In quantum networks, long-distance quantum communication relies on Einstein–Podolsky–Rosen (EPR) pairs. However, EPR pairs undergo decoherence over time, while their on-demand generation incurs latency. Pre-Storing EPR pairs in quantum memories can mitigate this latency, but such memories are costly and scarce resources, making their efficient placement an important design problem. While prior studies have mainly focused on routing or entanglement distribution, the placement of quantum memories under fidelity and purification constraints has received limited attention. In this paper, we propose an optimization framework for quantum memory placement that minimizes the average entanglement generation latency under a limited budget of deployable memories. Simulations across multiple real-world topologies show that our method consistently achieves lower latency than random and centrality-based placements, suggesting its potential to improve the scalability of large-scale quantum communication networks.

Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.

In this work, an industrial Wireless Local Area Network (iWLAN) system used to control industrial robots (iRBs) in factory automation (FA) environments is addressed. For fast and deterministic communication, we propose a synchronous multi-user round-robin transmission protocol. To reduce the overhead caused by the conventional multiple downlink access, we also propose a low overhead Packet Division Multiple Access (PDMA) technique. The analysis and simulation results show that our proposals provide 100% and 300% better throughput than the conventional industrial Point Coordination Function (iPCF) and Space Division Multiple Access (SDMA) protocol schemes, respectively. In particular, with the control duration per iRB of being better than 100 μsec, the proposal completely satisfies the demand for fast control of FA systems. Furthermore, the hardware architecture of MAC layer of iWLAN system is also proposed and designed to successfully implement the proposed protocol.

In this paper, a new approach is examined for controlling the prosthetic hand: the torque control of each joint. The joint torque is estimated from EMG signals using an artificial neural network. The learning system is based on a feedback error learning schema. Two controlled objects were used in the experiments, i.e. a hand dynamic model and prosthetic hand. These experimental results show the possibility to apply the proposed method to the prosthetic hand control under the conditions that the controlled object is a second order delay system.

In this paper, we propose a direct torque control method for the prosthetic hand. In order to estimate the joint torque from EMG signals, an artificial neural network by the feedback error learning schema is used. 2-DOF motions, i.e. hand grasping/opening and arm flexion/extension, are picked up. In the experiments, two measurement conditions of EMG signal are prepared: the forearm from which the EMG signal is measured is free or fixed. Then it is verified that the neural network can learn the relation between the EMG signal and the joint torque under these two measurement conditions.