615 research outputs found
Fabricating colloidal crystals and construction of ordered nanostructures
Colloidal crystals of polymeric or inorganic microspheres are of extensive interest due to their potential applications in such as sensing, optics, photonic bandgap and surface patterning. The article highlights a set of approaches developed in our group, which are efficient to prepare colloidal crystals with ordered voids, patterned colloidal crystals on non-planar surfaces, heterogeneous colloidal crystals of different building blocks, colloidal crystals composed of non-spherical polyhedrons, and colloidal crystals of non-close-packed colloidal microspheres in particular. The use of these colloidal crystals as templates for different microstructures range from nanoscale to micron-scale is also summarized
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance
by fusing the distinctive temporal information from multiple views. Existing
methods mainly focus on fusing multi-view information at an early stage, e.g.,
by learning a common feature subspace among multiple views. However, these
early fusion methods may not fully exploit the unique temporal patterns of each
view in complicated time series. Moreover, the label correlations of multiple
views, which are critical to boost-ing, are usually under-explored for the
MVTSC problem. To address the aforementioned issues, we propose a Correlative
Channel-Aware Fusion (C2AF) network. First, C2AF extracts comprehensive and
robust temporal patterns by a two-stream structured encoder for each view, and
captures the intra-view and inter-view label correlations with a graph-based
correlation matrix. Second, a channel-aware learnable fusion mechanism is
implemented through convolutional neural networks to further explore the global
correlative patterns. These two steps are trained end-to-end in the proposed
C2AF network. Extensive experimental results on three real-world datasets
demonstrate the superiority of our approach over the state-of-the-art methods.
A detailed ablation study is also provided to show the effectiveness of each
model component
On the Pulse Shaping for Delay-Doppler Communications
In this paper, we study the pulse shaping for delay-Doppler (DD)
communications. We start with constructing a basis function in the DD domain
following the properties of the Zak transform. Particularly, we show that the
constructed basis functions are globally quasi-periodic while locally
twisted-shifted, and their significance in time and frequency domains are then
revealed. We further analyze the ambiguity function of the basis function, and
show that fully localized ambiguity function can be achieved by constructing
the basis function using periodic signals. More importantly, we prove that time
and frequency truncating such basis functions naturally leads to approximate
delay and Doppler orthogonalities, if the truncating windows are periodic
within the support. Motivated by this, we propose a DD Nyquist pulse shaping
scheme considering signals with periodicity. Finally, our conclusions are
verified by using various strictly or approximately periodic pulses
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