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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suresh, Helina Rajini Neelaveni, R. Kumar, Bonda Kiran Arunadevi, R. Indhumathi, G. Varun, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Tamilnadu Chennai India Srm Institute of Science and Technology Department of Cse Tamilnadu Chennai India Kl School of Architecture Kl Deemed to Be University Andhrapradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification. © 2023 IEEE.

关键词： Internet of things

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Image super resolution using Enhanced Attention Block (EAB) for Grape Leaf Disease Detection

Image super resolution using Enhanced Attention Block (EAB) ...

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International Conference on computer Applications In Electrical Engineering-Recent Advances (CERA)

作者： P.V. Yeswanth Y Thanya S Deivalakshmi K M Lokesh Kumar Johaan George Department of Electronics and communication engineering National Institute of Technology Tiruchirappalli India Department of Computer and communication engineering Amrita Vishwa Vidyapeetham Chennai India Department of Ocean engineering and Naval Architecture Indian Insitute of Technology Kharagpur India

Accurate disease diagnosis in grape leaf analysis is heavily reliant on high-resolution (HR) images. To meet this need, we propose a model that incorporates the Enhanced Attention Block (EAB) to generate HR images for improved disease detection. The EAB employs advanced attention mechanisms and residual learning to effectively capture critical, spatial and channel-wise relationships. Proposed model is assessed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics, yielding PSNR scores of 31. 902dB, 32.785dB, and 34. 853dB, and SSIM score of 0.8709, 0.9058, and 97.46 for factors 2,4 and 6 respectively. This proposed approach holds great potential for widespread implementation in disease detection across various plant species, allowing for timely and accurate interventions for plant health management and increasing industry productivity.

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Design and Experimental Investigation of Trikarenos: A Fault-Tolerant 28nm RISC-V-based SoC

arXiv

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arXiv 2024年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gürkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca The Integrated Systems Laboratory ETH Zurich Switzerland The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Industrial Engineering The University of Trento Italy The Department of Space Engineering The Delft University of Technology Netherlands The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Electronic Engineering The University of Rome Tor Vergata Italy The Integrated Systems Laboratory ETH Zurich Switzerland The Department of Electrical Electronic and Information Engineering The University of Bologna Italy

We present a fault-tolerant by-design RISC-V SoC and experimentally assess it under atmospheric neutrons and 200 MeV protons. The dedicated ECC and Triple-Core Lockstep countermeasures correct most errors, guaranteein... 详细信息

关键词： Fault tolerance

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Supporting Disaster Resilience Building through Cross-Community Knowledge Sharing

Supporting Disaster Resilience Building through Cross-Commun...

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IEEE Global Humanitarian technology Conference (GHTC)

作者： Motoya Koga Tetsuya Kaneko Emanuel Leleito Kazuki Karashima Ari Aharari Department of Architecture Sojo University Kumamoto Japan Graduate School of Engineering Nagoya University Nagoya Japan Dept.of Architecture Maebashi Institute of Technology Maebashi Japan Department of Computer and Information Sciences Sojo University Kumamoto Japan

Collaborative action in disaster mitigation efforts contributes to knowledge and experience sharing, resource sharing, improvement of response and coordination, and standardization of good practice. In this study, we propose a workshop model and develop a virtual collaboration tool to support and promote cross-community knowledge sharing and collaboration focusing on disaster mitigation efforts. The workshop model involves pre-fieldwork training, actual fieldwork, and post-fieldwork information exchange. The collaboration tool enables information collection, sharing and record keeping. The effectiveness of the workshop model and the collaboration tool is demonstrated through use in a citizen participatory disaster preparedness workshops linking two elementary schools located in the central and southern regions of Japan.

