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 r...
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The consensus algorithm is one of the core technologies of the blockchain, which determines how the nodes in the blockchain network reach a consensus and enable them to jointly maintain a piece of data. This paper foc...
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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...
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.
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...
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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 ...
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.
Sonic interaction design is defined as the study and exploitation of sound as one of the principal channels conveying information, meaning, and aesthetic/emotional qualities in interactive contexts. This field lies at...
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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 motiva...
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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 equipmen...
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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 inco...
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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...
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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