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Channel-Based Key Generation for Secure Underwater Acoustic Communications

IEEE Transactions on Wireless Communications 2025年

作者： Diamant, Roee Casari, Paolo Ardizzon, Francesco Tomasin, Stefano Sherlock, Benjamin Corner, Tom Neasham, Jeff University of Haifa Department of Marine Technologies Israel University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Trento CNIT DISI Italy University of Padova CNIT UNIPD Italy School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom

To protect underwater acoustic communications from interception, the exchange of encryption keys is necessary. Since underwater devices can be compromised, generating keys on site is a better option than predefined keys. In this paper, we present a solution that utilizes the characteristics of the underwater acoustic channel impulse response (CIR), which is highly variable in both space and time, while ensuring consistency between the communicating nodes (Alice and Bob). To compensate for temporal variations, the key is calculated from the CIR feature's distribution parameters, while the hard key is determined using a K-means strategy. To achieve key agreement, we exploit the long propagation delay in the underwater CIR and let Alice and Bob transmit simultaneously while their packets fly past each other. Due to the channel's reciprocity, this simultaneous transmission ensures that the same CIR is estimated at both ends of the communication link. Simulation and sea experiment results show that it is possible to extract at least three times as many secret bits as we would with a uniform quantizer. The results show a high matching rate between Alice and Bob and a high Hamming distance to Eve's key. For reproducibility, we share the CIRs from the sea trials. © 2002-2012 IEEE.

关键词： Underwater acoustics

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Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

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Mobile Network Traffic Prediction Using Temporal Fusion Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Kougioumtzidis, Georgios Poulkov, Vladimir K. Lazaridis, Pavlos I. Zaharis, Zaharias D. Technical University of Sofia Faculty of Telecommunications Sofia1000 Bulgaria University of Huddersfield School of Computing and Engineering HuddersfieldHD1 3DH United Kingdom Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki54124 Greece

The continuous development of mobile communication technologies has led to a rapid increase in cellular network traffic. Therefore, traffic prediction models have become very important for the design of mobile communication networks, as they are essential for increasing the quality of service (QoS) and ensuring a high level of quality of experience (QoE). Accurate and timely prediction of network traffic volume enables efficient planning of radio resource allocation, improves network energy efficiency, and reduces network congestion and operational costs. However, the task of mobile network traffic prediction is inherently challenging due to the dynamic, multivariate nature of traffic patterns that are influenced by diverse factors such as location, user behavior, and temporal variations. In this paper, we propose a novel prediction model based on deep learning techniques. Specifically, we develop a customized temporal fusion transformer (TFT) for accurate time series prediction that effectively captures the complex dependencies in mobile network traffic and ensures resilience to unexpected variations, which is critical for efficient network management and QoE enhancement. The prediction model is evaluated and tested against state-of-the-art prediction models using real-world cellular network data as the training dataset. The experimental results validate the excellence of this customized transformer architecture in capturing the complex temporal dynamics of cellular network traffic by exploiting attention-based mechanisms. © 2020 IEEE.

关键词： Prediction models

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SUDCOPA: WSN-based IoT Energy Optimization and Load Balancing through a Context-Sensitive Minimum Clustering Radius 24

SUDCOPA: WSN-based IoT Energy Optimization and Load Balancin...

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Proceedings of the 2024 13th International Conference on Software and Information engineering

作者： Foudil Mir Ahcene Bounceur Farid Meziane Medical Computing Laboratory (LIMED) Faculty of Exact Sciences University of Bejaia Bejaia Algeria College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates College of Science and Engineering Data Science Research Centre University of Derby Derby United Kingdom

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer Science, engineering, and Decision Sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural Science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow

IEEE Design and Test

引用

IEEE Design and Test 2025年

作者： Wiese, Philip Islamoglu, Gamze Scherer, Moritz Macan, Luka Jung, Victor J.B. Burrello, Alessio Conti, Francesco Benini, Luca ETH Zürich Switzerland Boǧaziçi University Turkey Faculty of Electrical Engineering and Computing University of Zagreb Croatia Juniata College United States Politecnico of Turin Italy University of Bologna Italy

One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address this by leveraging a heterogeneous architectural template coupling RISC-V processors with hardwired accelerators supported by an automated deployment flow. We demonstrate Attention-based models in a tinyML power envelope with an octacore cluster coupled with an accelerator for quantized Attention. Our deployment flow enables end-to-end 8-bit Transformer inference, achieving leading-edge energy efficiency and throughput of 2960 GOp/J and 154GOp/s (0.65 V, 22nm FD-SOI technology). © 2013 IEEE.

