A particle filter to reconstruct a free-surface flow from a depth camera

We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This data assimilation scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone

A decentralized scheduling algorithm for time synchronized channel hopping

Abstract. Time Synchronized Channel Hopping (TSCH) is an existing medium access control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with a mobile nodes. Two scheduling algorithms are presented. Aloha-based scheduling allocates one frequency channel for broadcasting advertisements for new neighbors. Reservation-based scheduling augments Aloha-based scheduling with a dedicated slot for targeted advertisements based on gossip information. A mobile ad-hoc network with frequent connectivity changes is simulated, and the performance of the two proposed algorithms is assessed against the optimal case. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption