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A blind watermarking method using maximum wavelet coefficient quantization

Expert Systems with Applications 2009年第9期36卷 11509-11516页

作者： Lin, Wei-Hung Wang, Yuh-Rau Horng, Shi-Jinn Kao, Tzong-Wann Pan, Yi Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Information Engineering St. John's University Taipei Taiwan Department of Electronic Engineering National United University Miao-Li Taiwan Department of Electronic Engineering Technology and Science Institute of Northern Taiwan Taipei Taiwan Department of Computer Science Georgia State University Atlanta GA 30302-4110 United States

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks. © 2009 Elsevier Ltd. All rights reserved.

关键词： Copyrights

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Chemical reaction neural networks to map lithium-ion battery thermal runaway gas generation

Cell Reports Physical Science

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Cell Reports Physical Science 2025年第5期6卷

作者： Zhang, Jiabo Ma, Changsheng Liu, Shuaiqi Guo, Qianzhen Liu, Shaoyan Han, Peng Huang, Zhen Han, Dong Key Laboratory for Power Machinery and Engineering Ministry of Education Shanghai Jiao Tong University Shanghai 200240 China Shanghai Non-carbon Energy Conversion and Utilization Institute Shanghai 200240 China School of Information Science and Engineering Lanzhou University Lanzhou 730000 China China-UK Low Carbon College Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science University of Electronic Science and Technology of China Chengdu 610054 China

The widespread application of lithium-ion batteries in electric vehicles is hindered by safety concerns, notably thermal runaway. Understanding gas generation during thermal runaway is crucial for battery safety design, early fault detection, and fire hazard assessment, but existing kinetic models remain inadequate. We investigate gas generation behavior of 18650-type LiNi_1/3Co_1/3Mn_1/3O₂ cells across varied states of charge, constructing a training dataset to develop a chemical reaction neural network model that autonomously explores unknown reaction pathways and corresponding chemical kinetic parameters. By incorporating an additional non-linear neural network to predict the mean molar weight of generated gases, the model trains a chemical kinetic mechanism comprising 7 species and 16 reactions. The resulting mechanism accurately predicts key parameters, including gas generation rates and amounts, during thermal runaway. This methodology addresses the current limitations in thermal runaway gas prediction, providing a robust framework for enhancing battery safety. © 2025 The Author(s)

关键词： chemical kinetic mechanism chemical reaction neural network gas generation lithium-ion battery thermal runaway reaction pathway

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Secure blockchain bidding auction protocol against malicious adversaries

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High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

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computer Modeling in engineering & Sciences 2022年第4期131卷 23-26页

作者： Qi Liu Xiaodong Liu Radu Grosu Ching-Nung Yang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburgh018373UK Institute of Computer Engineering Vienna University of TechnologyWien1040Austria Department of Computer Science and Information Engineering National Dong Hwa UniversityHualien97401Taiwan

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving ***-to-end procedures and new types of user-machine interaction are made possible via the *** the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

关键词： CPS hardware services

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Generation of High-Quality Relevant Judgments through Document Similarity and Document Pooling for the Evaluation of information Retrieval Systems 14

Generation of High-Quality Relevant Judgments through Docume...

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14th International Conference on Software, Knowledge, information Management and Applications, SKIMA 2022

作者： Joseph, Minnu Helen Ravana, Sri Devi University of Malaya Faculty of Computer Science & Information Technology Department of Information Systems Malaysia Faculty of Computer Science Engineering & Technology Malaysia

ISBN: (纸本)9781665493345

The information Retrieval System Evaluation have carried out through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of document corpus, topics, and a set of relevance judgements. The relevant judgements are the documents which retrieved from the test collections based on the topics. The precision of the evaluation process is based on the number of relevant documents in the relevant judgement list called qrels. This paper presents a study on how methodologies like pooling and document similarity helps to generate more relevant documents into the relevance judgments set in order to increase the accuracy of the evaluation process. The initial results have shown that combination of pooling with document similarity performs better compared to base clustering or classification. © 2022 IEEE.

关键词： information retrieval systems

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Detection and suppression of narrow band RFI for synthetic aperture radar imaging

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Chinese Journal of Aeronautics 2015年第4期28卷 1189-1198页

作者： Yang Lin Zheng Huifang Feng Jin Li Ning Chen Jiaqi College of Electronic Science and Engineering National University of Defense Technology Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences College of Computer and Information Engineering Hohai University

Radio frequency interference（RFI） is becoming more and more frequently, which makes it an important issue in SAR *** presented in synthetic aperture radar either on purpose or inadvertent will distort the useful SAR echoes, thus degrade the SAR image *** resolve this issue, a long time study was carried out to study the characteristic of the RFI through the RFIaffected spaceborne and airborne SAR *** on the narrow band nature of RFI, this paper proposes a new process which contains both RFI detection and RFI suppression.A useful subband spectral kurtosis detector is first used to detect RFI, and then its results are used for RFI *** proposed process has two advantages： one is the economization on the compute time for unnecessary interference suppression when no RFI existed; the other is improving the performance of the suppression method with knowing the exact position where RFI ***, the previous RFI suppression method––subband spectral cancelation（SSC） is supplemented and *** subband division step is also elaborated detail in this *** experiment results show that the subband spectral kurtosis detector exhibits good performance in recognizing both weak and narrow-band *** addition, the validity of the SSC method with subband spectral kurtosis detector is also validated on the real SAR echoes.

