Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery.

In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.

Self-Assembly of Silicon@Oxidized Mesocarbon Microbeads Encapsulated in Carbon as Anode Material for Lithium-Ion Batteries.

The utilization of silicon/carbon composites as anode materials to replace the commercial graphite is hampered by their tendency to huge volumetric expansion, costly raw materials, and complex synthesis processes in lithium-ion batteries. Herein, self-assembly method is successfully applied to prepare hierarchical silicon nanoparticles@oxidized mesocarbon microbeads/carbon (Si@O-MCMB/C) composites for the first time, in which O-MCMB core and low-cost sucrose-derived carbon shell not only effectively enhance the electrical conductivity of the anode, but also mediate the dramatic volume change of silicon during cycles. At the same time, the carbon can act as "adhesive", which is crucial in enhancing the adhesive force between Si and O-MCMB in the composites. The as-obtained Si@O-MCMB/C delivers an initial reversible capacity of 560 mAh g-1 at 0.1 A g-1, an outstanding cyclic retention of 92.8% after 200 cycles, and respectable rate capability. Furthermore, the synthetic route presented here is efficient, less expensive, simple, and easy to scale up for high-performance composites.