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Andrzej Skoczeń

Publikacje

[1]
W. Dabrowski, P. Grybos, P. Hottowy, A. Skoczen, K. Swientek, A.A. Grillo, S. Kachiguine, A.M. Litke, A. Sher
Development of front-end ASICs for imaging neuronal activity in live tissue.
Nuclear Instruments and Methods A (2005), Vol. 541, 405-411.

[2]
P. Grybos, M. Idzik, A. Skoczen
Design of low noise charge amplifier in sub-micron technology for fast shaping time .
Analog Integr Circ S 49 (2): 107-114 Nov 2006

[3]
P. Hottowy, W. Dąbrowski, A. Skoczeń, P. Wiącek
An integrated multichannel waveform generator for large-scale spatio-temporal stimulation of neural tissue.
Analog Integrated Circuits and Signal Processing (2008), Vol. 55, 239-248 Link  

[4]
A. Skoczeń, A. Skała
Zabezpieczenia nadprzewodzących elementów LHC przed skutkami utraty stanu nadprzewodzącego.
Przegląd Elektrotechniczny, Rocznik 2009, tom R. 85, nr 7, Str.: 65-72 Link  

[5]
A. Skoczeń, A. Skała
Testy zabezpieczeń przed skutkami quenchu w nadprzewodzących obwodach LHC.
Przegląd Elektrotechniczny, Rocznik 2009, tom R. 85, nr 7, Str.: 73-75 Link  

[6]
P. Hottowy, A. Skoczeń, D.E. Gunning, S. Kachiguine, K. Mathieson, A. Sher , P. Wiącek, A.M. Litke, W. Dąbrowski
Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue.
Journal of Neural Engineering 9 (2012), 066005 Link  

[7]
J.Steckert, A.Skoczen
Design of FPGA-based radiation tolerant quench detectors for LHC.
2017 JINST 12 T04005 Link  

[8]
M. Wielgosz, A. Skoczeń, M. Mertik
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets.
NIM A, vol. 867 (2017)40-50 Link  

[9]
M. Wielgosz, M. Mertik, A. Skoczeń, E. De Matteis
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization.
Engineering Applications of Artificial Intelligence, 2018, vol. 74, p. 166-185 Link  

[10]
M.Wielgosz, A.Skoczeń, E. De Matteis
Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor.
Sensors 2018, 18, 3933 Link  

[11]
Maciej Wielgosz, Andrzej Skoczeń
Using Neural Networks with Data Quantization for Time Series Analysis in LHC Superconducting Magnets.
Int. J. Appl. Math. Comput. Sci., 2019, Vol. 29, No. 3, 503–515 Link  

[12]
Maciej Wielgosz, Andrzej Skoczeń
Recurrent Neural Networks with Grid Data Quantization for Modeling LHC Superconducting Magnets Behavior.
Information Technology, Systems Research, and Computational Physics; ed.: Piotr Kulczycki, Janusz Kacprzyk, Laszlo T. Kóczy, Radko Mesiar, Rafał Wiśniewski; Springer International Publishing; Cham 2020; ISBN 978-3-030-18058-4; pages 177-190 Link  

[13]
P. Jurgielewicz, T. Fiutowski, E. Kublik, A. Skoczeń, M. Szypulska, P. Wiącek, P. Hottowy, B. Mindur
Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics.
Sensors 2021, 21, 4423. https://doi.org/10.3390/s21134423 Link  

Materiały konferencyjne

[1]
P. Gryboś, W. Dąbrowski, A. Skoczeń, R. Szczygieł
A very low frequency band-pass filter.
Proceedings of the 5-th International Conference „Mixed Design of Integrated Circuits and Systems” MIXDES’98, p. 271.Łódź, Poland 18-20 June 1998.

[2]
P. Gryboś, W. Dąbrowski, M. Idzik, A. Skoczeń, R. Szczygieł
Development of a multichannel chip for readout of signals from biological systems.
Proceedings of the 6-th International Conference „Mixed Design of Integrated Circuits and Systems” MIXDES’99, p. 483.Kraków, Poland 17-19 June 1999.

[3]
W. Dąbrowski, P. Gryboś, P. Hottowy, A. Skoczeń, K. Świentek, N. Bezayiff, A.A. Grillo, S. Kachiguine, A.M. Litke, A. Sher
Development of Integrated Circuits for Readout of Microelectrode Arrays to Image Neuronal Activity in Live Retinal Tissue.
Nuclear Science Symposium Conference Record, 2003 IEEE October 2003, Portland, USA, Vol. 2, 956 - 960

[4]
W. Dąbrowski, P. Gryboś, A.M. Litke, P. Hottowy, A. Skoczen, K. Świentek, N. Beyayiff, A.A. Grillo, S. Kachiguine, A.M. Litke, A. Sher
Development of integrated circuits for readout of microelectrode arrays to image neuronal activity in live retinal tissue.
2003-IEEE-Nuclear-Science-Symposium. -Conference-Record-IEEE-Cat- No.03CH37515. 2004: 956-60 Vol.2, IEEE, Piscataway, NJ, USA.

