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Joint SSK Assisted by Mirror Array Grouping and LEDs in Indoor VLC System

IEEE Transactions on Vehicular technology 2025年

作者： Sun, Yicong Wang, Fasong Li, Rui Shi, Shijie Li, Yitong Li, Xingwang Xiang, Wei Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan University of Technology School of Sciences Zhengzhou450001 China Henan Polytechnic University School of Physics and Electronics Information Engineering Jiaozuo454003 China La Trobe University School of Computing Engineering and Mathematical Sciences BundooraVIC3086 Australia

In the mirror array-assisted indoor visible light communication (VLC) system, activating all mirrors simultaneously can result in a complex system design and excessive consumption of resources. To address this issue and enhance the spectral efficiency, this paper proposes a joint space shift keying (SSK) technique, which is achieved by jointly utilizing the mirror array grouping and LED activation procedures in indoor VLC systems. By doing so, the SSK scheme utilizes the activation status of mirror array blocks to transmit information and can avoid excessive utilization of mirror resources while achieving efficient and reliable communication. The fairness of the considered system is improved by introducing a mirror array grouping technique that relies on the channel gains. Concurrently, a novel joint SSK modulation scheme is proposed, whereby the mirror array block is activated in conjunction with the LED. This scheme employs a information bit-based activation technique to enhance spectral efficiency. Additionally, the low complexity orthogonal matching pursuit (OMP) detection and the optimal maximum likelihood (ML) detection algorithms are employed. The effectiveness of the proposed schemes has been confirmed through extensive simulations. © 1967-2012 IEEE.

关键词： Mirrors

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Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Deng, Zehang Sun, Ruoxi Xue, Minhui Wen, Sheng Camtepe, Seyit Nepal, Surya Xiang, Yang Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC Australia CSIRO's Data61 Cybersecurity and Quantum Systems Group Australia

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6× to over 100× more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead. © 2004-2012 IEEE.

关键词： Federated learning

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Enhancing Disaster Response: A Dual-Drone Approach for Efficient Area Scanning and Life Detection 7th

Enhancing Disaster Response: A Dual-Drone Approach for Effic...

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7th International Conference on Soft computing and Signal Processing, ICSCSP 2024

作者： Mummoorthy, A. Balamurugan, P. Patibandla, Anitha Department of Information Technology Malla Reddy College of Engineering and Technology Hyderabad India School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of ECE Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819799251

The paper proposes a new methodology for utilizing drones and UAVs during natural disasters such as floods and earthquakes. Natural disasters represent some of the most devastating events imaginable, underscoring the critical importance of preparedness to swiftly aid affected individuals. While natural disasters occur with varying frequency across the globe, their occurrence can severely impede human communication and mobility within affected regions. Providing assistance and procuring necessary equipment become significantly challenging, compounded by the constrained time available for response efforts. The initial 72 h following a disaster is pivotal for rescue operations. Developing an optimal rescue plan from ground-level assessments proves impractical. This is where drones and Unmanned Aerial Vehicles (UAVs) come into play. With advancements in drone technology, it becomes imperative to devise intelligent strategies for their deployment during natural disasters. This paper endeavors to propose an intuitive methodology aimed at analyzing affected areas, providing optimized routes, and predicting safe zones through modern machine learning algorithms. The proposed methodology seeks to expedite the analysis of current conditions and enhance the efficiency and safety of rescue operations. Central to this project is the establishment of reliable communication between the two drones and the server, along with the development of efficient task allocation strategies for each drone. Additionally, the methodology includes an interactive user interface housing all the information communicated and computed by the system. In conclusion, the proposed methodology aims to streamline rescue operations during natural disasters by harnessing the capabilities of drones and UAVs. Through improved efficiency and informed decision-making, this approach endeavors to enhance the effectiveness of disaster response efforts, ultimately contributing to saving lives and mitigating the impact

关键词： Drones

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Gated Fusion and Dual Attention Network for Road Extraction from Satellite Imagery 8

Gated Fusion and Dual Attention Network for Road Extraction ...

