We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks 1 . Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: "how to select the best paths and how to allocate rates over these paths subject to latency constraints?" To answer these questions, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

We conducted a series of experiments in a flume with a cavity that provided refuge from the current to assess the feeding behavior of the stone moroko Pseudorasbora parva. Feeding behavior on commercial food pellets was quantified by assigning fish activity to one of the components of their predation cycle: pause, moving, pursuit (i.e., approach, chase, and attack), coasting, and resting within the cavity. Swimming speed was highest during attack, followed by that during chase, moving, approach, and coasting. As flow velocity increased, the approach and moving distances decreased while swimming speed increased. The body angle of a fish in pursuit relative to the flow generally increased in the latter stages of approach, chase, and attack and markedly decreased as flow increased. When flow velocity increased, the percentage of lateral captures decreased and fish rested in the cavity longer. Thus, the average energy cost of a feeding fish did not increase in higher flow velocities as much as was expected from estimations made at low flow velocity. The processes of approach, chase, and attack were defined as the unsteady swimming motions of linear or centripetal acceleration. Energy expenditure in the unsteady swimming mode was estimated using the theory of hydrodynamics. The main energy expenditure of the feeding cycle shifted from moving in slow flow to chase in fast flow because search time was shorter in fast flow. The net energy gain from feeding and swimming implied that the fish used in this experiment tended to express specific behaviors based on the trade-off between food uptake and energy expenditure up to a flow velocity of 7 cm/s. However, in faster flows, fish seemingly avoided a large drag force from the flowing water even if the net energy gain was positive.

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In this paper, a low-power and low-noise capacitive-coupled chopper instrumentation amplifier (CCIA) is proposed for biopotential sensing applications. A chopping technique is applied to mitigate the domination of flicker noise at low frequency. A new offset cancellation loop is also used to deal with the intrinsic offset, originating from process variation, to reduce ripple noise at the output of CCIA. Moreover, the optimization of the chip area was resolved by adding a T-network capacitor in the negative feedback loop. The CCIA is designed on 0.18 µm process CMOS technology with a total chip area of 0.09 mm2. The post-simulation results show that the proposed architecture can attenuate the output ripple up to 41 dB with a closed-loop gain of 40 dB and up to 800 Hz of bandwidth. The integrated input referred noise (IRN) of the CCIA is 1.8 µVrms over a bandwidth of 200 Hz. A noise efficiency factor (NEF) of 5.4 is obtained with a total power dissipation of 1.2 µW and a supply voltage of 1 V, corresponding to a power efficiency factor of 9.7 that is comparable with that of state-of-the-art studies.

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