In Urban Air Mobility, the approach and landing procedure of Vertical Take-off and Landing aircraft is recognized as a critical phase which needs innovative solutions to guarantee reliable operations. This paper focus...
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In order to explore the reasons for the insufficient positioning accuracy of the crane, to ensure the personal safety of the staff, and to improve the reliability of production operations and work efficiency. Focus on...
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Mobile robots and autonomous vehicles rely on 3-D point cloud technology for environmental perception, which often employ various visual perception sensors within their Internet of Things (IoT) systems to acquire poin...
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Mobile robots and autonomous vehicles rely on 3-D point cloud technology for environmental perception, which often employ various visual perception sensors within their Internet of Things (IoT) systems to acquire point cloud information. Nevertheless, the obtained point cloud data is frequently incomplete, making it difficult to effectively perform perception tasks. Multitask point cloud learning networks can not only achieve point cloud reconstruction under high occlusion rates but also assist the system in accomplishing various point cloud tasks. However, existing multitask point cloud learning models based on advanced Transformer frameworks often suffer from quadratic complexity, limiting their performance in massive point cloud data learning. In this article, we propose a novel approach named Hybrid-Fusion Mamba (HFMamba), as a pretraining network model specifically designed for multitask point cloud learning. Compared to Transformer-based networks, the proposed HFMamba possesses linear complexity, which can significantly reduce IoT systems' computational cost. HFMamba employs a unique first-layer feature fusion design that integrates point cloud features from three different perspectives, enabling it to capture deeper dependencies among the point clouds. Moreover, a hybrid scan strategy is proposed to separately scan the hidden states and residuals, aiming to model the sequence from different directions. Experimental results demonstrate that the proposed HFMamba network model outperforms many state-of-the-art methods without applying any serialization strategies to the original point cloud data. In particular, the proposed HFMambda approach achieves classification accuracies of 93.7% and 93.89% on ModelNet40 and ScanObjectNN, respectively.
In Japan, the demand for nursing care is increasing with the aging of the population. On the other hand, the nursing care industry is facing a serious problem of increasing the burden on care workers due to a shortage...
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Advances in wearable technology have enabled ubiquitous use of wearable devices in remote patient monitoring, particularly in clinical trials. Because of the reliance on high-quality data in these endeavors, the first...
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ISBN:
(纸本)9798331530143
Advances in wearable technology have enabled ubiquitous use of wearable devices in remote patient monitoring, particularly in clinical trials. Because of the reliance on high-quality data in these endeavors, the first and often the most time-consuming step is to build a data collection system. While many systems have been developed to address this, they are often highly specific and customized to the task at hand, and are often not generalized enough to support other tasks. To remedy this, we developed Raproto, an open-source easy-to-use rapid prototyping platform that does not require the time, effort, and expertise needed for custom development. The Raproto platform consists of three components, the wearable device(s), communication protocol, and remote storage. These components support the collection, transmission, storage, analysis, and visualization of large-scale data with applications from smaller-scale research studies to large clinical trials. To reduce the burden of device and application development, we created multipurpose and customizable smartwatch applications on both the Android and Tizen operating systems. We evaluate our platform in a lab setting as well as in two real-world case studies. Overall, we find that we can collect data using our application for over 24 hours on a single charge and there is little to no data loss, thus making it an ideal tool to preface customized device development for real-world impact and commercialization.
The possibility of exploiting the natural motion of the vehicle for synthesizing antenna arrays makes the automotive scenario an interesting application for Synthetic Aperture radar (SAR) technology. The latter enhanc...
