Biao Sun

Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.

A compact planar eight-way power divider with ultra-wide bandwidth (UWB) is introduced in this paper. To ensure a relative bandwidth of 100% (6~18 GHz), high power capacitance and small size in horizontal direction, a combination of single-ridged waveguide and microstrip line is adopted. In structure, the proposed eight-way power divider presents a relatively small size which meets the requirement of compact distance between output ports. While in electrical performances, the power divider performs well on VSWR (less than 1.38), phase differences (within ±4 degree) and amplitude differences (less than 0.4 dB). The simulation and measured results are provided for comparison.

Traditional silicon solar cells extract holes and achieve interface passivation with the use of a boron dopant and dielectric thin films such as silicon oxide or hydrogenated amorphous silicon. Without these two key components, few technologies have realized power conversion efficiencies above 20%. Here, a carbon nanotube ink is spin coated directly onto a silicon wafer to serve simultaneously as a hole extraction layer, but also to passivate interfacial defects. This enables a low‐cost fabrication process that is absent of vacuum equipment and high‐temperatures. Power conversion efficiencies of 21.4% on an device area of 4.8 cm² and 20% on an industrial size (245.71 cm²) wafer are obtained. Additionally, the high quality of this passivated carrier selective contact affords a fill factor of 82%, which is a record for silicon solar cells with dopant‐free contacts. The combination of low‐dimensional materials with an organic passivation is a new strategy to high performance photovoltaics.