关键词： Convolutional neural networks

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DPS: Design Pattern Summarisation Using Code Features

arXiv

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arXiv 2025年

作者： Nazar, Najam Sikka, Sameer Treude, Christoph School of Computer and Mathematical Sciences The University of Adelaide Australia School of IT and Engineering Ada University Azerbaijan Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques—and more recently, large language models—have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored. Our approach utilises code features and JavaParser to parse the code and create a JSON representation. Using an NLG library on this JSON representation, we convert it into natural language text that acts as a summary of the code, capturing the contextual information of the design pattern. Our empirical results indicate that the summaries generated by our approach capture the context in which patterns are applied in the codebase. Statistical evaluations demonstrate that our summaries closely align with human-written summaries, as evident from high values in the ROUGE-L, BLEU-4, NIST, and FrugalScore metrics. A follow-up survey further shows that DPS summaries were rated as capturing context better than human-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Software design

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Multi-Dimensional Spectrum Prediction Method Based on Effici...

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IEEE Conference on Wireless Communications and Networking

作者： Niancong Ji Shufei Wang Yibin Zhang Yang Peng Tomoaki Otsuki Dusit Niyato Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China Faculty of Science and Technology Keio University Kanagawa Japan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

关键词： Wireless communication Adaptation models Adaptive systems Computational modeling Dynamic spectrum access Training data Manuals Predictive models Data models Robustness

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Practical Quantum Circuit Implementation for Simulating Coupled Classical Oscillators

arXiv

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arXiv 2025年

作者： Luangsirapornchai, Natt Sanglaor, Peeranat Sornsaeng, Apimuk Bressan, Stéphane Chotibut, Thiparat Suksen, Kamonluk Chongstitvatana, Prabhas Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Chula Intelligent and Complex Systems Lab Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore School of Computing National University of Singapore 13 Computing Drive Singapore117417 Singapore

Simulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in the thermodynamic limit. Motivated by the quantum algorithm framework proposed by Babbush et al. [1], we present and implement a detailed quantum circuit construction for simulating one-dimensional spring-mass systems. Our approach incorporates key quantum subroutines, including block encoding, quantum singular value transformation (QSVT), and amplitude amplification, to realize the unitary time-evolution operator associated with simulating classical oscillators dynamics. In the uniform spring-mass setting, our circuit construction requires a gate complexity of (formula presented), where N is the number of oscillators and Ε is the target accuracy of the approximation. For more general, heterogeneous spring-mass systems, the total gate complexity is O(N log2 N log2(1/Ε)). Both settings require O(log2 N) qubits. Numerical simulations agree with classical solvers across all tested configurations, indicating that this circuit-based Hamiltonian simulation approach can substantially reduce computational costs and potentially enable larger-scale many-body studies on future quantum hardware. © 2025, CC0.

关键词： Hamiltonians

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Learning To Explore With Predictive World Model Via Self-Supervised Learning

arXiv

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arXiv 2025年

作者： Santana, Alana Costa, Paula P. Colombini, Esther L. Institute of Computing State University of Campinas Campinas São Paulo Brazil Faculty of Electrical and Computer Engineering State University of Campinas Campinas São Paulo Brazil

Autonomous artificial agents must be able to learn behaviors in complex environments without humans to design tasks and rewards. Designing these functions for each environment is not feasible, thus, motivating the development of intrinsic reward functions. In this paper, we propose using several cognitive elements that have been neglected for a long time to build an internal world model for an intrinsically motivated agent. Our agent performs satisfactory iterations with the environment, learning complex behaviors without needing previously designed reward functions. We used 18 Atari games to evaluate what cognitive skills emerge in games that require reactive and deliberative behaviors. Our results show superior performance compared to the state-of-the-art in many test cases with dense and sparse rewards. © 2025, CC BY.

关键词： Self-supervised learning

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