Autor: Mikołajczyk, S. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Explosives vapors–concentrating and optoelectronic detection
Autorzy:: Wojtas, J.
Rutecka, B.
Popiel, S.
Nawała, J.
Wesołowski, M.
Mikołajczyk, J.
Cudziło, S.
Bielecki, Z.
Powiązania:: https://bibliotekanauki.pl/articles/221395.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: explosives concentrator
explosive detection
optoelectronic sensors
CEAS sensors
Opis:: Detection of explosives vapors is an extremely difficult task. The sensitivity of currently constructed detectors is often insufficient. The paper presents a description of an explosive vapors concentrator that improves the detection limit of some explosives detectors. These detector s have been developed at the Institute of Optoelectronics. The concentrator is especially dedicated to operate with nitrogen oxide detectors. Preliminary measurements show that using the concentrator, the recorded amount of nitrogen dioxide released from a 0.5 ng sample of TNT increases by a factor of approx. 20. In the concentrator an induction heater is applied. Thanks to this and because of the miniaturization of the container with an adsorbing material (approx. 1 cm³), an extremely high rate of tempera ture growth is achieved (up to 500°C within approx. 25 s). The concentration process is controlled by a microcontroller. Compact construction and battery power supply provide a possibility of using the concentrator as a portable device.
Źródło:: Metrology and Measurement Systems; 2014, 21, 2; 177-190
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Machine learning system for automated blood smear analysis
Autorzy:: Grochowski, Michał
Wąsowicz, Michał
Mikołajczyk, Agnieszka
Ficek, Mateusz
Kulka, Marek
Wróbel, Maciej S.
Jędrzejewska-Szczerska, Małgorzata
Powiązania:: https://bibliotekanauki.pl/articles/220750.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: optical microscopy
blood cells
biophotonics
image analysis
classification
eigenfaces
neural networks
decision support
nanodiamonds
bioimaging
Opis:: In this paper the authors propose a decision support system for automatic blood smear analysis based onmicroscopic images. The images are pre-processed in order to remove irrelevant elements and to enhancethe most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.
Źródło:: Metrology and Measurement Systems; 2019, 26, 1; 81-93
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

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