Wireless power transfer (WPT) with dynamic charging capabilities is a promising technology that can charge moving objects in real-time. However, maintaining high-efficiency power transfer during vehicle movement continues to be a significant challenge. To address this challenge, this study proposes a dynamic WPT system that utilizes orthogonal transmitter and receiver coils, offering the advantages of stable output power and efficiency, even when the vehicle is in motion. Unlike other systems, the proposed topology eliminates the need for a complicated feedback control system, thereby reducing hardware costs. To verify the effectiveness of the proposed topology, a dynamic WPT system was implemented in this study. Measurement results demonstrate that even when the vehicle moves a distance of 400 mm (four times the length of the receiving coil), the output voltage and power variations are only 4.9% and 9.6%, respectively.
Compelling evidence suggests that the interaction between electromagnetic metasurfaces and deep learning gives rise to the proliferation of intelligent metasurfaces in the past decade. In general, deep learning offers a transformative force to reform the design and working style of metasurfaces. A majority of the inverse-design literature announce that, given a user-defined input, the pre-trained deep learning models can quickly output the metasurface candidates with high fidelity. However, they largely ignore an important fact, that is, the practical input is always semi-known. In this work, we introduce a generation-elimination network that is robust to semi-known input and information pollution. The network is composed of a generative network to generate a number of possible answers and then a discriminative network to eliminate suboptimal answers. We benchmark the feasibility via two scenes, the on-demand metasurface design of the reflection spectra and the far-field pattern. In the microwave experiment, we fabricated and measured the reconfigurable metasurfaces to automatically meet the semi-known beam steering requirement that widely exist in wireless communication. Our work for the first time answers the question of how to cope with semi-known input, which is ubiquitous in a panoply of real-world applications, such as imaging, sensing, and communication across noisy environment.
The linear sampling method (LSM) is a qualitative inverse scattering technique for reconstructing the shape of a target. It has several beneficial qualities, including the avoidance of nonlinear optimization and simplified scattering approximations. However, it often struggles when sensors can only be placed on one side of the target. In this paper, we investigate two alternative LSM formulations for overcoming the limited-aspect challenge. The first, the phase-delay frequency variation LSM (PDFV-LSM), incorporates coherent processing across frequency to improve discrimination in the range direction. The second, the phase-encoded LSM (PE-LSM), enhances the PDFV-LSM approach with a receive-beamforming operation to decrease the complexity of the inverse problem. We apply both techniques to simulated data from three-dimensional targets and three-dimensional limited-aspect arrays. We generate three-dimensional reconstructions and compare them to reconstructions from both the conventional LSM and conventional backprojection-based processing. The results demonstrate superior reconstruction fidelity for either the PDFV-LSM, the PE-LSM, or both, across a wide variety of imaging scenarios due to finer range resolution. They also demonstrate trade-offs between the two enhanced LSM techniques, with the PE-LSM achieving better range resolution and robustness to noise and the PDFV-LSM achieving better lateral resolution.
In this paper, we developed a hyperspectral transmission microscopic imaging (HTMI) system for rapid detection of pathogenic bacteria, which can realize precise identification and classification of mixed pathogenic bacteria to a single-bacterium level. The system worksin trans-illumination patterns and a self-developed dispersive hyperspectral imaging module is usedas the detection setup, providing spectral images with high SNR, and showing excellent performances with spatial resolution of 2.19 µm and spectral resolutions less than 1 nm. Hyperspectral microscopic imaging of five types of bacteria in low concentration were performed. The merging spatial-spectral profiles of individual bacteria for each species were extracted and utilized for species identification, achieving high classification accuracy of 93.6% using a simple PCA-SVM method. Species identification experiments of the mixed bacterial sampleswere further carried out, and the results demonstrate the validity and capability of the system assisted with simple machine learning methods to be used as an effective and rapid diagnostic tool for elaborate identification of mixed bacterial pathogen samples, providing guidance for the use of correct antibiotics.
Plasmonic topological states, providing a new way to bypass the diffraction limits and against fabrication disorders, have attracted intense attention. In addition to the near-field coupling and band topology, the localized surface plasmonic resonance modes can be manipulated with far-field degrees of freedom (DoFs), such as polarization. However, changing the frequency of the topological edge states with different polarized incident waves remains a challenge, which has led to significant interest in multiplexed radiative topological devices. Here, we report the realization of polarization-wavelength locked plasmonic topological edge states on the Su-Schrieffer-Heeger (SSH) model. We theoretically and numerically show that such phenomenon is based on two mechanisms, i.e., the splitting in the spectra of plasmonic topological edge states with different intrinsic parity DoF and projecting the far-field polarizations to the parity of lattice modes. These results promise applications in robust optical emitters and multiplexed photonic devices.
In emergency departments and ICUs, a novel noncontact thermometer is urgently required to measure physical temperatures through common clothing to accomplish body temperature precise measurement for critical patients. Hence, a Ku band digital auto gain compensative microwave radiometer is proposed to get a higher theoretical temperature measurement sensitivity than a Dicke radiometer, benefit miniaturization design and reduce attenuation caused by common clothing. Meanwhile, a novel compensation method for receiver calibration is proposed to improve temperature sensitivity under non-ideal conditions, and the revised systematic calibration method is elaborated. Furthermore, in order to invert body physical temperatures through clothing, a microwave thermal radiation transmission model of clothed human body is constructed, and the microwave radiation apparent temperature equation of clothed human body is derived. Importantly, three groups of experiments are set up to confirm the designed radiometer's performance, especially the biological tissue temperature measurement. Results show that: 1) the designed radiometer has high temperature sensitivity and accuracy for unsheltered targets; 2) amplitude attenuation caused by cotton cloth for Ku band microwave is much smaller than that for infrared thermal radiation; 3) the designed radiometer can track physical temperatures of targets (such as water and swine skin tissue) sheltered or covered by cotton cloth relatively accurately. In conclusion, our designed Ku band microwave radiometer is certificated to have outstanding performance in temperature measurement for biological tissue through common clothing, which can be developed into a promising product in medical monitoring.
Nonlinear photonic sources including semiconductor lasers have been recently utilized as ideal computation elements for information processing. They supply energy-efficient way and rich dynamics for classification and recognition tasks. In this work, we propose and numerically study the dynamics of complex photonic systems including high-β laser element with delayed feedback and functional current modulation, and employ nonlinear laser dynamics of near-threshold region for the application in reservoir computing. The results indicate a perfect (100%) recognition accuracy for the pattern recognition task and an accuracy about 98% for the Mackey-Glass chaotic sequences prediction. Therefore, the system shows an improvement of performance with low-power consumption. In particular, the error rate is an order of magnitude smaller than previous works. Furthermore, by changing the DC pump, we are able to modify the number of spontaneous emission photons of the system, which then allows us to explore how the laser noise impacts the performance of the reservoir computing system. Through manipulating these variables, we show a deeper understanding on the proposed system, which is helpful for the practical applications of reservoir computing.
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