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Current substrate-integrated-waveguide (SIW) filter design methodologies can be extremely computational and time-inefficient when a narrow-band filter is required. A new approach to designing compact, highly selective narrow-band filters based on smartly positioned obstacles is thus presented here. The proposed modal-cancellation approach is achieved by translating or eliminating undesired modes within the frequency of interest. This is performed by introducing smartly located obstacles in the maxima and nulls of the modes of interest. This approach is different from the traditional inverter technique, where a periodic number of inductive irises are coupled in a ladder configuration to implement the desired response of an nth-order filter, and significantly reduces the complexity of the resulting filter structure. Indeed, the proposed method may be used to design different filters for several frequency bands and various applications. The methodology was experimentally verified through fabricated prototypes.

In this paper, we propose the theoretical framework for a reconfigurable radiation pattern modulation (RRPM) scheme, which is reminiscent of the index modulation technique. In the proposed scheme, information is encoded using far-field radiation patterns generated by a set of programmable radiating elements. A considerable effort has been invested to allow for high transmission of the reconfigurable radiation pattern symbols; yet, the receiving system has received little attention and has always been considered ideal. Depending on the number of receivers and their respective positions, two variables are considered here for data transmission: the sampling resolution and the fraction of the covered space by the receiving antennas. Hence, we quantitatively investigate their effect on the bit-error-rate (BER) by making use of a limited number of measurements that approximate the behavior of the system under real-field conditions.

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Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users' satisfaction.