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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Liu, Chunhong Zhao, Mengdi Wang, Huaichen Cheng, Bo Liu, Jialei Yuan, Peiyan Engineering Lab of Intelligence Business & Internet of Things and School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility. IEEE

关键词： Vehicles

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Brain Abnormality Detection and Analysis by Using MRI of Brain Through Multiple Sclerosis Lesions Detection and Analysis 1

Brain Abnormality Detection and Analysis by Using MRI of Bra...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Ali Ibrahim, S. Jafar Rayudu, K.M. Anitha, Setti Anitha, K. Arun Nambi, P. Erfan, S.K. School of Computer Science and Engineering Department of Internet of Things Vellore Institute of Technology Tamilnadu Vellore632014 India QIS College of Engineering and Technology AP Ongole India

ISBN: (纸本)9798350345957

Progressive MS lesion formation is a primary cause of cognitive decline and physical impairment, making MS one of the major disorders. The rate of disease progression and the efficacy of treatments depend critically on having a quick and accurate method for assessing the number and size of MS lesions in the brain. The position, size, morphological variation, intensity resemblance with normal brain tissues, and inter-subject anatomical variance of MS lesions in brain MRI make precise diagnosis, characterisation, and quantification exceedingly challenging. In this paper, we explored a technique for detecting and segmenting MS lesions in which the fundamental processes are background creation and binarization with a global threshold. To link the normal tissues close to the margins, we perform a three-phase level set and then add a third-phase segmented region using a contour of the brain. To create an adaptable setting, we must first eliminate lesions from the brain, saving only the brain's largest linked area and the corpus callosum. Selecting the threshold using entropy and standard deviation via the binarization approach is followed by non-gamut image enhancement. The proposed method detects lesions in the same locations as a radiologist and with the same precision in terms of size and number. As a result of its flexibility, the suggested method has a range of applications in clinical practise for identifying MS lesions in MRI. When compared to more contemporary methods, the proposed method has a smaller margin of error and higher accuracy. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of Data/Data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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CT Image Segmentation Algorithm for Liver Tumors Based on 3D AGCH-Net and Multi-Scale Fusion 14

CT Image Segmentation Algorithm for Liver Tumors Based on 3D...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Zhang, Lihui Fu, Zihan Liu, Yingxue Wang, Xiaoyu Zhang, Lijuan School of Management Engineering Jilin University of Architecture and Technology Changchun130114 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Liver tumor segmentation remains a challenging task. Previous methods often relied on simple feature fusion (e.g., concatenation or summation), leading to suboptimal results. This paper proposes a segmentation method based on AGCHNet with multi-scale feature fusion. Built on the U-Net framework, we replace standard convolutions with Fused-MBConv, improving multi-scale feature extraction and representation. The SE_ASPP module at the bottleneck enhances salient feature learning, while the AGCH attention mechanism in skip connections mitigates semantic conflicts and suppresses irrelevant information. Our method, evaluated with five-fold cross-validation on the LiTs dataset, achieved 96.6% Dice, 7.3 mm HD, and 1.0 mm ASD for liver segmentation. For tumor segmentation, it reached 80.9% Dice, 14.3 mm HD, and 2.3 mm ASD, outperforming mainstream methods. © 2024 The Authors.

关键词： Convolution

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WiFo: wireless foundation model for channel prediction

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science China(Information sciences) 2025年第6期68卷 372-384页

作者： Boxun LIU Shijian GAO Xuanyu LIU Xiang CHENG Liuqing YANG State Key Laboratory of Photonics and Communications School of Electronics Peking University Internet of Things Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology

Channel prediction permits to acquire channel state information(CSI) without signaling overhead. However,almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency(STF) wireless foundation model(WiFo) to address time-frequency channel prediction tasks in a unified manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder(MAE)-based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160k CSI samples for *** validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art(SOTA) zero-shot generalization performance via comparisons with other full-shot baselines.

关键词： channel prediction channel state information foundation model self-supervised pre-training zero-shot learning

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models 10

Primeval Identification and Diagnosis of Alzheimer's Disease...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujitha, K.L. Lynsha Helena Pratheeba, H.P. Rajeshwari, R. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Image Subject Accessing Model under Foggy Weather 14

Research on Image Subject Accessing Model under Foggy Weathe...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Li, Zhiyi Zhang, Songtao Meng, Fanlei Zhang, Lijuan School of Instrument Science and Electrical Engineering Jilin University Jilin China Computer Science and Technology Guohao Academy Tongji University Shanghai200092 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Imaging results under outdoor conditions are easily affected by foggy weather which may produce blur, low contrast, intensity shift and other visual degradation effects. This paper proposed an accessing model of fog density based on discrete wavelet transform features of multi-channel mean subtracted contrast normalized coefficients. Besides, corner feature intensity is utilized to extract highlevel features of foggy images. The accessing model used support vector regression to predict fog density with the help of a fog density accessing dataset collected from human eyes. The experimental results show that the subjective accessing method proposed in this paper has a high correlation with human eyes accessing results on the testing dataset, and is better than the general referenceless image spatial quality evaluator model. The accessing model can reflect the image quality as human eyes in foggy scenes. © 2024 The Authors.

关键词： Support vector regression

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Fundamental Limits of Doppler Shift-Based, ToA-Based,and TDoA-Based Underwater Localization

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1637-1639页

作者： Zijun Gong Cheng Li Ruoyu Su Hong Kong University of Science and Technology(Guangzhou) IOT ThrustGuangzhou 511400 Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Faculty of Engineering and Applied Science Memorial University of NewfoundlandNewfoundland A1B 3X5Canada School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China

Dear Editor,This paper is concerned with the underwater localization based on acoustic signals. Specifically, we will focus on the search of an underwater target that can constantly broadcast a beacon signal, such as a black box. Common measurements for localization are Doppler shift [1], time of arrival(ToA) [2]–[4], time difference of arrival(TDoA) [5], [6], angle of arrival(AoA) [7], etc.

关键词： TDoA ToA underwater

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A random number generator with hardware switching devices 3

A random number generator with hardware switching devices

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3rd IEEE International Conference on computer Systems, ICCS 2023

作者： Shen, Ying Gao, Xin Sun, Yu Li, Zongrui Xie, Jianfei Si, Nong Beijing University of Technology Faculty of Information Technology Beijing China North China Institute of Science & Technology Department of Internet of Things Engineering Hebei China

ISBN: (纸本)9798350313666

Utilizing a hardware-based random number generator is a viable strategy for enhancing the security of the fundamental procedures in handling sensitive data within a computer system. Given the substantial volume of data generated by intelligent devices, there is an increasing need for a random number generator that is quick, cost-effective, simplified, and trustworthy. Such a generator is considered the most suitable fundamental for performing encryption algorithms. This work presents a novel approach to developing a real random number generator for general uses, utilizing MEMS switch technology. In order to enhance the level of unpredictability, the implementation involves the utilization of a voltage-controlled switch array and the allocation of dedicated reference comparison devices. The examination of circuit simulation performance demonstrates that the suggested method exhibits verifiable randomness in the test model and efficiency in energy consumption. © 2023 IEEE.

关键词： Sensitive data

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Fast target-aware learning for few-shot video object segmentation

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science China(Information sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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