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Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

International Journal of Information technology (Singapore) 2025年第2期17卷 941-949页

作者： Pavithra, P. Hariharan, B. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

Secure multi-keyword search for outsourced cloud data has gained popularity, especially for scenarios involving multiple data owners. This work proposes a method for secure multi-keyword searches across encrypted cloud data from various owners, utilizing an administrator or trustworthy third party for key management. The approach employs the TF-IDF model and Vector Space Model (VSM) to generate index and query vectors, and uses an advanced Attribute-Based Encryption (ABE) algorithm for data privacy. The Improved Dingo Optimization Algorithm (IDOA) optimally selects the key value. Encrypted emails are sent to a cloud server, and a Grouped Balanced Binary (GBB) tree structure is used for index construction. The Greedy Depth-First Search (GDFS) method efficiently searches the index, grouping relevant documents to exclude unnecessary ones. Performance is evaluated based on memory usage, execution time, encryption and decryption times, and search times, implemented in Java and tested on a cloud simulator. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Attribute-based encryption and vector space model Cloud computing Dingo optimization algorithm Multi keyword search scheme Multiple data owner

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SDD-SLAM: Semantic-Driven Dynamic SLAM with Gaussian Splatting

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IEEE Robotics and Automation Letters 2025年第6期10卷 5721-5728页

作者： Liu, Haosong Wang, Long Luo, Haiyong Zhao, Fang Chen, Runze Chen, Yushi Xiao, Mingyu Yan, Jiaquan Luo, Dan Beijing100876 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Beijing Forestry University School of Information Science and Technology School of Artificial Intelligence Beijing100083 China

Recently, significant advancements have been made in 3D Gaussian Splatting SLAM for dynamic environments. However, most existing methods primarily address active dynamic objects, such as people and vehicles, and fail to account for the impact of passive dynamic objects on localization and mapping. This results in the presence of numerous artifacts left by dynamic objects in the scene, which diminishes the accuracy of pose estimation. To address these challenges, we propose SDD-SLAM, a semantic-driven SLAM system based on 3D Gaussian Splatting. Extensive experiments conducted on the TUM and BONN datasets demonstrate that the proposed methods, including refined mask expansion, edge noise filtering, object-level dynamic object removal based on semantic Gaussians, and object-level density control strategy for Gaussian ellipsoids, significantly enhance the accuracy of camera pose estimation and the quality of map reconstruction in dynamic environments, achieving state-of-the-art performance. © 2016 IEEE.

关键词： Gaussian distribution

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Precision bounds for quantum phase estimation using two-mode squeezed Gaussian states

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Chinese Physics B 2025年第1期34卷 228-233页

作者： Jian-Dong Zhang Chuang Li Lili Hou Shuai Wang School of Mathematics and Physics Jiangsu University of TechnologyChangzhou 213001China Research Center for Novel Computing Sensing and Intelligent Processing Zhejiang LaboratoryHangzhou 311121China

Quantum phase estimation based on Gaussian states plays a crucial role in many application *** this paper,we study the precision bound for the scheme using two-mode squeezed Gaussian *** quantum Fisher information is calculated and its maximization is used to determine the optimal *** find that two single-mode squeezed vacuum states are the optimal Gaussian inputs for a fixed two-mode squeezing *** corresponding precision bound is sub-Heisenberg-limited and scales as N^(-1)/*** practical purposes,we consider the effects originating from photon *** precision bound can still outperform the shot-noise limit when the lossy rate is below *** work may demonstrate a significant and promising step towards practical quantum metrology.

