When a magnetic field is applied along a direction deviated from the quantization $Z$-axis, the conservation of total magnetization holds no more. In this case the inclined field can cause a strong spin-evolution via the linear Zeeman term even the field is as weak as a percentage of $mG$. An approach beyond the mean field theory is proposed to study the evolution of small $^{87}$Rb condensates under the weak inclined fields. The time-dependent populations of spin-components are given in analytical forms. The evolution is found to be highly sensitive to the magnitude and direction of the field.
The detection and prediction of sea clutter power is the basis of inversing atmospheric duct. At present, the technology of atmospheric duct within radar detection range is relatively perfect, but the long-distance inversion of atmospheric duct is limited by radar detection range, and the prediction of the echo power of the measured sea clutter is the basis of long-distance inversion of atmospheric duct. Based on the theory of weighted Markov model and grey Markov model, a weighted grey Markov model is constructed, and the sliding method is introduced to establish the sliding weighted grey Markov model. The relative error between the measured sea clutter power and predicted values of the above four models is calculated and analyzed using the experimental data collected. The results show that the sliding weighted grey Markov model has better accuracy not only in short-range prediction but also in long-distance prediction, which could provide data support for inversing atmospheric duct.
The technique of solid titration was developed to explore the true solution equilibria for complex solids and solutions, and in particular for biominerals. It has been shown that the conventional method of equilibration over a large excess of solid is inappropriate for the investigation of solubility when there is incongruent dissolution. The new method shows great sensitivity, reliability and reproducibility, and can be applied to many systems in medical and dental contexts, as well as general chemistry, geology, and the pharmaceutical industry.
A dynamic process of repeating collisions of a pair of trapped neutral particles with weak spin-dependent interaction is designed and studied. A related theoretical derivation and numerical calculation have been performed to study the inherent coordinate-spin and momentum-spin correlations. Because of the repeating collisions, the effect of the weak interaction can be accumulated and enlarged, and therefore can be eventually detected. Numerical results suggest that the Cr-Cr interaction, which has not yet been completely clarified, could thereby be determined. The design can be used in general to determine various interactions among neutral atoms and molecules, in particular for the determination of very weak forces.
Poor communication environments always lead to unstable communication in unmanned aerial vehicle swarms. To solve the problem of task assignment in poor communication environments, this study proposed an information fusion strategy (IFS) based on information integrity and authenticity. The proposed IFS was embedded into the classical sequential and the Prim assignment and its generalization (G-Prim) decentralized task assignment algorithms, and these two improved variants with the proposed IFS were denoted as sequential auction with IFS (Seq-IFS) and G-Prim-IFS, respectively. The Bernoulli and Gilbert–Elliott models, which can model communication delay and packet loss, were adopted to describe unstable communication channels. A series of test instances with different swarm sizes and levels of communication channel reliability was used to test the performances of Seq-IFS and G-Prim-IFS in their original forms. Numerical experimental results demonstrated that the proposed Seq-IFS and G-Prim-IFS significantly outperformed their original versions in most test instances, particularly in cases with low communication environments.
The Ising machine (IM) has emerged as a promising tool for tackling nondeterministic polynomial-time hard combinatorial optimization problems in real-world applications. Among various types of IMs, optoelectronic IMs based on electro-optical (EO) modulators stand out as an impressive platform for Ising computations. They offer a simple and stable architecture, with the EO modulator providing a natural inline nonlinear transfer function for the Ising model. However, integrated optoelectronic IMs have not been demonstrated until now, and exploring large-scale computations within the constraints of digital hardware resources remains an open challenge for these systems. In this paper, an integrated optoelectronic IM based on a thin-film lithium niobate (TFLN) photonic chip is presented, in conjunction with a sparse matrix–vector multiplication algorithm embedded in a field-programmable gate array that optimizes hardware resource utilization and minimizes computational latency. This setup allows us to solve multiple types of MAX-CUT problems with up to 2048 spins and achieve a remarkably low iteration latency of 1.78 μs. To further address the constraints posed by digital devices when tackling larger-scale Ising problems, we extend the application of the TFLN chip to yet another new scheme in which the single, compact on-chip modulator concurrently performs operations of linear multiplication and nonlinear transformation. This scheme demonstrates the capability to address large-scale MAX-CUT problems involving up to 16,384 spins, which, to the best of our knowledge, are the largest-scale problems solved on an on-chip IM, highlighting its potential to overcome digital limitations. The TFLN-based optoelectronic IMs provide a compact solution with high scalability for potentially practical applications in addressing complex combinatorial optimization problems.
An optoelectronic analog Ising machine is experimentally demonstrated. The SpMV algorithm is applied to accomplish two MAX-CUT tasks mapped into 2048-spin Ising networks, taking only 1.68μs per iteration.
An optoelectronic analog Ising machine is experimentally demonstrated. The SpMV algorithm is applied to accomplish two MAX-CUT tasks mapped into 2048-spin Ising networks, taking only 1.68μs per iteration.
This paper considers the problem of real-time reconstruction of ocean wave field with a network of discrete wave height sensors. Being able to predict incoming wave characteristics helps individual or collections of wave energy converters to increase the amount of energy that they can capture. In this paper, the wave field is modeled to consist of a frequency spectrum of monotone Airy waves with unknown strengths and phases. Kalman filter based observers are then designed to estimate the wave fields. The observers' performance in reconstructing the wave field accurately is validated in simulation for 1-D and 2-D linear and nonlinear waves. Wave tank experiments have also been performed to validate its ability to reconstruct a wave field in real-time using noisy data obtained from a vision-based wave height sensor.
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