[5]
P. Gryboś, M. Idzik, A. Skoczeń
Design of low noise charge amplifier in submicron technology for fast shaping time.
Proceedings of the 12th International Conference "Mixed Design of Integrated Circuits and Systems" MIXDES 2005, Kraków, Poland, 22-25 June 2005, 105-110

[6]
P. Hottowy, W. Dąbrowski, A. Skoczeń
A multichannel ASIC for stimulation of live neural tissue – analysis of stimulation artifacts and design considerations.
Proceedings of the 12th International Conference "Mixed Design of Integrated Circuits and Systems" MIXDES 2005, Kraków, Poland, 22-25 June 2005, 591-596

[7]
P. Hottowy, W. Dąbrowski, A. Skoczeń
Stimchip – a multichannel ASIC for programmable in real time stimulation of neural cells using MEAs.
Proceedings of 5th International Meeting on Substrate-Integrated Micro Electrode Arrays - MEA Meeting 2006, July 4–7, 2006, Reutlingen, Germany, 184–185.

[8]
P. Hottowy, W. Dąbrowski, A. Skoczeń, P. Wiącek
Design of a multichannel ASIC for large scale spatio-temporal distributed simulation of neural tissue.
Proceedings of the 13th International Conference "Mixed Design of Integrated Circuits and Systems" MIXDES 2006, Gdynia, Poland, 22-24 June 2006, 746-751.

[9]
P. Hottowy, W. Dąbrowski, S. Kachiguine, A. Skoczeń, T. Fiutowski, A. Sher, P. Rydygier, A.A. Grillo, A.M. Litke
An MEA-based system for multichannel, low artifact stimulation and recording of neural activity.
Proceedings of 6th International Meeting on Substrate-Integrated Micro Electrode Arrays - MEA Meeting 2008, July 8–11, 2008, Reutlingen, Germany, 259–262 Link  

[10]
P. Hottowy, W. Dabrowski, S. Kachiguine, A. Skoczen, A. Sher, A.M. Litke
High-resolution multielectrode array system for spatio-temporal distributed stimulation and recording of neural activity.
Neuroscience 2008, the 38th annual meeting of the Society for Neuroscience, November 15 - 19, 2008, Washington, DC. Presentation Abstract 101.11/VV6. Link  

[11]
Paweł Hottowy, John M. Beggs, E. J. Chichilnisky, Władysław Dąbrowski1, Tomasz Fiutowski, Deborah E. Gunning, Jon Hobbs, Lauren Jepson, Sergei Kachiguine, Keith Mathieson, Przemysław Rydygier, Alexander Sher, Andrzej Skoczeń, Alan M. Litke
512-electrode MEA System For Spatio-TemporalDistributed Stimulation and Recording of Neural Activity.
Proceedings of 7th International Meeting on Substrate-Integrated Micro Electrode Arrays - MEA Meeting 2010, June 29 - July 2, 2010, Reutlingen, Germany, 327-330 Link  

[12]
P. Hottowy, A. Skoczeń, D.E. Gunning, S. Kachiguine, K. Mathieson, A. Sher , P. Wiącek, A.M. Litke, W. Dąbrowski
Replicating light-evoked activity in a population of retinal ganglion cells with MEA-based electrical stimulation.
Proceedings of 8th International Meeting on Substrate-Integrated Micro Electrode Arrays - MEA Meeting 2012, July 10 - July 13, 2012, Reutlingen, Germany, 200-201

[13]
A. Skoczeń, J. Steckert, O. Bitterling
 Zastosowanie FPGA w systemie ochrony magnesów LHC przed skutkami quenchu.
II Sympozjum FPGA, Kraków 2016 Link  

[14]
M. Szypulska, M. Dwużnik, P. Wiącek, A. Skoczeń, T. Fiutowski1, M. Jędraczka, J. Dusik, M. I. Ahmed, W. Dąbrowski, P. Hottowy, E. Kublik
Modular ASIC-based System for Large-Scale Electrical Stimulation and Recording of Brain Activity in Behaving Animals.
MIXDES 2016, 23rd International Conference "Mixed Design of Integrated Circuits and Systems", June 23-25, 2016, Łódź, Poland

[15]
M. Wielgosz, M. Mertik, A. Skoczeń
Monitoring the LHC magnets.
DS@HEP2017, Fermilab Link  

[16]
M. Wielgosz, A. Skoczeń
Recurrent Neural Networks with grid data quantization for modeling LHC superconducting magnets behavior.
ITSRCP18, Proceedings of the International Multiconference on Computational Physics (CS 2018), 2-5 July. Kraków, Poland Link  

[17]
M. Wielgosz, A. Skoczeń, K. Wiatr
Looking for a Correct Solution of Anomaly Detection in the LHC Machine Protection System.
2018 International Conference on Signals and Electronic Systems (ICSES) Link  

Raporty

[1]
M. Wielgosz, A. Skoczeń, M. Mertik
Using LSTM recurrent neural networks for detecting anomalous behavior of LHC superconducting magnets.
arXiv:1611.06241 Link  

[2]
M. Mertik, M. Wielgosz, A. Skoczeń
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework.
arXiv:1610.09201 Link  

[3]
M. Wielgosz, A. Skoczen, M. Mertik
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnet.
arXiv:1702.00833v1 Link  

[4]
M. Wielgosza, M. Mertik, A. Skoczeń
The prototype of the HL-LHC magnets monitoring system based on Recurrent Neural Networks and adaptive quantization.
arXiv:1709.09883v1 Link  


 
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