scholarly journals Real-Time Compact Environment Representation for UAV Navigation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4976 ◽  
Author(s):  
Kaitao Meng  ◽  
Deshi Li  ◽  
Xiaofan He  ◽  
Mingliu Liu  ◽  
Weitao Song 
Keyword(s):  
Real Time ◽  
Low Cost ◽  
High Mobility ◽  
Sensor Data ◽  
Depth Sensor ◽  
Extraction Algorithm ◽  
Environment Representation ◽  
And Storage ◽  
Uav Navigation ◽  
Efficient Environment

Recently, unmanned aerial vehicles (UAVs) have attracted much attention due to their on-demand deployment, high mobility, and low cost. For UAVs navigating in an unknown environment, efficient environment representation is needed due to the storage limitation of the UAVs. Nonetheless, building an accurate and compact environment representation model is highly non-trivial because of the unknown shape of the obstacles and the time-consuming operations such as finding and eliminating the environmental details. To overcome these challenges, a novel vertical strip extraction algorithm is proposed to analyze the probability density function characteristics of the normalized disparity value and segment the obstacles through an adaptive size sliding window. In addition, a plane adjustment algorithm is proposed to represent the obstacle surfaces as polygonal prism profiles while minimizing the redundant obstacle information. By combining these two proposed algorithms, the depth sensor data can be converted into the multi-layer polygonal prism models in real time. Besides, a drone platform equipped with a depth sensor is developed to build the compact environment representation models in the real world. Experimental results demonstrate that the proposed scheme achieves better performance in terms of precision and storage as compared to the baseline.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals IOTFlood: hardware and software platform using internet of things to monitor floods in real time

2021 ◽  
Vol 16 (4) ◽  
pp. 1
Author(s):  
Francisco Vital Da Silva Júnior ◽  
Mônica Ximenes Carneiro Da Cunha ◽  
Marcílio Ferreira De Souza Júnior
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Research Design ◽  
Open Source Software ◽  
Low Cost ◽  
Sensor Data ◽  
Software Platform ◽  
Web Interface ◽  
Iot Platform

Floods are responsible for a high number of human and material losses every year. Monitoring of river levels is usually performed with radar and pre-configured sensors. However, a major flood can occur quickly. This justifies the implementation of a real-time monitoring system. This work presents a hardware and software platform that uses Internet of Things (IoTFlood) to generate flood alerts to agencies responsible for monitoring by sending automatic messages about the situation of rivers. Research design involved laboratory and field scenarios, simulating floods using mockups, and later tested on the Mundaú River, state of Alagoas, Brazil, where flooding episodes have already occurred. As a result, a low-cost, modular and scalable IoT platform was achieved, where sensor data can be accessed through a web interface or smartphone, without the need for existing infrastructure at the site where the IOTFlood solution was installed using affordable hardware, open source software and free online services for the viewing of collected data.

Real-Time Motion Capture for a Human Body using Accelerometers

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 601-610 ◽  
Author(s):  
Jihong Lee ◽  
Insoo Ha
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Human Body ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Human Motion ◽  
The Body ◽  
Sensor Data ◽  
Time Motion ◽  
Gravity Acceleration

In this paper we propose a set of techniques for a real-time motion capture of a human body. The proposed motion capture system is based on low cost accelerometers, and is capable of identifying the body configuration by extracting gravity-related terms from the sensor data. One sensor unit is composed of 3 accelerometers arranged orthogonally to each other, and is capable of identifying 2 rotating angles of joints with 2 degrees of freedom. A geometric fusion technique is applied to cope with the uncertainty of sensor data. A practical calibration technique is also proposed to handle errors in aligning the sensing axis to the coordination axis. In the case where motion acceleration is not negligible compared with gravity acceleration, a compensation technique to extract gravity acceleration from the sensor data is proposed. Experimental results not only for individual techniques but also for human motion capturing with graphics are included.

scholarly journals Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3079
Author(s):  
André Glória ◽  
João Cardoso ◽  
Pedro Sebastião
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
New Technologies ◽  
Irrigation System ◽  
Low Cost ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Sensors And Actuators

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

scholarly journals Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

2020 ◽  
Vol 18 (4) ◽  
pp. 214-228
Author(s):  
Abdalla Eldesoky ◽  
Ahmed M. Kamel ◽  
M. Elhabiby ◽  
Hadia Elhennawy
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Sensor Data ◽  
Observation Angle ◽  
Integrated Sensors ◽  
Land Vehicle ◽  
Position Errors

