POS> Vol.2 No.2, May 2011

Machine Perception Through Natural Intelligence

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ABSTRACT

The sensing organs are exponentially better than any of analogous artificial ones. That is why using them in full scale is a perspective trend to the efficient (advanced) machine perception. On the other hand, limitations of sensing organs could be replaced by the perfect artificial ones with the subsequent training the nervous system on their output signals. An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial transducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in relation to the nervous system is performed. The advances in nanotechnology are opening the way to achieving direct electrical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors' signals to a processing unit has been maintaining by an electromagnetic transistor/memristor (externally) and superconducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals' spreading.The measuring method and necessary performance data of the sensor for the robot's orientation in the ambient magnetic field with living being-machine interaction in order to obtain input and output signals from brain and motor nerves to the measurement system and vice versa are introduced. The range of applied sensors differs from an induction sensor to superconducting induction magnetometer. The analytical expressions for arrangements of the head sensors in differential and vector (3D) relative positions are deduced. Sensitivity of the perception method makes it possible to recognize the linear translation of 10?2 m and disposal in space of 10?3 m3. Interaction between living beings and robotic equipment is given analytical treatment.

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Cite this paper

R. Sklyar, "Machine Perception Through Natural Intelligence," Positioning, Vol. 2 No. 2, 2011, pp. 65-77. doi: 10.4236/pos.2011.22007.

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