• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.85-94
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• 2019

Warhead and debris show the different micro-Doppler frequency shape in the spectrogram because of the different micro motion. So we can classify them using the micro-Doppler features. In this paper, we classified warhead and debris in the separation phase using CNN(Convolutional Neural Networks). For the input image of CNN, we used micro-Doppler spectrogram. In addition, to improve classification performance of warhead and debris, we applied the preprocessing using CA-CFAR to the micro-Doppler spectrogram. As a result, when the preprocessing of micro-Doppler spectrogram was used, classification performance is improved in all signal-to-noise ratio(SNR).

• Journal of Sensor Science and Technology
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• v.17 no.4
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• pp.317-324
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• 2008

This paper presents an optical doppler tomography(ODT) system using a speaker as a method to achieve depth measurement in a flowing sample. The use of the speaker provides easy implementation with a low cost. The nonlinear characteristics of the speaker has hindered its adaptation because it produces inconsistent fringe frequencies at different depths. This paper reports an adaptive algorithm to compensate the nonlinear characteristics, and could, resultantly, acquire the Doppler frequency shift caused by the sample. The experiment utilizes a flowing scattering particle solution in a capillary tube at a certain flow rate. The Doppler frequency profile over the lumen was calculated by using spectrogram method. and we obtained the velocity image of the sample.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1217-1220
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• 1987

We have developed a Ultra Sound Pulsed Doppler System with two-dimensional M-mode image and Spectrum analyzer. The image of the M-mode is composed of time and depth axes. The Spectrum analyzer shows the spectrum of Doppler signal which represents the velocity component of time dependent blood-flow behavior. The spectrogram using Spectrum analyzer is composed of frequency and amplitude axes. The outputs of the system are audio signals, velocity curves, velocity profiles, M-mode images and spectrogram.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.169-182
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• 1986

Pulsed ultrasonic Doppler system is a useful diagnostic instrument to measure blood-flow-velocity, velocity profile, and volume-blood-flow. This system is more powerful compare with 2-dimensional B-scan tissue image. A system has been deve- loped and ii being evaluated using TMS 32010 DSP. We use this DSP for the purpose of real-time spectrum analyzer to obtain spectrogram in singlegate pulsed Doppler system and for the serial comb filter to cancel clutter and zero crossing counter to estimate Doppler mean frequency in multigate pulsed Doppler system. The Doppler shift of the backscattered signals is sensed in a phase detector. This Doppler signal corresponds to the mean velocity over a some region in space defined by the ultrasonic beam dimensions, transmitted pulse duration, and transducer ban(iwidth. Multi- gate pulsed Doppler system enable the transcutaneous and simultaneous assessment of the velocities in a number of adjacent sample volumes as a continuous function of time. A multigate pulsed Doppler system processing the information originating from presented.

• Journal of Biomedical Engineering Research
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• v.7 no.2
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• pp.139-146
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• 1986

Pulsed ultrasonic Doppler system is a useful diagnostic instrument to measure blood-flow-velocity, velocity profile, and volume-blood-flow. This system is more powerful compare with 2-dimensional B-scan tissue image. A system has been deve- loped and ii being evaluated using TMS 32010 DSP. We use this DSP for the purpose of real-time spectrum analyzer to obtain spectrogram in singlegate pulsed Doppler system and for the serial comb filter to cancel clutter and zero crossing counter to estimate Doppler mean frequency in multigate pulsed Doppler system. The Doppler shift of the backscattered signals is sensed in a phase detector. This Doppler signal corresponds to the mean velocity over a some region in space defined by the ultrasonic beam dimensions, transmitted pulse duration, and transducer ban(iwidth. Multi- gate pulsed Doppler system enable the transcutaneous and simultaneous assessment of the velocities in a number of adjacent sample volumes as a continuous function of time. A multigate pulsed Doppler system processing the information originating from presented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.11
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• pp.996-1003
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• 2016

Trilateration technique using time of arrival(TOA) is generally used for single target position estimation in radar system. However, trilateration technique has limitation in case of multiple targets, since it is difficult to distinguish the measurements corresponding to the respective targets. In this study, to eliminate ambiguity of relation between measurements and targets, micromotion of each target is measured by micro Doppler which is actively studied in radar industry nowadays and these information are used to distinguish measurements used at trilateration technique. Resultingly, the trilateration technique is applied successfully for each target. The targets are considered as multiple submissiles separated from the missile. Simulation results shows the performance of the proposed algorithm.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of IKEEE
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• v.24 no.1
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• pp.200-207
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• 2020

In this paper, an area-efficient feature extractor was proposed for security surveillance radar systems and FPGA-based implementation results were presented. In order to reduce the memory requirements, features extracted from Doppler profile for FFT window-size are used, while those extracted from total spectrogram for frame-size are excluded. The proposed feature extractor was design using Verilog-HDL and implemented with Xilinx Zynq-7000 FPGA device. Implementation results show that the proposed design can reduce the logic slice and memory requirements by 58.3% and 98.3%, respectively, compared with the existing research. In addition, security surveillance radar system with the proposed feature extractor was implemented and experiments to classify car, bicycle, human and kickboard were performed. It is confirmed from these experiments that the accuracy of classification is 93.4%.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.489-498
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• 2018

In this paper, a target detection method based on beamspace-domain multichannel nonnegative matrix factorization is studied when an echo of continuous-wave ping is received from a low-Doppler target in reverberant environment. If the receiver of the continuous-wave active sonar moves, the frequency range of the reverberation is broadened due to the Doppler effect, so the low-Doppler target echo is interfered by the reverberation in this case. The developed algorithm analyzes the multichannel spectrogram of the received signal into frequency bases, time bases, and beamformer gains using the beamspace-domain multichannel nonnnegative matrix factorization, then the algorithm estimates the frequency, time, and bearing of target echo by choosing a proper basis. To analyze the performance of the developed algorithm, simulations were performed in various signal-to-reverberation conditions. The results show that the proposed algorithm can estimate the frequency, time, and bearing, but the performance was degraded in the low signal-to-reverberation condition. It is expected that modifying the selection algorithm of the target echo basis can enhance the performance according to the simulation results.

• 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.