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Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

computers, Materials & Continua 2024年第3期78卷 4071-4089页

作者： Lujuan Deng Ruochong Fu Zuhe Li Boyi Liu Mengze Xue Yuhao Cui School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou450002China

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.

关键词： Multispectral pedestrian detection convolutional neural networks depth separable convolution spatially reweighted attention mechanism

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AQINM: an adaptive QoS management framework based on intelligent negotiation and monitoring in cloud

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International Journal of Information Technology and Management 2024年第1期23卷 33-47页

作者： Saifeng, Zeng School of Computer and Communication Hunan Institute of Engineering Xiangtan411104 China

More and more federated cloud platforms have been constructed to deal with non-trivial large-scale applications, which typically require certain level of quality-of-service (QoS) guarantee. However, most of existing cloud-oriented QoS solutions are likely to introduce extra overheads on either resource allocation or task execution, which is especially true in federated cloud environments. In this paper, we design and implement a QoS-enhancing framework, namely adaptive QoS management based on intelligent negotiation and monitoring (AQINM), which provides three QoS-enhancing services including policy management, service level agreement (SLA) negotiation, and SLA monitoring. Unlike the conventional QoS-enable middleware, these services in the AQINM framework introduce several novel mechanisms to offer more cost-effective and efficient solutions to enforcing the QoS management in federated clouds. The implementation of our AQINM framework are tested in a campus federated cloud platform by using different applications as experimental benchmarks, and its performance are compared with other similar solutions. The experimental results show that the proposed AQINM is capable of reducing the costs of SLA negotiation and monitoring for large-scale cloud application that deployed in federated cloud environments. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Quality of service

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Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

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computers, Materials & Continua 2024年第1期78卷 1157-1170页

作者： Lujuan Deng Boyi Liu Zuhe Li School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou450002China

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse ***-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion *** fusion method does not utilize the correlation information between *** solve this problem,this paper proposes amodel based on amulti-head attention ***,after preprocessing the original ***,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input ***,the input coding sequence is fed into the transformer model for further processing and *** the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between ***,the processed results are input into the feedforward neural network to obtain the emotional output through the classification *** the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing *** model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

关键词： Emotion analysis deep learning cross-modal attention mechanism

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Low-profile,low sidelobe array antenna with ultrawide beam coverage for UAV communication

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Chinese Journal of Aeronautics 2025年第1期38卷 444-454页

作者： Mei LI Zhiliang SHANG Lin PU Ming-Chun TANG Lei ZHU School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering University of MacaoMacao SAR 999078China

This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage,low profile,and low Sidelobe Level(SLL)for the application of Unmanned Aerial Vehicle(UAV)air-to-ground *** array consists of ten broadside-radiating,ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low ***,an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented.A low-profile,half-mode Microstrip Patch Antenna(MPA)is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole,constructing the compact,end-fire,grounded Huygens source ***,two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide,broadside-radiating *** consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation *** indicates an ultrawide Half-Power Beamwidth(HPBW)of 161°and a low SLL of-25 dB with a high gain of 12 d Bi under a single-layer *** concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.

关键词： Array antenna Broad beam Fan beam Low sidelobe level Chebyshev synthesis UAV communication

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HA-FGOVD: Highlighting Fine-grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection

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IEEE Transactions on Multimedia 2025年 27卷 3171-3183页

作者： Ma, Yuqi Liu, Mengyin Zhu, Chao Yin, Xu-Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-grained category rather than focus on their fine-grained attributes, e.g., colors or materials, thus failed to identify objects specified with certain attributes. Despite being pretrained on large-scale image-text pairs with rich attribute information, their latent feature space does not highlight these fine-grained attributes. In this paper, we introduce HA-FGOVD, a universal and explicit method that enhances the attribute-level detection capabilities of frozen OVD models by highlighting fine-grained attributes in explicit linear space. Our approach uses a LLM to extract attribute words in input text as a zero-shot task. Then, token attention masks are adjusted to guide text encoders in extracting both global and attribute-specific features, which are explicitly composited as two vectors in linear space to form a new attribute-highlighted feature for detection tasks. The composition weight scalars can be learned or transferred across different OVD models, showcasing the universality of our method. Experimental results show that HA-FGOVD achieves state-of-the-art performance on the FG-OVD benchmark and demonstrates promising generalization on the OVDEval benchmark, suggesting that our method addresses significant limitations in fine-grained attribute detection and has potential for broader fine-grained detection applications. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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A novel feature selection method based on adaptive search particle swarm optimization

