Bohr’s complementarity is one central tenet of quantum physics. The paradoxical wave-particle duality of quantum matters and photons has been tested in Young’s double-slit (double-path) interferometers. The object exclusively exhibits wave and particle nature, depending measurement apparatus that can be delayed chosen to rule out too-naive interpretations of quantum complementarity. All experiments to date have been implemented in the double-path framework, while it is of fundamental interest to study complementarity in multipath interferometric systems. Here, we demonstrate generalized multipath wave-particle duality in a quantum delayed-choice experiment, implemented by large-scale silicon-integrated multipath interferometers. Single-photon displays sophisticated transitions between wave and particle characters, determined by the choice of quantum-controlled generalized Hadamard operations. We characterise particle-nature by multimode which-path information and wave-nature by multipath coherence of interference, and demonstrate the generalisation of Bohr’s multipath duality relation. Our work provides deep insights into multidimensional quantum physics and benchmarks controllability of integrated photonic quantum technology.

Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.

In wireless sensor networks (WSNs), energy efficiency can simultaneously guarantee robustness to link loss and node failure and is a key design goal of routing protocols because WSNs are strongly constrained in terms of transmission reliability, transmission delay, and energy consumption. Braided multipath routing has become a powerful tool for tolerating node failures and link losses, with high reliability and efficient data transmission rates. In this paper, we propose a novel network coding-based braided multipath routing called NC-BMR protocol. It integrates a data compression-based network coding method with the construction of hierarchical multiparent nodes (HMPNs) topology for the routing with coordinated data forwarding manner and a multipackets-based time scheduler strategy (MTSS). Its perfect transmission efficiency is achieved by only attaching a little control information with data packets. We validated NC-BMR based on the TOSSIM platform and compared it to several previous methods. Theoretical analysis and simulation results demonstrate its performance improvement in terms of the transmission reliability, delay, and overhead.

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.

VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.