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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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Frequency Response and Design Based on gm/IDof Amplifier in CNFET Technology 6

Frequency Response and Design Based on gm/IDof Amplifier in ...

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6th Iranian International Conference on Microelectronics, IICM 2024

作者： Zanjani, S. Mohammadali Dolatshahi, Mehdi Dousti, Massoud Alaie, Zahra Jahangir Moshayedi, Ata Mehrabi, Arash Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran Science and Research Branch Islamic Azad University Department of Electrical and Computer Engineering Tehran Iran School of Information Engineering Jiangxi University of Science and Technology Jiangxi341000 China

ISBN: (纸本)9798331511371

The downscaling of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) elements in the nanoscale, leads to the deviation of V-I characteristics compared with the ideal behavior. For instance, in the elements with short channels, Drain voltage considerably impacts the potential barrier in a way that the Source injects the carriers into the channel without any Gate role. This is due to the decrease in the height of the barrier by the Drain. An appropriate alternative for the MOS technology is the Carbon Nanotube Field Effect Transistors (CNFETs). The high mobility of carriers in the CNT results in their higher speed in CNFET transistors. Low power dissipation, nano dimension, low noise figure, the similarity of performance with the MOSFETs, and the ballistic transition of the carriers are their advantages. In this paper, together with the study of CNFET's structure and models, the impact of variations of input voltage, the number of nanotubes, pitches, and chirality on the current, threshold voltage, and V-I characteristics of a common source amplifier are studied. Moreover, it is observed that in the same specifications of the aspect ratio and bias, in the CNFET transistors, the gm/ID figure according to the normalized current in the Weak Inversion and Moderate regions is higher than the MOSFETs, shows that this technology as the fair choice can be an appropriate alternative for the MOSFETs. © 2024 IEEE.

关键词： Chirality

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Identifying Source Speakers for Voice Conversion Based Spoofing Attacks on Speaker Verification Systems 48

Identifying Source Speakers for Voice Conversion Based Spoof...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Danwei Cai, Zexin Li, Ming Duke University Department of Electrical and Computer Engineering Durham United States Duke Kunshan University Data Science Research Center Kunshan China

ISBN: (纸本)9781728163277

An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to make it sound like another speaker's voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification - inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models. © 2023 IEEE.

关键词： Speech recognition

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FIN: A Deep Multi-task Model for Face Information Detection

FIN: A Deep Multi-task Model for Face Information Detection

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Lai, Yi-Shan Chen, Yi Fu, Li-Chen Hsiao, Pei-Yung Wang, Yu-Chieh National Taiwan University Department of Computer Science and Information Engineering Taiwan National University of Kaohsiung Department of Electrical Engineering Taiwan Automotive Research and Testing Center Taiwan Taiwan

ISBN: (纸本)9798350382204

In recent years, with the development of autonomous driving technology, designing a driver monitoring system that can run on vehicles has become increasingly important. The driving system analyzes various factors such as head pose, gaze direction, and eye state to determine the driver's attentiveness and aid the system in evaluating the driver's capability to control the vehicle. In this paper, we present a lightweight multi-task deep learning model that utilizes full-face images to detect head pose, gaze direction, and eye state simultaneously. We propose a task-based cross-dimensional attention module (TCAM) that selectively filters and enhances relevant features for each specific task. Additionally, to tackle the lack of diverse head pose angles in the eye state dataset, we introduce a data augmentation technique to generate eye state data under different head poses, enhancing the model's robustness in detecting eye states under varying head poses. We demonstrate the results of our work on the 300WLP, AFLW2000, BIWI, Gaze360, and MRL datasets. Extensive experiments show that our method can obtain competitive results with lower parameter costs. Furthermore, we establish a baseline for eye state detection using full-face images as the input on the CEW datasets. © 2023 IEEE.

关键词： Autonomous vehicles

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 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： Time domain analysis

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

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Augmented Reality in Higher Education: Literature Review 25

Augmented Reality in Higher Education: Literature Review

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Zamahsari, Gamal Kusuma Adi, Puput Dani Prasetyo Arifuddin, Rahman Dirgantara, Wahyu Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Communication Science Department Jakarta11480 Indonesia Research Center for Telecommunication Bandung Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350378573

Augmented Reality (AR) is rapidly transforming educational landscapes, offering an immersive and interactive learning experience. This literature mapping review explores the current landscape of AR in higher education research. By systematically analyzing peer-reviewed publications in the last five years from 2019 to 2023, the review identifies key trends, application areas, author collaboration patterns, and research foci, were extracted, analyzed, and visualized using R and VOSViewer. The analysis reveals a growing body of research, with a surge in publications after 2020. science, Technology, engineering, and Mathematics (STEM) subjects dominate AR applications, highlighting its potential to visualize abstract concepts. The review further investigates the impact of AR on student outcomes, including engagement and learning processes. It also examines the geographical distribution of research and identifies knowledge gaps for future exploration. Overall, this review provides a comprehensive map of AR in higher education research, laying the groundwork for further investigation into its pedagogical effectiveness and broader implementation across disciplines. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water Harvesting 21

Prediction of Adsorption and Desorption Isotherms for Atmosp...

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21st International Learning and Technology Conference, L and T 2024

作者： Kabbaj, Narjisse El-Amin, Mohamed F. Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Nat. Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model's superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies. © 2024 IEEE.

关键词： Water conservation

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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