Simplified experiments on fine grain inputs to a coarse bed in mobile equilibrium were undertaken in a small, steep, narrow flume using spherical glass particles to study the influence upon the channel response of the size ratio between the bed (D_c) and the input fines (D_f). Size ratios (D_c/D_f) between 7.14 and 1.25 were tested, with a constant flow and coarse sediment feed rate and a variety of fine and total feed rates. Transition to a new, two‐size equilibrium occurs through slope adjustment (aggradation/degradation), accompanying a change in sediment mobility created by the addition of the fine material. Previous work has documented superior mobility following a fine grain input; the present experiments identify limits of this behavior related to the fine grain size, the proportion of fines introduced, and the total sediment supply. The mechanistic reasons for these limits are examined with respect to the grain sorting behavior, leading to the development (or not) of a quasi‐static layer of the fine material at the base of the transport layer. Despite the variation in bed slope response depending upon these factors, the slope transitions consistently follow an exponential profile.

Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on sediment transport experiments in a flow were sediments are represented by spherical calibrated beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long sequences with a high precision because they incorrectly handle both miss-detections and detector imprecision. Our contribution is to propose a particle filter-based algorithm including an adapted multiple motion model. Additionally, this algorithm integrates several improvements to account for the lack of precision of the detector. The evaluation was made using a test sequence with a dedicated ground-truth. The results show that the method outperforms state-of-the-art concurrent algorithms.

In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor–critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy.