• Title/Summary/Keyword: micro-doppler

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Angiogenic factor-enriched platelet-rich plasma enhances in vivo bone formation around alloplastic graft material

  • Kim, Eun-Seok;Kim, Jae-Jin;Park, Eun-Jin
    • The Journal of Advanced Prosthodontics
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    • v.2 no.1
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    • pp.7-13
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    • 2010
  • Although most researchers agree that platelet-rich plasma (PRP) is a good source of autogenous growth factors, its effect on bone regeneration is still controversial. The purpose of this study was to evaluate whether increasing angiogenic factors in the human PRP to enhance new bone formation through rapid angiogenesis. MATERIAL AND METHODS. In vitro, the human platelets were activated with application of shear stress, $20\;{\mu}g/ml$ collagen, 2 mM $CaCl_2$ and 10U thrombin/$1\;{\times}\;10^9$ platelets. Level of vascular endothelial growth factor (VEGF) and platelet microparticle (PMP) in the activated platelets were checked. In the animal study, human angiogenic factors-enriched PRP was tested in 28 athymic rat's cranial critical bone defects with $\beta$-TCP. Angiogenesis and osteogenesis were evaluated by laser Doppler perfusion imaging, histology, dual energy X-ray densinometry, and micro-computed tomography. RESULTS. In vitro, this human angiogenic factors-enriched PRP resulted in better cellular proliferation and osteogenic differentiation. In vivo, increasing angiogenic potential of the PRP showed significantly higher blood perfusion around the defect and enhanced new bone formation around acellular bone graft material. CONCLUSION. Angiogenic factor-enriched PRP leads to faster and more extensive new bone formation in the critical size bone defect. The results implicate that rapid angiogenesis in the initial healing period by PRP could be supposed as a way to overcome short term effect of the rapid angiogenesis.

Efficient Detection of Small Unmanned Aerial Vehicles in Cluttered Environment (클러터 환경을 고려한 효과적 소형 무인기 탐지에 관한 연구)

  • Choi, Jae-Ho;Kang, Ki-Bong;Sun, Sun-Gu;Lee, Jung-Soo;Cho, Byung-Lae;Kim, Kyung-Tae
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.30 no.5
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    • pp.389-398
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    • 2019
  • In this paper, we propose a method to detect small unmanned aerial vehicles(UAVs) flying in a real-world environment. Small UAV signals are frequently obscured by clutter signals because UAVs usually fly at low altitudes over urban or mountainous terrain. Therefore, to obtain a desirable detection performance, clutter signals must be considered in addition to noise, and thus, a performance analysis of each clutter removal technique is required. The proposed detection process uses clutter removal and pulse integration methods to suppress clutter and noise signals, and then detects small UAVs by utilizing a constant false alarm rate detector. After applying three clutter removal techniques, we analyzed the performance of each technique in detecting small UAVs. Based on experimental data acquired in a real-world outdoor environment, we found it was possible to derive a clutter removal method suitable for the detection of small UAVs.

Machine learning based radar imaging algorithm for drone detection and classification (드론 탐지 및 분류를 위한 레이다 영상 기계학습 활용)

  • Moon, Min-Jung;Lee, Woo-Kyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.5
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    • pp.619-627
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    • 2021
  • Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

Shear-wave elasticity imaging with axial sub-Nyquist sampling (축방향 서브 나이퀴스트 샘플링 기반의 횡탄성 영상 기법)

  • Woojin Oh;Heechul Yoon
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.5
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    • pp.403-411
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    • 2023
  • Functional ultrasound imaging, such as elasticity imaging and micro-blood flow Doppler imaging, enhances diagnostic capability by providing useful mechanical and functional information about tissues. However, the implementation of functional ultrasound imaging poses limitations such as the storage of vast amounts of data in Radio Frequency (RF) data acquisition and processing. In this paper, we propose a sub-Nyquist approach that reduces the amount of acquired axial samples for efficient shear-wave elasticity imaging. The proposed method acquires data at a sampling rate one-third lower than the conventional Nyquist sampling rate and tracks shear-wave signals through RF signals reconstructed using band-pass filtering-based interpolation. In this approach, the RF signal is assumed to have a fractional bandwidth of 67 %. To validate the approach, we reconstruct the shear-wave velocity images using shear-wave tracking data obtained by conventional and proposed approaches, and compare the group velocity, contrast-to-noise ratio, and structural similarity index measurement. We qualitatively and quantitatively demonstrate the potential of sub-Nyquist sampling-based shear-wave elasticity imaging, indicating that our approach could be practically useful in three-dimensional shear-wave elasticity imaging, where a massive amount of ultrasound data is required.