关键词： Kurtosis detector Radio frequency interference Subband spectralcancelation Synthetic aperture radar

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Event Recognition via Energy Efficient Voting for Wireless Sensor Networks

Event Recognition via Energy Efficient Voting for Wireless S...

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3rd Conference on Smart Spaces, ruSMART 2010 and 10th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking, NEW2AN 2010

作者： Baqer, Mohamed Department of Computer Engineering College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9783642148903

Physical world event monitoring and detection are two of the most dominating applications of wireless sensor networks. Combining sensor networks with artificial intelligence and pattern recognition is envisaged to deliver smart application to end-users. In this paper, a new distributed energy efficient pattern recognition approach is proposed to first depict events of interest as patterns and then detect the occurrence of events of interest, namely: the Voting with Random Node Selection (VRNS) approach. Sensor nodes may leverage their processing and storage capabilities by collectively handling sensory data to translate local sensory information of individual sensor nodes into meaningful results that depict the global status of the environment. In contrast to classical pattern recognition approaches, the VRNS approach operates within the constrained sensor network’s resources and infrastructures. Instead of utilising the entire sensor network, events of interest are recognised by employing an arbitrary sample of sensor nodes. The results show that the VRNS approach is capable of prolonging the longevity of the network’s lifetime, recognising event patterns effectively, and avoiding over-utilisation hotspots in the network. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Sensor nodes

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Tree-Structured Nuclear Norm Approximation with Applications to Robust Face Recognition

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IEEE Transactions on Image Processing 2016年第12期25卷 5757-5767页

作者： Luo, Lei Chen, Liang Yang, Jian Qian, Jianjun Zhang, Bob School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Computer and Information Science University of Macau Taipa999078 China

Structured sparsity, as an extension of standard sparsity, has shown the outstanding performance when dealing with some highly correlated variables in computer vision and pattern recognition. However, the traditional mixed (L1, L2) or (L1, L) group norm becomes weak in characterizing the internal structure of each group, since they cannot alleviate the correlations between variables. Recently, nuclear norm has been validated to be useful for depicting a spatially structured matrix variable. It considers the global structure of the matrix variable but overlooks the local structure. To combine the advantages of structured sparsity and nuclear norm, this paper presents a tree-structured nuclear norm approximation (TSNA) model assuming that the representation residual with tree-structured prior is a random matrix variable and follows a dependent matrix distribution. The extended alternating direction method of multipliers is utilized to solve the proposed model. An efficient bound condition based on the extended restricted isometry constants is provided to show the exact recovery of the proposed model under the given noisy case. In addition, TSNA is connected with some newest methods, such as sparse representation-based classifier, nuclear-L1 norm joint regression, and nuclear norm-based matrix regression, which can be regarded as the special cases of TSNA. Experiments with face reconstruction and recognition demonstrate the benefits of TSNA over other approaches. © 2016 IEEE.

关键词： Face recognition

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Fast Online Character Recognition Using a Novel Local-Global Feature Extraction Method 12

Fast Online Character Recognition Using a Novel Local-Global...

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12th International Conference on information and Knowledge technology, IKT 2021

作者： Parvizi, Ayoub Kazemifard, Mohammad Imani, Ziba Razi University Department of Computer Engineering and Information Technology Kermanshah Iran

ISBN: (纸本)9781665408493

Online handwriting recognition is one of the most active subjects of research in the field of pattern recognition. Feature extraction is one of the main steps in handwritten recognition which has a significant impact on accuracy and speed of recognition. Low time complexity of feature extraction method and short feature vector length reduce the computational cost and memory consumption. In this paper, a new feature extraction method is presented for fast online Persian/Arabic character recognition. In this method, in order to recognize the character pattern, the character signal sequence is first segmented into several equal parts in terms of number of points. Then the angle between horizon line and transient line of first and last points of each segment is considered as local feature. The global features are the angles between horizon line and transient line of first point of the character and the end point of segments. This method has been tested on two standard datasets including numbers and characters of Persian/Arabic languages. The experimental results show that the proposed method, in addition to high recognition accuracy, has less computational complexity and shorter vector length compared to other new methods of feature extraction and handwriting recognition. © 2021 IEEE.

关键词： Feature extraction

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