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10th International Conference on Information and Automation for Sustainability, ICIAfS 2021

作者： Thotawaththa, T.A.I. Gamage, Y.T. Gamlath, Damika Chee, Wei Meedeniya, D. University of Moratuwa Department of Computer Science and Engineering 10400. 17 Sri Lanka Sieve SCAPE HubQuarters Level 4 2 Orchard Link Singapore23978 Singapore

ISBN: (纸本)9781665444293

Data privacy deals with the sensitive information of individuals and has become a major topic in modern society. Although the practicability of mobile apps has become an essential part of the routines of many people, consumers are increasingly concerned about their data privacy. Most of the time, these privacy policies that are used to collect and share data are lengthy and complex to understand. Therefore, a common user finds it hard to understand before agreeing to the privacy policy terms. This study aims to propose an approach to convey privacy policies in a way that a common user can comprehend. In this paper, we present 10 categories to classify headers and sections of privacy policies, selected after considering both the users' and domain experts' views, and an automated privacy policy classification model. Bert, SVM, Naive Bayes, and BiLSTM models are used as the baseline classification models. Our best performing model Bert shows an F1-score of 81%. A dataset of 11K headers and sections of privacy policies classified under the 10 categories and high-level architecture of the privacy policy question answering model are also presented here. Thus, the proposed solution makes awareness and gives an insight to mobile app users on their data privacy. © 2021 IEEE.

关键词： Data privacy

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Exploring thermal images for object detection in underexposure regions for autonomous driving

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Applied Soft Computing 2022年第0期121卷

作者： Munir, Farzeen Azam, Shoaib Rafique, Muhammd Aasim Sheri, Ahmad Muqeem Jeon, Moongu Pedrycz, Witold School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of Islamabad Pakistan Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Underexposure regions are vital in constructing a complete perception of the surrounding environment for safe autonomous driving. The availability of thermal cameras has provided an essential alternative to explore regions where other optical sensors lack in capturing interpretable signals. A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions. Although object detection in the visible spectrum domain has matured, thermal object detection lacks effectiveness. A significant challenge is the scarcity of labeled data for the thermal domain, which is essential for SOTA artificial intelligence techniques. This work proposes a domain adaptation framework that employs a style transfer technique for transfer learning from visible spectrum images to thermal images. The framework uses a generative adversarial network (GAN) to transfer the low-level features from the visible spectrum domain to the thermal domain through style consistency. The efficacy of the proposed object detection method in thermal images is evident from the improved results when using styled images from publicly available thermal image datasets (FLIR ADAS and KAIST Multi-Spectral). © 2022 Elsevier B.V.

关键词： Object detection

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Darloc: Deep Learning and Augmentation Based Robust Magnetic Indoor Localization

SSRN

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SSRN 2022年

作者： Wang, Qinghu Jia, Jie Deng, Yansha Chen, Jian Wang, Xingwei Huang, Min Aghvami, Abdol Hamid School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computer Science and Technology Institute of Computer Engineering Inner Mongolia Minzu University Tongliao028000 China Department of Informatics King’s College London LondonWC2R 2LS United Kingdom School of Information Science and Engineering Northeastern University Shenyang110819 China

Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and augmentation-based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial-temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: 1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively;2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Optimal PMU Placement via Quantum Optimization

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Yuqi Liang, Zhiding Li, Yan Morstyn, Thomas Department of Electrical Engineering Pennsylvania State University University ParkPA16802 United States Rensselaer Polytechnic Institute Department of Computer Science TroyNY12180 United States University of Oxford Department of Engineering Science Wellington Square OxfordOX1 2JD United Kingdom

Phasor Measurement Units (PMUs) are essential for real-time monitoring and improving grid observability. Determining the optimal PMU installation is critical, especially as the penetration of renewable energy resources increases, as the Optimal PMU Placement (OPMUP) can guarantee full system observability and reduce the installation cost. However, identifying the optimal PMU installation is a prototypical combinatorial optimization problem, requiring substantial classical computational resources. Furthermore, the significant expansion of the grid in terms of renewable energy integration makes the determination of optimal PMU placement increasingly computationally intensive. In this work, a hybrid quantum-classical approach, Quantum Approximate Optimization Algorithm (QAOA), is developed to effectively solve the optimal PMU installation problem under normal and channel limitation scenarios. A tailored objective function is proposed for quantum optimization, which takes into account both PMU placement cost and system observability constraints. To analyze the observability of QAOA-generated solution distributions, recursion-based Depth-First Search and Breadth-First Search algorithms are proposed for normal and channellimited scenarios. These methods determine solution feasibility on classical computers with O(N + M) complexity, outperforming the O(N2) complexity of inequality-based approaches, where N and M denote the number of buses and branches, respectively. In addition, the proposed quantum optimization framework can significantly reduce the computational complexity from the polynomial or exponential levels required by previous classical methods, i.e., O(RN) on quantum circuits and O(R(M+N)) on classical resources, where R is the repetition times of quantum circuits executions. The proposed method is tested on IEEE 9-, 14-, 24-, and 30-bus systems, where better installation results are achieved compared to the state-of-the-art results, providing a new baseline for

