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Development of Multi-Agent-Based Indoor 3D Reconstruction

computers, Materials & Continua 2024年第10期81卷 161-181页

作者： Hoi Chuen Cheng Frederick Ziyang Hong Babar Hussain Yiru Wang Chik Patrick Yue Optical Wireless Lab Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling *** work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is *** visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional ***,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents *** 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural *** validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our *** challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding *** summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.

关键词： Multi-agent system multi-robot human collaboration visible light communication visible light positioning 3D reconstruction reinforcement learning multi-agent path finding

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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MIMO performance enhancement of MIMO arrays using PCS-based near-field optimization technique

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Science China(Information Sciences) 2023年第6期66卷 170-185页

作者： Yipeng WANG Xiaoming CHEN Huiling PEI Wei E.I.SHA Yi HUANG Ahmed A.KISHK School of Information and Communications Engineering Xi'an Jiaotong University Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic EngineeringZhejiang University Department of Electrical Engineering and Electronics University of Liverpool Department of Electrical and Computer Engineering Concordia University

Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.

关键词： antenna correlation array antenna multiple-input multiple-output (MIMO) capacity

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Deep convolutional generative adversarial networks: performance analysis in wireless systems

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Discover Internet of Things 2024年第1期4卷 29页

作者： Arnab, Shams Fardous Alam, S. M. Ibnul Ul Mahmud, Tasfin Sabuj, Saifur Rahman Ahmed, Shakil Department of Electrical and Electronic Engineering Brac University Dhaka1212 Bangladesh Department of Electrical and Computer Engineering Iowa State University AmesIA United States

This paper introduces Deep Convolutional Generative Adversarial Networks (DCGAN) as a potential solution for wireless systems aiming to enhance the Block Error Rate (BLER). The DCGAN under consideration consists of a generator and a discriminator, which utilizes the same network to differentiate between true and synthetic messages while simultaneously reconstructing the transmitted message. The wireless network is modeled as a DCGAN, where the desirable communication link is modeled between the transmitter and receiver. In addition, the expression for the transmitter and receiver of DCGAN is analytically derived. Based on the simulation outcomes, the proposed DCGAN has investigated a significantly lower BLER than the other models, namely convolutional neural networks, deep learning, and conditional GAN. © The Author(s) 2024.

关键词： Generative adversarial networks

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Enabling Efficient Sparse Multiplications on GPUs with Heuristic Adaptability

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2226-2239页

作者： Xu, Jiaming Huang, Shan Li, Jinhao Huang, Guyue Xie, Yuan Wang, Yu Dai, Guohao Shanghai Jiao Tong University Qing Yuan Research Institute Shanghai China University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States Hong Kong University of Science and Technology Department of Electronic and Computer Engineering China Tsinghua University Department of Electronic Engineering Beijing China

Sparse Matrix-Vector/Matrix Multiplication, namely SpMMul, has become a fundamental operation during model inference in various domains. Previous studies have explored numerous optimizations to accelerate it. However, to enable efficient end-to-end inference, the following challenges remain unsolved: (1) Incomplete design space and time-consuming preprocessing. Previous methods optimize SpMMul in limited loops and neglect the potential space exploration for further optimization, resulting in >30% waste of computing power. Additionally, the preprocessing overhead in SparseTIR and DTC-SpMM is 1000× larger than sparse computing. (2) Incompatibility between static dataflow and dynamic input. A static dataflow can not always be efficient to all input, leading to >80% performance loss. (3) Simplistic algorithm performance analysis. Previous studies primarily analyze performance from algorithmic advantages, without considering other aspects like hardware and data features. To tackle the above challenges, we present DA-SpMMul, a Data-Aware heuristic GPU implementation for SpMMul in multi-platforms. DA-SpMMul creatively proposes: (1) Complete design space based on theoretical computations and nontrivial implementations without preprocessing. We propose three orthogonal design principles based on theoretical computations and provide nontrivial implementations on standard formats, eliminating the complex preprocessing. (2) Feature-enabled adaptive algorithm selection mechanism. We design a heuristic model to enable algorithm selection considering various features. (3) Comprehensive algorithm performance analysis. We extract the features from multiple perspectives and present a comprehensive performance analysis of all algorithms. DA-SpMMul supports PyTorch on both NVIDIA and AMD and achieves an average speedup of 3.33× and 3.02× over NVIDIA cuSPARSE, and 12.05× and 8.32× over AMD rocSPARSE for SpMV and SpMM, and up to 1.48× speedup against the state-of-the-art open-source algo