关键词： quantum metrology Gaussian state Heisenberg limit

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Detection of diabetic retinopathy and age-related macular degeneration using DenseNet based neural networks

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Multimedia Tools and Applications 2025年第1期84卷 289-316页

作者： Singh, Manpinder Dalmia, Saiba Ranjan, Ranjeet Kumar School of Computing DIT University Dehradun248009 India CSE Department Thapar Institute of Engineering & Technology Patiala147004 India

The eyes are the organ of sight and one of the most highly developed sensory organs in our body which covers a larger part of the brain. One of the most common problems that are spreading from kids to adults is an eye disorder which can be defined as abnormal functioning of the eye that can lead to vision disturbance. Examples of major eye problems are cataracts, glaucoma, etc. Artificial Intelligence has helped benefit research in the medical field. Ocular diseases can be detected automatically through Computer Vision and Deep Learning models when high-quality medical eye fundus images are provided to them. Inspired by this, we proposed three deep learning models based on the DenseNet pre-trained model for Ocular Disease Detection. Since the amount of eye scans being performed is rapidly growing at a much faster rate than the interpretation of the scan results, our proposed models could help automate the process thus increasing speed and efficiency. The proposed model performed the detection with an accuracy of 77%, 86%, and 98% for Ocular Disease Detection, Diabetic Retinopathy, and Age-Related Macular Degeneration tasks respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer vision

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Enhanced Coffee Bean Defect Detection Using Super-Resolution GANs and Deep Learning 2

Enhanced Coffee Bean Defect Detection Using Super-Resolution...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Prabu, S. Thanigaivelu, P.S. Poonkodi, S. Anand, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur TamilNadu Chennai603203 India

ISBN: (纸本)9798331530389

Image Super-Resolution (SR) is a key challenge in computer vision, focusing on enhancing low-resolution images while preserving details. This paper presents a novel method using Generative Adversarial Networks (GANs) for improving image quality, specifically targeting coffee bean defect detection. The proposed SR-GAN model includes a generator that upscales low-resolution inputs and a discriminator that differentiates between generated high-resolution images and real ones. Trained on a coffee bean dataset, the model optimizes loss functions to balance perceptual quality and fidelity, capturing subtle defects like cracks and discolorations. Enhanced images are processed by two Convolutional Neural Networks (CNNs): one identifies broad defect categories, such as defective black beans, brown beans, broken beans and non-defective high-quality beans. This layered approach ensures more precise defect identification and classification, overcoming limitations of previous methods that struggled with subtle defects. Performance evaluations show that SR-GAN and CNN significantly outperforms existing models in resolution enhancement and classification accuracy, offering a robust solution for improving quality control in coffee production. © 2025 IEEE.

关键词： Convolutional neural networks

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Multi-view stereo algorithms based on deep learning: a survey

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Multimedia Tools and Applications 2025年第6期84卷 2877-2908页

作者： Huang, Hongbo Yan, Xiaoxu Zheng, Yaolin He, Jiayu Xu, Longfei Qin, Dechun Computer School Beijing Information Science & Technology University Beijing China Institute of Computing Intelligence Beijing Information Science & Technology University Beijing China Foreign Languages School Beijing Information Science & Technology University Beijing China

Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixels across images to compute dense correspondences and recover 3D points. Traditional MVS methods often encounter difficulties in addressing complex scenes due to limitations in robustness. However, recent advancements in deep-learning-based MVS have demonstrated remarkable performance gains, attributed to their enhanced feature extraction and cost volume processing capabilities. This comprehensive survey delves into deep-learning-based MVS algorithms, highlighting key challenges and avenues for future exploration. Our focus is on studies aimed at mitigating algorithmic memory requirements and elevating processing speeds. To better understand the developments in this field, we propose a new taxonomy. The deep-learning-based MVS algorithms are categorized into two categories: scene-oriented and view-oriented methods. Scene-oriented methods typically employ voxels or neural implicit representations as the representative form of the scene and infer the complete scene directly. View-oriented methods focus on estimating the depth map of a single view. Detailed classifications and reviews have been conducted for each category. Furthermore, we introduce several widely used datasets and metrics and empirical evaluation of representative algorithms across three popular datasets. This article concludes by discussing prevalent challenges in the MVS domain and promising research directions for the future. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Stereo image processing

