The European VLBI Network, its new data processor at JIVE, and opportunities for expansion

2003 ◽  
Author(s):  
R.T. Schilizzi
Keyword(s):  
Data Processor

scholarly journals Electronic Textiles

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 115-130
Author(s):  
Guido Ehrmann ◽  
Andrea Ehrmann
Keyword(s):  
Power Supply ◽  
Sensor Data ◽  
Electronic Textiles ◽  
Sensors And Actuators ◽  
External Communication ◽  
Data Processor ◽  
Temperature Strain

Electronic textiles belong to the broader range of smart (or “intelligent”) textiles. Their “smartness” is enabled by embedded or added electronics and allows the sensing of defined parameters of their environment as well as actuating according to these sensor data. For this purpose, different sensors (e.g., temperature, strain, light sensors) and actuators (e.g., LEDs or mechanical actuators) are embedded and connected with a power supply, a data processor, and internal/external communication.

scholarly journals EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

2021 ◽  
Vol 13 (13) ◽  
pp. 2622
Author(s):  
Haozhou Wang ◽  
Yulin Duan ◽  
Yun Shi ◽  
Yoichiro Kato ◽  
Seish Ninomiya ◽  
...  
Keyword(s):  
Data Processing ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Data Annotation ◽  
Intermediate Data ◽  
Reconstruction Quality ◽  
Open Source Software Package ◽  
Machine Learning Applications ◽  
Data Processor ◽  
Uav Images

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include 1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and 2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

A 4 b Josephson data processor chip

2003 ◽  
Author(s):  
Y. Hatano ◽  
H. Mori ◽  
H. Yamada ◽  
H. Nagaishi ◽  
H. Nakane ◽  
...  
Keyword(s):  
Data Processor

scholarly journals Purwarupa Alat Deteksi Ketinggian Banjir Perbantuan SMS Gateway Dan Mikrontroler

INOVA-TIF ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 51
Author(s):  
Muhammad Ardi
Keyword(s):  
Detection System ◽  
Water Levels ◽  
Design Flood ◽  
Loss Of Life ◽  
Data Processor ◽  
Working Principle ◽  
Arduino Uno ◽  
Flood Elevation ◽  
Opening And Closing ◽  
The Impact

<em>Flood disasters still occur regularly and continuously in Indonesia. Flooding can occur due to the volume of water in the river beyond the river body. Many impacts caused by flooding, not only material losses, flooding can also cause loss of life. The impact of flooding can be reduced if people are better prepared to face the flood. One way is to quickly disseminate information on river water levels to the community. It is necessary to make a solution on how to design an automatic sluice using Arduino UNO R3 and how to monitor the water situation during floods. The working principle of this tool uses an ultrasonic sensor as a water level detector, Arduino as a data processor, servo motor as opening and closing the door bar automatically and the modem as an SMS notification. Because design based detection system is needed In this study there are two formulations of the problem (i) How to design flood altitude detection devices using Arduino uno r3 which can open and close automatically. (ii) How to test the flood altitude detection system using a wavecome modem. The research objective is divided into two parts (i) Creating a series of flood elevation devices using Arduino r3 so that it can open and close automatically (ii) Gets the results of flood elevation system testing with an sms gateway</em>

scholarly journals ANALISIS KOMPARASI PRESTASI BELAJAR DITINJAU DARI MINAT BELAJAR DAN IPK PADA MATA KULIAH STATISTIKA

2016 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Herlina Herlina
Keyword(s):  
Sample Size ◽  
Student Learning ◽  
Analysis Of Variance ◽  
Homogeneity Test ◽  
Learning Achievement ◽  
Normality Test ◽  
Data Processor ◽  
The Subject ◽  
The Difference ◽  
Learning Interest

The purpose of this study is to know the difference of the average of students learning achievement in the subject of Statistics in views of student learning interest and students cumulative achhievement index (IPK) and also to know the interaction between students learning interests and IPK. The population of this research are 209 students with the sample size is 146. The data in this research are analyzed using the tests of model assumptions : a normality test in eleven times, a homogeneity test in seven times, and an analysis of variance (ANOVA) using SPSS data processor. The results of this study is that there is a difference of the average of students learning achievement between high, medium and low learning interests with sig 0,03 <0,05, that between IPK <3,00 and IPK≥3,00 with sig 0,000<0,05, and there is no interaction between learning interest and IPK with sig 0,936> 0,05.

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