关键词： Graphics processing unit

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Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

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computers, Materials & Continua 2023年第3期74卷 5507-5525页

作者： Subhajit Chatterjee Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-si63243Korea Department of Computer Engineering Major of Electronic EngineeringJeju National UniversityInstitute of Information Science&TechnologyJeju63243Korea

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education-centric *** the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is *** to its freefloating nature,the shared electric kickboard is a common and practical means of *** plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is *** demand accurately with small data is *** data is necessary for training machine learning algorithms for effective *** generation is a method for expanding the amount of data that will be further accessible for *** this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original *** proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction *** modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster ***,we applied a regression-based blending ensemble technique that can help us to improve performance of demand *** used various evaluation criteria and visual representations to compare our proposed model’s *** data generated by our suggested GAN model is also *** TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.

关键词： Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Synthesis of Wideband Bandpass Filter With In-Band Complex Transmission Zeros for Frequency- and Attenuation-Reconfigurable In-Band Notch

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IEEE Transactions on Microwave Theory and Techniques 2024年第2期72卷 1162-1175页

作者： Liu, Bin Li, Kun Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic function with wideband bandpass response is constructed by merging two BPFs with relatively narrow bandwidths. With the newly constructed characteristic function, complex transmission zeros will be introduced in the filter passband, facilitating the generation and control of the in-band notch. Moreover, quasi-Chebyshev filtering response (with quasi-equal ripple level) is also guaranteed in the whole passband. Based on the new characteristic function, a series of filter topologies can be derived using matrix similarity transformations. By only tuning the resonant frequencies of particular resonators, these topologies can exhibit wideband bandpass responses with frequency- and attenuation-reconfigurable in-band notch. To demonstrate the proposed techniques, one prototype of the wideband BPF using waveguide resonators is designed, simulated, and fabricated. The fabricated wideband BPF exhibits a passband frequency from 8.12 to 11.76 GHz with one transmission zero at 13.36 GHz and a reconfigurable in-band notch with frequency tuning from 9.3 to 10.4 GHz and attenuation tuning from 2 to 60 dB. The simulated and measured results are in good agreement, validating the proposed technique. © 1963-2012 IEEE.

关键词： Topology

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Towards LLM-Generated Affective Phrase Sets for Text Entry Evaluation 24

Towards LLM-Generated Affective Phrase Sets for Text Entry E...

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23rd International Conference on Mobile and Ubiquitous Multimedia, MUM 2024

作者： Komninos, Andreas Simou, Ioulia Fotopoulos, Angelos Michanetzi, Eleftheria Lito Xenos, Michalis Computer Engineering & Informatics Department University of Patras Rio Greece

ISBN: (纸本)9798400712838

We present a novel method to generate affective phrase sets using Large Language Models (LLMs), for use in text entry study transcription tasks. These phrases could be used to adjust participant emotions, and enable performance assessment under various affective conditions. We found that the affective phrases impacted participants' valence, but not arousal or text entry performance. Though partially succesful, our work provides a foundation for exploring the relationship between emotion and text entry performance in lab studies. © 2024 Copyright held by the owner/author(s).

关键词： Text Entry

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Decentralized Federated Deep Reinforcement Learning Framework for Energy-Efficient Base Station Switching Control 11

Decentralized Federated Deep Reinforcement Learning Framewor...

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11th International Symposium on Telecommunication, IST 2024

作者： Movahedkor, Narges Shahbazian, Reza Department of Computer Engineering Tehran Iran Department of Informatics Rende Italy

ISBN: (纸本)9798350356250

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service (QoS) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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