Here we present and analyze an optical spectrum analyzer at the nanometer scale that is able to distribute different frequency contents of the radiation of an optical dipole source into different directions in the space. The spectrum analyzer is composed of arrays of optical Yagi-Uda nanoantennas, forming relatively narrow radiation patterns operating at different frequencies. The optical Yagi-...

The transfer learning method, based on unsupervised domain adaptation (UDA), has been broadly utilized in research fault diagnosis under variable working conditions with certain results. However, traditional UDA methods pay more attention to extracting information for the class labels and of data, ignoring influence data structure extracted features. Therefore, we propose a domain-adversarial m...

This paper proposes a channel capacity maximization method for Multiple-Input Multiple-Output (MIMO) antennas with parasitic elements. Reactive terminations are connected to the parasitic elements, and the reactance values are determined to achieve stochastically high channel capacity for the environment targeted. This method treats the S-parameter and propagation channel of the antenna, includ...

We present cathodoluminescence experiments that quantify the response of plasmonic Yagi-Uda antennas fabricated on one-dimensional silicon nitride waveguides as function of electron beam excitation position and emission wavelength. At the near-infrared antenna design wavelength cathodoluminescence signal robustly is strongest when exciting the antenna at the reflector element. Yet at just sligh...

Unsupervised domain adaptation (UDA) deals with the task that labeled training and unlabeled test data collected from source and target domains, respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across domains to be the same, and we also allow the data in each domain to be coll...

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the learning (FSL) to leverage low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, goal of FS-UDA and FSL are relevant yet distinct, since aims classify samples in target rather than source domain. We found that insufficient FS-UDA, which could int...

Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence Invariant Representations (IR) improved drastically transferability representations from labelled source domain to new and unlabelled target domain. However, potential pitfall this approach, namely presence label shift, been brought light. Some works address issue with relaxed version inva...