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6th Australian Microwave Symposium, AMS 2025

作者： Raza Naqvi, Syed Akbar Abbosh, Amin The University of Queensland School of Electrical Engineering and Computer Science Queensland Australia

ISBN: (纸本)9798350379969

This paper presents the current challenges of expanding our handheld skin cancer detection system towards large-scale application on different skin regions across the human body. The analysis is based on temporal dielectric trend assessment of common moles and adjacent skin regions. The study indicates that developing a robust skin cancer detection system requires consideration of additional factors, such as hair strand interference compensation and dermal hydration levels in parallel with dielectric data acquisition. These biomechanical descriptor variations, including trans-epidermal water loss, contribute to fluctuations in the dielectric properties of the same skin region measured at different times. © 2025 IEEE.

关键词： Lung cancer

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Poster: Platooning Revisited: What is the Personal Benefit Compared to ACC and Human Driving? 15

Poster: Platooning Revisited: What is the Personal Benefit C...

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15th IEEE Vehicular Networking Conference, VNC 2024

作者： Heinovski, Julian Ergenc, Doganalp Dressler, Falko School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350362701

Platooning has been researched for decades but debate about its lasting impact is still ongoing. Meanwhile, adaptive cruise control (ACC) became de facto standard for all new cars as well as for automated driving on the freeway. An evaluation of the personal benefit these systems offer remains difficult. To this end, we propose to assess driving systems by looking at the overall trip cost to incentivize drivers accordingly. For this, we define a novel metric to quantify the total trip cost, combining fuel cost and travel time within a monetary unit. We show the application of our new metric in a case study, comparing human driving, ACC, and platooning. Our results indicate that human driving always loses against ACC and that platooning has a significant advantage in mid to high traffic densities. © 2024 IEEE.

关键词： Adaptive cruise control

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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Content-Aware Entity Alignment: Utilizing Structural and Semantic Similarities for Enhanced Inter-Knowledge Graph Integration 4

Content-Aware Entity Alignment: Utilizing Structural and Sem...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehboob, Faiqa Satti, Fahad Ahmed Ali, Syed Imran Fraz, Muhammad Moazam NUST School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

Entity alignment in knowledge graphs is critical for maximizing the utility of interconnected data, especially in domains where data interoperability is essential. Entity alignment is the process of identifying and matching equivalent entities across different knowledge graphs. Traditional entity alignment methods often treat nodes as monolithic entities, which can lead to issues such as loss of granularity, semantic heterogeneity, and inadequate semantic understanding. These challenges reduce the effectiveness of the alignment process. Our research addresses these limitations by recognizing the heterogeneous nature of node information. We propose a novel approach to entity alignment that integrates structural similarities between nodes with their semantic and content information. This methodology aims to improve precision and context awareness in entity alignment, with potential applications in various fields, including healthcare, where efficient data exchange and collaborative research are crucial. © 2024 IEEE.

关键词： Knowledge graph

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Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6601-6616页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Serpedin, Erchin Florida State University Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States Lebanese American University Department of Computer Science and Mathematics Beirut1102-2801 Lebanon Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR compared to a random selection;2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%-13% higher degradation in DR compared to traditional FDIAs;and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5-53% compared to benchmark detectors against FDIEAs. © 2020 IEEE.

关键词： Cyber attacks

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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai980-8577 Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 1967-2012 IEEE.

关键词： Convex optimization

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A New Multi Chaos-Based Compression Sensing Image Encryption

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computers, Materials & Continua 2023年第7期76卷 437-453页

作者： Fadia Ali Khan Jameel Ahmed Suliman A.Alsuhibany Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia

The advancements in technology have substantially grown the size of image *** image encryption algorithms have limited capabilities to deal with the emerging challenges in big data,including compression and noise *** image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this *** proposed scheme is built on the intriguing characteristics of an orthogonal *** Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic *** the diffusion process,a block-wise random permutation of the data is performed using *** proposed scheme provides sufficient security and resilience to JPEG compression and channel noise through a series of experiments and security *** enables Partial Encryption(PE)for faster processing as well as complete encryption for increased *** higher values of the number of pixels change rates and unified average change intensity confirm the security of the encryption *** contrast to other schemes,the proposed approach can perform full and partial encryption depending on security requirements.

关键词： Chaos compression image encryption

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Efficient Algorithms for Attributed Graph Alignment With Vanishing Edge Correlation

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IEEE Transactions on Information Theory 2025年第6期71卷 4556-4580页

作者： Wang, Ziao Wang, Weina Wang, Lele The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada University of Michigan Department of Electrical and Computer Engineering Ann ArborMI48109 United States Carnegie Mellon University Computer Science Department PittsburghPA15213 United States

Graph alignment refers to the task of finding the vertex correspondence between two correlated graphs of n vertices. Extensive study has been done on polynomial time algorithms for the graph alignment problem under the Erdős–Rényi graph pair model, where the two graphs are Erdős–Rényi graphs with edge probability qu , correlated under certain vertex correspondence. To achieve exact recovery of the correspondence, all existing algorithms at least require the edge correlation coefficient ρu between the two graphs to be non-vanishing as n → ∞. Moreover, it is conjectured that no polynomial-time algorithm can achieve exact recovery under vanishing edge correlation ρu u ≥ n−Θ(1). We identify a local tree structure, which incorporates one layer of user information and one layer of attribute information, and apply the subgraph counting technique to such structures. A polynomial-time algorithm is proposed that recovers the vertex correspondence for most of the vertices, and then refines the output to achieve exact recovery. The consideration of attribute information is motivated by real-world applications like LinkedIn and Twitter, where user attributes like birthplace and education background can aid alignment. © 1963-2012 IEEE.

关键词： Graph algorithms

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ResCTC: Resilience in Wireless Networks through Cross-Technology Communication 35

ResCTC: Resilience in Wireless Networks through Cross-Techno...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Zubow, Anatolij Stebut, Isabel Von Rosler, Sascha Dressler, Falko Tu Berlin School of Electrical Engineering and Computer Science Germany

ISBN: (纸本)9798350362244

Resilient wireless networks ensure that the network maintains functionality despite adverse conditions like hardware and software failures, adversarial jamming attacks, or environmental changes like wireless channel fading which can disrupt connectivity. We show that Cross-Technology Communication (CTC), which was developed to enable direct communication between otherwise incompatible radio technologies, can be utilized to improve the resilience in wireless networks. This is achieved by increasing the number of independent wireless links between adjacent wireless nodes as well as by the increase in communication distance resulting in increased path diversity. Results from our analysis reveal that the same level of resilience can be achieved with reduced number of radio interfaces per node while tolerating an order of magnitude higher node failure rate. Moreover, such CTC-enabled networks are more resilient to advanced technology-specific jamming attacks. © 2024 IEEE.

关键词： Fading (radio)

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Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning 18

Emergency Vehicle Direction Detection Using Mel-Frequency Ce...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Bhushan, Shourya Chaturvedi, Manish Iitram Department of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350387278

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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