In this paper, a spherical helix module based on 3D printing and microfluidic technologies was fabricated, tested and applied to drug delivery applications. A liquid metal alloy is introduced to enhance the sensitivit...
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In this paper, a spherical helix module based on 3D printing and microfluidic technologies was fabricated, tested and applied to drug delivery applications. A liquid metal alloy is introduced to enhance the sensitivity of the complex and drug-/liquid-reconfigurable helix topology. Moreover, the influence of the dimensions of the hemisphere inscribed by the helical turns on the resonant frequency of the structure is researched. The microfluidic channels with 1 mm radius are shaped in the form of double semispherical helix using a Form-2 3D printer. To fulfill effectively the liquid metal (LM) injection, movement and recycle for reuse, a syringe pump is integrated to flexibly control the flow of the fluid. Besides, the proof-of-concept prototype achieves excellent radiation gain and impedance matching. The realized sensitivity is 0.96 mL/GHz and 0.33 mL/GHz corresponding with the frequency shift from 2.28 to 2.01 GHz and 2.01 to 1.65 GHz.
Illumination, occlusion, pose and expression variations are the most common challenging problems for face recognition in many real-world applications. However, existing face recognition methods are proposed to handle ...
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Nowadays, the cameras of traffic monitoring systems are mounted toward roads or interactions. The views are fixed and limited. To extend the monitoring area, a novel onboard abnormal event detection system is proposed...
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Traffic sign classification is a significant issue in the intelligent vehicle domain, which helps vehicles to follow the traffic rules and ensure the safety. Feature selection and description are very important and di...
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In order to reduce traffic accidents and road congestion in many cities, vehicle speed estimation is very critical and important to observe speed limitation law and traffic conditions. In this paper, we present a spee...
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This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of ...
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This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.
Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakag...
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Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.
Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal...
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In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social network...
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In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to...
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