scholarly journals Accuracy and Precision of Low-Cost Echosounder and Automated Data Processing Software for Habitat Mapping in a Large River

Diversity ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 116 ◽  
Author(s):  
Jani Helminen ◽  
Tommi Linnansaari ◽  
Meghann Bruce ◽  
Rebecca Dolson-Edge ◽  
R. Allen Curry
Keyword(s):  
Data Processing ◽  
Low Cost ◽  
Absolute Error ◽  
Large River ◽  
Habitat Characteristics ◽  
Vegetation Height ◽  
River Bed ◽  
Data Processing Tool ◽  
Automated Data Processing ◽  
Vegetation Maps

The development of consumer hydroacoustic systems continues to advance, enabling the use of low-cost methods for professional mapping purposes. Information describing habitat characteristics produced with a combination of low-cost commercial echosounder (Lowrance HDS) and a cloud-based automated data processing tool (BioBase EcoSound) was tested. The combination frequently underestimated water depth, with a mean absolute error of 0.17 ± 0.13 m (avg ± 1SD). The average EcoSound bottom hardness value was high (0.37–0.5) for all the substrate types found in the study area and could not be used to differentiate between the substrate size classes that varied from silt to bedrock. Overall, the bottom hardness value is not informative in an alluvial river bed setting where the majority of the substrate is composed of hard sands, gravels, and stones. EcoSound separated vegetation presence/absence with 85–100% accuracy and assigned vegetation height (EcoSound biovolume) correctly in 55% of instances but often overestimated it in other instances. It was most accurate when the vegetation canopy was ≤25% or >75% of the water column. Overall, as a low-cost, easy-to-use application EcoSound offers rapid data collection and allows users with no specialized skill requirements to make more detailed bathymetry and vegetation maps than those typically available for many rivers, lakes, and estuaries.

scholarly journals An Alternative Statistical Model for Predicting Salinity Variations in Estuaries

2020 ◽  
Vol 12 (24) ◽  
pp. 10677
Author(s):  
Ronghui Ye ◽  
Jun Kong ◽  
Chengji Shen ◽  
Jinming Zhang ◽  
Weisheng Zhang
Keyword(s):  
Statistical Model ◽  
River Discharge ◽  
Numerical Models ◽  
Low Cost ◽  
River Estuary ◽  
Pearl River Estuary ◽  
Large River ◽  
Physical Models ◽  
Long Term Effects ◽  
Salinity Variations

Accurate salinity prediction can support the decision-making of water resources management to mitigate the threat of insufficient freshwater supply in densely populated estuaries. Statistical methods are low-cost and less time-consuming compared with numerical models and physical models for predicting estuarine salinity variations. This study proposes an alternative statistical model that can more accurately predict the salinity series in estuaries. The model incorporates an autoregressive model to characterize the memory effect of salinity and includes the changes in salinity driven by river discharge and tides. Furthermore, the Gamma distribution function was introduced to correct the hysteresis effects of river discharge, tides and salinity. Based on fixed corrections of long-term effects, dynamic corrections of short-term effects were added to weaken the hysteresis effects. Real-world model application to the Pearl River Estuary obtained satisfactory agreement between predicted and measured salinity peaks, indicating the accuracy of salinity forecasting. Cross-validation and weekly salinity prediction under small, medium and large river discharges were also conducted to further test the reliability of the model. The statistical model provides a good reference for predicting salinity variations in estuaries.

