We demonstrate a photonic crystal resonator on hybrid silicon nitride-on-lithium niobate (SiN-on-LN) platform, designed for microwave-assisted optical-frequency conversion applications. We measure a large intrinsic qu...
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Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protect...
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In terms of increasing terrorism, criminal behaviour, and anti-social events, there has been a need for safety systems to identify criminals. Face Recognition is one of the vibrant technologies that are very useful fo...
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Electric vehicles (EVs) have emerged as a quick solution and eco-friendly alternative to internal combustion engine vehicles (ICEVs), offering fewer emissions and better energy consumption. Lithium-ion batteries are u...
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All messaging platforms rely on a centralized server which has the vulnerability of getting hacked by outside agencies and posing risk to private data. Authentication between users is an important asset in electronic ...
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Healthcare data continues to grow in size and complexity. Hence, there's a rising need for more scalable and accurate prediction models. This study presents a framework that combines big data processing with advan...
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In linguistics, all languages can be considered as symbolic systems, with each language relying on symbolic processes to associate specific symbols with meanings. In the same language, there is a fixed correspondence ...
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Pulmonary artery (PA) segmentation is an important step in the diagnosis and treatment of pulmonary diseases. In the present work, a novel method for PA segmentation from CTPA images is proposed. First, the mediastinu...
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In today's fast-paced healthcare environment, efficient management of patient records and seamless patient-provider communication is crucial. This study proposes a comprehensive solution integrating blockchain tec...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and *** classification is an important method to prevent network ***,due to the tremendous traffic volume and limitations of computing,mos...
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Traffic encryption techniques facilitate cyberattackers to hide their presence and *** classification is an important method to prevent network ***,due to the tremendous traffic volume and limitations of computing,most existing traffic classification techniques are inapplicable to the high-speed network *** this paper,we propose a High-speed Encrypted Traffic Classification(HETC)method containing two ***,to efficiently detect whether traffic is encrypted,HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted ***,HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest *** experimental results show that HETC can achieve a 94%F-measure in detecting encrypted flows and a 85%–93%F-measure in classifying fine-grained flows for a 1-KB flow-length dataset,outperforming the state-of-the-art comparison ***,HETC does not need to wait for the end of the flow and can extract mass computing *** average time for HETC to process each flow is only 2 or 16 ms,which is lower than the flow duration in most cases,making it a good candidate for high-speed traffic classification.
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