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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Scaling Up Multi-Agent Reinforcement Learning via Graph Decomposition Invariant Network 5

Scaling Up Multi-Agent Reinforcement Learning via Graph Deco...

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5th International Seminar on artificial intelligence, Networking and Information technology, AINIT 2024

作者： Fu, Hongyi Ji, Jianmin School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798350385557

This paper focuses on the dilemma faced by largescale multi-agent systems. With the increase of agent size, the policy search space grows exponentially, reaching the task complexity that is difficult for the current mainstream multiagent algorithms to deal with, so that the optimal strategy is converged in large-scale multi-agent systems. In order to make the learning process of large-scale multi-agent tasks more smooth, many researchers have introduced the method of course learning, setting the scale from small to large courses, compared with learning training from scratch, this way can continue to inherit the knowledge learned in the smaller scale tasks, making the training smoother. However, in order to realize the multiagent course learning from small to large scale, we face the following difficulties. First, the neural network structure we designed can adapt to the observation input of agents of different scales, rather than only applicable to a fixed scale. Second, in the multi-stage course learning, due to the network's inefficient representation of multi-agent systems, the knowledge learned in small-scale courses cannot be effectively utilized in large-scale courses, and even the problem of catastrophic forgetting occurs in the neural network. Specifically, we view multi-agent systems from a graph perspective and derive the Graph Decomposition Invariant (GDI) for the first time. We prove that neural network architectures satisfying the GDI are of great help in realizing efficient course learning. We design a model architecture called GDI model suitable for multi-agent curriculum learning, which satisfies the graph decomposition invariant proposed by us. Finally, we designed small-scale to large-scale multi-agent tasks on two experimental platforms, StarCraft and Neural MMO, and conducted a large number of comparative experiments, which verified that our proposed GDI model has a great promotion effect on course learning. © 2024 IEEE.

关键词： Multi agent systems

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Energy-efficient multiuser and multitask computation offloading optimization method

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Intelligent and Converged Networks 2023年第1期4卷 76-92页

作者： Meini Pan Zhihua Li Junhao Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China School of IoT Engineering Jiangnan UniversityWuxi 214122China

For dynamic application scenarios of Mobile Edge Computing(MEC),an Energy-efficient Multiuser and Multitask Computation Offloading(EMMCO)optimization method is *** the consideration of multiuser and multitask computation offloading,first,the EMMCO method takes into account the existence of dependencies among different tasks within an implementation,abstracts these dependencies as a Directed Acyclic Graph(DAG),and models the computation offloading problem as a Markov decision ***,the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism,the long-term dependencies among different tasks are successfully captured by this ***,the Improved Policy Loss Clip-based PPO2(IPLC-PPO2)algorithm is developed,and the RNN encoder-decoder neural network is trained by the developed *** loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process,and the neural network parameters are continuously updated to select the optimal offloading scheduling *** results demonstrate that the proposed EMMCO method can achieve lower latency,reduce energy consumption,and obtain a significant improvement in the Quality of Service(QoS)than the compared algorithms under different situations of mobile edge network.

关键词： Mobile Edge Computing(MEC) computation offloading Reinforcement Learning(RL) optimization model

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Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

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Journal of Bionic Engineering 2023年第3期20卷 1198-1262页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,image segmentation is a critical stage in image processing and *** achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image *** image segmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

关键词： COVID-19 Multilevel threshold image segmentation Kapur’s entropy Multi-verse optimizer Meta-heuristic algorithm Bionic algorithm

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VF-Lens: Enhancing Visual Perception of Visually Impaired Users in VR via Adversarial Learning with Visual Field Attention 32

VF-Lens: Enhancing Visual Perception of Visually Impaired Us...

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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Chen, Xiaoming Han, Dehao Qu, Qiang Shen, Yiran Beijing Technology and Business University School of Computer and Artificial Intelligence China The University of Sydney Australia The University of Sydney School of Computer Science Australia Shandong University School of Software China

ISBN: (纸本)9798331536459

This research aims to enhance the image perception of visually impaired users in VR environments. We propose VF-Lens, a model that adaptively compensates for light sensitivity based on the user's visual field impairment, acting as a virtual lens between the visually impaired users and the VR world. VF-Lens is designed as a tailored generative adversarial learning model with a generator and discriminator, offering applicability to various types of visual impairments while bypassing engineering complexities. The generator creates a "hyperimage"tailored to the user's visual field impairment, which then undergoes a particular regression process to predict and replicate the real perception of the visually impaired user. The discriminator then evaluates the similarity between the replicated perception and the original image. Through adversarial training, the generator can produce hyperimages that adapt to the user's visual field parameters, enabling them to perceive the image more similarly to normal-vision users. We further improve VF-Lens by proposing new "visual field attention"mechanisms that prioritize and refine visual information in the user's visual field. Extensive evaluation, encompassing both visually impaired participants and simulations, has been conducted to demonstrate the effectiveness of VF-Lens in improving visual perception for visually impaired users. Moreover, we establish a standardized evaluation process involving tailored metrics as well as objective and subjective evaluations to promote reusability and comparability for future research in this field. © 2025 IEEE.

