Review on UWB Bandpass Filters

Rapid development of a number of wireless communication systems imposed an urgent requirement for a technology which contains multi-wireless communication standard. Since the ultra-wideband (UWB) technologies are of advantage in broad bandwidth and high-speed transmission, much attention has been paid to exploiting the UWB bandpass filters. In this chapter, the development process of the UWB bandpass filters and the regulation of the UWB bandpass filter are initially introduced. Subsequently, the application scenarios of UWB filters in UWB communication systems and unique merits of UWB filters were explored. In addition, the primary performance specifications of the UWB filters, including insertion loss, return loss, the level of out-of-band attenuation, and roll-off rate, are also presented. After a brief discussion of microwave network theory, several methods for implementing UWB filters are summarized. Furthermore, the design of the UWB filter with notch band is presented in Section 5. The last section, the Conclusion section, is given at the end of this chapter

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable of effectively detecting co-salient objects in real time (~150 fps). To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. Finally, to improve the quality and integrity of the predicted maps, we develop an Adversarial Integrity Learning (AIL) strategy to make the segmented regions more likely composed of complete objects with less surrounding noise. Extensive experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art (~5.9% and ~6.2% improvement in S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency maps, and online demos are publicly available at https://github.com/ZhengPeng7/MCCL.Comment: AAAI 202