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Redefining Effusive-Constrictive Pericarditis with Echocardiography
Pieter van der Bijl,Philip Herbst,Anton F. Doubell 한국심초음파학회 2016 Journal of Cardiovascular Imaging (J Cardiovasc Im Vol.24 No.4
Background: Effusive-constrictive pericarditis (ECP) is traditionally diagnosed by using the expensive and invasive techniqueof direct pressure measurements in the pericardial space and the right atrium. The aim of this study was to assess the diagnosticrole of echocardiography in tuberculous ECP. Methods: Intrapericardial and right atrial pressures were measured pre- and post-pericardiocentesis, and right ventricular andleft ventricular pressures were measured post-pericardiocentesis in patients with tuberculous pericardial effusions. Echocardiographywas performed post-pericardiocentesis. Traditional, pressure-based diagnostic criteria were compared with post-pericardiocentesissystolic discordance and echocardiographic evidence of constriction. Results: Thirty-two patients with tuberculous pericardial disease were included. Sixteen had ventricular discordance (invasivelymeasured), 16 had ECP as measured by intrapericardial and right atrial invasive pressure measurements and 17 had ECP determinedechocardiographically. The sensitivity and specificity of pressure-guided measurements (compared with discordance) forthe diagnosis of ECP were both 56%. The positive and negative predictive values were both 56%. The sensitivity of echocardiography(compared with discordance) for the diagnosis of ECP was 81% and the specificity 75%, while the positive and the negativepredictive values were 76% and 80%, respectively. Conclusion: Echocardiography shows a better diagnostic performance than invasive, pressure-based measurements for the diagnosisof ECP when both these techniques are compared with the gold standard of invasively measured systolic discordance.
Automatic Sugar Beet Phenotyping in Open Field by a Computer Vision System
( Pieter M. Blok ),( Jochen Hemming ),( Youngki Hong ),( Jaesu Lee ),( Daehyun Lee ),( Gookhwan Kim ) 한국농업기계학회 2016 한국농업기계학회 학술발표논문집 Vol.21 No.2
Crop growth is an important quality assessment in plant breeding, especially in open field crops which grow in fluctuating and unfavorable outdoor conditions. To evaluate the growth potential of different plant varieties, researchers conduct leaf area measurements of emerged plants to evaluate its growth potential. This is a time consuming and labor intensive activity and therefore often only conducted on random spots on the field. An automatic computer vision system was built to automate and to speed up this plant phenotyping process. The system consist of three color cameras mounted on an implement facing straight downwards, lamps for illumination, an encoder wheel and a computer system. Natural light was blocked by a surrounding cover to limit the effect of variable outdoor light conditions on the image quality. The computer vision software makes use of an excessive green algorithm (2G - R - B) to segment the plant material from the soil. As the crop plants are sown by a precision sowing device in a regular pattern a method based on the fast-fourier transform (FFT) is used to distinguish crop plants from weed plants. A rectangular based clustering algorithm, based on 8-pixel nearest-neighbor connectivity, is used to cluster separated plant-parts together as one individual plant object used to measure the leaf area. The system was validated in an open-field sugar beet crop at the growing stage off our leaves. Fifty-five sugar beet plants were manually measured by experienced plant scouts(“ground truth”). The same plants were measured with the computer vision system. An ANOVA F-test(P<0.05) was used to discriminate the two measurement methods. The F-probability was 0.055 an djust above the significance level. So the H0 hypothesis that there is not a difference between human measurement and machine vision measurement was no trejected. Possible causes of difference was the inability of the system to detect and measure plants damaged by animals and very small plants which were occluded by clods or bigger plants. Nevertheless,with improvements on the vision software and camera/lamp configuration, the system is profitable for a fast and accurate leaf area measurement and corresponding plant phenotyping.
Pieter Simoens,Farhan Azmat Ali,Bert Vankeirsbilck,Lien Deboosere,Filip De Turck,Bart Dhoedt,Piet Demeester,Rodolfo Torrea-Duran,Liesbet Van der Perre,Antoine Dejonghe 한국통신학회 2012 Journal of communications and networks Vol.14 No.1
Thin client computing trades local processing for network bandwidth consumption by offloading application logic to remote servers. User input and display updates are exchanged between client and server through a thin client protocol. On wireless devices, the thin client protocol traffic can lead to a significantly higher power consumption of the radio interface. In this article, a cross-layer framework is presented that transitions the wireless network interface card (WNIC) to the energy-conserving sleep mode when no traffic from the server is expected. The approach is validated for different wireless channel conditions, such as path loss and available bandwidth, as well as for different network roundtrip time values. Using this cross-layer algorithm for sample scenario with a remote text editor, and through experiments based on actual user traces, a reduction of the WNIC energy consumption of up to 36.82% is obtained, without degrading the application’s reactivity.
2D LIDAR 스캐너와 파티클 필터 레이저빔 모델 기반의 과수 로봇의 주간 내 자율주행
Pieter M. Blok,서현권(Hyun Kwon Suh),Koen van Boheemen,김학진(Hak-Jin Kim),김국환(Gook-Hwan Kim) 제어로봇시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.8
In mountainous orchards, agricultural tasks, such as crop protection and harvesting, are characterized as being labor intensive and dangerous. An autonomous orchard robot that can execute these unattended seems a promising alternative to increase task operability. An essential function in the development of an autonomous orchard robot is navigation, which is usually based on tree-row detection from LIDAR scan data by using navigational algorithms. This research applies a probabilistic particle filter (PF) algorithm with a novel laser-beam model for the autonomous in-row navigation of an orchard robot. The navigational accuracy of the algorithm is assessed in a Dutch apple orchard over a distance of 500 m, with the robot driving at two velocities: 0.25 m/s and 0.50 m/s. At both speeds, almost 50% of the observed lateral deviations were lower than 0.05 m from the optimal navigation line. With the use of the PF algorithm, the robot navigated itself between six patterns of tree rows with artificially removed trees. Some lateral deviations exceeded 0.10 m when three adjacent trees were missing in both tree rows. Based on these results, a PF with a laser beam model is an accurate and robust algorithm for the autonomous in-row navigation in semi-structured outdoor environments, such as orchards.