In this study, the authors propose an adaptive channel estimation algorithm for asymmetrically clipped optical orthogonal frequency‐division multiplexing visible light communication (VLC) systems and analyse its optimal signal‐to‐noise ratio (SNR) threshold through the simulation experiments. The novel estimation scheme combines the advantages of orthogonal matching pursuit algorithm and least‐square discrete Fourier transform algorithm. It is robust to the changes in channel distribution and SNR range. The performance for a VLC system using 16‐quadrature amplitude modulation is presented in terms of mean square error (MSE) and uncoded bit error rate. In the high SNR range, the proposed scheme improves the MSE up to more than 10 dB.

Background: A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset.Methods: The 82 pancreatic contrast-enhanced abdominal CT volumes were split via four-fold cross validation to test the model performance. In order to achieve accurate segmentation, we firstly involved residual learning into an adversarial U-Net to achieve a better gradient information flow for improving segmentation performance. Then, we introduced a multi-level pyramidal pooling module (MLPP), where a novel pyramidal pooling was involved to gather contextual information for segmentation, then four groups of structures consisted of a different number of pyramidal pooling blocks were proposed to search for the structure with the optimal performance, and two types of pooling blocks were applied in the experimental section to further assess the robustness of MLPP for pancreas segmentation. For evaluation, Dice similarity coefficient (DSC) and recall were used as the metrics in this work.Results: The proposed method preceded the baseline network 5.30% and 6.16% on metrics DSC and recall, and achieved competitive results compared with the-state-of-art methods.Conclusions: Our algorithm showed great segmentation performance even on the particularly challenging pancreas dataset, this indicates that the proposed model is a satisfactory and promising segmentor.

Owing to the irregular shape and high anatomical variability of the pancreas in abdominal CT images, pancreas segmentation is regarded as a challenging task. To address this issue, we propose an automatic segmentation model using double adversarial networks with a pyramidal pooling module. First, we introduce double adversarial networks that double-check whether the obtained segmentation results are similar to their ground truths owing to the special competing mechanism of adversarial learning, which contributes to the capturing of spatial information for segmentation and prompts the obtained samples to be more realistic, to improve the network segmentation performance. Second, we design a pyramidal pooling module to collect multi-level features and retain substantial information for segmentation in order to further boost the network performance. Finally, to assess the segmentation performance of our model, we use several indexes, namely the Dice similarity coefficient (DSC), Jaccard index, precision, and recall, as evaluation indicators. Experimental results show that the proposed model outperforms most existing pancreas segmentation methods.

Purpose: Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape.Methods: To mitigate this issue, an attention‐guided duplex adversarial U‐Net (ADAU‐Net) for pancreas segmentation is proposed in this work. First, two adversarial networks are integrated into the baseline U‐Net to ensure the obtained prediction maps resemble the ground truths. Then, attention blocks are applied to preserve much contextual information for segmentation. The implementation of the proposed ADAU‐Net consists of two steps: 1) backbone segmentor selection scheme is introduced to select an optimal backbone segmentor from three two‐dimensional segmentation model variants based on a conventional U‐Net and 2) attention blocks are integrated into the backbone segmentor at several locations to enhance the interdependency among pixels for a better segmentation performance, and the optimal structure is selected as a final version.Results: The experimental results on the National Institutes of Health Pancreas‐CT dataset show that our proposed ADAU‐Net outperforms the baseline segmentation network by 6.39% in dice similarity coefficient and obtains a competitive performance compared with the‐state‐of‐art methods for pancreas segmentation.Conclusion: The ADAU‐Net achieves satisfactory segmentation results on the public pancreas dataset, indicating that the proposed model can segment pancreas outlines from CT images accurately.

An iterative pseudo-inverse ghost imaging (IPGI) method is proposed based on iterative denoising and pseudo-inverse ghost imaging (PGI). The background noise in the imaging is eliminated in iterations by setting an appropriate threshold. The IPGI method provides a significantly larger enhancement of the peak-signal-to-noise ratio (PSNR) than the PGI technique for binary objects. Experiments and data analyses are performed to evaluate the performance of the proposed method. Compared with conventional GI, differential GI (DGI), and PGI methods, the proposed method has the highest performance in visual effects and significantly improves the imaging quality. For a certain PSNR, the proposed method provides satisfactory performance in terms of computing time.

In this paper, we propose two kinds of translation type chaotic systems for creating 2 N + 1-and 2(N + 1)-scrolls chaotic attractors from a simple three-dimensional system, which are named the translation-2 chaotic system (a12a21 < 0) and the translation-3 chaotic system (a12a21 > 0). We also propose the successful design criterion for constructing 2 N + 1-and 2(N + 1)-scrolls, respectively. Then, the dynamics property of the translation-2 chaotic system is studied in detail. MATLAB simulation results show that very sophisticated dynamical behaviors and unique chaotic behaviors of the system. Finally, the definition and criterion of multi-scroll attractors for the translation-3 chaotic system is obtained. Three representative examples are shown in some classical chaotic systems that can be equally obtained via the set parameters of the translation type chaotic system. Furthermore, we show that the translation type chaotic systems have similar but topologically non-equivalent chaotic attractors, and they are the three-dimensional ordinary differential equations.