Twin-field quantum key distribution (TFQKD) systems have shown great promise for implementing practical long-distance secure quantum communication due to its measurement-device-independent nature and its ability to of...
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Twin-field quantum key distribution (TFQKD) systems have shown great promise for implementing practical long-distance secure quantum communication due to its measurement-device-independent nature and its ability to offer fundamentally superior rate-loss scaling than point-to-point QKD systems. A surge of research and development effort in the last two years has produced many variants of protocols and experimental demonstrations. In terms of hardware topology, TFQKD systems interfering quantum signals from two remotely phase-locked laser sources are in essence giant Mach-Zehnder interferometers (MZIs) requiring active phase stabilization. Such configurations are inherently unsuitable for a TFQKD network, where more than one user pair share the common quantum measurement station, because it is practically extremely difficult, if not impossible, to stabilize MZIs of largely disparate path lengths, a situation that is inevitable in a multi-user-pair TFQKD network. On the other hand, Sagnac interferometer-based TFQKD systems exploiting the inherent phase stability of the Sagnac ring can implement asymmetric TFQKD, and are therefore eminently suitable for implementing a TFQKD network. In this work, we experimentally demonstrate a proof-of-principle multi-user-pair Sagnac TFQKD network where three user pairs sharing the same measurement station can perform pairwise TFQKD through time multiplexing, with channel losses up to 58.00 dB, and channel loss asymmetry up to 15.00 dB. In some cases, the secure key rates still beat the rate-loss bound for point-to-point repeaterless QKD systems, even in this network configuration. Our demonstration of this multi-user-pair TFQKD network is a step in advancing quantum-communication network technologies.
Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Fur...
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Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research – showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://***/BruceFang123.
Modern district heating technologies have a great potential to make the energy sector more flexible and sustainable due to their capabilities to use energy sources of varied nature and to efficiently store energy for ...
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The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swar...
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The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm are used to tune linear PID (L-PID) and nonlinear PID(NL-PID) controllers to implement performance and execution control of permanent magnet brushed DC (PMBDC) motor actuated robotic manipulator. The MOGA, APSO optimised nonlinear and linear PID controllers have been validated for their response efficacy and compared in respect of the steady-state error, overshoot and settling time of PMBDC driven robotic Manipulator. The efficacy of metaheuristics such as APSO and MOGA tuned NL-PID controller are better as compared to the L- PID controlled objects. Experimental results show that the NL-PID controller mends the controlled objects’ performace with a reduction in both the overshoot as well as the settling time, if tuned either with MOGA or APSO for L-PID controllers while NL-PID controller tuned with APSO gives satisfactory dynamic response of the system.
Phase reduction is an effective theoretical and numerical tool for studying synchronization of coupled deterministic oscillators. Stochastic oscillators require new definitions of asymptotic phase. The Q-function, i.e...
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In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an ...
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We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitra...
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Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-...
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Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing...
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ISBN:
(纸本)9781665481106
Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.
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