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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images 24

Dual Cross-Stage Partial Learning for Detecting Objects in D...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

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A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

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computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

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Algorithm Optimization of Image Generation Model based on Diffusion Model 8

Algorithm Optimization of Image Generation Model based on Di...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chong, Zhang Yadav, Amit Tangliangyao Khan, Asif Chengdu Neusoft University Software Engineering Department Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350376425

Image synthesis and generation is an important research direction in the fields of computer vision and deep learning. These generative models not only enrich visual content and reduce acquisition difficulty, but also provide training support for model algorithms through high-quality data augmentation;Innovation in lineage simulation and multimedia content production can also be achieved, and image generation technology shows great commercial potential. This article uses diffusion models in technology and studies how to improve the quality of generated images, control model generation results, and accelerate image generation speed. By using model fusion technology and combining the advantages of different image generation models, the diversity and realism of generated images are improved, while adding conditional constraints to control the generation results of the model. Through a series of carefully designed experiments, the method adopted has been tested and validated in practice, achieving significant performance improvements and revealing the operational mechanism of image generation models under specific conditions and constraints. © 2024 IEEE.

关键词： Digital elevation model

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Enhancing Pakistan Rice Plant Disease Detection: A Highly Effective Pre-Trained CNN Model 25

Enhancing Pakistan Rice Plant Disease Detection: A Highly Ef...

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25th International Multi Topic Conference, INMIC 2023

作者： Arshed, Muhammad Asad Akram, Zeeshan Bajwa, Haroon Zahid Azeem, Muhammad Huzaifa Dawood, Muhammad Ahmad Riaz, Faisal Department of Software Engineering Lahore Pakistan Department of Computer Science Lahore Pakistan Department of Infs Lahore Pakistan

ISBN: (纸本)9798350317701

Rice, a crucial global food source, faces numerous challenges impacting its quality and quantity. Pakistan is a significant rice producer and needs more agricultural technological advancements. Conventional disease detection methods for rice plants are time-consuming and ineffective. However, recent progress in agricultural technology, particularly machine learning & deep learning, holds great promise for improved disease identification. In this study, different datasets were considered for preparing a dataset that is specifically related to Pakistan's environmental rice diseases. This study uses a prepared dataset to train the proposed fine-tuned ResNet101V2 model and for the robustness of the proposed model comparison with ResNet101, ResNet50V2, and DenseNet121 also performed in this study. Among the models evaluated, ResNet101V2 exhibited superior performance with a test accuracy of 81%, demonstrating high accuracy in identifying six distinct classes, including five rice leaf diseases (Leaf Blight, Leaf Smut, Hispa, Leaf Blast, and Brown Spot) and one healthy category specific to Pakistan environments. This study represents a significant advancement toward developing more efficient disease detection and management systems in rice farming, ultimately leading to reduced financial losses and improved agricultural practices. © 2023 IEEE.

关键词： Agricultural technology

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Handwritten Text Recognition using Deep Learning Methods 7

Handwritten Text Recognition using Deep Learning Methods

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7th International Electromagnetic Compatibility Conference, EMC Turkiye 2023

作者： Hassan, Hagar Hany Gülcü, Ayla Bahçeşehir University Computer Engineering Department Istanbul Turkey Bahçeşehir University Software Engineering Department Istanbul Turkey

ISBN: (纸本)9798350342130

Offline handwritten text recognition has been widely utilized in various fields including historical document analysis. Deep learning techniques have demonstrated their effectiveness in digitizing handwritten text as each technique is precisely designed to tackle a specific task or solve a particular problem. In this article, we use convolutional neural network for extracting distinct character features and a recurrent neural network for handling character combinations within sequential data. By combining these models, we create a hybrid deep neural network consisting of three CNN layers followed by a bidirectional LSTM layer. This architecture effectively encodes input images and generates character probability matrices with which the connectionist temporal classification operation computes the loss function. Extensive experimentation with various parameter values allowed us to optimize our model, which we evaluated on the IAM dataset, yielding a reasonably low error rate. © 2023 IEEE.

关键词： Convolutional neural networks

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Extracting Driving Styles from Automotive Sensor Data to Develop Personas 9

Extracting Driving Styles from Automotive Sensor Data to Dev...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Kaya, M. Cagri Bouraffa, Tayssir Wnuk, Krzysztof Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering GothenburgSE-41296 Sweden Blekinge Institute of Technology Department of Software Engineering KarlskronaSE-37179 Sweden

ISBN: (纸本)9798350365887

Creating accurate and insightful personas for automotive applications requires understanding the diverse driving styles that emerge from sensor data. This paper introduces initial results of our methodology for developing personas based on the analysis of automotive sensor data, which captures various driving behaviors. We present a comprehensive process that begins with the extraction of driving events from sensor data, followed by the identification of distinct driving styles through K-Means clustering. Our approach is innovative in its use of dynamic, real-world driving data as opposed to static or direct user interaction data commonly employed in persona development. This allows for a deeper understanding of driver behaviors, which are indirectly inferred from sensor data, thus providing a foundation for creating detailed personas. © 2024 IEEE.

关键词： Automotive sensor data clustering driving styles event detection persona development

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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AI-Driven Innovations: Transforming Data Support and Environmental Management for the Future

AI-Driven Innovations: Transforming Data Support and Environ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Mahfuri, Mahmoud Ibrahim, Amer International University for Science and Technology Computer and Information Technology Department Syria United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents the evolving role of artificial intelligence (AI) in improving internal control and management processes. AI-driven technologies, including Generative Adversarial Networks (GANs) and ontologies, in addition to enabling data-supported decision-making, and empowering local governments to execute administrative tasks more efficiently and effectively. The study explores the wide range of opportunities that AI offers, highlighting the potential for optimizing internal workflows. Besides, it delves into key challenges, models, and methods for fully integrating AI into organizational processes, providing actionable insights for achieving streamlined, data-driven management systems. © 2024 IEEE.

关键词： Generative adversarial networks

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