Low Cost IoT based Flood Monitoring System Using Machine Learning and Neural Networks: Flood Alerting and Rainfall Prediction

2020 ◽  
Author(s):  
Dola Sheeba Rani ◽  
G N Jayalakshmi ◽  
Vishwanath P Baligar
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Monitoring System ◽  
Low Cost ◽  
Rainfall Prediction ◽  
Flood Monitoring

Using AI at the Edge and Incremental Machine Learning to Process Onboard Instrument Data

2021 ◽  
Author(s):  
Nicholas Parkyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Data Storage ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Low Cost ◽  
Machine Intelligence ◽  
Edge Computing ◽  
Continual Learning

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals Rainfall Prediction using Machine Learning and Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 1954-1961
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Weather Forecasting ◽  
Research Work ◽  
Primary Source ◽  
Coastal Areas ◽  
Complex Data ◽  
Linear Pattern ◽  
Rainfall Prediction ◽  
The People

Rainfall prediction model mainly based on artificial neural networks have been proposed in India until now. This research work does a comparative study of two rainfall prediction approaches and finds the more accurate one. The present technique to predict rainfall doesn’t work well with the complex data present. The approaches which are being used now-a-days are statistical methods and numerical methods, which don’t work accurately when there is any non-linear pattern. Existing system fails whenever the complexity of the datasets which contains past rainfall increases. Henceforth, to find the best way to predict rainfall, study of both machine learning and neural networks is performed and the algorithm which gives more accuracy is further used in prediction. Recently, rainfall is considered the primary source of most of the economy of our country. Agriculture is considered the main economy driven source. To do a proper investment on agriculture, a proper estimation of rainfall is needed. Along with agriculture, rainfall prediction is needed for the people in coastal areas. People in coastal areas are in high risk of heavy rainfall and floods, so they should be aware of the rainfall much earlier so that they can plan their stay accordingly. For areas which have less rainfall and faces water scarcity should have rainwater harvesters, which can collect the rainwater. To establish a proper rainwater harvester, rainfall estimation is required. Weather forecasting is the easiest and fastest way to get a greater outreach. This research work can be used by all the weather forecasting channels, so that the prediction news can be more accurate and can spread to all parts of the country

scholarly journals Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 208 ◽  
Author(s):  
Jongryun Roh ◽  
Hyeong-jun Park ◽  
Kwang Lee ◽  
Joonho Hyeong ◽  
Sayup Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Low Cost ◽  
Load Cell ◽  
Sitting Posture ◽  
Posture Monitoring

scholarly journals On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3479
Author(s):  
Maria Pia Del Rosso ◽  
Alessandro Sebastianelli ◽  
Dario Spiller ◽  
Pierre Philippe Mathieu ◽  
Silvia Liberata Ullo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Low Cost ◽  
Volcanic Eruptions ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Multispectral Imagery

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

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