3-D RECONSTRUCTION OF OBJECTS BY ARTIFICIAL INTELLIGENCE: APPARATUS AND METHOD

An unknown object is non-destructively and quantitatively evaluated for three-dimensional spatial distribution of a set of material constitutive parameters, using a multi-element array-source transducer and a multi-element array-detector transducer in spaced, mutually facing relation. The array-source transducer exposes the array-detector transducer to a set of source-field patterns pursuant to a set of electrical input signals. Either a known object or an unknown object positioned between these transducers will be the cause of scattering, thus presenting a scattered-field pattern to the array detector transducer, for each pattern of the set of source-field patterns. A computer, a signal processor and a neural network operate from detector response to each set of scattered-field patterns, in each of two modes. In an initial mode, the neural network is "trained" or configured to process a set of transfer functions involved in array-detector response to scattered-field patterns produced by the known object; in another mode, the neural network utilizes its "trained" configuration in application to a set of transfer functions involved in array-detector response to scattered-field patterns produced by an unknown object, to generate estimates of the three-dimensional spatial distribution of the material constitutive parameters of the unknown object.