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ISBN:
(纸本)9781665436694
The possibility of exploiting the natural motion of the vehicle for synthesizing antenna arrays makes the automotive scenario an interesting application for Synthetic Aperture radar (SAR) technology. The latter enhances the imaging resolution without increasing the hardware cost. A fundamental requirement of SAR imaging is the knowledge of position and velocity of the the radar platform along the synthetic aperture, with an accuracy that shall be in the order of the wavelength. Navigation techniques fusing data from heterogeneous onboard sensors provide a good positioning solution, to be possibly refined with radardata. This paper addresses the question of whether there exist a minimum set of navigation sensors that guarantees sufficiently accurate SAR imaging in realistic urban scenarios, to minimize the hardware cost. We develop a multi-sensor navigation algorithm, considering different sets of sensors, and we discuss the impact of each one on navigation accuracy. The latter is validated against experimental data using Real Time Kinematics (RTK) positioning measurements as ground truth and SAR imaging results.
Micro-Doppler radar signatures of helicopters and drones are gaining increasing importance. However, collecting data under controlled conditions on drones in flight can be difficult. The ability to use predictive code...
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ISBN:
(纸本)9781510661844;9781510661851
Micro-Doppler radar signatures of helicopters and drones are gaining increasing importance. However, collecting data under controlled conditions on drones in flight can be difficult. The ability to use predictive codes to produce moving target and micro-Doppler radardata is becoming more important. In order to demonstrate the potential use of computer code predictions, this report will describe the X, V, and W-Band micro-Doppler signatures for the DJI Phantom 2 quadcopter. The predictions are generated using the Xpatch prediction code. The motion of all 4 propellers are simulated for realistic flight conditions. Predictions were performed at multiple viewing angles and using various PRF values. Additionally, different range resolutions were also predicted. The data is analyzed using a series of Range-Doppler spectrograms and short time Fourier transforms. The equations for the motion of the blades are examined in the context of the minimum PRF that is needed for capturing the micro-Doppler information. A discussion is included for finding the best frequency band to operate which balances the tradeoff of information content with operating frequency and PRF value. It is shown in the standard analysis that the unique shape of the blades produced patterns in the micro-Doppler signature that may be of use in target identification. Application of Time-Frequency-Analysis is also demonstrated. The predicted data is compared with micro-Doppler data measured in the laboratory using a 100 GHz compact range on a real Phantom 2 drone.
Due to the unbalanced and insufficient distribution of meteorological radar in China, there is a large blind area of radar network in the southwest and the maritime region. With its larger observation range and less e...
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Monitoring vital signs such as breathing or heart rates as well as other physical movements in complex environments is the basis for many emerging applications spanning from healthcare to autonomous vehicles. Designin...
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ISBN:
(纸本)9798350329216;9798350329209
Monitoring vital signs such as breathing or heart rates as well as other physical movements in complex environments is the basis for many emerging applications spanning from healthcare to autonomous vehicles. Designing radar systems capable of remotely monitoring these movements necessitates measurement campaigns in combination with advanced machine-learning algorithms. Despite the compelling applications and the need for large and diverse data sets for validation of design, there are few examples of simulated human movement in multipath environments in the literature. To address this gap, the work presented here outlines a method to accurately simulate radar back-scatter from time varying human movement. Specifically, we animate human breathing with anatomically accurate mathematical models through physical-optics-based simulation and validated them against monostatic radar measurements with a 28.5 GHz channel sounder in a semi-anechoic chamber by the National Institute of Standards and technology, capturing phase and path loss over time from a human breathing positioned 2 m away. Using vital sensordata as ground truth, we demonstrate the animations to match the simulated human's breathing patterns and heart rate. Furthermore, the simulation resulted in excellent agreement with the measured phase across ten breaths, and had a root-mean-square error (RMSE) of 2.1 dB in path loss.
This work addresses the design considerations, simulations, and fundamental framework of a novel Relative Navigation (RN) radarsensor operating at millimeter-wave bands, aiming at operating in ocean environments. A &...
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ISBN:
(纸本)9781510650930;9781510650923
This work addresses the design considerations, simulations, and fundamental framework of a novel Relative Navigation (RN) radarsensor operating at millimeter-wave bands, aiming at operating in ocean environments. A "multi-loop" system structure is suggested, which is believed to be the method to achieve the accuracy and reliability of a non-traditional tracking radarsensor. Simulation studies and data collection verification for preliminary hardware and software elements are presented.
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