AN INTERNET-OF-THINGS APPLICATION TO ASSIST THE DETECTION OF FALLING TO THE GROUND
Yifei Yu1, Yu Sun2, Fangyan Zhang3, 1USA, 2California State Polytechnic University, USA, 3ASML, USA
As people get old, the risk of them falling increases; the fall will impact senior citizens more negatively than younger people. My grandmother once fell and hit her when she was alone at home, and she instantly became unconscious. Frequently, senior citizens are unable to help themselves after they fall, even if they remain conscious. However, there isn’t a product that senior citizens can use to notify their relatives right away if they fall, and this leads to the question of how we can bring immediate aid to all senior citizens after they fall. This paper brings forward the product and software that can solve this problem. The product is a small wristband that detects any falls or collisions and notifies relatives right away. The software is an accompanying app that shows the data recorded from those falls or collisions, specifically designed for family members to keep track of their elders. We applied our application during our test sessions and conducted a qualitative evaluation of the approach. The results show that this experiment is a great solution to our problem, but with a few limitations and weaknesses.
Detection of falling, wristband, iOS, Android.
Full Paper
https://aircconline.com/csit/papers/vol10/csit101206.pdf
Volume Link :
http://airccse.org/csit/V10N12.html
SMARTTANK: AN INTERNET-OF-THINGS (IOT) APPLICATION TO AUTOMATE THE WATER TANK REFILLING USING COMPUTER VISION AND AI
Henry Hamilton1, Yu Sun2, Fangyan Zhang3, 1USA, 2California State Polytechnic University, USA, 3ASML, USA
This system provides a method of automatically keeping water bowls full and refilling every time it is detected that they are not. This is highly useful for anyone who owns a pet, as it decreases the amount of work the owner will need to do. The system uses an AI model, trained with over a thousand images of water bowls. This allows it to accurately determine when a bowl needs filling. When an empty bowl is spotted, a subsystem consisting of a valve and other electronic parts releases stored water into the bowl. Through experimentation it has been shown the accuracy of the system is about 97% under optimal lighting conditions. Without a light source, the system does not function. Currently, the components are not of the highest quality and the system only works with the bowl used in testing. There are future plans to train the model with new pictures featuring an assortment of bowls. Additionally, an LED could be added to the system to solve the issue of it not working without external light.
Artificial Intelligence, image detection, RPI system processor.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101213.pdf
Volume Link :
http://airccse.org/csit/V10N12.html
A SMART INTERNET-OF-THINGS APPLICATION FOR SHOE RECOMMENDATIONS USING PRESSURE SENSOR AND RASPBERRY PI
VYutian Fan1, Yu Sun2 and Fangyan Zhang3, 1Milton Academy, USA, 2California State Polytechnic University, USA 3ASML, USA
Running is one of the most important and simple sports spanning various ages, which can train throughout body and muscle. For running, proper shoes not only improve runners’ performance but also protect them from injury to some extent. However, runners have difficulty in finding a pair of shoes which fit runners’ gait patterns and feet shape very well. The process of selection of shoes is not effective and necessarily accurate. In this paper, we propose a new tool which facilitates the process by employing electronic sensors to the insoles of shoes and collecting feet information for runner accurately. It is helpful for runners to find the best fit shoes.
Machine learning, Firebase, Mobile application, Model fitting.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101214.pdf
Volume Link :
http://airccse.org/csit/V10N12.html
AN INTERNET OF THINGS (IOT) SOLUTION TO OPTIMISE THE LIVESTOCK FEED SUPPLY CHAIN
David Raba1, Salvador Gurt2, Oriol Vila2 and Esteve Farres2, 1Universitat Oberta de Catalunya, Spain and 2Insylo Technologies Inc., Spain
The animal feed supply chain to farm, mainly represented by the feed suppliers and livestock farmers, currently faces great inefficiencies due to outdated supply chain management. Stakeholders struggle with the timing and quantity evaluation when restocking their feed bins, significantly affecting cost and labour efficiency. However, the lack of accurate and cost-effective sensors to measure stock levels of solid materials stored in containers and open piles is preventing the implementation of these strategies in a large number of industrial sectors. In these cases, traditional technologies cannot offer a convenient solution due to an inevitable trade-off between accuracy and cost. This work develops an integral feedstock management system to optimise the entire supply chain. A new monitoring system based on an RGB-D sensor is presented as well as the data processing pipeline from raw depth measurements to bin specific daily consumption rates.
Inventory management, Vendor Managed Inventories, Internet of Things
For More Details :
http://aircconline.com/csit/papers/vol10/csit100409.pdf
Volume Link :
http://airccse.org/csit/V10N04.html
DATA PREDICTION OF DEFLECTION BASIN EVOLUTION OF ASPHALT PAVEMENT STRUCTURE BASED ON MULTI-LEVEL NEURAL NETWORK
OShaosheng Xu, Jinde Cao and Xiangnan Liu, Southeast University, China
Aiming at reducing the high cost of test data collection of deflection basins in the structural design of asphalt pavement and shortening the long test time of new structures, this paper innovatively designs a structure coding network based on traditional neural networks to map the pavement structure to an abstract space. Therefore, the generalization ability of the neural network structure is improved, and a new multi-level neural network model is formed to predict the evolution data of the deflection basin of the untested structure. By testing the experimental data of RIOHTRACK, the network structure predicts the deflection basin data of untested pavement structure, of which the average prediction error is less than 5%.
multi-level neural network, Encoding converter, structural of asphalt pavement, deflection basins, RIOHTRACK
For More Details :
: https://aircconline.com/csit/papers/vol10/csit101304.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
THE TEMTUM CONSENSUS ALGORITHM – A LOW ENERGY REPLACEMENT TO PROOF OF WORK
Richard Dennis and Gareth Owenson, University of Portsmouth, United Kingdom
This paper presents a novel consensus algorithm deployed within the Temtum cryptocurrency network. An overview of the proof of work consensus algorithm is presented, and gaps in the research are outlined. The Temtum consensus algorithm's unique components, including the Node Participation Document (NPD) and the use of the NIST randomness beacon, are outlined and explained. Comparisons on the cost to attack the consensus algorithm and energy consumption between the Temtum consensus algorithm and Bitcoin’s proof of work is presented and evaluated. We conclude this paper summarising the findings of the research and presenting future work to be conducted. .
Blockchain, Peer-to-Peer Networks, Crypto currencies, Consensus, Byzantine Fault Tolerance.
For More Details :
http://aircconline.com/csit/papers/vol10/csit101301.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
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