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Point spread function modeling and engineering in black-box lens systems

Optics Express 2025年第3期33卷 4211-4224页

作者： Xiao, Dafei Kedem, Reut Orange Shechtman, Yoav Russell Berrie Nanotechnology Institute Technion–Israel Institute of Technology Haifa Israel Faculty of Biomedical Engineering Technion–Israel Institute of Technology Haifa Israel Faculty of Electrical and Computer Engineering Technion–Israel Institute of Technology Haifa Israel

Point spread function (PSF) engineering in an imaging system involves the introduction of additional optical components to efficiently encode object information. It typically relies on a well-established mapping relation between object space, PSF engineering plane, and image plane. However, this reliance can limit its application in imaging systems with unknown ("black box") attributes, e. g. commercial photographic lenses, where a theoretical imaging model is not readily available. To address this limitation, we conduct wave-optics analysis of general lens systems, explicitly derive a general imaging model based on scaled Fourier transform, and develop an overall pipeline for automatic parameter determination of the model. When applied to a photographic lens, this pipeline demonstrates high PSF modeling accuracy, showcasing the capability of "seeing what you design". We then implemented PSF engineering in this lens to find its optimal PSF for 3D localization. This work improves computational imaging by enabling accurate wavefront shaping design in arbitrary imaging systems. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Optical transfer function

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A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

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computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

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Co-clustering method for cold start issue in collaborative filtering movie recommender system

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Multimedia Tools and Applications 2024年 1-25页

作者： AbbasiRad, Ensieh Keyvanpour, Mohammad Reza Tohidi, Nasim Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Computer Engineering Faculty K.N.Toosi University of Technology Tehran Iran Computer Science & amp Engineering University of Michigan Ann ArborMI United States

Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great opportunities and challenges for business, government, education, and other fields. The cold start problem is a significant issue in these systems. If recommender systems fail to provide satisfactory personalized recommendations for new users, the user’s trust can easily be lost. Hence, in this paper, using co-clustering and utilizing user demographic information and the behavioral history of users, a solution to this critical issue for recommending movies is introduced. In the proposed method, in addition to dealing with the problem of relative cold start, the problem of absolute cold start is also addressed. The proposed method was evaluated via two RMSE and MAE criteria, which accordingly has achieved 0.85 and 0.49 on the Movielens dataset and 1.05 and 0.6 on the EachMovie dataset, respectively, according to the number of comments that Cold Start users have registered. Moreover, it achieved 0.9 and 0.55 on the Movielens dataset and 1.42 and 0.89 on the EachMovie dataset, respectively, according to the number of registered comments for the cold start items. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Individualized Data Generation in Personalized Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6628-6642页

作者： Cai, Yunyun Xi, Wei Shen, Yuhao Sun, Cerui Wang, Shuai Gong, Wei Zhao, Jizhong Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Southeast University School of Computer Science and Engineering Nanjing China University of Science and Technology of China School of Computer science and Technology Hefei China

Most Personalized Federated Learning (PFL) algorithms merge the model parameters of each client with other (similar or generic) model parameters to optimize the personalized model (PM). However, the merged model parameters in these algorithms may fit low relevance data, thereby limiting the performance of PM. In this paper, we generate similar data for each client through the collaboration of a generic model (GM) on the server, rather than merging model parameters. To train a generator capable of generating data for all classes on the server without real data, we employ the GM as the discriminator in adversarial training with the generator. Additionally, we introduce a similarity assessment metric, which allows for the assessment of the similarity between local data and data from other classes. Nevertheless, the presence of non-IID data among clients can weaken the performance of the GM, consequently impacting the training of the generator and similarity assessment. To address this issue, we design a directive mechanism so that GM can be optimized during adversarial training without the need for additional training. The experimental results validate the superiority of our algorithm over state-of-the-art algorithms in terms of accuracy, loss, and convergence speed. © 2002-2012 IEEE.

关键词： Federated learning

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Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protection

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International Journal of computers and Applications 2024年第4期46卷 252-265页

作者： Barua, Eirene Dutta, Mala Faculty of Engineering Assam down town University Guwahati India Faculty of Computer Technology Assam down town University Guwahati India

Over the past few decades, pervasive computing has generated increasing amounts of data. Data mining (DM) technology is becoming more widely used. Due to the potential threats to the confidentiality of people and organizations, the massive collecting and analysis of data might raise privacy issues. A new research area termed privacy-preserving association rule mining (PPARM), which has attracted the interest of many researchers, has emerged to prevent privacy disclosure during DM. The research field PPARM, aims to prevent association rule mining privacy infractions from happening. The study uses a systematic survey to understand the literature on PPARM techniques for sensitive rule identification and enhanced data protection. Here, the application area of PPARM has been explored, summarized, and concluded with some open challenges and future directions. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Association rules

