In this study, we propose a method to facilitate the observation and analysis of pressure data. In modern times, research utilizing pressure is advancing in various fields. However, the aspect of visualizing invisible...
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ISBN:
(纸本)9798350374537;9798350374544
In this study, we propose a method to facilitate the observation and analysis of pressure data. In modern times, research utilizing pressure is advancing in various fields. However, the aspect of visualizing invisible pressure is often overlooked and not well-developed. Therefore, we have developed a pressure distribution visualization system that allows users to easily acquire and analyze pressure data transmitted from pressure sensors through a user interface (UI). The application is not limited to just one type of pressure sensor device with specific specifications. it needs to meet various requirements. The system is developed as a mobile application for mobile devices, capable of displaying pressure in both grid and hand-shaped modes. Furthermore, its versatility allows it to accommodate various requirements of pressure sensors. In the usability evaluation experiment conducted on the application developed in this study, an impressive score of 83.5 was achieved, indicating a high level of usability.
In the field of urban safety, extensive research is being conducted on the application of mmWave radarsensors for both indoor and outdoor environments. These advanced sensing technologies are gaining attention due to...
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With the exacerbation of global population aging, the issue of falls among the elderly is increasingly drawing attention from various sectors of society. Consequently, the development of an effective human fall detect...
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This paper focuses on the utilization of gyro sensordata, particularly from the MPU6050 sensor, for sequence prediction and positional analysis for many Industry 4.0 application and asset monitoring. Leveraging machi...
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sensordata fusion helps to derive more specific inferences than what could be achieved using a single independent sensor. Multi-sensordata Fusion (MSDF) technology enhance the capability of improving accuracy, infor...
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This paper is about a method for extracting the target point of a signal received from a radarsensor through spatial voxelization in an indoor environment with many metal objects such as partitions, PCs, and monitors...
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Self-driving vehicles need to detect the environment, systematically process the input data make decisions, and assign tasks to the executing agency until the task is executed. The environmental data processed by the ...
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In this paper the topic of joint active and passive (hybrid) radar detection is introduced and the theoretical benefits are outlined. An experimental hybrid radar setup is presented where a low-cost Software Defined R...
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ISBN:
(纸本)9781665436694
In this paper the topic of joint active and passive (hybrid) radar detection is introduced and the theoretical benefits are outlined. An experimental hybrid radar setup is presented where a low-cost Software Defined Radio (SDR) based radar system is used for hybrid sensing of targets using active and Passive Bistatic radar (PBR). Experimental results are presented for simultaneously sensing using an active 2.4 GHz radar and 690 MHz Digital Video Broadcasting - Terrestrial (DVB-T) based PBR mode. The detection performance of each sensor and a joint sensor performance are evaluated, where the joint detection performance is found to exceed that of the individual sensors alone. The ability to reduce active radar transmissions, but still retain a reasonable detection performance, is investigated using experimental data and the case is made for adaptive behaviour in order to exploit the benefits available to hybrid radars.
For safe driving, it is essential to accept reliable information from recognition sensors. In this paper, we present a deep learning model that classifies whether radar signals coming in are normal or abnormal. The ab...
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ISBN:
(纸本)9781665456456
For safe driving, it is essential to accept reliable information from recognition sensors. In this paper, we present a deep learning model that classifies whether radar signals coming in are normal or abnormal. The abnormal signal is defined as noise from the radar and all signals received when the radar fails or is in trouble. It is difficult to determine whether reflected signals are normal or not based only on radardata. Therefore, the camera and radarsensors are used together, considering the radar cross section (RCS) distribution varies by the angle and distance of the object. The proposed model uses data received from camera and radarsensors to determine the normality of object signals. The model shows an accuracy of 96.24%. Through the results of this study, the reliability of radar signals can be determined in the actual driving environment, thereby ensuring the safety of vehicles and pedestrians.
An automotive radarsensor can be misaligned compared to the initial installation state due to various external shocks while driving, and it can cause deterioration of radar detection performance. To guarantee the sta...
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ISBN:
(纸本)9798350303872
An automotive radarsensor can be misaligned compared to the initial installation state due to various external shocks while driving, and it can cause deterioration of radar detection performance. To guarantee the stable detection performance of the radar, a method of estimating the deviation angle compared to the initial state is required. To directly check the radar misalignment, an inefficient bumper removal process is required, so a method of indirectly determining the mounting state of the radar is required. Therefore, in this paper, we propose a method for estimating the tilt angle of the radarsensor using deep neural networks (DNNs). First, radarsensordata are obtained at various radar tilt angles and measurement distances to identify the characteristics of received signals. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input vectors. The proposed angle estimator consists of several DNNs in parallel, and the input vector passes through one of them according to the distance estimated from the range profile. Finally, the DNN determines the tilt angle for the input vector. In our datasets, the average classification accuracy of the proposed DNN-based classifier is over 98%.
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