This letter proposes a low-complexity estimation method of integer frequency offset in orthogonal frequency division multiplexing (OFDM) systems. The performance and complexity of the proposed method are compared with that of Morelli and Mengali's method based on maximum likelihood (ML) technique. The results show that the performance of the proposed method is comparable to that of M&M method with reduced complexity.

The Euler number of a binary image is an important topological property for pattern recognition, and can be calculated by counting certain bit-quads in the image. This paper proposes an efficient strategy for improving the bit-quad-based Euler number computing algorithm. By use of the information obtained when processing the previous bit quad, the number of times that pixels must be checked in processing a bit quad decreases from 4 to 2. Experiments demonstrate that an algorithm with our strategy significantly outperforms conventional Euler number computing algorithms.

This letter proposes non-pilot-aided symbol timing and carrier frequency estimation methods in a multicarrier transmission system. To do this, multicarrier system uses a frequency diversity scheme over two consecutive data symbols with the combination of a cyclic time shift. Using the multicarrier signal equipped with frequency diversity, however, time and frequency are accurately estimated without any training symbol.

Non-autoregressive generation has attracted more and more attention due to its fast decoding speed. Latent alignment objectives, such as CTC, are designed to capture the monotonic alignments between the predicted and output tokens, which have been used for machine translation and sentence summarization. However, our preliminary experiments revealed that CTC performs poorly on document abstractive summarization, where a high compression ratio between the input and output is involved. To address this issue, we conduct a theoretical analysis and propose Hierarchical Latent Alignment (HLA). The basic idea is a two-step alignment process: we first align the sentences in the input and output, and subsequently derive token-level alignment using CTC based on aligned sentences. We evaluate the effectiveness of our proposed approach on two widely used datasets XSUM and CNNDM. The results indicate that our proposed method exhibits remarkable scalability even when dealing with high compression ratios.

Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

This letter proposes a low-complexity scheme for estimating the frequency of a complex sinusoid in flat fading channels. The proposed estimator yields an estimation performance that is comparable to the existing autocorrelation-based frequency estimator, while retaining the same frequency range. Its implementation complexity is much lower than the conventional scheme, thus this allows for fast estimation in real time.