Treatment of MZI sensor signal with an improved wavelet neighboring coefficients

Label free biosensors based on electrolyte gated organic transistors (EGOTs) are ultra-sensitive and versatile sensing devices. The dose curve represents the change of the sensor signal as a function of...

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Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors

Sensors ◽

10.3390/s21144917 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4917

Author(s):

Beata Bąk ◽

Jakub Wilk ◽

Piotr Artiemjew ◽

Jerzy Wilde

Keyword(s):

Gas Sensors ◽

Culture Media ◽

Laboratory Diagnosis ◽

Baseline Correction ◽

American Foulbrood ◽

Sensor Signal ◽

Sodium Pyruvate ◽

Mueller Hinton ◽

Regeneration Phase ◽

Mueller Hinton Broth

American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers.

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Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

Atmosphere ◽

10.3390/atmos12050645 ◽

2021 ◽

Vol 12 (5) ◽

pp. 645

Author(s):

Kristen Okorn ◽

Michael Hannigan

Keyword(s):

Metal Oxide ◽

Low Cost ◽

Air Pollutant ◽

Sensor System ◽

Physical Factors ◽

Time Averaging ◽

Calibration Model ◽

Sensor Signal ◽

Metal Oxide Sensors ◽

Signal Normalization

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

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Section 4: Sensor, Signal and Imaging Informatics: Sensors, Medical Image and Signal Processing

Yearbook of Medical Informatics ◽

10.1055/s-0038-1638489 ◽

2006 ◽

Vol 15 (01) ◽

pp. 70-73

Author(s):

R.W Westphal ◽

Keyword(s):

Medical Image ◽

Selection Process ◽

Imaging Informatics ◽

Sensor Signal ◽

Tissue Modeling ◽

Excellent Research ◽

Body Sensors ◽

Selection For ◽

Soft Tissue Modeling ◽

Future Patient

SummaryObjectives:To summarize current excellent research in the field of sensor, signal and imaging informatics.Method:Synopsis of the articles selected for the IMIA Yearbook 2007.Results:The selection process for this yearbook section “Sensor, signal and imaging informatics” results in five excellent articles, representing research in four different nations. Papers from the fields of brain machine interfaces, sound surveillance in telemonitoring, soft tissue modeling, and body sensors have been selected.Conclusion:The selection for this yearbook section can only reflect a small portion of the worldwide copious work in the field of sensors, signal and image processing with applications in medical informatics. However, the selected papers demonstrate, how advances in this field may positively affect future patient care.

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Study of the Metal Crack Propagation by Acoustic Emission Testing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.105-107.2179 ◽

2011 ◽

Vol 105-107 ◽

pp. 2179-2182

Author(s):

Wei Min Zhang ◽

Shu Xuan Liu ◽

Yong Qiu ◽

Cheng Feng Chen

Keyword(s):

Acoustic Emission ◽

Crack Propagation ◽

Acquisition System ◽

Signal Acquisition ◽

Sensor Signal ◽

Acoustic Emission Sensor ◽

Axial Tension ◽

Emission Testing ◽

Acoustic Emission Testing ◽

Signal Operating

Crack propagation is the main reason which leads to the invalidity of the metal components. A set of detecting equipment based on the acoustic emission method was designed, and it was mainly composed of acoustic emission sensor, signal operating circuits and signal acquisition system. Specimens of 16MnR material were manufactured and the static axial tension test of them was carried on. Acoustic emission signals from the specimen were detected by acoustic emission equipment by using piezoelectric ceramic sensor. Signal datum were acquired and operated by the acquisition system, as well as the acquisition program written for it. The final results has demonstrated that acoustic emission equipment designed for the test performed well in acquiring the signals induced by the metal crack propagation.

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Sensor, Signal, and Imaging Informatics

Yearbook of Medical Informatics ◽

10.15265/iy-2017-019 ◽

2017 ◽

Vol 26 (01) ◽

pp. 120-124

Author(s):

W. Hsu ◽

S. Park ◽

Charles Kahn

Keyword(s):

Medical Informatics ◽

Scientific Work ◽

Imaging Informatics ◽

Sensor Signal ◽

Imaging Data ◽

Extensive Search ◽

Challenges And Opportunities ◽

Final Selection ◽

Selection Of

Summary Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

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