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Comparative Analysis of Lenet, Resnet, and InceptionV3 Models for Pest Detection in Agriculture 1

Comparative Analysis of Lenet, Resnet, and InceptionV3 Model...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Mallela, Rajya Lakshmi Kamepalli, Sujatha Guttula, Sri Naga Sandhya Mendu, Sravanthi Pechetti, Bharath Raj Mendu, Monika Parvathi School of Computing Vignans Foundation for Science Technology and Research Guntur India

ISBN: (纸本)9798350355611

The detection of pests in agricultural fields is essential for advancing smart agriculture, aiding in optimizing resource management and enhancing crop yields. This study conducts a thorough performance assessment of three leadingedge deep learning architectures Lenet, Resnet, and InceptionV3specifically tailored for pest detection tasks. Utilizing a meticulously curated dataset consisting of annotated images of crops affected by various pests, each model was trained and evaluated under identical conditions to ensure a fair comparison. Each model's performance was thoroughly assessed using several key metrics, including accuracy, precision, recall, and F1-score, to determine their efficiency in accurately identifying and categorizing pest infestations. The comparative analysis highlights the unique strengths and limitations of each architecture, offering a comprehensive understanding of their capabilities and limitations in real-world agricultural applications. The findings from this study provide critical insights into the applicability of Lenet, Resnet, and InceptionV3models in developing robust pest detection systems. By highlighting the most effective deep learning model for this purpose, the research contributes to ongoing efforts to enhance pest management strategies, thereby supporting sustainable agricultural practices. The results underscore the importance of selecting the appropriate deep-learning architecture to improve the performance and efficiency of automated pest detection, ultimately fostering smarter, more resilient agricultural systems. © 2025 IEEE.

关键词： Deep Learning Inception V3 Lenet Pest Detection Resnet

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CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and Hardware Architecture for CRYSTALS-Kyber 38

CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and ...

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38th International Conference on VLSI Design, VLSID 2025

作者： Majumdar, Alip Shrestha, Rahul School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9798331522445

This paper presents highly reliable algorithm and high-speed hardware architecture for a unified modulo reduction for CRYSTALS-Kyber. This new architecture for modulo reduction is capable of operating at a maximum clock frequency of 351.62 MHz on Artix-7 FPGA platform, consuming a latency of 5 clock cycles, which is approximately 35.24% faster than state-of-the-art implementations. In this work, we have alleviated the size expansion problem of intermediate value of the conventional Barrett algorithm (which is one of the fastest and most reliable algorithm for modulo reduction, reported in literature) by suggesting a new technique, referred as 'Conditional R-index Switching for b-value Minimization (CRIS-b)'. We propose a unified modulo reduction design handling both positive and negative spectrum numbers for all arithmetic modulo operations in a butterfly unit of Number Theoretic Transform(NTT). © 2025 IEEE.

关键词： Quantum cryptography

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RF-Sauron:Enabling Contact-Free Interaction on Eyeglass Using Conformal RFID Tag

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IEEE Internet of Things Journal 2025年第10期12卷 14320-14332页

作者： Yang, Baizhou Chen, Ling Peng, Xiaopeng Chen, Jiashen Tang, Yani Wang, Wei Fang, Dingyi Feng, Chao Northwest University Shaanxi Key Laboratory of Passive Internet of Things and Neural Computing School of Information Science and Technology Xi'an710127 China Northwest University Xi'an Key Laboratory of Advanced Computing and Software Security School of Information Science and Technology Xi'an710127 China Rochester Institute of Technology RochesterNY14623 United States Northwest University Xi'an Key Laboratory of Advanced Computing and System Security School of Information Science and Technology Xi'an710127 China Northwest University Shaanxi International Joint Research Centre for the Battery-Free Internet of Things School of Information Science and Technology Xi'an710127 China

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%. © 2014 IEEE.

关键词： Contrastive Learning

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, Information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： Optimization algorithms

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