• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.175-180
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• 2015

Worldwide, studies on intelligent vehicles for the convenience of drivers have been actively conducted as the number of cars has increased. However, vehicle convenience enabled by buttons lowers the concentration on driving and hence poses as a huge threat to the safety of the driver. The use of one of the convenient features, impaired driving auxiliary equipment, is limited because of its complex usage, and this device also hinders the front view of the driver. This paper proposes a vehicle-control device for controlling the convenient features as well as changes in speed and direction using gestures and motions of the driver. This device consists of an ultrasonic sensor for recognizing movement, an arduino for accepting signal control functions and servo and DC motors apply to various vehicle parts. Firstly, the vehicle-control device was designed using a 3D CAD program known as Solid-works based on the size of the steering wheel. Then, through simulations, a suitable length for minimizing the absorbent between ultrasonic sensors was confirmed using a program known as COMSOL Multiphysics. Finally, simulation results were verified through experiments, and the optimal size of the device was identified through the number of errors.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.426-432
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• 2021

Continuous wave (CW) Doppler radar has the advantage of being able to solve the privacy problem unlike camera and obtains signals in a non-contact manner. Therefore, this paper proposes a human activity recognition (HAR) system using CW Doppler radar, and presents the hardware design and implementation results for acceleration. CW Doppler radar measures signals for continuous operation of human. In order to obtain a single motion spectrogram from continuous signals, an algorithm for counting the number of movements is proposed. In addition, in order to minimize the computational complexity and memory usage, binarized neural network (BNN) was used to classify human motions, and the accuracy of 94% was shown. To accelerate the complex operations of BNN, the FPGA-based BNN accelerator was designed and implemented. The proposed HAR system was implemented using 7,673 logics, 12,105 registers, 10,211 combinational ALUTs, and 18.7 Kb of block memory. As a result of performance evaluation, the operation speed was improved by 99.97% compared to the software implementation.