MassyTools: A High-Throughput Targeted Data Processing Tool for Relative Quantitation and Quality Control Developed for Glycomic and Glycoproteomic MALDI-MS

2015 ◽  
Vol 14 (12) ◽  
pp. 5088-5098 ◽  
Author(s):  
Bas C. Jansen ◽  
Karli R. Reiding ◽  
Albert Bondt ◽  
Agnes L. Hipgrave Ederveen ◽  
Magnus Palmblad ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
High Throughput ◽  
Maldi Ms ◽  
Relative Quantitation ◽  
Data Processing Tool

scholarly journals An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3405 ◽  
Author(s):  
Manuel Espinosa-Gavira ◽  
Agustín Agüera-Pérez ◽  
Juan González de la Rosa ◽  
José Palomares-Salas ◽  
José Sierra-Fernández
Keyword(s):  
Sensor Networks ◽  
Low Cost ◽  
Monitoring Network ◽  
Absolute Error ◽  
Small Scale ◽  
Photovoltaic Systems ◽  
Real Time Control ◽  
Short Term ◽  
Time Control ◽  
Cloud Motion

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m 2 and a standard deviation of 36.1 W/m 2 . The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.

scholarly journals Analysis of Tools for The Description and Analytical Processing of Enviromental Data

2020 ◽  
Vol 20 (2) ◽  
pp. 129-132
Author(s):  
Vugar Abdullayev ◽  
N.A. Ragimova N.A ◽  
V.H Abdullayev ◽  
T.K Askerov
Keyword(s):  
Data Processing ◽  
Environmental Monitoring ◽  
Analytical Data ◽  
Environmental Data ◽  
Volume Data ◽  
Star Schema ◽  
Data Processing Tool ◽  
Data Volume ◽  
Speed Up ◽  
Analytical Processing

The objects of the research are tools that support the description and analytical processing of environmental data requests. These tools are used for environmental monitoring. Analytical processing of environmental data is necessary for this monitoring by the persons concerned. Here, a star schema is used to describe the data. Analytical data processing tools are required for analysis and research of environmental data. The results of analytical processing of environmental data are used to speed up decision-making. This article also describes the structure of the analytical data processing tool. Therefore, one of the problem points is how to describe the data. For this purpose, an environmental data relay scheme is defined, and the data description is implemented in multidimensional cubes. Due to the growth of data volume, data processing is carried out using multi-dimensional visualization methods. In addition, a visual user interface has been created for analytically processing queries based on scale data. The result of this research is to find a method for describing environmental data. At the end of the research, a hypercube was obtained, with the help of which it was possible to structure environmental data and carry out analytical processing of them. To this end, environmental data have been described using a multi-dimensional visualization method. And OLAP technologies were used to carry out analytical processing of this data. OLAP technologies allow aggregate data to be used and presented as a hypercube. The results of the research can be used as a basis for an environmental information system that is used for environmental monitoring.

scholarly journals Data Processing Tool for Calculation in Dioid

2000 ◽  
pp. 469-470
Author(s):  
Cottenceau Bertrand ◽  
Lhommeau Mehdi ◽  
Hardouin Laurent ◽  
Boimond Jean-Louis
Keyword(s):  
Data Processing ◽  
Data Processing Tool

scholarly journals Arduino based platform for process control learning

2020 ◽  
Vol 6 (5) ◽  
pp. 0585-0593
Author(s):  
Bruna Couto Molinar Henrique ◽  
Leonardo Couto Molinar Henrique ◽  
Humberto Molinar Henrique
Keyword(s):  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Control Unit ◽  
Absolute Error ◽  
Internal Model Control ◽  
Pid Controllers ◽  
Experimental Unit ◽  
Source Codes ◽  
Model Control

This work deals with implementation of an experimental flowrate control unit using free and low-cost hardware and software. The open-source software Processing was used to develop the source codes and user graphical interface and the open-source electronic prototyping platform Arduino was used to acquire data from an experimental unit. Work presents descriptions of the experimental setup, the real-time PID controllers used and theoretical/conceptual issues of Arduino. PID controllers based on internal model control, minimization of the integral of time-weighted absolute error, Ziegler-Nichols, and others were tuned for setpoint and load changes and real-time runs were carried out in order to make real-time use of  control theory learned in academy. Results showed the developed platform proved to be suitable for use in experimental setups allowing users compare their ideas and expectations with the experimental evidence in a real and low-cost fashion. In addition, the instrumentation is simple to configure with acceptable level noise and particularly useful for control/automation learning with educational purposes.

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