articular energy levels emitted from the explosive material in the object as a result of the neutron bombardment of step (a);
(c) presenting data derived from the gamma ray counts obtained from the explosive material to an artificial neural system (ANS) for the purpose of teaching the ANS how to properly classify the known explosive material;
(d) irradiating at least one object, such as a parcel or a suitcase, containing unknown materials with neutrons of substantially the same energy and concentration as in step (a);
(e) detecting and recording the number of gamma rays at particular energy levels emitted from the unknown material in the object as a result of the neutron bombardment of step (d); and
(f) presenting data derived from the gamma ray counts obtained from the unknown material to the ANS for the purpose of having the ANS classify the object as one having or not having an explosive material.
2. The method of claim 1 further including repeating steps (a)-(c) a sufficient number of times to teach the ANS how to properly classify a plurality of known explosive materials.
3. The method of claim 2 wherein the repeating of steps (a)-(c) is automatically carried out under control of a computer.
4. The method of claim 1 wherein steps (c) and (f) comprise presenting the gamma ray counts directly to the ANS.
5. The method of claim 1 wherein steps (c) and (f) comprise developing feature data from the gamma ray counts and presenting the feature data to the ANS.
6. The method of claim 5 wherein the step of developing feature data comprises selectively combining the gamma ray counts.
7. The method of claim 6 wherein the step of selectively combining gamma ray counts includes generating three-dimensional image data of at least one particular element within said object, said three-dimensional image data being based on the detection of gamma rays of a particular energy level associated with said at least one particular element.
8. A method of detecting the presence of a prescribed material in an object, such as a suitcase or a parcel, comprising the steps of:
(a) teaching an Artificial Neural System (ANS) how to detect the presence of said prescribed material within said object based on a quantitative analysis of particular emissions emitted from said prescribed material when irradiated with radiant energy, said ANS comprising a plurality of layers of processors connected in a parallel network, said teaching including adjusting the interconnections between said processors with an appropriate weighting factor; and
(b) presenting emissions from said object to the ANS trained in step (a), whereby the ANS detects whether said prescribed material is present within said object.
9. The method of claim 8 wherein the step of teaching said ANS includes the iterative process of:
(1) presenting the emissions emitted from a first configuration of said prescribed material to an input processor layer of said ANS,
(2) incrementally adjusting selected weighting factors of said ANS until an output processor layer of said ANS signals the detection of said prescribed material,
(3) repeating steps (1) and (2) for additional configurations of said prescribed material, and
(4) repeating steps (1) through (3) a sufficient number of times to train the ANS to correctly detect said prescribed material for all tested configurations.
10. A method for non-invasively detecting explosives in an object under investigation comprising the steps of:
(a) generating thermal neutrons;
(b) irradiating said object with said thermal neutrons, the interactions of said thermal neutrons with atomic nuclei within said object giving rise to the emission of gamma rays, said gamma rays having an energy level characteristic of the nuclear species contained in said object;
(c) detecting emitted gamma rays having an energy level characteristic of at least one specific element and the approximate location of the origin of said gamma rays within said object, thereby providing a measure of the density distribution of said at least one specific element within said object;
(d) detecti