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Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 8651

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Editors

Prof. Dr. Shaozhang Niu

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Guest Editor

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: multimedia security; image recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Jiwei Zhang

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Guest Editor

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Interests: computer vision; information security
Special Issues, Collections and Topics in MDPI journals

Dr. Feifei Kou

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Guest Editor

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Interests: artificial intelligence; big data mining; semantic learning; search and recommendation
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Dr. Yongmei Tan

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Guest Editor

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: AI; NLP

Dr. Renzhi Yuan

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Guest Editor

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: space communication; satellite optical communication; ultraviolet optical communication; underwater optical communication; optical receivers; optimal quantum detection; quantum detection in classical and quantum optical communications
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Dr. Chunping Li

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Guest Editor

School of Software, Tsinghua University, Beijing 100084, China
Interests: artificial intelligence; data analysis and data mining; machine learning and automated reasoning

Special Issue Information

Dear Colleagues,

Founded in 1955, Beijing University of Posts and Telecommunications (BUPT) has emerged as one of China's leading institutions, renowned for its contributions to the fields of telecommunications, computer science, and engineering. As a cornerstone in national technological development, BUPT was among the first to be included in the national “211 Project” and the "985 Project Advantage Discipline Innovation Platform". Furthermore, it has consistently been recognized as a top-tier university in the fields of computer science and engineering.

Over the past 70 years, BUPT has made groundbreaking advancements in computer science, ranging from theoretical foundations in algorithms and software engineering to innovations in network infrastructure, intelligent systems, and cybersecurity. To commemorate the 70th anniversary of Beijing University of Posts and Telecommunications, this Special Issue aims to highlight the latest research, cross-disciplinary developments, and emerging technologies in the field of computer science and engineering. We welcome contributions that cover a wide array of theoretical innovations, practical breakthroughs, system-level designs, and comprehensive reviews.

The scope of this Special Issue includes, but is not limited to, the following topics:

Computer architecture and systems;
Distributed systems and cloud computing;
Network protocols and communication systems;
Machine learning and deep learning;
Artificial intelligence in engineering applications;
Data science and big data technologies;
Cybersecurity and privacy protection;
Blockchain and decentralized computing;
Internet of Things (IoT) and intelligent systems;
High-performance computing and edge computing;
Human–computer interaction and user interface design;
Software engineering and development practices;
Embedded systems and system-on-chip technologies;
Digital signal processing and multimedia systems;
Computational intelligence and optimization.

We invite submissions that explore the latest advancements in computer science and engineering, contributing to the growing body of knowledge and celebrating BUPT’s 70 years of excellence and innovation in the field.

Prof. Dr. Shaozhang Niu
Dr. Jiwei Zhang
Dr. Feifei Kou
Dr. Yongmei Tan
Dr. Renzhi Yuan
Dr. Chunping Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

computer architecture
distributed systems
cloud computing
network protocols
machine learning
deep learning
artificial intelligence
data science
big data
cybersecurity
blockchain
IoT (Internet of Things)
high-performance computing
edge computing
software engineering

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Published Papers (7 papers)

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Research

30 pages, 45966 KB

Open AccessArticle

DriveTDPA: Trajectory-Decision Preference Alignment for Vision-Language Autonomous Driving Planning

by Dingqi Liu and Jiayu Qin

Electronics 2026, 15(11), 2378; https://doi.org/10.3390/electronics15112378 - 1 Jun 2026

Viewed by 243

Abstract

Autonomous driving planning requires not only accurate trajectory prediction but also coherent semantic alignment across perception, decision making, and motion generation. Existing vision-language-based approaches predominantly focus on improving trajectory accuracy, which may lead to limited behavioral consistency. In this paper, we reformulate planning as a structured autoregressive generation task, where reasoning, actions, and future trajectories are jointly produced from multimodal observations. Based on this formulation, we propose Trajectory-Decision Joint Preference Optimization (TDJPO), which is a rollout-based alignment framework equipped with a unified reward that simultaneously captures physical trajectory quality and decision-level coherence. Starting from a supervised fine-tuned model, we construct preference pairs through stochastic rollouts and optimize the model using direct preference optimization. Experimental results on the NuScenes-TP benchmark demonstrate that our approach consistently enhances both trajectory accuracy and semantic consistency compared with supervised fine tuning, trajectory-only optimization, and lightweight vision-language baselines. These findings emphasize the necessity of jointly aligning physical feasibility and decision-level reasoning for achieving coherent and human-like autonomous driving behavior. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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25 pages, 3938 KB