关键词： Qubits

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Optical Anisotropy in van der Waals materials: Impact on Direct Excitation of Plasmons and Photons by Quantum Tunneling

引用

Light(science & Applications) 2021年第12期10卷 2387-2398页

作者： Zhe Wang Vijith Kalathingal Thanh Xuan Hoang Hong-Son Chu Christian A.Nijhuis Department of Chemistry National University of Singapore3 Science Drive 3Singapore 117543Singapore Centre for Advanced 2D Materials National University of Singapore6 Science Drive 2Singapore 117564Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Department of Electronics and Photonics Institute of High Performance ComputingA*STAR(Agency for ScienceTechnology and Research)1 Fusionopolis WaySingapore 138632Singapore Hybrid Materials for Opto-Electronics Group Department of Molecules and MaterialsMESA+Institute for Nanotechnology and Center for Brain-Inspired Nano SystemsFaculty of Science and TechnologyUniversity of Twente7500 AE EnschedeThe Netherlands

Inelastic quantum mechanical tunneling of electrons across plasmonic tunnel junctions can lead to surface plasmon polariton(SPP)and photon *** far,the optical properties of such junctions have been controlled by changing the shape,or the type of the material,of the electrodes,primarily with the aim to improve SPP or photon emission *** we show that by tuning the tunneling barrier itself,the efficiency of the inelastic tunneling rates can be improved by a factor of *** exploit the anisotropic nature of hexagonal boron nitride(hBN)as the tunneling barrier material in Au//hBN//graphene tunnel junctions where the Au electrode also serves as a plasmonic strip *** this junction constitutes an optically transparent hBN–graphene heterostructure on a glass substrate,it forms an open plasmonic system where the SPPs are directly coupled to the dedicated strip waveguide and photons outcouple to the far *** experimentally and analytically show that the photon emission rate per tunneling electron is significantly improved(~×3)in Au//hBN//graphene tunnel junction due to the enhancement in the local density of optical states(LDOS)arising from the hBN *** the dedicated strip waveguide,SPP outcoupling efficiency is quantified and is found to be∼80%stronger than the radiative outcoupling in Au//hBN//graphene due to the high LDOS of the SPP decay channel associated with the inelastic *** new insights elucidated here deepen our understanding of plasmonic tunnel junctions beyond the isotropic models with enhanced LDOS.

关键词： tunneling strip coupling

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A comprehensive evaluation of polygon-based hologram algorithms 14

A comprehensive evaluation of polygon-based hologram algorit...

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Holography, Diffractive Optics, and Applications XIV 2024

作者： Wang, Fan Wang, Pin Zhang, Yaping Blinder, David Gupta, Anuj Poon, Ting-Chung Ito, Tomoyoshi Shimobaba, Tomoyoshi Graduate School of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba Japan Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Kunming Yunnan650500 China Pleinlaan 2 BrusselB-1050 Belgium IMEC Kapeldreef 75 LeuvenB-3001 Belgium CSIR-Central Scientific Instruments Organisation Chandigarh160030 India Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781510682085

Polygon-based computer-generated holography algorithms can achieve high realism at low computational cost. Many recent contributions improve the hologram generation efficiency and solve many issues of 3D realistic rendering. This manuscript provides a comprehensive evaluation of their performance and potential through a detailed classification and comparison. Although the numerical-based algorithms using spectral interpolation offer greater flexibility in achieving realistic 3D displays, further phase-optimization algorithms are needed to render 3D reconstructions with higher definition, reduced noise, and faster updates. © 2024 SPIE. All rights reserved.

关键词： computer generated holography

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Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits 30th

Reconfigurable Virtual Accelerator (ReVA) for Large-Scale A...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Yaguchi, Kazuki Maeda, Eriko Kawai, Shunya Teruya, Daichi Osana, Yasunori Miyoshi, Takefumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, hardware acceleration in large-scale computing fields such as Artificial Intelligence and High Performance Computing, faces hardware resource shortages. To overcome this problem, we propose Reconfigurable Virtual Accelerator (ReVA), which allows a large-scale acceleration circuit built using multiple FPGAs, processors and memory subsystems, to accelerate application programs. We have designed and implemented a prototype of Virtual Accelerator (VA) Generator for ReVA to investigate its performance. The VA Generator prototype employs an open-source HLS automated split compilation tool, RapidStream, to automatically generate placed-and-routed virtual accelerators that can be implemented on multiple FPGAs based on HLS dataflow designs. The VA Generator, which places VAs on the appropriate regions of FPGAs, allows large circuits to be performed like a single accelerator using several FPGAs. In addition, RapidStream’s parallel compilation technology allows the VA Generator to suppress the increasing compilation time according to the circuit size. Moreover, with our VA Generator prototype we have built and evaluated a VA of a large-scale circuit which cannot be fit in a single FPGA with multiple FPGAs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reconfigurable hardware

