Multi-modal low cost mobile indoor surveillance system on the Robust Artificial Intelligence-based Defense Electro Robot (RAIDER)

2012 ◽  
Author(s):  
Binu M. Nair ◽  
Yakov Diskin ◽  
Vijayan K. Asari
Keyword(s):  
Artificial Intelligence ◽  
Surveillance System ◽  
Low Cost

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

Artificial Intelligence-based Surveillance System for Railway Crossing Traffic

2020 ◽  
pp. 1-1
Author(s):  
Pavel Sikora ◽  
Lukas Malina ◽  
Martin Kiac ◽  
Zdenek Martinasek ◽  
Kamil Riha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Surveillance System ◽  
Railway Crossing

scholarly journals Low-Cost National Media-Based Surveillance System for Public Health Events, Bangladesh

2016 ◽  
Vol 22 (4) ◽  
pp. 720-722 ◽  
Author(s):  
Trong T. Ao ◽  
Mahmudur Rahman ◽  
Farhana Haque ◽  
Apurba Chakraborty ◽  
M. Jahangir Hossain ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Low Cost ◽  
Health Events ◽  
National Media

scholarly journals Study on the Surveillance System of Water Stage Used the Resonator Pressure Transducer and Wireless Connectivity

2018 ◽  
Vol 228 ◽  
pp. 02001
Author(s):  
Bing Han ◽  
Qiang Fu
Keyword(s):  
Surveillance System ◽  
Pressure Transducer ◽  
Oil And Gas ◽  
Low Cost ◽  
Regional Scale ◽  
Time Data ◽  
Long Distance ◽  
Hydrogeological Parameters ◽  
Water Stage ◽  
Wireless Connectivity

For the sake of ameliorating the faultiness of low precision for conventional surveillance methods of water stage, and realize the goal of real time data collection, automated actions and long-distance conveying, we have designed a novel surveillance system of water stage with the resonator pressure transducer and wireless connectivity technologies. The surveillance system of water stage has come into service in a field experiment project of a certain oil and gas pipeline engineering. By analyzing and comparing the results of experiments, the system has the merits of high agility, reliability, instantaneity and accuracy, low cost, capacity of resisting disturbance, which making it ideal for use in unattended supervising of water stage for multi-spots observation based on regional scale. The surveillance system can well satisfy the actual demand of auto hydrogeological parameters monitoring for geotechnical engineering.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

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