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DNA coding and chaos-based image encryption algorithm

Journal of Computational and Theoretical Nanoscience 2013年第2期10卷 341-346页

作者： Zhang, Qiang Liu, Lili Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

In this paper, an image encryption algorithm based on combination of DNA coding with chaotic map is proposed. The proposed algorithm uses chaotic sequence generated by Logistic system to scramble the image pixels which is encoded through the DNA encoding;then makes use of DNA addition operation for the rows of DNA sequence matrix and implements image encryption at last. The algorithm is simple and can resist a variety of attacks. Copyright © 2013 American Scientific Publishers.

关键词： DNA

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

arXiv

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arXiv 2025年

作者： Tang, Fenghe Yao, Qingsong Ma, Wenxin Wu, Chenxu Jiang, Zihang Zhou, S. Kevin Anhui Hefei230026 China Suzhou Institute for Advanced Research USTC Suzhou215123 China Stanford University Palo AltoCA94305 United States State Key Laboratory of Precision and Intelligent Chemistry USTC Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China

Medical image segmentation remains a formidable challenge due to the label scarcity. Pre-training Vision Transformer (ViT) through masked image modeling (MIM) on large-scale unlabeled medical datasets presents a promising solution, providing both computational efficiency and model generalization for various downstream tasks. However, current ViT-based MIM pre-training frameworks predominantly emphasize local aggregation representations in output layers and fail to exploit the rich representations across different ViT layers that better capture fine-grained semantic information needed for more precise medical downstream tasks. To fill the above gap, we hereby present Hierarchical Encoder-driven MAE (Hi-End-MAE), a simple yet effective ViT-based pre-training solution, which centers on two key innovations: (1) Encoder-driven reconstruction, which encourages the encoder to learn more informative features to guide the reconstruction of masked patches;and (2) Hierarchical dense decoding, which implements a hierarchical decoding structure to capture rich representations across different layers. We pre-train Hi-End-MAE on a large-scale dataset of 10K CT scans and evaluated its performance across seven public medical image segmentation benchmarks. Extensive experiments demonstrate that Hi-End-MAE achieves superior transfer learning capabilities across various downstream tasks, revealing the potential of ViT in medical imaging applications. The code is available at: https://***/FengheTan9/Hi-End-MAE. Copyright © 2025, The Authors. All rights reserved.

关键词： Computerized tomography

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Adaptive frequency-domain enhanced deep model driven by heterogeneous networks for medical image segmentation

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Knowledge-Based Systems 2025年 319卷

作者： Liu, Dong Kuang, Jin Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423300 China School of Computer and Artificial Intelligence Xiangnan University Chenzhou423300 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423300 China School of Geosciences Yangtze University Wuhan430100 China

Accurate medical image segmentation necessitates precise localization of global structures and local boundaries due to the high variability in lesion shapes and sizes. However, existing models are limited by conventional spatiotemporal features and single-network architectures, which restrict the simultaneous captures of semantic information and boundary details, thereby challenging generalizable medical image segmentation. To overcome these limitations, we propose a heterogeneous network-driven adaptive frequency-domain enhanced deep model(AFDSeg). First, we introduce the Frequency Domain Adaptive High-Frequency Feature Selection(FAHS) module, which adaptively extracts high-frequency features to enhance contour and detail representation while integrating spatiotemporal and frequency-domain features for improved consistency. Additionally, Prototype-Guided Low-Frequency Feature Aware(PFLA) and Local High-Frequency Salient-Feature Denoising (LHSD) modules are developed, which extract discriminative low-frequency features while suppressing local noise in high-frequency components, thereby facilitating efficient multi-scale feature fusion. Furthermore, the Multi-Level Prototype Feature Refinement(MPFR) Module is introduced to align low- and high-dimensional features during decoding and enhance semantic consistency. Finally, a heterogeneous network framework capable of accommodating multiple network architecture for medical image segmentation is proposed. Our method achieves mDice scores of 93.91%, 88.64%, 91.27%, 90.74%, and 81.38% on the Kvasir-SEG, BUSI, ISIC-2017, ACDC, and Synapse datasets, respectively, and attains 92.09%, 93.50%, and 83.92% in cross-domain experiments on three unseen datasets (Kvasir Capsule-SEG, BUS42, and M&Ms). Our approach consistently outperforms state-of-the-art methods on both benchmark and cross-domain datasets. Extensive quantitative and qualitative experiments demonstrated that AFDSeg accurately segments global structures and local detai

关键词： Semantic Segmentation

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Logistics Demand Forecasting and Scheduling Optimization Using K-Means Clustering and Deep Q-Network

Logistics Demand Forecasting and Scheduling Optimization Usi...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Kun Liang Hang Zhang School of Management Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep $\mathbf{Q}$ network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction.

