Abstract: A neural net sonic identification system includes an ultra-sonic emitter and receiver configured to detect characteristics of a subject by detecting reflections of the ultrasonic waves. The sonic reflections are processed by a neural network pattern recognition system that learns to recognize a particular subject and distinguish it from other subjects. The emitter/receiver is continuously optimized for phase, wavelength, spectral, and power frequencies, and the received sound “picture” is written-over the previously stored encrypted image data file for a particular individual, so as to continuously update the processor's retained identification information. In one embodiment, the emitter/receiver comprises a solid-state multi-layer tunable array of piezoelectric emitters and receivers that can be selectively “aimed” in different directions without physically moving by independently varying the wavelength and power of the various emitters/receivers.

Abstract: A neural net sonic identification system includes an ultra-sonic emitter and receiver configured to detect characteristics of a subject by detecting reflections of the ultrasonic waves. The sonic reflections are processed by a neural network pattern recognition system that learns to recognize a particular subject and distinguish it from other subjects. The emitter/receiver is continuously optimized for phase, wavelength, spectral, and power frequencies, and the received sound “picture” is written-over the previously stored encrypted image data file for a particular individual, so as to continuously update the processor's retained identification information. In one embodiment, the emitter/receiver comprises a solid-state multi-layer tunable array of piezoelectric emitters and receivers that can be selectively “aimed” in different directions without physically moving by independently varying the wavelength and power of the various emitters/receivers.