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DDVC: Deep Distributed Video Coding Using Quality Enhancement Network

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhou, Junwei Ye, Zhuang Yi, Xiangbo Zhang, Weijian Lin, Qiuzhen Xiang, Jianwen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China

Distributed video coding (DVC) transfers the complex process of the encoder to the decoder, which is suitable for video applications with limited encoding resources. Deep learning has shown impressive performance in video coding tasks in learning nonlinear compact representations of input frames and reconstructing video frame details. It is worth exploring whether deep learning implementation of the DVC paradigm is feasible and whether performance gains can be obtained. This paper proposes a deep DVC scheme (DDVC) using a quality enhancement network (QEN), which maps pixels to a more compressible latent space via an autoencoder resulting in a compact representation of Wyner-Ziv (WZ) frames. Moreover, considering the spatio-temporal correlation between the WZ frame and the Key frame, the QEN on the decoder side, using CNN and LSTM iteratively extracts common information between the WZ frame and the Key frame, which could further finetune the WZ frame reconstruction. We evaluated DDVC in limited encoding resources application scenarios with 19 related video sequences. Results on the video sequences with different motion intensity levels show that DDVC significantly outperforms existing schemes in reconstruction quality with the same compression ratio. We open-sourced the implementation at GitHub1. © 1991-2012 IEEE.

关键词： Video streaming

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Demonstration of A Novel SLM-Based PAPR Reduction Method for GFDM using USRP Testbed

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IEEE Access 2025年 13卷 84399-84408页

作者： Vejandla, Kishore Ragam, Prashanth Valluri, Sivaprasad Gunturu, Chakravarthy Yaswanth, K.N.G.B. Bandari, Shravan Kumar Jyothi, Sankati Indian Institute of Information Technology Una. Saloh School of Electronics Himachal Pradesh Una India VIT AP School of Computer Science and Engineering Andhra PradeshInavolu Beside A.P.Secretatiat Amaravati India Indian Institute of Information Technology Design and Manufacturing Andhra Pradesh Kurnool India National Institute of Technology Meghalaya 793003 India VIT AP Software Development Cell Andhra PradeshInavolu Beside A.P.Secretatiat Amaravati India

The remarkable attributes like elevated transmission rates, low latency, minimal out band emission and enhanced spectral efficiency of the generalized frequency division multiplexing (GFDM) system have lured the significant attention from research community. However, the inherent superposition of multiple symbols within the time domain results in a notable amount of peak-to-average power ratio (PAPR) that affects the performance due to non-linear front end circuitry. The selected mapping (SLM) technique resolves this problem by employing phase rotation vectors to generate alternative signal representations. Conventionally, phase sequence vectors possess uniform magnitudes, thereby constraining the achievable PAPR reduction. To address this limitation, we propose the use of normalized rows from the Riemann matrix as phase sequence vectors, given their inclusion of components with substantial magnitudes. This approach mitigates excessive peaks and consequently yields superior PAPR reduction gains. To optimize real-time system performance, side information (SI) is also integrated into the transmitted signals alongside the normalized Riemann matrix sequences. Indeed, for validation purpose, an experimental setup utilizing the universal software radio peripheral (USRP) 2953R in conjunction with LabVIEW software is employed for real-time transmission. The observed spectral response from the USRP closely aligned with simulation results, affirming the efficacy of the proposed method. © 2013 IEEE.

关键词： Model checking

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TS-RePSO: A Three-Stage Feature Selection Method Combing ReliefF and PSO in Bioinformatics

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 640-652页

作者： Bin Pu Haining Wang Zhaozhao Xu Fangyuan Yang Xiangqiong Wu Jianguo Chen College of Computer Science and Electronic Engineering Hunan University Changsha China School of Software South China University of Technology GuangZhou China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China Department of Gynecologic Oncology The First Affiliated Hospital of Henan Polytechnic University Jiaozuo China School of Software Engineering Sun Yat-Sen University Guangzhou China

The inherent characteristics of high-dimensional feature redundancy of biomedical data lead to the “curse of dimensionality” in bioinformatics, which brings new challenges to feature selection problems. Recently, the two-stage approach combining the filter and wrapper methods has become popular for feature selection tasks. However, these two-stage or previous one-stage algorithms suffer from blindness in the setting of thresholds, and the search methods tend to fall into local optimum solutions. To this end, we propose a three-stage feature selection method that combines ReliefF and Particle swarm optimization as a specific case, called TS-RePSO, including the filter stage, grouping stage, and wrapper stage. Specifically, in the filter stage, ReliefF is utilized to compute the weights of the features and sort them in descending order. In the grouping stage, the ranked features are grouped based on the density equalization strategy so that the weight of groups in all groups is equal. In the wrapper stage, the proposed grouping PSO is employed to search for the grouped features and select them according to the in-group and out-group evaluation strategies. Extensive experiments are conducted on 5 benchmark datasets and 6 real-world datasets, and experiment results show that the proposed method achieves the best performance.