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Pagliari, Emanuele Davoli, Luca Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden Lab Department of Engineering and Architecture University of Parma Parma43124 Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system’s overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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SAMOYE: ZERO-SHOT SINGING VOICE CONVERSION MODEL BASED ON FEATURE DISENTANGLEMENT AND ENHANCEMENT

arXiv

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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangChina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

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Discover Cities 2024年第1期1卷 1-34页

作者： Omrany, Hossein Al-Obaidi, Karam M. Hossain, Mohataz Alduais, Nayef A. M. Al-Duais, Husam S. Ghaffarianhoseini, Amirhosein School of Architecture and Built Environment The University of Adelaide Adelaide Australia Department of the Natural and Built Environment College of Social Sciences and Arts Sheffield Hallam University Sheffield UK Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Architecture Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia School of Future Environments Auckland University of Technology Auckland New Zealand

Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

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Facial Recognition Impact in Smartphone Sector 4

Facial Recognition Impact in Smartphone Sector

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Jha, Amaresh Sudhakar, P. Lokhande, Sharayu Ashish Kumar Ishodzhanova, Galina Venkatrao, K. Zhakupova, Aliya School of Digital Media Journalism & Mass Communication Gd Goenka University India Vignan's Nirula Institute of Technology and Science for Women Department of Ece India Computer Engineering Army Institute of Technology India International Education Corporation Department of Architecture Kazakhstan Srkr Engineering College Department of Ece India International Education Corporation Department of Linguistics Kazakhstan

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In recent times, technological advancement brings many benefits for people and facial recognition technology is one of them. Nowadays, S martphone users increasingly use facial recognition features that help in verifying identity of users for different purposes, for example, accessing their device before making any payment from their phones. This study paper has analysed impact of facial recognition systems in S martphone sectors by discussing its advantages and challenges to understand both its negative and positive impact. Literature review section has provided relevant information on selected topic as it has elaborated that different large organisation have their own face recognition software for Apple, Facebook, and Google. Additionally, it has mentioned that there are two different types of facial recognition software such as camera-based and infrared;however, regarding effectiveness, infrared works more appropriate rather than camera-based recognition technologies. The paper has also elaborated that this technology offers automating time-tracking systems and it has touch less sign options, which help them in limiting physical contact with others. Result of this study showed that facial recognition helps to protect any type of unauthorised access in a Smartphone as well as it could prevent cyber threats by recognising face of S martphone users. Additionally, due to this software's fast identification feature, it could successfully verify faces of people within a few seconds. However, as this software is based on artificial intelligence, unlike other applications, it is highly vulnerable as hackers can hack Smartphones if strong algorithms are more used. Lastly, to prevent this issue a strong AI infrastructure and model security is required in future. © 2022 IEEE.

关键词： Smartphones

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Evaluation of Critical Factors in Urban Matrix Environment from the Perspective of Ecological Design

Evaluation of Critical Factors in Urban Matrix Environment f...

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2022 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2022

作者： Hou, Yi-You Liu, Kuang-Sheng Lin, Jiun-Hung Lin, Ming-Hung Tin, Lou-Yang National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung City Taiwan Cheng Shiu University Department of Architecture and Interior Design Kaohsiung City Taiwan College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Cheng Shiu University Department of Electrical Engineering Kaohsiung City Taiwan

ISBN: (数字)9781665495875

ISBN: (纸本)9781665495875

The ecological environment of human-centered habitats, cities, is gradually emphasized that the idea of sustainable development is proposed to stress on the integration of development and conservation. Besides, under current resource-applied environment, long-term management of resources is planned and used for preventing resources from shortage. Current development of urban central areas is about saturated, and the rapid expansion of urban fringe is about out of control. Effective recovery of urban landscape ecological structure and function as well as retention, restoration, and regeneration of urban natural environment to balance urban ecosystem and natural ecosystem are primary issues to improve urban environmental quality. Aiming at residents in Kaohsiung City, as the questionnaire objects, 360 copies of questionnaire are distributed as the research samples, and 294 valid copies are retrieved, with the retrieval rate 82%. The research results show *** ecology as the most emphasized dimension in the evaluation of urban matrix environment from the perspective of ecological design, and *** five mostly emphasized indicators, among 20, are green coverage ratio, provision of learning opportunities, diversity of plant species, recoverable materials, and participation ways. According to the results to propose the conclusion. It is expected to dig out specific opportunities for the sustainable use and development of urban matrix and to develop a complete and systematic landscape ecological network, with the combination with other landscape elements, to promote urban ecological quality. © 2022 IEEE.

关键词： Ecosystems

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