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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent griculturalquipment RearchInstitute Hebei Agricultural UniverityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Dual-Antenna GNSS/MEMS INS Tightly Coupled Algorithm for Agricultural Machinery Based on Adaptive Federated Filtering

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IEEE Sensors Journal 2025年第9期25卷 14780-14792页

作者： Feng, Yihang Huang, Guanwen Li, Xin Li, Zhenhong Zhang, Kai Li, Hang Jing, Ce Chang’an University College of Geological Engineering and Geomatics Xi’an710054 China Chang’an University College of Geological Engineering and Geomatics Key Laboratory of Ecological Geology and Disaster Prevention Ministry of Natural Resources Xi’an710054 China Universities of Shaanxi Province Engineering Research Center of Intelligent BDS Xi’an710054 China Chang’an University College of Geological Engineering and Geomatics Key Laboratory of Western China’s Mineral Resource and Geological Engineering Ministry of Education Big Data Center for Geosciences and Satellites Xi’an710054 China

Modern agricultural machinery relies on high-accuracy navigation systems;however, the common loosely coupled (LC) solution of dual-antenna global navigation satellite system (GNSS) and micro-electromechanical system inertial navigation system (MEMS INS) often fails to meet accuracy requirements in complex environments. Theoretically, the tightly coupled (TC) solution of the dual-antenna baseline constraint and MEMS INS offers better attitude accuracy. However, its state space is incomplete, comprising only attitude, gyro biases, and ambiguity. Moreover, previous studies have not conducted a state observability analysis on this model, which is essential for understanding its state estimation capabilities. Therefore, we derived the TC model of dual-antenna baseline constraint and MEMS INS within a complete state space and performed an observability analysis. Based on these results and considering computational efficiency, we integrated this model into the GNSS/MEMS INS TC model using federated filtering. To further improve the algorithm’s accuracy in complex agricultural environments, an adaptive robust positioning algorithm is proposed based on turning state detection. The proposed algorithm was validated through three sets of experiments, demonstrating position accuracy within 2 cm in both open and slightly occluded environments, with heading accuracy within 0.6°, and maintaining optimal accuracy even in severely occluded environments. © 2001-2012 IEEE.

关键词： Adaptive filtering

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Laplacian Sparse Dynamic Slow Feature Analysis for Process Monitoring and Fault Isolation

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IEEE Sensors Journal 2025年第9期25卷 15653-15667页

作者： Wu, Ping Xue, Yikai Ni, Yuxuan Pan, Haipeng Zhang, Xi He, Yuchen Gao, Jinfeng Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China China Jiliang University Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province Hangzhou314423 China

The process monitoring and fault isolation are essential for ensuring operational safety and maintaining product quality in the industrial processes. This study introduces a novel approach to process monitoring and fault isolation based on the Laplacian sparse dynamic slow feature analysis (LSDSFA). To simultaneously account for sparsity, dynamics, and global structural information, a new optimization framework for LSDSFA is proposed. This framework incorporates the n l1 norm and a Laplacian graph into dynamic slow feature analysis (SFA), with enhancements to process variable representation. The optimization problem is addressed using the alternating direction method of multipliers (ADMMs). The proposed method decomposes the process variable space into latent and residual subspaces. In addition, three monitoring statistics are developed to facilitate effective process monitoring, while fault isolation is achieved through a two-level contribution plot. Specifically, the intrinsic structure of the process data is captured by employing a graph Laplacian operator to explore intervariable correlations. Simultaneously, the incorporation of the graph Laplacian operator and l1 norm into dynamic SFA (DSFA) reduces variable redundancy, thereby exploiting the intrinsic structure of the process data more effectively and improving interpretability through the sparsity. Experimental evaluations conducted on the widely adopted Tennessee Eastman Process (TEP) benchmark and a real-world blast furnace ironmaking process (BFIP) demonstrate the superior performance of the proposed LSDSFA-based method in comparison with related techniques. © 2001-2012 IEEE.

关键词： Blast furnaces

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Predicting Lactobacillus delbrueckii ***-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method

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Science China(Life Sciences) 2025年第2期68卷 558-574页

作者： Shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong Sun Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China Key Laboratory of Dairy Products Processing Ministry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China College of Computer and Information Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot 010018China

Lactobacillus delbrueckii ***(***)and Streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb St I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,this study aimed to develop a semi-supervised learning framework for predicting Lb St I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb St I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an

关键词： Lactobacillus delbrueckii subsp.bulgaricus and Streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Listen with Seeing: Cross-modal Contrastive Learning for Audio-Visual Event Localization

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IEEE Transactions on Multimedia 2025年 27卷 2650-2665页

作者： Sun, Chao Chen, Min Zhu, Chuanbo Zhang, Sheng Lu, Ping Chen, Jincai Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Wuhan430074 China South China University of Technology School of Computer Science and Engineering Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

In real-world physiological and psychological scenarios, there often exists a robust complementary correlation between audio and visual signals. Audio-Visual Event Localization (AVEL) aims to identify segments with Audio-Visual Events (AVEs) that contain both audio and visual tracks in unconstrained videos. Prior studies have predominantly focused on audio-visual cross-modal fusion methods, overlooking the fine-grained exploration of the cross-modal information fusion mechanism. Moreover, due to the inherent heterogeneity of multi-modal data, inevitable new noise is introduced during the audio-visual fusion process. To address these challenges, we propose a novel Cross-modal Contrastive Learning Network (CCLN) for AVEL, comprising a backbone network and a branch network. In the backbone network, drawing inspiration from physiological theories of sensory integration, we elucidate the process of audio-visual information fusion, interaction, and integration from an information-flow perspective. Notably, the Self-constrained Bi-modal Interaction (SBI) module is a bi-modal attention structure integrated with audio-visual fusion information, and through gated processing of the audio-visual correlation matrix, it effectively captures inter-modal correlation. The Foreground Event Enhancement (FEE) module emphasizes the significance of event-level boundaries by elongating the distance between scene events during training through adaptive weights. Furthermore, we introduce weak video-level labels to constrain the cross-modal semantic alignment of audio-visual events and design a weakly supervised cross-modal contrastive learning loss (WCCL Loss) function, which enhances the quality of fusion representation in the dual-branch contrastive learning framework. Extensive experiments conducted on the AVE dataset for both fully supervised and weakly supervised event localization, as well as Cross-Modal Localization (CML) tasks, demonstrate the superior performance of our model compa

关键词： Information fusion

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Personalized Federated Learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micro Base Stations 5

LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micr...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Gao, Mingming Wang, Xinyu Wang, Congying Zhang, Zhongxiang Shan, Shiquan Song, Yonghui College of Electronics and Information Engineering Liaoning Technicial University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Huludao China

ISBN: (纸本)9798331533694

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications. © 2025 IEEE.

关键词： Doherty amplifiers

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