Open AccessArticle

Hybrid Deep Learning Techniques Integrated with Machine Learning for Foreign Exchange Rate Forecasting

by Yu Cui and Jingjing Jiang

Electronics 2026, 15(7), 1463; https://doi.org/10.3390/electronics15071463 - 1 Apr 2026

Viewed by 868

Abstract

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–2018 training data + 2019–2023 validation data + 2024 testing data) to train and evaluate the model provides realistic forecasting and prevents temporal leakage. The global panel dataset for more than 250 and 60+ year countries and regions demonstrate that all of the proposed models are better than all classical machine learning models, stand-alone deep learning models, and naive persistence models. The hybrid model shows the most significant prediction error reduction with R² as 0.98, proving long-horizon currency forecasting is extremely robust. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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20 pages, 2021 KB

Open AccessArticle

TPSTA: A Tissue P System-Inspired Task Allocator for Heterogeneous Multi-Core Systems

by Yuanhan Zhang and Zhenzhou Ji

Electronics 2026, 15(6), 1339; https://doi.org/10.3390/electronics15061339 - 23 Mar 2026

Viewed by 429

Abstract

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of

O (M / W)

, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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23 pages, 11570 KB

Open AccessArticle

Geometric Graph Learning Network for Node Classification

by Lei Wang, Xitong Xu and Zhuqiang Li

Electronics 2026, 15(3), 696; https://doi.org/10.3390/electronics15030696 - 5 Feb 2026

Viewed by 590

Abstract

Graph attention improves neighbor discrimination, but it remains limited by local receptive fields and by a strong dependence on the input topology, which is often unreliable on heterophilous graphs. We propose Geometric Graph Learning Network (G2LNet), a structure-learning framework that infers message-passing probabilities from an explicit geometric topology learned in latent Euclidean or hyperbolic spaces. G2LNet combines (i) a geometric mapping module, (ii) distance- or inner-product-based relation operators with perceptual connectivity to control the influence of the given graph, and (iii) end-to-end constraint objectives enforcing stability, sparsity, and (optional) symmetry of the learned topology. This design yields unified local, non-local, and graph-free neighborhoods, enabling systematic analysis of when non-local aggregation helps. Experiments on node classification across nine publicly available benchmark datasets demonstrate that G2LNet’s controlled variant consistently achieves higher accuracy than representative strong baseline models–both local and non-local–on most datasets. This establishes a robust alternative for smaller scale node classification tasks. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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18 pages, 2458 KB

Open AccessArticle

An Interpretable CPU Scheduling Method Based on a Multiscale Frequency-Domain Convolutional Transformer and a Dendritic Network

by Xiuwei Peng, Honghua Wang, Guohui Zhou, Jun Jiang, Hao Fang, Zhengxing Wu and Xiaohui Li

Electronics 2026, 15(3), 693; https://doi.org/10.3390/electronics15030693 - 5 Feb 2026

