scholarly journals Control - Monitoring System Of Oxygen Level, Ph, Temperature And Feeding in Pond Based on Iot

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Jaja Kustija ◽  
Furqon Andika
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Fish Farming ◽  
Temperature Data ◽  
Management Systems ◽  
Oxygen Level ◽  
Oxygen Levels ◽  
Fish Management

Fish management systems have an important role in fish farming. One aspect of fish management is water quality which includes several things such as temperature, pH, oxygen levels and also feeding. So far, monitoring of water quality and feeding of fish has been done manually. This study aims to design a control-monitoring system for oxygen levels, pH, temperature and automatic feeding based on IoT. The reading data from the sensor and also the RTC will be forwarded by the microcontroller to the server to be displayed to the user. This system is automated with actuators in the form of aerators and motors, so that feeding and adding oxygen levels to the pond will be automatically carried out by the microcontroller. The results of this study indicate the system can work, temperature data, oxygen levels, pH can be monitored through the server and feeding can also be done.

scholarly journals A Review on Visual Water Quality Monitoring System in Precision Aquaculture

2019 ◽  
Vol 892 ◽  
pp. 23-30
Author(s):  
Alter Jimat Embug ◽  
Ag Asri Ag Ibrahim ◽  
Muzaffar Hamzah ◽  
Mohammad Fadhli Asli
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Current Practice ◽  
Fish Farming ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Auditory Display

This paper presents the review of available visual water quality monitoring and proposes a conceptual sonification model of audiovisual analytics for precision aquaculture. This study reviews the current practice of the visual water quality monitoring system used to interpret the complex fish farming data. This study also explores the possibility of using an auditory display, by using sound as complementary elements to communicate information from the system to the user.

In vivo Monitoring of Oxygen Levels in Human Brain Tumor Between Fractionated Radiotherapy Using Oxygen-enhanced MR Imaging

2020 ◽  
Vol 16 (4) ◽  
pp. 427-432
Author(s):  
Junchao Qian ◽  
Xiang Yu ◽  
Bingbing Li ◽  
Zhenle Fei ◽  
Xiang Huang ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Tumor Cells ◽  
Normal Tissue ◽  
Oxygen Level ◽  
Tissue Oxygen ◽  
Human Brain Tumor ◽  
Fractionated Radiotherapy ◽  
Oxygen Levels ◽  
Oxygen Breathing

Background:: It was known that the response of tumor cells to radiation is closely related to tissue oxygen level and fractionated radiotherapy allows reoxygenation of hypoxic tumor cells. Non-invasive mapping of tissue oxygen level may hold great importance in clinic. Objective: The aim of this study is to evaluate the role of oxygen-enhanced MR imaging in the detection of tissue oxygen levels between fractionated radiotherapy. Methods: A cohort of 10 patients with brain metastasis was recruited. Quantitative oxygen enhanced MR imaging was performed prior to, 30 minutes and 22 hours after first fractionated radiotherapy. Results: The ΔR1 (the difference of longitudinal relaxivity between 100% oxygen breathing and air breathing) increased in the ipsilateral tumor site and normal tissue by 242% and 152%, respectively, 30 minutes after first fractionated radiation compared to pre-radiation levels. Significant recovery of ΔR1 in the contralateral normal tissue (p < 0.05) was observed 22 hours compared to 30 minutes after radiation levels. Conclusion: R1-based oxygen-enhanced MR imaging may provide a sensitive endogenous marker for oxygen changes in the brain tissue between fractionated radiotherapy.

Optimization of Energy and Water Quality Management Systems for Drinking Water Utilities

2015 ◽  
Vol 2015 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Carla Cherchi ◽  
Mohammad Badruzzaman ◽  
Joan Oppenheimer ◽  
Matthew Gordon ◽  
Simon Bunn ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Quality Management ◽  
Water Quality Management ◽  
Water Utilities ◽  
Management Systems ◽  
Quality Management Systems

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Research on Fishery Water Quality Monitoring System Based on 6LoWPAN

2020 ◽  
Vol 1624 ◽  
pp. 042057
Author(s):  
Xueying Wang ◽  
Yanli Feng ◽  
Jiajun Sun ◽  
Dashe Li ◽  
Huanhai Yang
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring
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