Design and Development of A-vent: A Low-Cost Ventilator with Cost-Effective Mobile Cloud Caching and Embedded Machine Learning

2021 ◽  
Author(s):  
Paul M. Cabacungan ◽  
Carlos M. Oppus ◽  
Nerissa G. Cabacungan ◽  
John Paul A. Mamaradlo ◽  
Paul Ryan A. Santiago ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Cost Effective ◽  
Mobile Cloud ◽  
Design And Development

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Design and Development of Perimeter Security Platform

2020 ◽  
Vol 9 (2S5) ◽  
pp. 64-66
Keyword(s):  
Sensor Network ◽  
Low Cost ◽  
Cost Effective ◽  
Vibration Sensor ◽  
Smart Devices ◽  
Effective Solution ◽  
Design And Development ◽  
Specific Location ◽  
Sensor Information ◽  
Ethernet Interface

There are many integrated perimeter security solutions available, the main objective of this paper is to provide cost effective solution. This paper mainly focuses on design of a Low-cost vibration module with RS485 interface, Controller hub with RS485 interface and Ethernet interface and a Command Centre server. The platform consists of RS485 daisy chained vibration sensor network connected to control server via Controller hub. It is designed to pin point the area of intrusion and cueing the camera to that specified location. It can be integrated with smart devices like PTZ/Thermal/IR cameras or radars. Each daisy chain consists of 250 sensors with each sensor 3 metres apart, each ethernet hub can handle 2 daisy chains. The Controller hub gets vibration sensor information via RS485 and transmits data to Command centre using TCP/IP protocol. The Controller centre identifies the location of the sensor and moves the PTZ camera to the specific location and live streams the data to the user.

scholarly journals Deep Learning-Enabled Point-of-Care Sensing Using Multiplexed Paper-Based Sensors

2019 ◽  
Author(s):  
Zachary Ballard ◽  
Hyou-Arm Joung ◽  
Artem Goncharov ◽  
Jesse Liang ◽  
Karina Nugroho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Cost Effective ◽  
Serum Samples ◽  
Cvd Risk ◽  
Medical Test ◽  
Sensing Membrane

ABSTRACTWe present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications.

scholarly journals Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

2019 ◽  
Vol 11 (22) ◽  
pp. 2596 ◽  
Author(s):  
Luca Zappa ◽  
Matthias Forkel ◽  
Angelika Xaver ◽  
Wouter Dorigo
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Learning Algorithm ◽  
Low Cost ◽  
Cost Effective ◽  
Training Data ◽  
Field Scale ◽  
Training Set

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype

2021 ◽  
pp. 154851292110272
Author(s):  
Dimitrios Paraschos ◽  
Nikolaos K. Papadakis
Keyword(s):  
Machine Learning ◽  
System Integration ◽  
Modular Design ◽  
Autonomous Underwater Vehicle ◽  
Low Cost ◽  
Cost Effective ◽  
Underwater Vehicle ◽  
Stability Control ◽  
Operational Environment ◽  
Image Capturing

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.

ASSESSMENT OF THE PLATELET COUNT IN THE PREGNANT WOMEN IN IGIMS, PATNA, BIHAR

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Tanwi Singh ◽  
Anshuman Sinha
Keyword(s):  
Platelet Count ◽  
Pregnant Women ◽  
Screening Test ◽  
Low Cost ◽  
Medical Science ◽  
Cost Effective ◽  
Platelet Indices ◽  
Increased Risk ◽  
Low Platelet Count ◽  
Severity Of The Disease

The major risk associated with low platelet count in pregnancy is the increased risk of bleeding during the childbirth or post that. There is an increased blood supply to the uterus during pregnancy and the surgical procedure requires cutting of major blood vessels. Women with thrombocytopenia are at increased risk of losing excessive blood. The risk is more in case of caesarean delivery as compared to vaginal delivery. Hence based on above findings the present study was planned for Assessment of the Platelet Count in the Pregnant Women in IGIMS, Patna, Bihar. The present study was planned in Department of Pathology, Indira Gandhi Institute of Medical Science, Patna, Bihar, India. The present study was planned from duration of January 2019 to June 2019. In the present study 200 pregnant females samples received for the platelet estimation were enrolled in the present study. Clinically platelet indices can be a useful screening test for early identification of preeclampsia and eclampsia. Also platelet indices can assess the prognosis of this disease in pregnant women and can be used as an effective prognostic marker because it correlates with severity of the disease. Platelet count is a simple, low cost, and rapid routine screening test. Hence the data generated from the present study concludes that platelet count can be used as a simple and cost effective tool to monitor the progression of preeclampsia, thereby preventing complications to develop during the gestational period. Keywords: Platelet Count, Pregnant Women, IGIMS, Patna, Bihar, etc.

Janus – A New Approach to Air Combat Pilot Training

2019 ◽  
Vol 2019 (4) ◽  
pp. 7-22
Author(s):  
Georges Bridel ◽  
Zdobyslaw Goraj ◽  
Lukasz Kiszkowiak ◽  
Jean-Georges Brévot ◽  
Jean-Pierre Devaux ◽  
...  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Energy ◽  
Training System ◽  
Embedded Sensors ◽  
Pilot Training ◽  
New Approach ◽  
Combat Aircraft ◽  
Air Combat ◽  
The Cost

Abstract Advanced jet training still relies on old concepts and solutions that are no longer efficient when considering the current and forthcoming changes in air combat. The cost of those old solutions to develop and maintain combat pilot skills are important, adding even more constraints to the training limitations. The requirement of having a trainer aircraft able to perform also light combat aircraft operational mission is adding unnecessary complexity and cost without any real operational advantages to air combat mission training. Thanks to emerging technologies, the JANUS project will study the feasibility of a brand-new concept of agile manoeuvrable training aircraft and an integrated training system, able to provide a live, virtual and constructive environment. The JANUS concept is based on a lightweight, low-cost, high energy aircraft associated to a ground based Integrated Training System providing simulated and emulated signals, simulated and real opponents, combined with real-time feedback on pilot’s physiological characteristics: traditionally embedded sensors are replaced with emulated signals, simulated opponents are proposed to the pilot, enabling out of sight engagement. JANUS is also providing new cost effective and more realistic solutions for “Red air aircraft” missions, organised in so-called “Aggressor Squadrons”.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

Sign in / Sign up

Export Citation Format

Share Document