hh.sePublications
Change search
Refine search result
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Stasiunas, Antanas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, LT-3031 Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, LT-3031 Kaunas, Lithuania.
    Miliauskas, Rimvydas
    Department of Physiology, Kaunas University of Medicine, LT-3000 Kaunas, Lithuania.
    Stasiuniene, Natalija
    Department of Biochemistry, Kaunas University of Medicine, LT-3000 Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Compression, adaptation and efferent control in a revised outer hair cell functional model2005In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 27, no 9, p. 780-789Article in journal (Refereed)
    Abstract [en]

    In the cochlea of the inner ear, outer hair cells (OHC) together with the local passive structures of the tectorial and basilar membranes comprise non-linear resonance circuits with the local and central (afferent–efferent) feedback. The characteristics of these circuits and their control possibilities depend on the mechanomotility of the OHC. The main element of our functional model of the OHC is the mechanomotility circuit with the general transfer characteristic y = k tanh(x − a). The parameter k of this characteristic reflects the axial stiffness of the OHC, and the parameter a working position of the hair bundle. The efferent synaptic signals act on the parameter k directly and on the parameter a indirectly through changes in the membrane potential. The dependences of the sensitivity and selectivity on changes in the parameters a and k are obtained by the computer simulation. Functioning of the model at low-level input signals is linear. Due to the non-linearity of the transfer characteristic of the mechanomotility circuit the high-level signals are compressed. For the adaptation and efferent control, however, the transfer characteristic with respect to the initial operating point should be asymmetrical (a > 0). The asymmetry relies on the deflection of the hair bundle from the axis of the OHC.

  • 2.
    Stasiunas, tanas
    et al.
    Kaunas University of Technology.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Kemesis, povilas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Miliauskas, Rimvydas
    Kaunas University of Medicine.
    Stasiuniene, Natalija
    Kaunas University of Medicine.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system2005In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 35, no 6, p. 495-510Article in journal (Refereed)
    Abstract [en]

    An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.

  • 3.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bacauskiene, Marija
    Kaunas University of Technology, Department of Electric Power Systems, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Selecting features for neural network committees2002In: Proceedings of the International Joint Conference on Neural Networks, Piscataway: IEEE, 2002, p. 215-220Conference paper (Refereed)
    Abstract [en]

    We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We compared the approach with two other neural network based feature selection methods. The algorithm developed outperformed the methods by achieving a higher classification accuracy on three real world problems tested. ©2002 IEEE

  • 4.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts1999In: Multiple approaches to intelligent systems: 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31 - June 3, 1999. Proceedings / [ed] Ibrahim Imam, Yves Kodratoff, Ayman El-Dessouki and Moonis Ali, Berlin: Springer Berlin/Heidelberg, 1999, p. 550-559Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to using both labelled and unlabelled data to train a multi-layer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train networks for colour classification in graphic arts.

  • 5.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Increasing colour image segmentation accuracy by means of fuzzy post-processing1995In: 1995 IEEE International Conference on Neural Networks: Proceedings, the University of Western Australia, Perth, Western Australia, 27 November-1 December 1995 (Vol. 4), Piscataway, NJ: IEEE Press, 1995, p. 1713-1718Conference paper (Refereed)
    Abstract [en]

    This paper presents a colour image segmentation method which attains a high segmentation accuracy even when regions of the image that have to be separated are very similar in colour. The proposed method classifies pixels into colour classes. Competitive learning with `conscience' is used to learn reference patterns for the different colour classes. A nearest neighbour classification rule followed by a block of fuzzy post-processing attains a high classification accuracy even for very similar colour classes. A correct classification rate of 97.8% has been achieved when classifying two very similar black colours, namely, the black printed with a black ink and the black printed with a mixture of cyan, magenta and yellow inks.

  • 6.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS). Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images2000In: IJCNN 2000: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, 24-27 July 2000, Vol. 2 / [ed] Shun Ichi-Amari, C. Lee Giles, Marco Gori & Vincenzo Piuri, Los Alamitos: IEEE Computer Society, 2000, p. 297-302Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to determining colours of specks in an image taken from a pulp sample. The task is solved through colour classification by an artificial neural network. The network is trained using possibilistic target values. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.

  • 7.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Bergman, Lars
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Nilsson, Kenneth
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Hierarchical neural network for color classification1994In: The 1994 IEEE International Conference on Neural Networks: IEEE World Congress on Computational Intelligence, June 27-June 29, 1994, Walt Disney World Dolphin Hotel, Orlando, Florida, Vol. 5 / [ed] IEEE, Piscataway, NJ: IEEE Press, 1994, p. 2938-2941Conference paper (Refereed)
    Abstract [en]

    To make the hierarchical architecture, the neural networks of different type and different unsupervised learning techniques were combined. The classification accuracy obtained from such architecture is high enough to use it in the print quality control.

  • 8.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Lipnickas, Arunas
    Kaunas University of Technology, Kaunas, Lithuania.
    Soft fusion of neural classifiers1998In: ICONIP'98: The Fifth International Conference on Neural Information Processing, jointly with JNNS'98, the 1998 annual conference of the Japanese Neural Network Society : Kitakyushu, Japan, October 21-23, 1998 : proceedings, Volume 1 / [ed] Shiro Usui, Takashi Omori, Burke, VA: IOS Press, 1998, p. 195-198Conference paper (Refereed)
    Abstract [en]

    This paper presents three schemes for soft fusion of outputs of multiple neural classifiers. The weights assigned to classifiers or groups of them are data dependent. The first scheme performs linear combination of outputs of classifiers and, in fact, is the BADD defuzzification strategy. The second approach involves calculation of fuzzy integrals. The last scheme performs weighted averaging with data dependent weights. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data dependent weights compared to various existing combination schemes of multiple classifiers. The majority rule, combination by averaging, the weighted averaging, the Borda count, and the fuzzy integral have been used for the comparison.

  • 9.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bergman, Lars
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031 Kaunas, Lithuania.
    Detecting and measuring rings in banknote images2005In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 18, no 3, p. 363-371Article in journal (Refereed)
    Abstract [en]

    Various intelligent systems show a rapidly growing potential use of visual information processing technologies. This paper presents an example of employing visual information processing technologies for detecting and measuring rings in banknote images. The size of the rings is one of parameters used to inspect the banknote printing quality. The approach developed consists of two phases. In the first phase, based on histogram processing and data clustering, image areas containing rings are localized and edges of the rings are detected. Then, in the second phase, applying the hard and possibilistic spherical shell clustering to the extracted edge pixels the ring centre and radii are estimated. The experimental investigations performed have shown that even highly occluded rings are robustly detected. Several prototypes of the system developed have been installed in two banknote printing shops in Europe.

  • 10.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Busk, Hans
    Department of Physics, Lund University, Lund, Sweden .
    Image analysis and artificial neural network for colour classification in multicolour newspaper printing1994In: 1994 International Printing and Graphic Arts Conference: October 17-20, 1994 Chateau Halifax Hotel, Halifax, Canada, Atlanta: TAPPI Press , 1994, p. 89-92Conference paper (Refereed)
    Abstract [en]

    Automatic inspection of printed multicoloured screen pictures demands methods for colour classification of screen dots and pans of screen dots directly in an arbitrary picture. The paper describes a technique using colour image analysis and artificial neural network for inverse colour separation. For every arbitrary small part of a coloured picture it is determined which coloured inks that have been printed in that part. Special attention is paid to the problem of seperating between black colour produced by black ink and black colour produced by combining cyan, magenta and yellow ink. The technique is tested on multicoloured newsprint and a high correct colour classification rate has been demonstrated.

1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf