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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Federated learning-based lightweight selective feature fusion and irregular-aware network for crack detection

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Optoelectronics Letters 2025年

作者： LIU Hui HE Tian CHENG Xu SHI Fan NIKOLI? Sa?a V. ZHANG Jianhua School of Computer Science and Engineering， Tianjin University of Technology Department of Electronics， Faculty of Electronic Engineering， University of Nis

Employing deep learning for pavement crack detection can significantly enhance accuracy， and federated learning can help to overcome the challenges of data silos and data security. This paper proposes a lightweight method to detect cracks. We use lightweight encoder modules to extract multi-scale features， and further feature fusion and modelling by Selective Fusion Blocks and Irregular-aware Blocks. Moreover， this method is the first to combine federated learning with crack detection， effectively resolving the tension between data privacy and data sharing in decentralised devices. Experiments were conducted to compare our proposed method with other crack detection methods on two publicly available datasets， including the original method without federated learning and five state-of-the-art methods combined with the same federated learning framework. The experiment proves that our model obtains comparable F1 score with minimal parameters and computational effort. For example， compared to the original method， the number of parameters and computation are reduced by 67.1% and 73.6%， respectively.

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A Whispering Widgets and Wireless Wonders Pioneering Change in the World of IoT 7th

A Whispering Widgets and Wireless Wonders Pioneering Change ...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Tripathi, Amit Kumar Islam, Baharul Murugan, R. Adhvaryu, Rachit Department of Electronical and Electronics Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Engineering Assam Down Town University Assam Guwahati India Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9789819753369

The IoT has made it possible to connect and control more devices than ever before, opening up new avenues for efficiency and creativity. However, new problems have emerged as a result of this explosion of linked devices, notably concerning the security and privacy of personal information. To meet these difficulties, we provide a new approach within the context of "Whispering Widgets and Wireless Wonders: Ushering in a New Era for the Internet of Things." Our suggested approach safeguards data during transit and storage by using state-of-the-art cryptographic methods including Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES). Furthermore, we use blockchain technology to guarantee the immutability and transparency of data by creating a public and unchangeable ledger of all data exchanges. Our research shows that our suggested strategy for securing IoT data is superior to more conventional methods. We test how well our system protects sensitive information, keeps data intact, keeps users anonymous, and how efficient it is overall. When compared to conventional approaches, the "Proposed Method" regularly performs better, demonstrating its strength and applicability for protecting IoT data. High security is guaranteed by the combination of ECC and AES, and data integrity is improved by blockchain technology. Furthermore, our approach places a premium on user privacy, giving people more say over their data inside the IoT ecosystem. Finally, we believe that our suggested solution is a major advancement in achieving more data security and privacy in the IoT context. To the problems caused by the proliferation of linked gadgets, it provides a thorough and effective answer. Our approach prioritizes security, data integrity, and user privacy to let people and businesses get the benefits of the Internet of Things (IoT) without compromising on data privacy or security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025

关键词： Data integrity

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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN computer Science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Ferroelectrically gated two-dimensional bismuth oxyselenides for strain-invariant flexible synaptic thin-film transistors

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Science China(Information Sciences) 2025年第5期68卷 396-403页

作者： Jie WEN Fei XIAO Zheng-Dong LUO Dongxin TAN Xuetao GAN Dawei ZHANG Zhufei CHU Yinshui XIA Yan LIU Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University School of Materials Science and Engineering The University of New South Wales ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) The University of New South Wales Faculty of Electrical Engineering Computer Science Ningbo University

Two-dimensional van der Waals（2D vdW） semiconductors have proven to be of great importance for flexible thinfilm transistors（TFTs） owing to their intrinsic mechanical flexibility and superior electronic *** particular,bismuth oxyselenide（Bi2O2Se）,featuring ultrahigh electron mobility along with facile scalable thin-film growth methods,could offer a new option to deliver massively enhanced potential for flexible ***,it has remained a challenge to achieve nonvolatile flexible memory devices based on Bi2O2Se TFTs,thereby hindering the extension of Bi2O2Se TFTs to storage and emerging neuromorphic computing ***,a flexible synaptic TFT is demonstrated through the creation of a Bi2O2Se-based ferroelectric field-effect transistor（FeFET） structure on the flexible mica *** proposed device exhibits excellent nonvolatile memory characteristics,including a large memory window,excellent current modulation ratio,great retention,and strong ***,the Bi2O2Se-based FeFET can be operated as a synaptic device with analog conductance-modulating *** to the superior mechanical flexibility of the component materials and the mica substrate,the Bi2O2Se-based FeFETs can retain their performance against various bending states,showing a straininvariant electrical *** study marks the advancement of Bi2O2Se-based TFTs toward flexible nonvolatile memories and synaptic devices.

关键词： Bi$_{2}$O$_{2}$Se FeFET nonvolatile memory flexible device synaptic transistor

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Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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Optimized Metamaterials for Design of Enhanced-Performance High Order Mode Dipole-Driven Yagi-Uda Antenna for Millimeter Wave Applications

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IEEE Access 2025年 13卷 79385-79396页

作者： Esmail, Bashar A. F. Isleifson, Dustin Koziel, Slawomir Pietrenko-Dabrowska, Anna University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 5V6 Canada Reykjavik University Department of Engineering Reykjavík102 Iceland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Gdańsk80-233 Poland

This paper presents the development of a Yagi antenna optimized for third-order mode operation within the millimeter-wave (mmWave) spectrum. A third-order mode driven dipole, along with a reflector, is introduced to enhance the antenna’s gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna operates within the fifth generation (5G) band at 28 GHz, with a bandwidth of 2.6 GHz. Radiation pattern analysis indicates that the gain of the antenna in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. To further increase the gain and address the path loss challenges in the mmWave spectrum, a near-zero index metamaterial (NZIM) array was integrated. A 5 x 5 unit cell array was embedded into the same antenna substrate, positioned in front of the reflector. The metamaterial array was optimized using the trust-region (TR) algorithm, resulting in a significant gain enhancement, reaching 13.8 dBi at 28 GHz while maintaining the operational bandwidth in terms of impedance matching. The antenna was subsequently fabricated and tested, with experimental results demonstrating a strong correlation with the simulated outcomes across all key parameters. © 2013 IEEE.

关键词： Antenna reflectors

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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