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Łukasz Lentka
Researcher at Gdańsk University of Technology
Publications - 14
Citations - 181
Łukasz Lentka is an academic researcher from Gdańsk University of Technology. The author has contributed to research in topics: Noise & Capacitor. The author has an hindex of 6, co-authored 14 publications receiving 154 citations.
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Journal ArticleDOI
Determination of gas mixture components using fluctuation enhanced sensing and the ls-svm regression algorithm
TL;DR: In this article, the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing was analyzed by using fluctuation-enhanced sensing.
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Methods of trend removal in electrochemical noise data - Overview
Łukasz Lentka,Janusz Smulko +1 more
TL;DR: A comparison of popular methods of trend removal from electrochemical noise time records and an indication of the most suitable one for removing the drift component from the acquired electrochemical data is summarized.
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Detection of Gaseous Compounds with Different Techniques
Janusz Mikołajczyk,Zbigniew Bielecki,Tadeusz Stacewicz,Janusz Smulko,Jacek Wojtas,D. Szabra,Łukasz Lentka,Artur Prokopiuk,P. Magryta +8 more
TL;DR: In this article, the authors presented the description, comparison and recent progress in some existing gas sensing technologies, such as catalytic, thermal conductivity, electrochemical, semiconductor and surface acoustic wave ones, and discussed some examination results of the constructed devices.
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Fluctuation-enhanced sensing with organically functionalized gold nanoparticle gas sensors targeting biomedical applications.
Łukasz Lentka,Mateusz Kotarski,Janusz Smulko,Umut Cindemir,Zareh Topalian,Claes-Göran Granqvist,Raul Calavia,Radu Ionescu +7 more
TL;DR: Results on formaldehyde detection using fluctuation-enhanced gas sensing showed that formaldehyde was easily detected via intense fluctuations of the gas sensor's resistance, while the cross-influence of ethanol vapor was negligible.
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Application of Statistical Features and Multilayer Neural Network to Automatic Diagnosis of Arrhythmia by ECG Signals
Amine Ben Slama,Łukasz Lentka,Aymen Mouelhi,Mohamed Fethi Diouani,Mounir Sayadi,Janusz Smulko +5 more
TL;DR: This paper explores a method of de-noising ECG signal by the discrete wavelet transform (DWT) and further detecting arrhythmia by estimated statistical parameters and provides more accurate diagnosis based on the examined ECGs.