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8th International Conference on Image, Vision and computing, ICIVC 2023

作者： Wang, Haijuan Bai, Lin Ji, Bo Shen, Jiaming Jin, Ying Ye, Fei Zhu, Chunlin School of Electronics and Control Engineering Chang'An University Xi'an China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd. Hangzhou China Xi'An Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9798350335231

Automatic recognition and extraction of roads from high-resolution satellite images is a crucial task in remote sensing and computer vision. With the continuous development of remote sensing technology, more ground object information is contained in images, making it more challenging to extract roads due to increased interference. This paper proposes a gated fusion and dual attention network with an encoder-decoder structure to address this problem. The full-stage gated fusion module in skip connection selectively fuses feature maps of different scales using the Gated Fusion (GFF) unit, which increases the receptive field of the network and improves the accuracy of road extraction. The context extraction and dual attention module introduce rich global information and simultaneously weights features from both spatial and channel dimensions. This improves the semantic segmentation problem caused by focusing only on local information in current road extraction models. Experimental results on two public datasets show that GFDANet can effectively extract roads in complex scenes. © 2023 IEEE.

关键词： Roads and streets

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Machine Learning Model for Predicting Community Integrity Towards Solid Waste Management and Environmental Care 5

Machine Learning Model for Predicting Community Integrity To...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Zulkipli, Faridah Jamian, Nurul Husna Masrom, Suraya Khalid, Khairul Irwan Nopiah, Zulkifli Mohd Kerdprasop, Nittaya College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah CampusTapah Road Malaysia Block J MKN Embassy Techzone Jalan Teknokrat 2 Cyberjaya Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Selangor Malaysia School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9798331528553

Solid waste management and environmental conservation are global concerns that play a crucial role in both developed and developing countries. This worldwide issue makes a substantial contribution to the achievement of sustainable development goals and, as a result, helps to create a brighter future for a sustainable environment. The primary objective of this project is to create a machine learning model that can accurately assess and monitor the level of community integrity in relation to the management of solid waste and environmental conservation. Community integrity was used as a dependent variable (predictor variable). The independent factors considered in this study include gender, cumulative grade point average (CGPA), residence status, awareness, practice, attitude, perception, and knowledge. Five machine learning algorithms were utilized to determine the optimal prediction model for assessing community integrity in solid waste management and environmental care. The models used were Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Model. The Generalized Linear Model machine learning algorithm was identified as the most appropriate model, based on its ability to predict community integrity in solid waste management and environmental care, as indicated by its lowest Root Mean Square Error (RMSE). © 2024 IEEE.

关键词： Adversarial machine learning

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Valorisation of macroalgae for biofuels in Indonesia: an integrated biorefinery approach

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Environmental technology Reviews 2024年第1期13卷 269-304页

作者： Suhartini, Sri Pangestuti, Martasari Beti Elviliana Rohma, Novita Ainur Junaidi, Mochammad Agus Paul, Roshni Nurika, Irnia Rahmah, Nur Lailatul Melville, Lynsey Department of Agro-industrial Technology Faculty of Agricultural Technology Universitas Brawijaya Malang Indonesia Research Group Universitas Brawijaya Malang Indonesia Centre of Excellence in Bioenergy and Biorefinery Faculty of Agricultural Technology Universitas Brawijaya Malang Indonesia INRAE OPAALE Rennes France Department of Agricultural Economic Faculty of Agriculture Universitas Brawijaya Malang Indonesia SPJAIN Global School of Management Dubai United Arab Emirates Bioresource and Bioeconomy Research Group Faculty of Computing Engineering and Built Environment Birmingham City University Birmingham United Kingdom

