Non-recursive max* operator with reduced implementation complexity for turbo decoding

In this study, the authors deal with the problem of how to effectively approximate the max?? operator when having n > 2 input values, with the aim of reducing implementation complexity of conventional Log-MAP turbo decoders. They show that, contrary to previous approaches, it is not necessary to apply the max?? operator recursively over pairs of values. Instead, a simple, yet effective, solution for the max?? operator is revealed having the advantage of being in non-recursive form and thus, requiring less computational effort. Hardware synthesis results for practical turbo decoders have shown implementation savings for the proposed method against the most recent published efficient turbo decoding algorithms by providing near optimal bit error rate (BER) performance

Endothelin-3 like immunoreactivity in plasma of patients with cirrhosis of the liver

A highly specific and sensitive radioimmunoassay (RIA) has been established for determination of endothelin-3 like immunoreactivity in human plasma to investigate its possible role in hemodynamic alterations due to liver disease. Crossreactivity with other endothelin isoforms was always below 4 %, the lower detection limit following extraction on Sep-Pak C18 cartridges was 0.5 pg/ml. The concentration of endothelin-3 (mean ± SEM) was 4.16 ± 0.56 pg/ml (n = 13) in plasma of patients with cirrhosis of the liver, three fold higher than in age matched controls (1.35 ± 0.27 pg/ml, n = 12, p < 0.01). Plasma immunoreactivity was confirmed to be endothelin-3 related by reverse-phase HPLC. These data could suggest a role of plasma endothelin-3 in circulatory changes, as they occur in cirrhosis of the liver

An fMRI-constrained MEG source analysis with procedures for dividing and grouping activation

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Full QCD Algorithms towards the Chiral Limit

I discuss the behaviour of algorithms for dynamical fermions as the sea-quark mass decreases. I focus on the Hybrid-Monte-Carlo (HMC) algorithm applied to two degenerate flavours of Wilson fermions. First, I briefly review the performance obtained in large scale HMC simulations. Then I discuss a modified pseudo-fermion action for the HMC simulation that has been introduced three years ago. I summarize recent results obtained with this pseudo-fermion action by the QCDSF and the ALPHA collaborations. I comment on alternatives to the HMC, like the Multiboson algorithm and variants of it.Comment: 7 pages, Lattice 2003 plenary talk, typos corrected, references updated, discussion of the MB algorithm corrected/extende

Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.Comment: 14 pages, 15 figures, Submitted to Journal of Marine Science and Technolog

Collision probability reduction method for tracking control in automatic docking/berthing using reinforcement learning

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled by tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.The version of record of this article, first published in Journal of Marine Science and Technology (Japan), is available online at Publisher’s website: https://doi.org/10.1007/s00773-023-00962-