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Number of results: 7
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Abstract

A novel all optical measurement scheme is proposed to measure wideband microwave frequencies up to 30 GHz. The proposed method is based on a four-wave mixing (FWM) approach in a semiconductor optical amplifier (SOA) of both even order side-bands generated by an unknown microwave frequency modulating an optical carrier. The optical power of a generated FWM signal depends on frequency spacing between extracted side-bands. A mathematical relation is established between FWM power and frequency of an unknown signal. A calibration curve is drawn based on the mathematical relation which predicts the unknown frequency from power withdrawn after FWM

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Authors and Affiliations

A. Kumar
V. Priye
R. Raj Singh
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Abstract

Atopic dermatitis (AD) is the most frequent allergic disease in dogs. AD can be treated using allergenspecific immunotherapy as well as symptomatic antipruritic treatment including the use of lokivetmab - caninized anti-interleukine-31 antibody.

The aim of the study was to evaluate the effectiveness of lokivetmab over 12 weeks of treatment. Studies have been carried out in 89 dogs. In all affected animals, the severity of lesions was assessed using the CADESI 04 and the pruritus was assessed using the VAS.

After the first dose of lokivetmab, both CADESI 04 and VAS statistical decreased by 4 weeks from 40.48 to 20.31, and from 7.42 to 2.48, respectively (p = 0.0000001) maintained significantly decresed values during the whole treatment period (CADESI 04 15.64, 15.07 after 8 and 12 weeks, respectively, PVAS 2.03, 1.95 after 8 and 12 weeks, respectively).

Lokivetmab leads to a significant reduction of CADESI 04 and pruritus, within four weeks and maximum effect is achived after the second dose.

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Authors and Affiliations

M.P. Szczepanik
J. Popiel
A. Cekiera
D. Pomorska-Handwerker
J. Karaś-Tęcza
M. Ściskalska
K. Oczkowska
M. Taube
V. Olender
P. Parys
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Abstract

This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (LSTM) hybrid used as an acoustic model for Speech Recognition. It was demonstrated by many independent researchers that DBNNs exhibit superior performance to other known machine learning frameworks in terms of speech recognition accuracy. Their superiority comes from the fact that these are deep learning networks. However, a trained DBNN is simply a feed-forward network with no internal memory, unlike Recurrent Neural Networks (RNNs) which are Turing complete and do posses internal memory, thus allowing them to make use of longer context. In this paper, an experiment is performed to make a hybrid of a DBNN with an advanced bidirectional RNN used to process its output. Results show that the use of the new DBNN-BLSTM hybrid as the acoustic model for the Large Vocabulary Continuous Speech Recognition (LVCSR) increases word recognition accuracy. However, the new model has many parameters and in some cases it may suffer performance issues in real-time applications.
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Authors and Affiliations

Łukasz Brocki
Krzysztof Marasek
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Abstract

Efficient ultrasonic noise reduction by using enclosures requires the knowledge of absorbing properties of materials in the frequency range above 4 kHz. However, standardized methods enable determination of absorption coefficients of materials in the frequency range up to 4 kHz. For this reason, it is proposed to carry out measurements of the sound absorption properties of materials in the free field by means of a tone-burst technique in the frequency range from 4 kHz to 40 kHz at angles of incidence varying from 0° to 60°. The absorption coefficient of a material is calculated from the reflection coefficient obtained by reflecting a tone-burst from both a perfectly reflecting panel and a combination of this panel and the sample of the tested material. The tests results show that mineral wool and polyurethane open-cell foam possess very good absorbing properties in this frequency range.
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Authors and Affiliations

Dariusz Pleban

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