Abstract: Pure random numbers from a sheet within a one-time pad are employed to encrypt the bytes of a source data packet and to order the encrypted bytes in a random order within the encrypted data packet. Pure random numbers fill remaining positions within the encrypted data packet. The resulting encrypted data packet is unconditionally secure (i.e., unbreakable). Sheets within the one-time pad are utilized only once, and the one-time pad is replaced when exhausted. For electronic checking applications, the one-time pad is distributed to the user stored in an electronic checkbook, with a copy retained by the bank. For cellular telephone applications, the one-time pad is stored in a replaceable memory chip within the mobile unit with a copy retained at a single, secured central computer. For client-server applications or applications involving sales over the Internet, the one-time pad may be provided to the user on a floppy disk or CD-ROM, with a copy retained by the vendor.

Abstract: Pure random numbers from a sheet within a one-time pad are employed to encrypt the bytes of a source data packet and to order the encrypted bytes in a random order within the encrypted data packet. Pure random numbers fill remaining positions within the encrypted data packet. The resulting encrypted data packet is unconditionally secure (i.e., unbreakable). Sheets within the one-time pad are utilized only once, and the one-time pad is replaced when exhausted. For electronic checking applications, the one-time pad is distributed to the user stored in an electronic checkbook, with a copy retained by the bank. For cellular telephone applications, the one-time pad is stored in a replaceable memory chip within the mobile unit with a copy retained at a single, secured central computer. For client-server applications or applications involving sales over the Internet, the one-time pad may be provided to the user on a floppy disk or CD-ROM, with a copy retained by the vendor.

Abstract: A system and method for training a neural network that ceases training at or near the optimally trained point is presented. A neural network having an input layer, a hidden layer, and an output layer with each layer having one or more nodes is presented. Each node in the input layer is connected to each node in the hidden layer and each node in the hidden layer is connected to each node in the output layer. Each connection between nodes has an associated weight. All nodes in the input layer are connected to a different historical datum from the set of historical data. The neural network being operative by outputting a prediction or classification, the output of the output layer nodes, when presented with input data. The weights associated with the connections of the neural network are first adjusted by a training device. The training device then iteratively applies a training set to the neural network, the training set consisting of historical data.

Abstract: A method and system for optimized material movement within a computer based manufacturing system utilizing global positioning systems. A tracking module which includes a wireless communication device, such as a cellular telephone chip set, and a position determination system, such as a global positioning receiver chip set, is built into each shipping container, vehicle or the like which is utilized to transport material for a computer based manufacturing system. The source and itinerary for each shipment of material, as well as a manufacturing schedule, are then loaded into the computer system which controls the manufacturing system. Periodically, the location of each shipment is then determined by querying the tracking module for a current actual location which is then compared within the computer system with a planned location, determined from the stored itinerary for that shipment.