Abstract: Embodiments of the disclosure provide for handover or roaming of unmanned vehicles and missions between ground control stations (GCSs) of the same or different ground control centers (GCCs). Some embodiments receive a control change indication indicative of reassignment of a vehicle associated with a first GCS that is associated with a first GCC. Some embodiments reassign the vehicle from a first GCS to a second GCS associated with the GCC to enable the second GCS to newly access data corresponding to the vehicle via the master control system to enable control of the vehicle. Some embodiments reassign the vehicle from a first GCC to a second GCC by copying data corresponding to the vehicle from the master control system of the first GCC to a second master control system of a second GCC to enable control of the vehicle via at least one GCS of the second GCC.

Abstract: The present invention discloses a method for semantically processing speech for speech recognition purposes. The method can reduce an amount of memory required for a Viterbi search of an N-gram language model having a value of N greater than two and also having at least one embedded grammar that appears in a multiple contexts to a memory size of approximately a bigram model search space with respect to the embedded grammar. The method also reduces needed CPU requirements. Achieved reductions can be accomplished by representing the embedded grammar as a recursive transition network (RTN), where only one instance of the recursive transition network is used for the contexts. Other than the embedded grammars, a Hidden Markov Model (HMM) strategy can be used for the search space.

Abstract: The present invention discloses a method for semantically processing speech for speech recognition purposes. The method can reduce an amount of memory required for a Viterbi search of an N-gram language model having a value of N greater than two and also having at least one embedded grammar that appears in a multiple contexts to a memory size of approximately a bigram model search space with respect to the embedded grammar. The method also reduces needed CPU requirements. Achieved reductions can be accomplished by representing the embedded grammar as a recursive transition network (RTN), where only one instance of the recursive transition network is used for the contexts. Other than the embedded grammars, a Hidden Markov Model (HMM) strategy can be used for the search space.