A rendezvous node selection protocol for drone-based data collection

2019 ◽  
Author(s):  
Hong Min ◽  
Kwangsoo Jo ◽  
Jinman Jung ◽  
Bongjae Kim ◽  
Junyoung Heo
Keyword(s):  
Data Collection ◽  
Node Selection ◽  
Selection Protocol

scholarly journals Efficient Data Collection Method in Sensor Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Keyan Cao ◽  
Haoli Liu ◽  
Yefan Liu ◽  
Gongjie Meng ◽  
Si Ji ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Dynamic Environment ◽  
Wireless Sensor ◽  
Node Selection ◽  
Collection Method ◽  
Random Path ◽  
Efficient Data ◽  
Efficient Data Collection

Wireless sensor networks are widely used in many fields, such as medical and health care, military monitoring, target tracking, and people’s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low battery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data collection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between nodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the construction of random paths to fail. This paper proposes an efficient data collection algorithm for this problem. The algorithm is improved on the basis of the random node selection algorithm. This method can effectively avoid the failure of random path node selection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic environment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction of extreme learning machines is proposed. This method uses extreme learning machine methods to perform trajectory prediction for nodes in a dynamic environment.

Electron Diffraction of Wet Biological Membranes

1974 ◽  
Vol 32 ◽  
pp. 158-159
Author(s):  
S.W. Hui ◽  
D.F. Parsons
Keyword(s):  
Electron Diffraction ◽  
Data Collection ◽  
Biological Membranes ◽  
Small Areas ◽  
Electron Diffraction Technique ◽  
Electron Microscopes ◽  
Collection Time ◽  
Camera Length

The development of the hydration stages for electron microscopes has opened up the application of electron diffraction in the study of biological membranes. Membrane specimen can now be observed without the artifacts introduced during drying, fixation and staining. The advantages of the electron diffraction technique, such as the abilities to observe small areas and thin specimens, to image and to screen impurities, to vary the camera length, and to reduce data collection time are fully utilized. Here we report our pioneering work in this area.

Automated electron tomography: from data collection to image processing

1995 ◽  
Vol 53 ◽  
pp. 26-27
Author(s):  
Weiping Liu ◽  
Jennifer Fung ◽  
W.J. de Ruijter ◽  
Hans Chen ◽  
John W. Sedat ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Collection ◽  
Structural Information ◽  
Imaging Technique ◽  
Electron Tomography ◽  
Ccd Camera ◽  
Automated Data Collection ◽  
Full Control ◽  
Transmission Electron ◽  
Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

Productivity of Emerging Word Combinations in Toddlers With Specific Expressive Language Impairment

1997 ◽  
Vol 6 (4) ◽  
pp. 34-47 ◽  
Author(s):  
Steven H. Long ◽  
Lesley B. Olswang ◽  
Julianne Brian ◽  
Philip S. Dale
Keyword(s):  
Data Collection ◽  
Young Children ◽  
Language Impairment ◽  
Treatment Approach ◽  
Expressive Language ◽  
Semantic Consistency ◽  
Session Analysis

This study investigated whether young children with specific expressive language impairment (SELI) learn to combine words according to general positional rules or specific, grammatic relation rules. The language of 20 children with SELI (4 females, 16 males, mean age of 33 months, mean MLU of 1.34) was sampled weekly for 9 weeks. Sixteen of these children also received treatment for two-word combinations (agent+action or possessor+possession). Two different metrics were used to determine the productivity of combinatorial utterances. One metric assessed productivity based on positional consistency alone; another assessed productivity based on positional and semantic consistency. Data were analyzed session-by-session as well as cumulatively. The results suggest that these children learned to combine words according to grammatic relation rules. Results of the session-by-session analysis were less informative than those of the cumulative analysis. For children with SELI ready to make the transition to multiword utterances, these findings support a cumulative method of data collection and a treatment approach that targets specific grammatic relation rules rather than general word combinations.

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

EI Outcomes Forum Launched to Explore Data Collection

ASHA Leader ◽  
2009 ◽  
Vol 14 (4) ◽  
pp. 7-28
Author(s):  
Jaumeiko Brown
Keyword(s):  
Data Collection
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