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In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
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The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network is used.
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This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.
Real-time monitoring system for weather and air pollutant measurement with HT...
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Presentation of the UAE solar Atlas by Masdar Institute http://atlas.masdar.ac.ae/
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IAEME
Ijcet 06 07_003
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http://www.iosrjournals.org/iosr-jce/pages/v12i2.html
An Enhanced Support Vector Regression Model for Weather Forecasting
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IOSR Journals
https://www.irjet.net/archives/V4/i6/IRJET-V4I6111.pdf
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Assessment of solar energy distribution for installing solar panels using remote sensing and gis techniques
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Assessment of solar energy distribution for installing solar panels using rem...
IAEME Publication
Wireless sensor network (WSN) is a key to access the internet of things (IoT). The popularity of IoT and the prediction that there will be more devices connected to the Internet cause difficulties in integrating and making connected devices. The problem of IoT implementation are the lack of real-time data collection, processing, and the inability to provide continuous monitoring. To overcome these problems, this paper proposes an IoT device for monitoring environmental conditions through the IoT KAA platform that can be monitored anywhere and anytime in real time. The end device node consists of several sensors such as as temperature, humidity, carbon monoxide (CO) and carbon dioxide (CO2) sensors. The collected data from the end device node will be transmitted via a communication based on IEEE 802.15.4 to Raspberry Pi gateway, then sent to the KAA cloud server and saved into the database. The environmental data can be accessed via a web-based sensor application. We Analize the performance evaluation in terms of transaction, availability, data transfer, response time, transaction rate, throughput, and concurrency. The experimental result shows that the use of KAA IoT platform is better than that without platform.
Implementation of environmental monitoring based on KAA IoT platform
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journalBEEI
This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method.
Stereo vision-based obstacle avoidance module on 3D point cloud data
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TELKOMNIKA JOURNAL
That internship report was presented at Department of Atmospheric Science (CUSAT) for the 3rd semester M.Tech examination.
Internship Report Presentation
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Devanil Choudhury
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Hannover 2008 V2
1.
Service-oriented infrastructure for
environmental and economic security Space Research Institute NASU-NSAU, Ukraine
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Environmental security: flood
estimation
6.
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Environmental security: flood
prediction
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17.
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Grid - heterogeneous
infrastructure
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SRI NASU-NSAU segment
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Segment functioning Raw
data acquisition
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Segment functioning Data
processing on cluster
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Segment functioning Data
processing on cluster
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Segment functioning Data
processing on cluster
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Segment functioning Results
visualization
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