This paper explores the feasibility of using wearable laser-induced graphene (LIG) sensors to estimate oxygen saturation (SpO2) as an alternative to traditional photoplethysmography (PPG) oximeters, particularly in ma...
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The emergence of autonomous vehicles marks a transformative moment in the transportation sector, significantly propelled by the integration of Light Detection and Ranging (LiDAR) technology. LiDAR revolutionizes how v...
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ISBN:
(纸本)9780791888469
The emergence of autonomous vehicles marks a transformative moment in the transportation sector, significantly propelled by the integration of Light Detection and Ranging (LiDAR) technology. LiDAR revolutionizes how vehicles perceive their environment, emitting laser beams to measure distances to objects and creating highly accurate three-dimensional maps. This innovation is pivotal for enhancing the operational efficiency and safety of autonomous vehicles by providing instantaneous and detailed environmental mappings. However, the efficacy of LiDAR sensors is compromised by environmental factors such as dust, dirt, snow, and rain, which can severely affect their accuracy and, consequently, the safety of the vehicles they guide. Despite the importance of this issue, the research field has shown a notable lack of investigation into predicting LiDAR sensor contamination and its reliability. This gap underscores a critical need for dedicated research efforts to ensure the reliability and safety of autonomous driving technologies, making it a pressing challenge for researchers and developers alike. Therefore, it is imperative to develop a novel way to detect LiDAR sensors'reliability like contamination level. In response to the urgent need to address LiDAR sensor contamination, our team have initiated a comprehensive data collection journey, spanning from California's diverse climates to Michigan's variable weather conditions. This dataset contains multi-level features such as contamination levels on multiple sensors, environmental factors, and sensor images across varying weather conditions and geographical locales. The dataset is a groundbreaking contribution to the field, playing a vital role in developing our machine learning models and offering novel insights that could dramatically advance research and practical applications. In this work, our framework employs machine learning methods across two distinct but complementary strategies: sensor data analysis and imag
As people's awareness of environmental protection has increased over the years, air quality, particularly air pollution related to particulate matter 2.5 (PM 2.5), has become a concern for the general public. Thus...
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In intelligent transportation systems, accurate positioning is crucial for ensuring the safe and efficient operation of vehicles. This paper addresses the positioning challenges in satellite-constrained scenarios by p...
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The paper showcases experimental testing of a phased array antenna for connecting and tracking vehicles in Vehicle-to-Everything technology applications. The study involved measuring the performance of a microstrip ha...
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Landslides are hazards triggered from natural factors such as geomorphology and climatic phenomena. The impacts of these events are not negligible in terms of both economic and social impacts, therefore the topic of r...
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Industrial Wireless sensor Networks (IWSNs) are a type of ubiquitous computing technology that is increasingly being used in industrial settings. They consist of a network of wireless sensors that monitor and collect ...
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In wireless sensor networks (WSNs), sensors are scattered in a particular region to record and evaluate the physical data from the environment. They are widely utilised in various applications, including healthcare, e...
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Underwater robot technologies are crucial for underwater resource exploration, and many developments have been achieved in this direction. Robotics researchers have published and built a wide range of fish-shaped robo...
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The Maximization of network lifetime by energy balancing algorithm is a research topic that focuses on the development of distributed algorithms for enhancing the lifetime of wireless sensor networks (WSNs). With the ...
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