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Neural Computing and Applications 2025年第12期37卷 7767-7783页

作者： Han, Fei Wang, Yi-Huai Li, Fan-Yu School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China

As an effective method for data dimensionality reduction, feature selection could improve the classification accuracy and reduce the computational cost when dealing with high-dimensional data. Feature selection is essentially a complex optimization search problem. Among many optimization algorithms, particle swarm optimization (PSO) has been widely used due to its good global search ability and easy to implement. However, most feature selection methods based on PSO ignore the correlations between the features of data, which may lead to more redundant features, and the feature selection methods are prone to fall into local minima. This study proposes an improved feature selection method based on adaptive search particle swarm optimization (AS-BPSO-FS). On one hand, AS-BPSO-FS is designed to consider the feature correlation according to the feature correlation information. The particle position adaptive update strategy selects features based on the correlation coefficient between features, ensuring that features with higher correlation are more likely to be culled, so as to obtain a subset of features with less redundancy. On the other hand, AS-BPSO-FS identifies particles trapped in local minima by calculating the update time of individual and global optimal positions, and uses an adaptive particle neighborhood search strategy to help particles escape from local minima. The AS-BPSO-FS has been tested on 10 UCI data and compared with some state-of-the-art feature selection methods. The results verify that the proposed method could obtain feature subsets with better classification performance and less redundancy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Feature Selection

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Individualized Driving Intention Prediction With Inverse Reinforcement Learning

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8125-8139页

作者： Liu, Siqi Li, Xinyang Chen, Jiansheng Guo, Chenghao Wu, Jiehui Luo, Qifeng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers' awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers' actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver's historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios. © 2000-2011 IEEE.

关键词： Advanced driver assistance systems

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A computational offloading algorithm for cloud-edge collaboration in smart agriculture

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International Journal of Simulation and Process Modelling 2024年第2期21卷 121-129页

作者： Li, Feng Li, Yiyuan Pan, Yuqing School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang China

To solve the problem that the massive amount of information and real-time processing in the IoT system puts pressure on the computing resources of the whole system, the industry often adopts the computation offloading algorithm for cloud-edge collaboration. However, at this stage, conventional computation offloading algorithms frequently fail to account for dynamic conflicts between terminal tasks and offloaded tasks, and the results of offloading are inefficient. To that purpose, this paper introduces the task volume prediction model, the response time model and the power consumption model to represent the whole computing process of agricultural activities from various angles. On that basis, the DQN algorithm is used to solve the model’s mixed-integer nonlinear optimisation issue. Experiments show that the algorithm proposed in this paper can increase the offloading accuracy and the speed and accuracy of cloud-edge collaborative computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： smart agriculture cloud-edge collaboration computation offloading deep reinforcement learning

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A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

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China communications 2023年第2期20卷 179-197页

作者： Long Cheng Peng Zhao Dacheng Wei Yan Wang Department of Computer and Communication Engineering Northeastern UniversityQinhuangdao 066004China

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of ***-line-of sight(NLOS)is a primary challenge in indoor complex *** this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS ***,fitting polynomials are used to predict the measured *** residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering *** measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in *** distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to ***,the maximum likelihood method is used to calculate the position coordinate *** simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this *** it shows strong robustness in strong NLOS environment.

关键词： wireless sensor network indoor localization NLOS environment gaussian mixture model(GMM) fitting polynomial

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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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