In this paper a compact Ultra Wide Band filter with notch at WLAN frequency is designed. The filter is a designed using coupled lines arranged in inter digital capacitor form. The filter is designed to allow microwave signals in the range of 2GHz to 7GHz with a notch at 5GHz. The measured results show the notch at 5GHz with transmission coefficient of -14dB. With uniform group delay in pass band and nonlinear group delay at notch frequency.
Generating representations of video data is of key importance in advancing the field of machine perception. Most current techniques rely on handannotated data, which can be difficult to work with, expensive to generate, and hard to scale. In this work, we propose a novel learning approach based on contrastive learning, LAVA, which is capable of learning joint language, audio, and video representations in a self-supervised manner. We pre-train LAVA on the Kinetics 700 dataset using transformer encoders to learn representations for each modality. We then demonstrate that LAVA performs competitively with the current state-ofthe-art self-supervised and weakly-supervised pretraining techniques on UCF-101 and HMDB-51 video action recognition while using a fraction of the unlabeled data.
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