Reasoning about sensor data for automated system identification

The implementation of an automatic irrigation system based on the microcontroller and a wireless system network is presented in this paper. This implementation aims to demonstrate that automatic irrigation can be used to minimize and optimize water use. The automated irrigation system consists of the master control unit (MCU) and a distributed wireless sensor network (WSN). The communication between the WSN and the MCU is via a radio frequency (NRF25L01). The MCU has a radio transceiver that receives the sensor data from the wireless sensor network also has a communication link based cellular-internet interface using general packet radio service and a global system for mobile (GSM/GPRS). The activation of the automated system is done when the threshold value of the sensors in the WSN is reached. Each WSN consists of a soil moisture sensor probe, soil temperature probe, radio transceiver, and a microcontroller. The sensor measurements are transmitted to the MCU to analyze and activate/deactivate the automatic irrigation system. The internet connection using GPRS allows the data inspection in real-time on a server, where the temperature and soil moisture data are graphically displayed on the server using a graphical application and stored these data in a database server.

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City Wide Participatory Sensing of Air Quality

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.773778 ◽

2021 ◽

Vol 9 ◽

Author(s):

Andrew Rebeiro-Hargrave ◽

Pak Lun Fung ◽

Samu Varjonen ◽

Andres Huertas ◽

Salla Sillanpää ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Age Groups ◽

Personal Exposure ◽

Urban Environments ◽

Sensor Data ◽

Automated System ◽

Participatory Sensing ◽

Good Correspondence

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

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Communicable Knowledge in Automated System Identification

Lecture Notes in Computer Science - Computational Discovery of Scientific Knowledge ◽

10.1007/978-3-540-73920-3_2 ◽

2007 ◽

pp. 17-43 ◽

Cited By ~ 1

Author(s):

Reinhard Stolle ◽

Elizabeth Bradley

Keyword(s):

System Identification ◽

Automated System

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Real-Time Bradycardia Prediction in Preterm Infants Using a Dynamic System Identification Approach

Journal of Engineering and Science in Medical Diagnostics and Therapy ◽

10.1115/1.4045147 ◽

2019 ◽

Vol 3 (1) ◽

Author(s):

Soovadeep Bakshi ◽

Tianheng Feng ◽

Dongmei Chen ◽

Wei Li

Keyword(s):

System Identification ◽

Dynamic System ◽

Preterm Infants ◽

Neonatal Intensive Care ◽

Automated System ◽

Developmental Problems ◽

Identification Approach ◽

Future Work ◽

Electrocardiogram Ecg ◽

Ecg Data

Abstract Chronic bradycardia, or slowing of heart rate, is common in preterm infants, and may often lead to neuropsychiatric disorders, developmental problems, and impaired cognitive functions in the long term. Therefore, early detection and treatment of bradycardia is important. To this end, we present a system identification-based approach to the prediction of bradycardia in preterm infants. This algorithm is based on the notion that the cardiovascular system can be treated as a dynamic system, and that under bradycardia, this system reacts abnormally due to temporal and spatial destabilization. This paper presents a proof-of-concept of the proposed methodology by testing its performance using electrocardiogram (ECG) data collected from ten preterm infants. We show that the proposed algorithm is correctly able to predict bradycardia occurrences (mean area under the receiver operating characteristic (ROC) curve = 0.782 and variance = 0.0039) while minimizing the training or burn-in period. The physical interpretation of the results using the system dynamics approach is discussed. The developed algorithm performs well on not only classifying normal to abnormal conditions, but also showing a trend of transition between the two conditions. Future work is also discussed to further improve the algorithm and implement the algorithm in the neonatal intensive care unit. Our proposed method is able to predict bradycardia using only ECG data with minimal training period and can be integrated into an automated system for bradycardia detection and treatment, and therefore, reduce the risks related to bradycardia in preterm infants.

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