Abstract: A method for parameterization of data distributions for efficient information sharing in distributed sensor networks including a plurality of sensors, comprising the steps of performing Bayesian multi-source data fusion and sharing probalistic data information.

Abstract: A method for parameterization of data distributions for efficient information sharing in distributed sensor networks including a plurality of sensors, comprising the steps of performing Bayesian multi-source data fusion and sharing probalistic data information.

Abstract: Techniques for multiple access differential M-ary coding applications that can be optionally aided by multiuser detection (MUD) are disclosed. An initial hopped feature decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation can then be performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.

Abstract: A method and system begin at a random node of a trellis model having a set of at least two axes, wherein one axis corresponds to time and a second axis corresponds to frequency, a set of M states, corresponding to a set of all possible frequencies that may be transmitted by the system, and branches leaving each state, that terminate at allowable transmit frequencies for a next frequency hop. The number of branches leaving each state is dependent on the number of encoded bits per hop. Next, B bits of data are passed to a buffer, where B is the number of bits that will be transmitted per frequency hop. The method and system then combine the B bits of data with information for previously selected frequencies to select a current frequency, transmit the selected frequency, and feedback the currently selected frequency information to be used as previously selected frequency information when selecting a next frequency.

Abstract: A method and system begin at a random node of a trellis model having a set of at least two axes, wherein one axis corresponds to time and a second axis corresponds to frequency, a set of M states, corresponding to a set of all possible frequencies that may be transmitted by the system, and branches leaving each state, that terminate at allowable transmit frequencies for a next frequency hop. The number of branches leaving each state is dependent on the number of encoded bits per hop. Next, B bits of data are passed to a buffer, where B is the number of bits that will be transmitted per frequency hop. The method and system then combine the B bits of data with information for previously selected frequencies to select a current frequency, transmit the selected frequency, and feedback the currently selected frequency information to be used as previously selected frequency information when selecting a next frequency.

Abstract: Techniques for multiple access differential M-ary coding applications that can be optionally aided by multiuser detection (MUD) are disclosed. An initial hopped feature decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation can then be performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.