missing measurements
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 418
Author(s):  
Mohammad Al-Sa’d ◽  
Serkan Kiranyaz ◽  
Iftikhar Ahmad ◽  
Christian Sundell ◽  
Matti Vakkuri ◽  
...  

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.


2021 ◽  
Author(s):  
Hua Wang ◽  
Yixian Yuan ◽  
Suikang Zeng ◽  
Wuyan Li ◽  
Xiaobo Tang

Abstract The three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.


Author(s):  
Eamon Harrity ◽  
Lauren E. Michael ◽  
Courtney J. Conway

Many applications in wildlife management require knowledge of the sex of individual animals. The Yuma Ridgway’s Rail Rallus obsoletus yumanensis is an endangered marsh bird with monomorphic plumage and secretive behaviors, thereby complicating sex determination in field studies. We collected morphometric measurements from 270 adult Yuma Ridgway’s Rails and quantified the plumage and mandible color of 91 of those individuals throughout their geographic range to evaluate inter-sexual differences in morphology and coloration. We genetically sexed a subset of adult Yuma Ridgway’s Rails ( N =101) and used these individuals to determine the optimal combination of measurements (based on discriminant function analyses) to distinguish between sexes. Males averaged significantly larger than females in all measurements and the optimal discriminant function contained whole-leg, culmen, and tail measurements and classified correctly 97.8% (95% CI: 92.5-100.0%) of genetically sexed individuals. We used two additional functions that correctly classified ≥95.5% of genetically sexed Yuma Ridgway’s Rails to assign sex to individuals with missing measurements. These simple models provide managers and researchers with a practical tool to determine the sex of Yuma Ridgway’s Rails based on morphometric measurements. Although color measurements were not in the most accurate discriminant functions, we quantified subtle inter-sexual differences in the color of mandibles and greater coverts of Yuma Ridgway’s Rails. These results document sex-specific patterns in coloration that allow future researchers to test hypotheses to determine the mechanisms underlying sex-based differences in plumage coloration.


2021 ◽  
Vol 118 (31) ◽  
pp. e2102721118
Author(s):  
Chun-Teh Chen ◽  
Grace X. Gu

Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity—the conditions for equilibrium—can be learned by ElastNet.


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