关键词： Generative adversarial networks

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AIGLLM: An Action Instruction Generation Method with Visually Enhanced LLM 7

AIGLLM: An Action Instruction Generation Method with Visuall...

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7th International Conference on Pattern Recognition and artificial intelligence, PRAI 2024

作者： Xia, Yudong Zhao, Sirui Wu, Tong Tang, Huaying Xu, Tong University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350350890

Action Instruction Generation (AIG) is a key step in semantic-driven action sequence generation, which aims to embed text instructions describing specific actions into the driving text. AIG is a challenging task that requires accurately understanding the potential action semantics contained in the driving text. Besides, the challenge is exacerbated by the lack of paired text-action instructions data. To address these challenges and motivated by the powerful capabilities of LLMs in text generation, we propose AIGLLM, a novel AIG framework with visually enhanced Large Language Model (LLM). Specifically, we perform low-rank adaptation (LoRA) on the off-the-shelf LLM to enable the model understand action semantics and generate action instructions at appropriate positions within the original text sequence. Particularly, to address the current lack of data that can be used to fine-tune LLMs for AIG, we construct a dataset containing 12,000 text-action instruction pairs and 15,000 image-action instruction pairs. Subsequently, a visual module is designed to enable the LLM to learn action knowledge from external visual signals of actions, thereby enhancing the model's understanding of action semantics. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our method has an impressive key benefit: improved accurate action instruction embedding. © 2024 IEEE.

关键词： Semantics

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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PhysDiff: Physiology-based Dynamicity Disentangled Diffusion Model for Remote Physiological Measurement 39

PhysDiff: Physiology-based Dynamicity Disentangled Diffusion...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Qian, Wei Su, Gaoji Guo, Dan Zhou, Jinxing Li, Xiaobai Hu, Bin Tang, Shengeng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China School of Cyber Science and Technology Zhejiang University China School of Information Science and Engineering Lanzhou University China

ISBN: (纸本)157735897X

Recent works on remote PhotoPlethysmoGraphy (rPPG) estimation typically use techniques like CNNs and Transformers to encode implicit features from facial videos for prediction. These methods learn to directly map facial videos to the static values of rPPG signals, overlooking the inherent dynamic characteristics of rPPG sequence. Moreover, the rPPG signal is extremely weak and highly susceptible to interference from various sources of noise, including illumination conditions, head movements, and variations in skin tone. To address these limitations, we propose a Physiology-based dynamicity disentangled diffusion (PhysDiff) model particularly designed for robust rPPG estimation. PhysDiff leverages the diffusion model to learn the distribution of quasi-periodic rPPG signal and uses a dynamicity disentanglement strategy to capture two dynamic characteristics in temporal rPPG signal, i.e., trend and amplitude. This disentanglement is motivated by the underlying dynamic physiological processes of vasodilation and vasoconstriction, ensuring a more precise representation of the rPPG signal. The disentangled components are then used as pivotal conditions in the proposed spatial-temporal hybrid denoiser for rPPG reconstruction. Besides, we introduce a periodicity-based multi-hypothesis selection strategy in model inference, which compares the natural periodicity of multiple generated rPPG hypotheses and selects the most favorable one as the final prediction. Extensive experiments on four datasets demonstrate that our PhysDiff significantly outperforms prior methods on both intra-dataset and cross-dataset testing. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Physiological models

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Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild 48

Quaternion Orthogonal Transformer for Facial Expression Reco...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhou, Yu Guo, Liyuan Jin, Lianghai Huazhong University of Science and Technology School of Computer Science and Technology China Huazhong University of Science and Technology Institute of Artificial Intelligence China

ISBN: (纸本)9781728163277

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the *** are available at https://***/Gabrella/QOT. © 2023 IEEE.

关键词： Face recognition

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CHADTI: A Sequence-Based Model with Attention Mechanism for Drug-Target Interaction Prediction

CHADTI: A Sequence-Based Model with Attention Mechanism for ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chen, Zhuang Peng, Jing School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798350337020

In the new drug development process, drug discovery and repositioning can significantly reduce development costs and shorten development time. Drug-target interactions(DTIs) prediction is a crucial step for in-silico drug discovery and drug repurposing. However, manual identification of DTIs is extremely costly and time-consuming due to the large-scale chemical space. Using virtual screening to identify DTIs can significantly shorten development time and reduce costs. Recently, various attention models have been used to extract the features of drugs and proteins. However, these methods ignore the complex interactions between drugs and proteins. In this paper, we propose a sequence-based model, CHADTI, for DTIs prediction. We use stacked 1D-CNN layers to learn the features of drugs and a pre-trained model(ESM) to embed proteins. We design a novel attention mechanism to extract the complex interactions between drugs and proteins. Finally, We evaluate CHADTI on two datasets, and the results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Attention mechanism Drug discovery Drug Repositioning Drug-target interactions pre-trained model

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