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  • 1995-1999  (2)
  • 1998  (2)
  • Home-navigation  (1)
  • crystal structure  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Electroless deposition of Ni–B, Co–B and Ni–Co–B alloys using dimethylamineborane as a reducing agent (1998)
    Springer
    Journal of applied electrochemistry 28 (1998), S. 559-563 
    Details
    ISSN: 1572-8838
    Keywords: electroless Ni–Co–B alloy ; dimethylamineborane ; complexing agent ; deposition rate ; crystal structure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Notes: Abstract Fundamental aspects of electroless Ni–B, Co–B and Ni–Co–B alloys have been systematically examined. The composition, crystal structure and deposition rate of the alloys were determined as a function of the concentration of reducing agent (dimethylamineborane) and complexing agents (tartrate, citrate, malonate and succinic acid), bath pH and Ni2+/Co2+ ratio. Changes in the deposition rate and metallurgical features of the alloys induced by the change in plating parameters are discussed, based on electrochemical polarization data and the formation enthalpy of the nickel and cobalt borides.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1003233715362
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Hippocampal neural network model performing navigation by homing vector field adhesion to sensor map (1998)
    Springer
    Artificial life and robotics 2 (1998), S. 129-133 
    Details
    ISSN: 1614-7456
    Keywords: Hippocampal neural network ; Hebbian learning ; Phase coding ; Self-orientation ; Home-navigation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We propose an artificial neural network model for autonomous agents, i.e., mobile robots, to learn maps of environments and acquire the ability to perform home-navigation autonomously. The networks consists of two subnetworks, each of which has a similar structure with hippocampal lamellar neuronal circuits. Hebbian learning procedures self-organize the first subnetwork to output the distributed sinusoidal activity of the cells by accumulating motor information generated during movement, and the second subnetwork to output localized activity by prototyping sensory information. These patterns represent a homing vector providing the relative coordinates of the agent from a starting point, and a place code corresponding uniquely to a point of the environment. By attaching homing vectors to the sensor map, the homing vector is associated with the sensory stimuli. Then the agents can perform home-navigation autonomously by this association.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02471169
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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