关键词： Training Accuracy Costs Computational modeling Customer satisfaction Demand forecasting Clustering algorithms Predictive models Scheduling Logistics

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Analysis on exponential stability of genetic regulatory networks with variable delays

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Journal of Computational and Theoretical Nanoscience 2013年第11期10卷 2604-2608页

作者： Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

This paper presents an analysis method to the global exponential stability for the delayed genetic regulatory networks with SUM regulatory functions. By transforming the kinetics of networks into a single delay differential equation and utilizing the method of delay differential inequalities, several sufficient conditions for ensuring global exponential stability of the genetic regulatory networks with variable delays are derived. Since our method does not make use of Lyapunov functionals, it is simpler and more straightforward. Two numerical examples are provided to show the validity of the obtained results. Copyright © 2013 American Scientific Publishers.

关键词： Differential equations

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Research of DNA sequences sets based on the improved genetic algorithm

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Journal of Computational and Theoretical Nanoscience 2012年第7期9卷 969-973页

作者： Xia, Xue Zhang, Qiang Wang, Bin Zhao, Yuxia Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

With the continuous development of biotechnology, DNA computing will bring rapid development to the mathematics, computer science and other fields. As the foundation and the core issue of DNA computing, DNA encoding can be divided into two aspects, one is the quality optimization and the other is set design. The main research methods are theoretical analysis and algorithm design. In this paper, the traditional genetic algorithm is improved in the initialization, selection and mutation operations. We use fixed probability selection and adaptive multi-bits mutation operations in the improved genetic algorithm (IGA). The IGA is used in the DNA code set design under the combinatorial constraints satisfied the Hamming distance and Reverse Hamming distance constraint. The experimental shows our results are better than the earlier results in the literature. © 2012 American Scientific Publishers All rights reserved.

关键词： Hamming distance

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Additive manufacturing process related mechanical performance of cobalt-chromium-molybdenum alloy: In-situ X-ray computed tomography study

引用

Materials Science and Engineering: A 2025年 935卷

作者： Zhou, Junhan Huang, Rongzheng Yang, Qidong Zhou, Ye Xie, Meiming Xie, Haiqiong Cai, Xu Wei, Kai Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment Ministry of Education Hunan University Changsha China Orthopedic Trauma Center General Hospital of Western Theater Command of PLA Chengdu China School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Institute of Bio-Intelligent Manufacturing Chongqing China Department of Spinal Surgery Hunan Xiangya Boai Rehabilitation Hospital Changsha China

Laser powder bed fusion (PBF-LB) shows significant potential in additively manufacturing personalized CoCrMo alloy orthopedic implants. However, the characteristics of microstructures and defects, and especially their influence on the mechanical performance of PBF-LB processed CoCrMo alloy remain underexplored. Here, CoCrMo alloy is additively manufactured, and we first employ an in-situ X-ray computed tomography (XCT) tensile test to investigate the exclusive microstructures and defects and their influence mechanism on mechanical properties. Adequate laser energy density (Ev = 106.67–152.38 J/mm3) reduces defect number and increases sphericity, improving manufacturing quality, while insufficient or excessive energy input leads to LOF or keyhole defects, respectively. Besides, by first conducting the in-situ XCT tensile tests, we originally 3D visually reveal that the underlying failure mechanism is the defect growth and coalescence, which lead to the crack initiation and eventual brittle fracture. At Ev = 152.38 J/mm3, the alloy exhibits the best mechanical performance (tensile strength: 1193.41 MPa, elongation: 4.99 %), with fracture behavior governed by intrinsic microstructural characteristics. The PBF-LB processed CoCrMo alloy exhibits even higher ultimate strength and comparable elongation than the conventionally casted ones. Moreover, the heat treatment enables the recrystallization and precipitate formation, significantly improving the elongation of the PBF-LB processed CoCrMo alloy to 26.65 % at Ev = 266.67 J/mm3, achieving a marvelous balance between strength and ductility. This study fills the gap in understanding the relationship between additive manufacturing, defect formation, and mechanical performance of CoCrMo alloy, establishing a basis for efficient manufacturing and superb mechanical performance for its biomedical applications. © 2025 Elsevier B.V.

关键词： Computerized tomography

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