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

scholarly journals Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

2019 ◽  
Vol 8 (2S5) ◽  
pp. 103-108
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Cost Effective ◽  
Daily Basis ◽  
Quality Parameters ◽  
Sensor Data ◽  
Sensor Technology ◽  
Real Time System ◽  
Quality Of Water ◽  
Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

Low-cost 3D scanning systems for cultural heritage documentation

2020 ◽  
Vol 10 (4) ◽  
pp. 437-455 ◽  
Author(s):  
Quentin Kevin Gautier ◽  
Thomas G. Garrison ◽  
Ferrill Rushton ◽  
Nicholas Bouck ◽  
Eric Lo ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Real Time ◽  
Low Cost ◽  
Three Dimensional ◽  
Archaeological Site ◽  
3D Models ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Content Type ◽  
Localization And Mapping

PurposeDigital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.Design/methodology/approachThe authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.FindingsWhile not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.Practical implicationsThe proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.Originality/valueThis article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.

scholarly journals Real-time and Low-cost IoT based farming using raspberry Pi

2020 ◽  
Vol 17 (1) ◽  
pp. 197
Author(s):  
Md. Wahidur Rahman ◽  
Md. Elias Hossain ◽  
Rahabul Islam ◽  
Md. Harun Ar Rashid ◽  
Md. Nur A Alam ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Ph Sensor ◽  
Sensor Data ◽  
Future Application ◽  
Raspberry Pi ◽  
Moisture Sensor ◽  
Security Issues ◽  
Temperature And Humidity ◽  
Level Sensor

<span>This paper reflects on the implementation of IoT enabled Farming, especially for the people needed a smart way of agriculture. This research focuses on real-time observation with efficient use of cheapest security system. The features of this research including i) Sensor data monitoring using soil moisture sensor which is responsible for measuring moisture of the filed, water level sensor which is liable for detecting flooded water, pH sensor which is accountable for measuring pH of the soil and Temperature and humidity sensor which is responsible for tracking out the present temperature and humidity in the atmosphere ii) Live monitoring of sensor’s value using cloud and a Dashboard iii) Security issues of the farming using Laser shield and IP-Camera through Wi-Fi which is conducted by android application. This paper also assures the analysis of the experimented data through various sensor’s value and gives a momentous way for future application. Result and discussion ensures the contribution in the field of Internet of things</span>

scholarly journals Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1192
Author(s):  
Dohyeong Kim ◽  
Yunjin Yum ◽  
Kevin George ◽  
Ji-Won Kwon ◽  
Woo Kyung Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Mobile Sensors ◽  
Sensor Data ◽  
Personal Monitoring ◽  
Aggregate Level ◽  
Individual Level ◽  
Asthmatic Children ◽  
Seoul Metropolitan Area ◽  
Substantial Discrepancy

This study aims to evaluate the accuracy and effectiveness of real-time personal monitoring of exposure to PM concentrations using low-cost sensors, in comparison to conventional data collection method based on fixed stations. PM2.5 data were measured every 5 min using a low-cost sensor attached to a bag carried by 47 asthmatic children living in the Seoul Metropolitan area between November 2019 and March 2020, along with the real-time GPS location, temperature, and humidity. The mobile sensor data were then matched with station-based hourly PM2.5 data using the time and location. Despite some uncertainty and inaccuracy of the sensor data, similar temporal patterns were found between the two sources of PM2.5 data on an aggregate level. However, average PM2.5 concentrations via personal monitoring tended to be lower than those from the fixed stations, particularly when the subjects were indoors, during nighttime, and located farther from the fixed station. On an individual level, a substantial discrepancy is observed between the two PM2.5 data sources while staying indoors. This study provides guidance to policymakers and researchers on improving the feasibility of personal monitoring via low-cost mobile sensors as an alternative or supplement to the conventional station-based monitoring.

Bioengineering Applications of 3D Scanning and Reconstruction Using a Depth Sensor

2015 ◽  
Vol 809-810 ◽  
pp. 920-925 ◽  
Author(s):  
Octavian Ciobanu
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
3D Model ◽  
Low Cost ◽  
Structured Light ◽  
3D Scanning ◽  
Kinect Sensor ◽  
Depth Sensor ◽  
3D Surfaces

Paper approaches some characteristics and bioengineering applications of a handheld depth sensor for low-cost 3D scanning and reconstruction. The Kinect depth sensor used in this work was launched on June 2009 and was based around a gaming webcam peripheral. The Kinect sensor uses a structured light technique in order to develop real-time 3D surfaces. The 3D model of anatomic surface may have a lot of bioengineering applications. Some observations and comparisons are presented in connection with the scanning and 3D reconstruction of different anatomic surfaces.

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