Autor: Herzog, J. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Artykuł

Skocz do pozycji: 2.

Tytuł:: DC and low-frequency noise analysis for buried SiGe channel metamorphic PMOSFETs with high Ge content
Autorzy:: Durov, S.
Mironov, O. A.
Myronov, M.
Whall, T. E.
Parker, E. H. C.
Hackbarth, T.
Hoeck, G.
Herzog, H. J.
König, U.
Känel von, H.
Powiązania:: https://bibliotekanauki.pl/articles/958103.pdf
Data publikacji:: 2005
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: SiGe
metamorphic MOSFET
LF-noise
I-V
C-V
effective hole mobility
Opis:: Measurements of current drive in p-Si1-xGex MOSFETs, with x = 0.7, 0.8 reveal an enhancement ratio of over 2 times as compared to a Si device at an effective channel length of 0.55 žm. They also show a lower knee voltage in the output I-V characteristics while retaining similar values of drain induced barrier lowering, subthreshold swing, and off current for devices with a Sb punch-through stopper. For the first time, we have quantitatively explained the low-frequency noise reduction in metamorphic, high Ge content, SiGe PMOSFETs compared to Si PMOSFETs.
Źródło:: Journal of Telecommunications and Information Technology; 2005, 1; 101-111
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 3.

Tytuł:: Teaching Machines on Snoring : A Benchmark on Computer Audition for Snore Sound Excitation Localisation
Autorzy:: Qian, K.
Janott, C.
Zhang, Z.
Deng, J.
Baird, A.
Heiser, C.
Hohenhorst, W.
Herzog, M.
Hemmert, W.
Schuller, B.
Powiązania:: https://bibliotekanauki.pl/articles/177964.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: snore sound
obstructive sleep apnea
acoustic features
machine learning
Opis:: This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband Energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.
Źródło:: Archives of Acoustics; 2018, 43, 3; 465-475
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

Artykuł

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