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Rushi Lan

Bio: Rushi Lan is an academic researcher from Guilin University of Electronic Technology. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 17, co-authored 86 publications receiving 898 citations. Previous affiliations of Rushi Lan include Chinese Academy of Sciences & Nanjing University.


Papers
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Journal ArticleDOI
TL;DR: Two integrated chaotic systems are proposed, which conduct cascade, nonlinear combination, and switch operations to three basic 1D chaotic maps to generate new structures to improve the randomicity behaviors of some existing chaotic maps.

202 citations

Journal ArticleDOI
TL;DR: This article proposes a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning, and presents a dual residual-path block (DRPB) that utilizes the hierarchical features from original low-resolution images.
Abstract: Recently, deep convolutional neural networks (CNNs) have been successfully applied to the single-image super-resolution (SISR) task with great improvement in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). However, most of the existing CNN-based SR models require high computing power, which considerably limits their real-world applications. In addition, most CNN-based methods rarely explore the intermediate features that are helpful for final image recovery. To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning. Specifically, a residual multiscale module with an attention mechanism (RMAM) is developed to enhance the informative multiscale feature representation ability. Furthermore, we present a dual residual-path block (DRPB) that utilizes the hierarchical features from original low-resolution images. To take advantage of the multilevel features, dense connections are employed among blocks. The comparative results demonstrate the superior performance of our MADNet model while employing considerably fewer multiadds and parameters.

190 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method, which improves the model's ability to capture weak features and enhances the use of context information.
Abstract: Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU, BLEU, METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

90 citations

Journal ArticleDOI
TL;DR: A cascading residual network (CRN) that contains several locally sharing groups (LSGs) that not only promotes the propagation of features and the gradient but also eases the model training is proposed, which outperforms most of the advanced methods while still retaining a reasonable number of parameters.
Abstract: Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.

88 citations

Journal ArticleDOI
TL;DR: A local descriptor called quaternionic local ranking binary pattern (QLRBP) for color images that works on the quaternionics representation of the color image that encodes a color pixel using a quaternion.
Abstract: This paper proposes a local descriptor called quaternionic local ranking binary pattern (QLRBP) for color images. Different from traditional descriptors that are extracted from each color channel separately or from vector representations, QLRBP works on the quaternionic representation (QR) of the color image that encodes a color pixel using a quaternion. QLRBP is able to handle all color channels directly in the quaternionic domain and include their relations simultaneously. Applying a Clifford translation to QR of the color image, QLRBP uses a reference quaternion to rank QRs of two color pixels, and performs a local binary coding on the phase of the transformed result to generate local descriptors of the color image. Experiments demonstrate that the QLRBP outperforms several state-of-the-art methods.

78 citations


Cited by
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Journal ArticleDOI

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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 Jan 2002
TL;DR: In this paper, the interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help.
Abstract: 1. Interaction. The interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help. 2. Interdependence. Learners must depend on one another to accomplish a common objective. Each group member has specific tasks to complete, and successful completion of each member’s tasks results in attaining the overall group objective.

2,171 citations

Journal ArticleDOI
TL;DR: Results showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
Abstract: COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.

460 citations

Journal ArticleDOI
TL;DR: Security and performance analysis indicates that the proposed scheme is highly resistant to various cryptanalytic attacks, is statistically superior and more secure than previously proposed chaos-based image ciphers.

277 citations