<p>The time-reversal method has been applied to the source location due to its spatiotemporal focusing properties. Much work on the topic is to locate the impulse or wideband sources. However, most sources in practical situations are narrowband. Therefore, it is desirable to develop a single-frequency time-reversal method to reconstruct narrowband-source locations. Unlike the conventional time-reversal method, the single-frequency time-reversal method we propose in this paper extracts the field signals at a single frequency at the time-reversal-mirror locations and reinjects them into the solution domain for the backward simulations. The preliminary experimental results with an ergodic cavity demonstrate the effectiveness of our proposed method and move one crucial step forward for the practical uses of the time-reversal method. They lay the foundations for further extensions of time-reversal theory and applications.</p>
<p>The time-reversal method has been applied to the source location due to its spatiotemporal focusing properties. Much work on the topic is to locate the impulse or wideband sources. However, most sources in practical situations are narrowband. Therefore, it is desirable to develop a single-frequency time-reversal method to reconstruct narrowband-source locations. Unlike the conventional time-reversal method, the single-frequency time-reversal method we propose in this paper extracts the field signals at a single frequency at the time-reversal-mirror locations and reinjects them into the solution domain for the backward simulations. The preliminary experimental results with an ergodic cavity demonstrate the effectiveness of our proposed method and move one crucial step forward for the practical uses of the time-reversal method. They lay the foundations for further extensions of time-reversal theory and applications.</p>
<p>Electromagnetic time-reversal (TR) has been proven to be an effective and efficient method for solving source reconstruction problems. Different algorithms are proposed to deal with difficulties in real-world scenarios. In our previous work, we proposed the electromagnetic kurtosis methods and discussed the resolution in band-limited TR. However, traditional kurtosis methods may not work effectively with narrow bandwidth. This work further extends the idea of arbitrary-order kurtosis and demonstrates its capabilities of achieving super-resolution in real applications. Compared to other super-resolution techniques, the proposed methods are algorithmic simple and do not incorporate subwavelength structures. Numerical experiments show the field concentration beyond the diffraction limit which can increase the accuracy of source reconstruction with band-limited TR. Quantitative analysis is also presented to illustrate the characteristics of arbitrary-order kurtosis and its applications in multiple sources reconstruction or future complex environments. </p>
<p>Electromagnetic time-reversal (TR) has been proven to be an effective and efficient method for solving source reconstruction problems. Different algorithms are proposed to deal with difficulties in real-world scenarios. In our previous work, we proposed the electromagnetic kurtosis methods and discussed the resolution in band-limited TR. However, traditional kurtosis methods may not work effectively with narrow bandwidth. This work further extends the idea of arbitrary-order kurtosis and demonstrates its capabilities of achieving super-resolution in real applications. Compared to other super-resolution techniques, the proposed methods are algorithmic simple and do not incorporate subwavelength structures. Numerical experiments show the field concentration beyond the diffraction limit which can increase the accuracy of source reconstruction with band-limited TR. Quantitative analysis is also presented to illustrate the characteristics of arbitrary-order kurtosis and its applications in multiple sources reconstruction or future complex environments. </p>
We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.