关键词： Filtering algorithms Classification algorithms Bioinformatics Particle swarm optimization Feature extraction Diseases Prediction algorithms Information filters Genetic algorithms Diversity reception

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - computer and Information Sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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POS tagging of low-resource Pashto language: annotated corpus and BERT-based model

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Language Resources and Evaluation 2025年 1-23页

作者： Haq, Ijazul Zhang, Yingjie Qadri, Intakhab Alam Shien-Ming Wu School of Intelligent Manufacturing South China University of Technology Guangzhou China Artificial Intelligence Department Guangdong CAS Angels Biotechnology Co. Ltd. Foshan China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

This paper presents the development of a comprehensive part-of-speech (POS) annotated corpus for the low-resource Pashto language, along with a deep learning model for automatic POS tagging. The corpus comprises approximately 700K words (30K sentences), labeled for word boundaries, considering Pashto lacks explicit delimiters for word segmentation. The corpus was then annotated for POS information using a concise and pragmatic tagset of 36 grammatical categories. We utilized this corpus to train a supervised POS tagging model. For the model development, we employed a multilingual BERT (Bidirectional Encoder Representations from Transformers), fine-tuning it for this specific task. The BERT-based model performance was evaluated against an RNN-based model that employs the BiLSTM-CRF network and word embeddings (Word2Vec, fastText, and GloVe). Experimental results demonstrated that the BERT-based model achieved the best performance, attaining an accuracy of 96.24% and an F1-score (weighted average) of 96.22%. The model’s performance is highly satisfactory, making it useful in practical applications. Furthermore, the applications of the annotated corpus are not limited to this study only;it can be employed in various NLP applications, including named entity recognition (NER), text proofing, and constituency and dependency parsing. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Artificial Intelligence BERT Corpus Linguistics LLMs Low-resource Languages Machine Learning NLP Pashto POS Tagging Transformers

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

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computer Modeling in engineering & Sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets

arXiv

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arXiv 2025年

作者： Ban, Lulu Zhu, Tao Lu, Xiangqing Qiu, Qi Han, Wenyong Li, Shuangjian Chen, Liming Wang, Kevin I-Kai Nie, Mingxing Wan, Yaping School of Computer Science University of South China 421001 China School of Computer Science and Technology Dalian University of Technology 116024 China Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the success of DoReMi in Large Language Models (LLMs), we introduce a data mixture optimization strategy for pre-training HAR models, aiming to improve the recognition performance across heterogeneous datasets. However, directly applying DoReMi to the HAR field encounters new challenges due to the continuous, multi-channel and intrinsic heterogeneous characteristics of IMU sensor data. To overcome these limitations, we propose a novel framework HAR-DoReMi, which introduces a masked reconstruction task based on Mean Squared Error (MSE) loss. By raplacing the discrete language sequence prediction task, which relies on the Negative Log-Likelihood (NLL) loss, in the original DoReMi framework, the proposed framework is inherently more appropriate for handling the continuous and multichannel characteristics of IMU data. In addition, HAR-DoReMi integrates the Mahony fusion algorithm into the self-supervised HAR pre-training, aiming to mitigate the heterogeneity of varying sensor orientation. This is achieved by estimating the sensor orientation within each dataset and facilitating alignment with a unified coordinate system, thereby improving the cross-dataset generalization ability of the HAR model. Experimental evaluation on multiple cross-dataset HAR transfer tasks demonstrates that HAR-DoReMi improves the accuracy by an average of 6.51%, compared to the current state-of-the-art method with only approximately 30% to 50% of the data usage. These results confirm the effectiveness of HAR-DoReMi in improving the generalization and data efficiency of pre-training HAR models, underscoring its significant potential to facilitate the practical deployment of HAR technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

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An Attribute-Based Pre-Authenticated Secure Communication Protocol Enabling Key Protection and Credential Online-Upgrading for 5G NR V2X

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Zhang, Zhishuo Huang, Wen Huang, Ying Liao, Yongjian Wu, Chunjiang Zhou, Shijie School of Information and Software Engineering Chengdu610054 China Sichuan University School of Computer Science Chengdu610065 China Sichuan University West China Hospital Chengdu610041 China Chengdu University of Information Technology School of Software Engineering Chengdu610225 China

Currently, no practical lightweight authenticated key agreement (AKA) protocol with fine-grained pre-authentication has been developed to address security issues such as data integrity, authenticity, traceability, tamper-proofing, and privacy in 5G NR V2X. In this paper, we introduce a lightweight anonymous attribute-based signature of knowledge (Lw-AABSoK) scheme built on Curve25519 to defend against key-leakage attacks. This scheme enables attribute revocation and online credential updating, serving as a fine-grained pre-authentication cryptographic module for V2X secure communication. Leveraging the proposed Lw-AABSoK, we design an end-to-end fine-grained pre-authenticated key agreement protocol (E2E-FGpAKA). The E2E-FGpAKA is UDP-compatible;all interactive messages are self-validated by the Lw-AABSoK, and their sizes are strictly below the 5G NR MAC Transport Block Size (TBS). Through rigorous comparative analysis, scientific experimental verification, and comprehensive evaluation, it is evident that the proposed scheme holds significant practical value for 5G NR V2X. © 2000-2011 IEEE.

关键词： Authentication Protocol

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