A multi-relay assisted orthogonal frequency division multiplexing (OFDM) system with index modulation (OFDM-IM) is proposed in this letter, assuming that amplify-and-forward (AF) relaying protocol is adopted at relays. Two commonly used AF protocols are considered: fixed-gain (FG) AF and variable-gain (VG) AF relaying protocols. To utilize relays in an efficient manner, we also employ two multi-carrier relay selection schemes termed bulk and per-subcarrier (PS) relay selection in the proposed system. We analyze the outage performance of the proposed system and derive closed-form expressions of the average outage probabilities (AOPs) for all cases with different AF relaying protocols and multi-carrier relay selection schemes. In addition, we obtain the asymptotic expressions of AOPs in the high signal-to-noise ratio (SNR) region, by which it is proven that the full diversity gain equaling the number of relays is attainable.
User reviews, like those found on Yelp and Amazon, have become an important reference for decision making in daily life, for example, in dining, shopping and entertainment. However, large amounts of available reviews make the reading process tedious. Existing word cloud visualizations attempt to provide an overview. However their randomized layouts do not reveal content relationships to users. In this paper, we present ReCloud, a word cloud visualization of user reviews that arranges semantically related words as spatially proximal. We use a natural language processing technique called grammatical dependency parsing to create a semantic graph of review contents. Then, we apply a force-directed layout to the semantic graph, which generates a clustered layout of words by minimizing an energy model. Thus, ReCloud can provide users with more insight about the semantics and context of the review content. We also conducted an experiment to compare the efficiency of our method with two alternative review reading techniques: random layout word cloud and normal text-based reviews. The results showed that the proposed technique improves user performance and experience of understanding a large number of reviews.
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