RF-DC Converter Using Loss Compensation and Adaptive Matching Network

Author(s):  
Kee Hoon Yang ◽  
Seob Oh ◽  
Jae Bin Kim ◽  
Jong Wan Jo ◽  
Kang-Yoon Lee
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jesus De Mingo ◽  
Pedro Luis Carro ◽  
Paloma Garcia-Ducar ◽  
Antonio Valdovinos

Author(s):  
Leonardo Zappelli

AbstractNowadays, the design of dividers is based on electromagnetic software that optimizes some geometric parameters to obtain the required performance. The choice of the geometry of the discontinuities contained in the divider and of the optimization initial point is quite critical to satisfy the divider requirements. In the last years, it is quite rare to find in the literature a theoretical approach helping the designers in the choice of the divider geometry. Helpful suggestion can derive by the analysis of the electric field in a trial divider that satisfies power division among the output ports in a thin band. In fact, the electric field null can be filled with metallic septa that ensure the same behavior at any frequency. The optimization of the septa position/form with numerical electromagnetic software permits to obtain divider with large bandwidth. A further analysis of the electric field null in the divider permits to add lateral metallic septa that further enlarge the transmission band. Finally, the design of an input matching network increases the transmitted power to the desired value.


Author(s):  
Xinfang Liu ◽  
Xiushan Nie ◽  
Junya Teng ◽  
Li Lian ◽  
Yilong Yin

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.


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