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Quad-Mode H-Beam Metasurface-Embedded Dual-Band Circularly-Polarized Multi-Tiered Antenna Scheme With 5G (n79) and Wi-Fi 6E Capabilities

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IEEE Access 2025年 13卷 89844-89857页

作者： Supreeyatitikul, N. Chomtong, P. Konpang, J. Thitimahatthanakusol, P. Akkaraekthalin, P. Rajamangala University of Technology Krungthep Faculty of Engineering Department of Electrical and Telecommunication Engineering Bangkok10120 Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok10800 Thailand Rajamangala University of Technology Isan Faculty of Engineering and Technology Department of Telecommunications Engineering and Digital Innovation Nakhon Ratchasima30000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

This research proposes a quad-mode H-beam metasurface (MTS)-embedded dual-band circularly-polarized (CP) multi-tiered antenna scheme with 5G (n79) and Wi-Fi 6E capabilities. The proposed antenna scheme consists of three layers of corner-truncated FR-4 substrates without an air gap. The upper substrate contains an array of 3× 4 H-beam MTS elemental units tilted at 49°. The middle substrate is a copper plate with a horizontal Z-shaped slot at its center functioning as the ground plane, and the lower substrate contains a microstrip feed line. Characteristic mode analysis (CMA) is employed to optimize the circular polarization of the MTS structure. Results show that Modes 1 & 2 at 4.81 GHz and Modes 4 & 9 at 6.2 GHz contribute to CP radiation. The measured return loss bandwidths of the antenna are 19.37% (4.63 - 5.56 GHz) and 9.51% (5.97 - 6.56 GHz) for the 5G (n79) and Wi-Fi 6E bands. The measured axial ratio bandwidths are 13.33% (4.6 - 5.24 GHz) and 1.77% (6.15 - 6.26 GHz), with maximum gains of 5.2 and 3.9 dBic at 5.5 and 6.2 GHz, respectively. Unlike conventional MTS-based CP antennas which employ rectangular- or square-shaped MTS elements for single-band operation, this work utilizes H-beam-shaped MTS elemental units to achieve dual-band CP capability. Additionally, rather than traditional full-wave methods, CMA is used to optimize circular polarization. Essentially, the proposed antenna scheme is suitable for 5G (n79) and Wi-Fi 6E communications. © 2013 IEEE.

关键词： Circular polarization

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Improving Camouflage Object Detection Using U-NET and VGG16 Deep Neural Networks and CBAM Attention Mechanism 10

Improving Camouflage Object Detection Using U-NET and VGG16 ...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Akbarnezhad, Erfan Naserizadeh, Fatemeh Faculty of Computer Engineering Quchan University of Technology Quchan Iran Faculty of Electrical & Computer Engineering Malek Ashtar University of Technology Tehran Iran

ISBN: (纸本)9798350348873

Camouflaged object detection is an essential concern in computer vision and is commonly applied in military, security, biological, and various other fields. In machine vision, camouflage pertains to the situation where the desired object in an image is often challenging to discern or nearly impossible to see due to patterns or visual characteristics that closely resemble the background or the surrounding environment. Essentially, camouflage involves an attempt to blend the texture of a foreground object with that of the background. Concealing objects can be accomplished through methods like color matching, pattern blending, or structural adaptation. Detecting camouflaged objects presents a significant challenge because of the inherent resemblance of camouflaged objects to their background and the indistinct boundaries between them. In this article, a deep neural network-based approach is proposed to address this issue. By utilizing the U-NET and VGG16 algorithms in combination with the CBAM attention mechanism, a solution for detecting camouflaged objects in the CAMO and CAMO-COCO datasets is presented. Also, a data augmentation process is employed to enhance the training of the model. The proposed method demonstrates remarkable performance in detecting camouflaged objects in both the CAMO and CAMO-COCO datasets. On the CAMO dataset, it outperforms the comparison methods with an F-measure value of 0.5867, along with an IOU value of 0.4168 and an MAE value of 0.1186. Similarly, on the CAMO-COCO dataset, the proposed method achieves superior results with an IOU value of 0.7101 and an MAE value of 0.0813 and an F-measure value of 0.7703. These findings highlight the high accuracy and minimal error of the proposed method in detecting camouflaged object boundaries, reinforcing its superiority over the compared methods on both datasets. ©2024 IEEE.

关键词： Object detection

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