scholarly journals Real-Time Monitoring and Reminding of Remote Peritoneal Dialysis System Based on the Principle of Least Squares

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Wu J ◽  
Ji Z ◽  
Pi M ◽  
Yi T
Keyword(s):  
Peritoneal Dialysis ◽  
Kidney Disease ◽  
Real Time ◽  
Low Cost ◽  
Mobile Network ◽  
Least Square ◽  
Transmission Protocol ◽  
Acquisition Device ◽  
Abnormal Points ◽  
Real Patients

Peritoneal dialysis has been widely studied and applied for kidney disease because of its low cost and easy operation. Given the development of chronic kidney disease worldwide, peritoneal dialysis has attracted more and more attention. At the same time, with the development and popularization of mobile network technology, mobile telematics has begun to become a mainstream trend. By integrating the experience of clinicians, the remote diagnosis and treatment system of the peritoneal dialysis developed by Shenzhen Traditional Chinese Medicine Hospital can monitor the entire peritoneal dialysis data of patients. The peritoneal dialysis data were analyzed by statistical methods. In this paper, we designed a data acquisition device with Bluetooth transmission protocol and a user APP to collect peritoneal dialysis data from experimental patients, and built a regression model based on the least square principle according to the clinical data of real patients. Through the model, abnormal or discrete points can be identified in real time. In clinical practice, by analyzing the possible medical risks and adverse events of patients according to the abnormal points, we realize the function of prediction and early reminding. The system indicates the results to patients according to the confidence interval of regression prediction, which greatly strengthens the interaction of the system and improves patient compliance.

Design of a wearable device for real-time screening of urinary tract infection and kidney disease based on smartphone

The Analyst ◽  
2018 ◽  
Vol 143 (12) ◽  
pp. 2812-2818 ◽  
Author(s):  
Jianyu Zhou ◽  
Tao Dong
Keyword(s):  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Quantitative Analysis ◽  
Kidney Disease ◽  
Tract Infection ◽  
Real Time ◽  
Low Cost ◽  
Wearable Device ◽  
Urine Samples

In this study, we developed a novel wearable and low-cost device for qualitative screening of glucose (GLU), leukocytes (LEU), and nitrite (NIT) and for semi-quantitative analysis of blood (BLD) and proteins (PRO) in the urine samples.

scholarly journals Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3031
Author(s):  
Paul English ◽  
Heather Amato ◽  
Esther Bejarano ◽  
Graeme Carvlin ◽  
Humberto Lugo ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Data Interpretation ◽  
Air Monitoring ◽  
Least Square ◽  
Quality Data ◽  
Monitoring Networks ◽  
Time Data ◽  
Ordinary Least Square Regression

Air monitoring networks developed by communities have potential to reduce exposures and affect environmental health policy, yet there have been few performance evaluations of networks of these sensors in the field. We developed a network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter. We report here on the performance of the Network to date by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015–2018) on a network-wide basis. Annual mean levels of PM10 did not differ statistically from regulatory annual means, but did for PM2.5 for two out of the 4 years. R2s from ordinary least square regression results ranged from 0.16 to 0.67 for PM10, and increased each year of operation. Sensor variability was higher among the Network monitors than the regulatory monitors. The Network identified a larger number of pollution episodes and identified under-reporting by the regulatory monitors. The participatory approach of the project resulted in increased engagement from local and state agencies and increased local knowledge about air quality, data interpretation, and health impacts. Community air monitoring networks have the potential to provide real-time reliable data to local populations.

scholarly journals An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7265
Author(s):  
Zhitao Lyu ◽  
Yang Gao
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Urban Environments ◽  
Least Square ◽  
Single Frequency ◽  
Line Of Sight ◽  
Support Vector ◽  
Positioning Accuracy ◽  
Gnss Receiver ◽  
Gnss Positioning

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

scholarly journals Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

2017 ◽  
Author(s):  
Sarab S. Sethi ◽  
Robert M. Ewers ◽  
Nick S. Jones ◽  
C. David L. Orme ◽  
Lorenzo Picinali
Keyword(s):  
Open Source ◽  
Real Time ◽  
Spatial Scales ◽  
Low Cost ◽  
Mobile Network ◽  
Cost Effective ◽  
Automated Analysis ◽  
Time Lapse ◽  
List Type ◽  
Ecosystem Monitoring

AbstractAutomated methods of monitoring ecosystems provide a cost-effective way to track changes in natural system’s dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort.Here we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography.We show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.We provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.

scholarly journals Real-time monitoring and reminding of remote peritoneal dialysis system based on the principle of least squares

2020 ◽  
Author(s):  
Jia Wu ◽  
Zheng Ji ◽  
Min Pi ◽  
Tiegang Yi
Keyword(s):  
Peritoneal Dialysis ◽  
Kidney Disease ◽  
Least Squares ◽  
Filtration Rate ◽  
Least Squares Method ◽  
Regression Equation ◽  
Least Square ◽  
Telemedicine System ◽  
Peritoneal Effusion ◽  
Standard Range

Abstract Background As an important treatment for the treatment of kidney disease, peritoneal dialysis has been widely studied and applied due to its low cost and easy operation. Given that chronic kidney disease is growing globally, peritoneal dialysis is receiving increasing attention. With the development and popularization of mobile network technology, mobile telematics began to become a mainstream trend. The emergence of mobile telemedicine system is an important result of applying the universal computing concept to medical purposes. However, as users are not familiar with the medical field, telemedicine technology depends to a large extent on the patient's acceptance of the use of them.Methods By integrating the experience of clinicians, the remote diagnosis and treatment system of peritoneal dialysis developed by Shenzhen Traditional Chinese Medicine Hospital can monitor the whole course of peritoneal dialysis data of patients. We used statistical methods to empirically analyze the peritoneal dialysis data. By exploring data over a standard duration of time, the filtration rate per minute of the peritoneal dialysis patients using a 1.5% low-calcium peritoneal solution was reduced over time and had a power function relationship which can help to remind incorrect data. The linear equation can be obtained by least square regression of the data after the time of peritoneal effusion and the weight of the effluent deformed.Results The least squares method was used to regress the patient's peritoneal dialysis data (logarithm of peritoneal dialysis time and filtration rate per minute), and the regression equation R square was equal to 0.95. The regression coefficient passed the T test and the regression equation fits well. According to the result parameters of the regression equation, we calculated the standard range of filtration rate for each peritoneal dialysis. Taking 441 cases of a random patient as an example, 438 cases of diafiltration rate met the standard range. 3 cases were filtered out below the standard.Conclusions The system can inform the patients of the results according to the confidence interval of the regression prediction, which greatly strengthens the interaction of the system and increases the patients' compliance.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

A Low-Cost, IP-Based Mobile Network Emulator (MNE)

2003 ◽  
Author(s):  
Joseph P. Macker ◽  
William Chao ◽  
Jeffrey W. Weston
Keyword(s):  
Low Cost ◽  
Mobile Network ◽  
Network Emulator

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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