Frequency-Shift Chirp Spread Spectrum Communications with Index Modulation

This paper introduces a novel frequency-shift chirp spread spectrum (FSCSS) system with index modulation (IM). By using combinations of orthogonal chirp signals for message representation, the proposed FSCSS-IM system is very flexible to design and can achieve much higher data rates than the conventional FSCSS system under the same bandwidth. The paper presents optimal detection algorithms, both coherently and non-coherently, for the proposed FSCSS-IM system. Furthermore, a low-complexity non-coherent detection algorithm is also developed to reduce the computational complexity of the receiver, which is shown to achieve near-optimal performance. Results are presented to demonstrate that the proposed system, while enabling much higher data rates, enjoys similar bit-error performance as that of the conventional FSCSS system.

Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods.