Echo intensity data as a directional antenna for observing processes above sloping ocean bottoms

2007 ◽  
Vol 57 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Hans van Haren
2014 ◽  
Vol 51 (7) ◽  
pp. 072801
Author(s):  
苍桂华 Cang Guihua ◽  
李明峰 Li Mingfeng ◽  
岳建平 Yue Jianping

Author(s):  
Douglas L. Dorset

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.


2010 ◽  
Vol E93-B (10) ◽  
pp. 2570-2577 ◽  
Author(s):  
Daisuke UCHIDA ◽  
Hiroyuki ARAI ◽  
Yuki INOUE ◽  
Keizo CHO

Author(s):  
Rinkle Chhabra ◽  
Anuradha Saini

Mobile Ad Hoc Networks (MANET) are autonomous, infrastructure less and self-configuring networks. MANETs has gained lots of popularity due to on the fly deployment i.e. small network setup time and ability to provide communication in obstreperous terrains. Major challenges in MANETs include routing, energy efficiency, network topology control, security etc. Primary focus in this article is to provide method and algorithm to ensure significant energy savings using re-configurable directional antennas. Significant energy gains can be clinched using directional antenna. Key challenges while using directional antenna are to find destination location, antenna focusing, signal power and distance calculations. Re-configurable directional antenna can ensure significant energy gains if used intelligently. This article provides a brief insight into improved energy savings using re-configurable directional antennas and an associated algorithm


2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.


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