TL;DR: The fusion method discussed in this paper uses the maxflow-mincut algorithm to do loop clustering and is efficient, and its usefulness for array contraction is demonstrated with a simple example.
Abstract: In this paper we propose a loop fusion algorithm specifically designed to increase opportunities for array contraction. Array contraction is an optimization that transforms array variables into scalar variables within a loop nest. In contrast to array elements, scalar variables have better cache behavior and can be allocated to registers. In past work we investigated loop interchange and loop reversal as optimizations that increase opportunities for array contraction [13]. This paper extends this work by including the loop fusion optimization. The fusion method discussed in this paper uses the maxflow-mincut algorithm to do loop clustering. Our collective loop fusion algorithm is efficient, and we demonstrate its usefulness for array contraction with a simple example.
TL;DR: In this paper, a linear transmitter includes an auxiliary loop coupled to the amplifier feedback loop that provides phase training for the feedback loop and power leveling for open loop transmission, without an associated training signal or training period.
Abstract: A linear transmitter includes an amplifier feedback loop for amplifying an input signal at a power amplifier. The feedback loop is operated in an open loop mode when the power amplifier is operating at a first operating point and is operated in a closed loop mode when the power amplifier is operating at a second operating point. The transmitter further includes an auxiliary loop coupled to the amplifier feedback loop that provides phase training for the feedback loop and power leveling when the feedback loop is operating open loop. Open loop phase training and power leveling is done during open loop transmission, without an associated training signal or training period. Stable closed loop operation can commence subsequently providing the higher power amplifier efficiency associated with the second operating point and maintaining off channel interference requirements.
TL;DR: A novel consistency based method to extract the loop closure regions that agree both among themselves and with the robot trajectory over time, using the very efficient graph optimization framework g2o as back-end.
Abstract: Long term autonomy in robots requires the ability to reconsider previously taken decisions when new evidence becomes available Loop closing links generated by a place recognition system may become inconsistent as additional evidence arrives This paper is concerned with the detection and exclusion of such contradictory information from the map being built, in order to recover the correct map estimate We propose a novel consistency based method to extract the loop closure regions that agree both among themselves and with the robot trajectory over time We also assume that the contradictory loop closures are inconsistent among themselves and with the robot trajectory We support our proposal, the RRR algorithm, on well-known odometry systems, eg visual or laser, using the very efficient graph optimization framework g2o as back-end We back our claims with several experiments carried out on real data