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A Gate Driver-Level Isolated Monitoring Technique for Gate Oxide Degradation in Silicon Carbide Power MOSFETs

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IEEE Transactions on Power Electronics 2024年

作者： Naghibi, Javad Mohsenzade, Sadegh Mehran, Kamyar Foster, Martin P. Queen Mary University of London School of Electronics Engineering and Computer Science LondonE1 4NS United Kingdom K.N. Toosi University of Technology Electrical Engineering Department Tehran16317-14191 Iran University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom

Gate oxide degradation accounts for one of the major chip-related failure modes both in Silicon (Si) and Silicon Carbide (SiC) power MOSFETs. Due to the thinner structure of the gate oxide layer as well as reduced conduction band offset between SiC and SiO2 in SiC MOSFETs, device failure with the root cause of gate oxide degradation is more probable than Si MOSFETs. In this paper, a real-time sensor-less monitoring technique based on the variations of the pulseswidth of the MOSFET gate driver input is proposed and validated. This method is implemented on the gate drive section (control section) of the converter which is galvanically isolated from the high voltage section of the converter. This basically means that for using the proposed condition monitoring (CM) method, no measurement in power section of the converter is required, and there is no need to use any high voltage/current or high bandwidth sensors for CM purposes. The output signal of the proposed CM technique is a voltage signal with DC value and provides a high resolution of gate oxide degradation level of the switch. The evaluation and validation of the proposed CM technique are done in a DC-DC Boost converter topology at 200kHz and up to 312V operation. The results show that the proposed CM technique can provide a high resolution assessment on gate oxide degradation level of the device on a real-time basis. © 1986-2012 IEEE.

关键词： Power MOSFET

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MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction

arXiv

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arXiv 2024年

作者： Zhang, Suyi Alam, Ekram Baber, Jack Bianco, Francesca Turner, Edward Chamanzar, Maysam Dehghani, Hamid MindPortal Ltd LondonEC2A 3PG United Kingdom Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Biomedical Engineering Carnegie Mellon University PittsburghPA15213 United States Neuroscience Institute Carnegie Mellon University PittsburghPA15213 United States School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

In the coming decade, artificial intelligence systems will continue to improve and revolutionise every industry and facet of human life. Designing effective, seamless and symbiotic communication paradigms between humans and AI agents is increasingly important. This paper reports a novel method for human-AI interaction by developing a direct brain-AI interface. We discuss a novel AI model, called MindSpeech, which enables open-vocabulary, continuous decoding for imagined speech. This study focuses on enhancing human-AI communication by utilising high-density functional near-infrared spectroscopy (fNIRS) data to develop an AI model capable of decoding imagined speech non-invasively. We discuss a new word cloud paradigm for data collection, improving the quality and variety of imagined sentences generated by participants and covering a broad semantic space. Utilising a prompt tuning-based approach, we employed the Llama2 large language model (LLM) for text generation guided by brain signals. Our results show significant improvements in key metrics, such as BLEU-1 and BERT P scores, for three out of four participants, demonstrating the method's effectiveness. Additionally, we demonstrate that combining data from multiple participants enhances the decoder performance, with statistically significant improvements in BERT scores for two participants. Furthermore, we demonstrated significantly above-chance decoding accuracy for imagined speech versus resting conditions and the identified activated brain regions during imagined speech tasks in our study are consistent with the previous studies on brain regions involved in speech encoding. This study underscores the feasibility of continuous imagined speech decoding. By integrating high-density fNIRS with advanced AI techniques, we highlight the potential for non-invasive, accurate communication systems with AI in the near future. © 2024, CC BY-NC-ND.

关键词： Near infrared spectroscopy

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Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey

arXiv

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arXiv 2024年

作者： Vu, Thai-Hoc Jagatheesaperumal, Senthil Kumar Nguyen, Minh-Duong Van Huynh, Nguyen Kim, Sunghwan Pham, Quoc-Viet Department of Electrical Electronic and Computer Engineering University of Ulsan Korea Republic of Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi626005 India Department of Information Convergence Engineering Pusan National University Busan46241 Korea Republic of Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom School of Electronic Engineering Kyonggi University Korea Republic of School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02 PN40 Ireland

The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet-of-Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, Generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm;thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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