. GeeNet leverages the mixing of two- and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the grid. For the first time, GeeNet has fulfilled ground elevation estimation from semantic scene completion. We use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet, demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point cloud. Moreover, the cross-dataset generalization capability of GeeNet is experimentally proven. GeeNet achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation, with a runtime of 0.88 ms.

Dy³⁺-doped fluoride fiber lasers have important applications in environment monitoring, real-time sensing, and polymer processing. At present, achieving a high-efficiency and high-power Dy³⁺-doped fluoride fiber laser in the mid-infrared (mid-IR) region over 3 µm is a scientific and technological frontier. Typically, Dy³⁺-doped fluoride fiber lasers use a unidirectional pumping method, which suffers from the drawback of high thermal loading density on the fiber tips, thus limiting power scalability. In this study, a bi-directional in-band pumping scheme, to address the limitations of output power scaling and to enhance the efficiency of the Dy³⁺-doped fluoride fiber laser at 3.2 µm, is investigated numerically based on rate equations and propagation equations. Detailed simulation results reveal that the optical–optical efficiency of the bi-directional in-band pumped Dy³⁺-doped fluoride fiber laser can reach 75.1%, approaching the Stokes limit of 87.3%. The potential for further improvement of the efficiency of the Dy³⁺-doped fluoride fiber laser is also discussed. The bi-directional pumping scheme offers the intrinsic advantage of mitigating the thermal load on the fiber tips, unlike unidirectional pumping, in addition to its high efficiency. As a result, it is expected to significantly scale the power output of Dy³⁺-doped fluoride fiber lasers in the mid-IR regime.

Currently, decarbonization has become an emerging trend in the power system arena. However, the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues, providing challenges for voltage regulation across a large-scale power grid network. Reinforcement learning based intelligent control of smart inverters and other smart building energy management (EM) systems can be leveraged to alleviate these issues. To achieve the best EM strategy for building microgrids in a power system, this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’ comfort while pursuing system-level objectives. The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels. The α-rank algorithm can solve the general-sum game and guarantee the ranking theoretically, but it is limited by the interaction complexity and hardly applies to the practical power system. A new evaluation algorithm (TcEval) is proposed by practically scaling the α-rank algorithm through a tensor complement to reduce the interaction complexity. Then, considering the noise prevalent in practice, a noise processing model with domain knowledge is built to calculate the strategy payoffs, and thus the TcEval-AS algorithm is proposed when noise exists. Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches, including ResponseGraphUCB (RG-UCB) and αInformationGain (α-IG). Finally, the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints. An asymmetric time-varying integral barrier Lyapunov function (ATIBLF) based integral reinforcement learning (IRL) control algorithm with an actor–critic structure is first proposed. The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated. Thus, optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item. Meanwhile, neural networks are used to approximate the gradient value functions. According to the Lyapunov stability theorem, the boundedness of all signals of the closed-loop system is proved, and the proposed control scheme ensures that the system states are within predefined compact sets. Finally, the effectiveness of the proposed control approach is validated by simulations.