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Antymateria i antyprotonowy atom helu

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
EN
Antimatter and the antiprotonic helium atom
Języki publikacji
PL
Abstrakty
EN
An exotic atom in which an electron and an antiproton orbit a helium nucleus could reveal if there are any differences between matter and antimatter. This unusual mirror on the antiworld is described,
Czasopismo
Rocznik
Strony
216--221
Opis fizyczny
Bibliogr. 8 poz., fot., rys., wykr.
Twórcy
autor
  • Department of Physics, University of Tokyo
Bibliografia
  • [1] W.S. McCulloch, W.H. Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bull. Math. Biophys. 5, 115 (1943).
  • [2] S. Ossowski, Sieci neuronowe w ujęciu algorytmicznym (WNT, Warszawa 1996).
  • [3] D.F. Specht, „Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition", IEEE Tr. Bio-Med. Eng. BME-14, 90 (1967).
  • [4] B. Heden, M. Ohlsson, L. Edenbrandt, “Value of Exercise Data for the Interpretation of Myocardial Perfusion”, J. Nucl. Cardiol. 9, 169 (2002).
  • [5] G. Dorffner, E. Leitgeb, H. Koller, “Toward Improving Exercise ECG for Detecting Ischemic Heart Disease with Recurrent and Feedforward Neural Nets”, w: Proc. IEEE Workshop on Neural Networks for Signal Processing, Ermioni, Greece, red. J. Ylontzos i in. (IEEE, New York 1994), s. 499.
  • [6] B. Heden i in., “Agreement between Artificial Neural Networks and Human Expert for the Electrocardiographic Diagnosis of Healed Myocardial Infarction”, J. Am. Col. Cardiol. 28, 1012 (1996).
  • [7] M. Ohlsson, H. Holst, L. Edenbrandt, “Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks”, w: Proc. Artificial Neural Networks in Medicine and Biology (ANNIMAB-1) Conference, Goeteborg, Sweden, May 2000.
  • [8] B. Heden i in., “Acute Myocardial Infarction Detected in 12-Lead ECG by Artificial Neural Networks”, Circulation 96, 1798 (1997).
  • [9] H. Holst i in., “A confident decision support system for interpreting electrocardiograms”, Clin. Physiol. 19, 410 (1999).
  • [10] H. Haraldsson, L. Edenbrandt, M. Ohlsson, “Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks”, Artif. Intell. Med. 32, 127 (2004).
  • [11] R.L. Kennedy i in., “An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and com-parison with serum myoglobin measurements”, Comput. Meth. Prog. Biomed. 52, 93 (1997).
  • [12] J. Kozłowski i in., “Classification of Iow and high risk for sudden cardiac death using neural network”, w: Simplicity behind Complexity, Proc. 3rd Interdisciplinary School on Nonlinear Dynamics for System and Signal Analysis EUROATTRACTOR 2002, Warsaw 2002, red. W. Klonowski (Pabst Science Publishers, Lengerich, Berlin 2004), s. 400.
  • [13] C. Papaloukasa i in., „An ischemia detection method based on artificial neural networks”, Artif. Intell. Med. 24, 167 (2002).
  • [14] M. Kukar i in., “An application of machine learning in the diagnosis of ischaemic heart disease”, w: Proc. 10th IEEE Symposium on Computer-based Medical Systems, Maribor, Slovenia, 1997, s. 70.
  • [15] G. Krishna Prasad, J.S. Sahambi, “Classification of ECG Arrhythmias using Multi-Resolution Analysis and Neural Networks”, Proc. IEEE Tencon, Bangalore, India 2003.
  • [16] I. Christov, G. Bortolan, “Ranking of pattern recognition parameters for premature ventricular contractions Classification by neural networks”, Physiol. Meas. 25, 1281 (2004).
  • [17] H. Al-Nashash, “Cardiac arrhythmia Classification using neural networks”, Technol. Health Care 8, 363 (2000).
  • [18] D. Cubański i in., “A neural network system for detection of atrial fibrillation in ambulatory electrocardiograms”, J. Cardiovasc. Electrophysiol. 5, 602 (1994).
  • [19] P.H.W. Leong, M.A. Jabri, “A Low Power VLSI Arrhythmia Classifier”, IEEE Tr. Neural Networks 6, 1435 (1995).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0011-0008
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