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Road Surface Analysis through Machine Learning Techniques

IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Performance Analysis of a Novel Relay Selection Scheme for Wireless-Powered Cluster-Based Multi-Hop Cognitive Relay Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1551-1562页

作者： Sun, Hui Naraghi-Pour, Mort Qian, Yuwen Sheng, Weixing Han, Yubing Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Baton RougeLA70803 United States

In this paper, we study the performance of wireless-powered cluster-based multi-hop cognitive relay networks (MCRNs), where secondary nodes harvest energy from multiple dedicated power beacons (PBs) and share the spectrum with multiple primary receivers (PRs) in the underlay paradigm. For this system, we propose a hop-by-hop relay selection scheme called the largest decoding set (LDS). In each stage, relay selection is based on the harvested energy from PBs and the channel state information (CSI) of both the interference links to PRs and the relaying links in the subsequent hop. Considering both harvested energy and maximum interference constraints, we derive the exact end-to-end outage probability and show that the results closely match those obtained from simulations. Moreover, the asymptotic end-to-end outage probabilities in two different scenarios are derived to provide more valuable insights. Numerical results show that the outage probability of the proposed LDS scheme is very close to that of the best-path scheduling (BPS) scheme, which provides a lower bound on outage probability but requires global CSI before the secondary source transmits. We also compare the LDS scheme with other relay selection schemes that have appeared recently in the literature and show that LDS has the best outage performance. © 2024 IEEE.

关键词： Cognitive radio

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Realistic Facial Expression Reconstruction Using Millimeter Wave

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IEEE Transactions on Mobile Computing 2025年第07期24卷 5964-5980页

作者： Kong, Hao Xie, Jiahong Yu, Jiadi Chen, Yingying Kong, Linghe Zhu, Yanmin Tang, Feilong Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Rutgers University WINLAB and Department of Electrical and Computer Engineering New BrunswickNJ08854 United States

The technology of facial expression reconstruction has paved the way for various face-centric applications such as virtual reality (VR) modeling, human-computer interaction, and affective computing. Existing vision-based solutions present challenges in privacy leakage and poor lighting conditions. In this paper, we introduce a nonintrusive facial expression reconstruction system, mm3DFace, which uses a millimeter wave (mmWave) radar to reconstruct facial expressions in a privacy-preserving and passive manner. mm3DFace first captures and pre-processes mmWave signals reflected by a human face, and extracts intricate facial geometric features using a ConvNeXt model integrated with triple loss embedding. Subsequently, mm3DFace derives pose-invariant facial representations utilizing region-divided affine transformation, and further generates individual facial shapes with 68 facial landmarks. Then, dynamic facial expressions with 3D facial avatars are reconstructed to exhibit realistic facial expressions. Finally, mm3DFace enables micro-expression recognition with mmWave signals, which ensures the capability of describing tiny facial changes. Through extensive real-world experiments involving 15 participants, mm3DFace achieves a normalized mean error of 3.94%, a mean absolute error of 2.30mm, and a 3D-mean absolute error of 4.10mm in tracking 68 facial landmarks, which demonstrates the efficacy and practicality of mm3DFace in real-world 3D facial reconstruction scenarios. © 2002-2012 IEEE.

关键词： Affine transforms

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Ill-condition enhancement for BC speech using RMC method

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International Journal of Speech Technology 2024年第4期27卷 1085-1092页

作者： Ohidujjaman Hasan, Mahmudul Zhang, Shiming Huda, Mohammad Nurul Uddin, Mohammad Shorif Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh Computer Science and Engineering Comilla University Comilla3506 Bangladesh School of Electrical and Information Northeast Agricultural University Harbin150030 China Computer Science and Engineering United International University Dhaka1212 Bangladesh Computer Science and Engineering Green University of Bangladesh Kanchon1460 Bangladesh Computer Science and Engineering Jahangirnagar University Savar1342 Bangladesh

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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Detecting Inaudible Voice Commands via Acoustic Attenuation by Multi-channel Microphones

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IEEE Transactions on Dependable and Secure Computing 2024年 1-17页

作者： Ji, Xiaoyu Zhang, Guoming Li, Xinfeng Qu, Gang Cheng, Xiuzhen Xu, Wenyuan Electrical Engineering of Zhejiang University Hangzhou China School of Computer Science and Technology of Shandong University Qingdao China Department of Electrical and Computer Engineering at the University of Maryland College Park MD USA

DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). Eliminating DolphinAttacks is challenging if ever possible since it requires to modify the microphone hardware. In this paper, we design EarArray, a lightweight method that can not only detect such attacks but also identify the direction of attackers without requiring any extra hardware or hardware modification. Essentially, inaudible voice commands are modulated on ultrasounds that inherently attenuate faster than the one of audible sounds. By inspecting the command sound signals via the built-in multiple microphones on smart devices, EarArray is able to estimate the attenuation rate and thus detect the attacks. We propose a model of the propagation of audible sounds and ultrasounds from the sound source to a voice assistant, e.g., a smart speaker, and illustrate the underlying principle and its feasibility. We implemented EarArray using two specially-designed microphone arrays and our experiments show that EarArray can detect inaudible voice commands with an accuracy of above 99% and recognize the direction of the attackers with an accuracy of 97.89% and can also detect the laser-based attack with an accuracy of 100%. IEEE

关键词： Microphones

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Super-resolution reconstruction of off-axis digital holography based on Real-ESRGAN

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Applied Optics 2025年第9期64卷 2245-2254页

作者： Chen, Geng Chen, Jie Liao, Houzhang Liu, Keke Tang, Xin Kong, Yong School of Electric and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Off-axis digital holography plays a crucial role in high-precision three-dimensional imaging. However, high-resolution phase images are often affected by the limited pixel size of the sensor. To address this issue, this study proposes, for the first time, to the best of our knowledge, the application of Real-ESRGAN for the super-resolution reconstruction of off-axis digital holography images. By directly applying super-resolution processing to the hologram, the limitations of the sensor's pixel size are overcome, followed by phase reconstruction to obtain high-resolution phase images. The Real-ESRGAN model enhances the texture information of the hologram through a deep network with multiple residual-in-residual dense blocks (RRDBs), restoring a clearer hologram. A U-Net architecture with spectral normalization, offering stronger discriminative capability, is used to provide detailed per-pixel feedback for the generator. The experimental results demonstrate that this method can be applied to multi-scale holograms, outperforming comparison networks in both visual quality and quantitative metrics, thus providing an innovative solution for the super-resolution reconstruction of off-axis digital holography. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Holograms

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