Globally, bioenergy development depends on the efficacy and affordability of conversion technologies and the availability of renewable biomass sources. As a tropical country, Indonesia has a wide range of biomass sources, either from land or marine biomass (i.e. macroalgae). The current estimation of macroalgae potential in Indonesia is estimated at approximately 9.96 million tonnes or about 26.86% share of world production in 2021. Specifically, marine macroalgae (wild or cultivated) have received attention for their potential as renewable resources for the sustainable bioenergy production, supporting a move towards a circular economy. However, as a developing country, Indonesia still needs to evolve and further advances its technology and skill capacity to address research, development, and innovation challenges in this area. Thus, this paper examines the potential biorefinery approach for application and commercialisation in Indonesia. It discusses cultivation practices and the future direction of the most sustainable and feasible routes for bioenergy production from macroalgae, exploring recent developments, opportunities, and challenges towards circular processes. The study proposed that the biorefining of macroalgae into bioethanol, biogas, compost, and solid fuels, either as mono–or co-production, are potential. Therefore, this paper may offer to narrowing the literature’s gap and adding a new perspective on the adoption of macroalgae-based bioenergy with integrated biorefinery and closed-loop systems approaches. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Supply chains

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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Learning manipulation of steep granular slopes for fast Mini Rover turning

Learning manipulation of steep granular slopes for fast Mini...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Deniz Kerimoglu Daniel Soto Malone Lincoln Hemsley Joseph Brunner Sehoon Ha Tingnan Zhang Daniel I. Goldman School of Physics Georgia Institute of Technology Atlanta USA Department of Applied Physics and Engineering Morehouse College Atlanta USA School of Interactive Computing Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Future planetary exploration missions will require reaching challenging regions such as craters and steep slopes. Such regions are ubiquitous and present science-rich targets potentially containing information regarding the planet’s internal structure. Steep slopes consisting of low-cohesion regolith are prone to flow downward under small disturbances, making it challenging for autonomous rovers to traverse. Moreover, the navigation trajectories of rovers are heavily limited by the terrain topology and future systems will need to maneuver on flowable surfaces without getting trapped, allowing them to further expand their reach and increase mission *** this work, we used a robophysical rover model and performed maneuvering experiments on a steep granular slope of poppy seeds to explore the rover’s turning capabilities. The rover is capable of lifting, sweeping, and spinning its wheels, allowing it to execute leg-like gait patterns. The high-dimensional actuation capabilities of the rover facilitate effective manipulation of the underlying granular surface. We used Bayesian Optimization (BO) to gain insight into successful turning gaits in high dimensional search space and found strategies such as differential wheel spinning and pivoting around a single sweeping wheel. We then used these insights to further fine-tune the turning gait, enabling the rover to turn nearly 90 degrees at just above 4 seconds with minimal downhill slip. Combining gait optimization and human-tuning approaches, we found that fast turning is empowered by creating anisotropic torques with the sweeping wheel.

关键词： Systematics Navigation Wheels Turning Bayes methods Trajectory Topology

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2023 7th International Conference on Communication and Information Systems An Improved IOTA Protocol Based on Optimal Midway Walk 7

2023 7th International Conference on Communication and Infor...

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7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Wu, Hongcheng Zhi, Hui Liu, Yangyang Anhui University School of Electronic and Information Engineering Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei230601 China Hefei Zhilian Technology Industry Investment Co. Ltd Hefei230601 China

ISBN: (纸本)9798350330540

IOTA (Internet of Things Applications) protocol aims to provide a secure and trustworthy protocol for realizing data-value interactions between IoT devices, and it has become the next possible direction for the development of Blockchain applications. IOTA protocol has the problem that the time-consuming cumulative weight update grows too fast with the increase of the number of transactions, this paper proposed an improved IOTA protocol based on optimal midway walk, the improved protocol uses cutting-edge transaction selection algorithm of weighted Monte Carlo Markov random walk to reduce cumulative weight update delay. Firstly, this improved IOTA protocol mainly ensures the efficiency of transaction confirmation without losing the security of consensus by introducing the optimal midway walk method, i.e., the starting point of each walk is chosen to depart at an optimal location. Secondly, this paper designs the indirect verification identity record algorithm and the layer degree of confirmation principle are designed to realize the improved IOTA protocol's resilience and resistance to Parasite chain attack. The experimental results show that the improved IOTA using random function to select the midway walk starting point from i-50λ, to i-75λsubstantially solves the system computational burden problem and greatly reduces the cumulative weight update delay. © 2023 IEEE.

关键词： cumulative weight IOTA midway walk Parasite chain attack

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