Cited by 1 | Viewed by 638

Abstract

In modern operating systems, CPU scheduling policy selection and evaluation still rely mainly on heuristic methods, especially at the single-processor level or the abstract ready-queue level, and there is still a lack of systematic modeling and interpretable analysis for complex workload patterns. Traditional approaches are easy to implement and respond quickly in specific scenarios, but they often fail to remain stable under dynamic workloads and high-dimensional features, which can harm generalization. In this work, we build a simulation dataset that covers five typical scheduling policies, redesign a deep learning framework for scheduling policy identification, and propose the MCFCTransformer-DD model. The model extends the standard Transformer with multiscale convolution, frequency-domain augmentation, and cross-attention to capture both low-frequency and high-frequency signals, learn local and global patterns, and model multivariate dependencies. We also introduce a Dendrite Network, or DD, into scheduling policy identification and decision support for the first time, and its gated dendritic structure provides a more transparent nonlinear decision boundary that reduces the black-box nature of deep models and helps mitigate overfitting. Experiments show that MCFCTransformer-DD achieves 94.50% accuracy, a 94.65% F1 score, and an AUROC of 1.00, which indicates strong policy identification performance and strong potential for decision support. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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25 pages, 3879 KB

Open AccessArticle

Robust Occluded Object Detection in Multimodal Autonomous Driving: A Fusion-Aware Learning Framework

by Zhengqing Li and Baljit Singh

Electronics 2026, 15(1), 245; https://doi.org/10.3390/electronics15010245 - 5 Jan 2026

Cited by 1 | Viewed by 1335

Abstract

Reliable occluded object detection remains a persistent core challenge for autonomous driving perception systems, particularly in complex urban scenarios where targets are predominantly partially or fully obscured by static obstacles or dynamic agents. Conventional single-modality detectors often fail to capture adequate discriminative cues for robust recognition, while existing multimodal fusion strategies typically lack explicit occlusion modeling and effective feature completion mechanisms, ultimately degrading performance in safety-critical operating conditions. To address these limitations, we propose a novel Fusion-Aware Occlusion Detection (FAOD) framework that integrates explicit visibility reasoning with implicit cross-modal feature reconstruction. Specifically, FAOD leverages synchronized red–green–blue (RGB), light detection and ranging (LiDAR), and optional radar/infrared inputs, employs a visibility-aware attention mechanism to infer target occlusion states, and embeds a cross-modality completion module to reconstruct missing object features via complementary non-occluded modal information; it further incorporates an occlusion-aware data augmentation and annotation strategy to enhance model generalization across diverse occlusion patterns. Extensive evaluations on four benchmark datasets demonstrate that FAOD achieves state-of-the-art performance, including a +8.75% occlusion-level mean average precision (OL-mAP) improvement over existing methods on heavily occluded objects

O = 2

in the nuScenes dataset, while maintaining real-time efficiency. These findings confirm FAOD’s potential to advance reliable multimodal perception for next-generation autonomous driving systems in safety-critical environments. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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22 pages, 3408 KB

Open AccessArticle

A High-Performance Branch Control Mechanism for GPGPU Based on RISC-V Architecture

by Yao Cheng, Yi Man and Xinbing Zhou

Electronics 2026, 15(1), 125; https://doi.org/10.3390/electronics15010125 - 26 Dec 2025

Viewed by 1288

Abstract

General-Purpose Graphics Processing Units (GPGPUs) rely on warp scheduling and control flow management to organize parallel thread execution, making efficient control flow mechanisms essential for modern GPGPU design. Currently, the mainstream RISC-V GPGPU Vortex adopts the Single Instruction Multiple Threads (SIMT) stack control mechanism. This approach introduces high complexity and performance overhead, becoming a major limitation for further improving control efficiency. To address this issue, this paper proposes a thread-mask-based branch control mechanism for the RISC-V architecture. The mechanism introduces explicit mask primitives at the Instruction Set Architecture (ISA) level and directly manages the active status of threads within a warp through logical operations, enabling branch execution without jumps and thus reducing the overhead of the original control flow mechanism. Unlike traditional thread mask mechanisms in GPUs, our design centers on RISC-V and realizes co-optimization at both the ISA and microarchitecture levels. The mechanism was modeled and validated on Vortex SimX. Experimental results show that, compared with the Vortex SIMT stack mechanism, the proposed approach maintains correct control semantics while reducing branch execution cycles by an average of 31% and up to 40%, providing a new approach for RISC-V GPGPU control flow optimization. Full article

(This article belongs to the Special Issue Celebrating the 70th Anniversary of Beijing University of Posts and Telecommunications—Computer Science and Engineering)

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