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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Exploring blockchain for the coordination of distributed energy resources

arXiv

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arXiv 2021年

作者： Yang, Qing Wang, Hao College of Electronics and Information Engineering Blockchain Technology Research Center Shenzhen University Shenzhen China Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia

The fast growth of distributed energy resources (DERs), such as distributed renewables (e.g., rooftop PV panels), energy storage systems, electric vehicles, and controllable appliances, drives the power system toward a decentralized system with bidirectional power flow. The coordination of DERs through an aggregator, such as a utility, system operator, or a third-party coordinator, emerges as a promising paradigm. However, it is not well understood how to enable trust between the aggregator and DERs to integrate DERs efficiently. In this paper, we develop a trustable and distributed coordination system for DERs using blockchain technology. We model various DERs and formulate a cost minimization problem for DERs to optimize their energy trading, scheduling, and demand response. We use the alternating direction method of multipliers (ADMM) to solve the problem in a distributed fashion. To implement the distributed algorithm in a trustable way, we design a smart contract to update multipliers and communicate with DERs in a blockchain network. We validate our design by experiments using real-world data, and the simulation results demonstrate the effectiveness of our algorithm. © 2021, CC BY-NC-SA.

关键词： Energy resources

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Opponent colour median-based scale invariant local ternary pattern: a new feature descriptor for moving object detection

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The Journal of Photographic science 2024年第4期72卷

作者： K. Kalirajan K. Balaji a Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India b Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India

ABSTRACTThis article presents a new colour-texture feature descriptor called opponent colour median-based scale invariant local ternary pattern (OCMD-SILTP) to deal with dynamic backgrounds, similar texture regions, moving cast shadow, and illumination changes. Here, the incoming image frames are first divided into several sub regions and then the colour-texture features are derived by applying OCMD-SILTP on the individual colour planes (R, G, and B) as well as opponent colour planes (R-G, R-B, and G-B). For opponent planes, the median of neighbourhood pixels of one color plane is used for thresholding the neighbourhood pixels of opponent color plane. Because of this, the proposed OCMD-SILTP colour-texture descriptors are more immune to scaling and video noises. The background changes are dynamically updated through OCMD-SILTP texture patterns using histogram intersection method. Experiments on publically available dataset prove the significance of proposed approach under complex environments.

关键词： Object detection colour texture dynamic background OCMD-SILTP surveillance tracking feature descriptor

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Synthetic dataset Generation Methods for Computer Vision Application

Synthetic Dataset Generation Methods for Computer Vision App...

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International Conference on Smart Systems and Technologies (SST)

作者： Matej Arlovic Davor Damjanovic Franko Hrzic Josip Balen Faculty of Electrical Engineering Computer Science and Information Technology J. J. Strossmayer University of Osijek Osijek Croatia Department of Orthopaedic Surgery and Sports Medicine Boston Children’s Hospital Harvard Medical School Boston MA USA Boston MA United States Center for Artificial Intelligence and Cybersecurity University of Rijeka Rijeka Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Deep learning models rely on datasets for training, validation, and testing. They require diverse and representative datasets to represent real-world scenarios effectively. The performance of deep learning models is heavily influenced by data quality and quantity. High-quality and diverse datasets are essential for developing robust and accurate models. However, acquiring data for datasets can pose challenges since the time and complexity associated with manual data labeling. In order to address this problem, synthetic data generated by 3D software like Unreal Engine 5, Blender, and NVIDIA Omniverse, as well as AI-generated images in Stable Diffusion and DALL-E 3, are employed. Synthetic data facilitate the rapid and straightforward generation of aggregated datasets that accurately represent real-world scenarios. The impact of synthetic data on the performance and quality of deep learning model outcomes requires further investigation.

关键词： Deep learning Solid modeling Three-dimensional displays Computational modeling Manuals Diffusion models data models Software Engines Synthetic data

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Convolutional Neural Networks for Enhanced Real-Time Traffic Incident Detection and Image Classification

Convolutional Neural Networks for Enhanced Real-Time Traffic...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Monisha J L. Ramalingam S. Mathankumar Narayanasamy P M. Rajmohan S. Murugan Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of Convolutional Neural Networks (CNNs), viz., Alex Net, is presented in this paper. Alex Net, a deep CNN architecture that is widely used for image recognition, is employed to classify traffic images and videos for incident identification. The system makes use of the ability of the model to learn robust features to identify images as different events, such as accidents, roadblocks, and vehicle breakdowns. Low latency and high accuracy incident identification is done by the system utilizing real-time traffic camera data and Alex Net. Differentiation between different types of events. This is for the sake of enhancing reaction time, enabling effective traffic management, and differentiating between events. AlexNet enables 92% accuracy in real-time traffic detection and image classification, enhancing the efficiency of intelligent transportation systems. The results demonstrate how deep learning (DL) methods can revolutionise traffic monitoring systems and improve safety protocols for drivers.

关键词： Deep learning Accuracy Electric breakdown Real-time systems data models Safety Convolutional neural networks Image classification Accidents Videos

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An Ensemble Deep Learning Approach for Diabetic Retinopathy Detection using Fundus Image

An Ensemble Deep Learning Approach for Diabetic Retinopathy ...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Sandra Johnson Lourdu Jennifer J R G. Karthikeyan Vengadapathiraj M D Sasireka Department of Artificial Intelligence & Data Science R.M.K. Engineering College Chennai Department of Electronics and Communication Engineering S.A Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Electronics and Communication Engineering Rajalakshmi Instittue of Technology Chennai Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai

ISBN: (纸本)9781665482721

Detection of diseases, including diabetic retinopathy, may be greatly improved by taking a fundus picture of the back of the eye (DR). Complications in diabetics are the most common cause of vision problems, notably in younger and much more financially secure age groups. The risk of blindness in patients with DR may be reduced if they are diagnosed early enough. An ophthalmologist examined the fundus picture and used DR screening to look for lesions. However, the increase in incidence of DR is not correlated with the number of ophthalmologists who are able to interpret fundus pictures. Delay in prevention and treatment of DR may result as a result of this. Consequently, an automated diagnosis system is required to assist ophthalmologists in increasing the diagnostic process efficiency. The concatenate model is used in this study to differ fundus images into three categories: those without diabetic retinopathy, those with non-proliferative diabetic retinopathy, and those with proliferative diabetic retinopathy. We're using DenseNet121 and Inception-ResNetV2 for our models. Two models' feature extraction findings are integrated using the multilayer perceptron (MLP) classification approach. Compared to a single model, our strategy provides an increase in accuracy, precision, and recall of 91 percent and 90 percent for the F1-score. Deep-learning-based DR categorization utilizing fundus picture data was successfully shown in this experiment.

关键词： Deep learning Retinopathy Blindness Multilayer perceptrons Aerospace electronics Feature extraction Diabetes

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Enhancing IoT Security with Self-Adaptive Blockchain-Based Intrusion Detection System

Enhancing IoT Security with Self-Adaptive Blockchain-Based I...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Pramod Kumar Naik Khaja Mannanuddin Sundaresan K Swati Sah Harshal Patil Firos A Department CSE Dayananda Sagar University Bangalore India School of computer science & AI SR University Warangal Telangana India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Anna University Chennai School of SET Sharda university Greater Noida India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of computer Science and Engineering Rajiv Gandhi University (A Central University)Rono-Hills Doimukh Arunachal Pradesh India

The extensive adoption of Internet of Things (IoT) devices has resulted in a revolution across a variety of business sectors by presenting previously unimaginable prospects for connection and automation. Despite this, there has been a significant increase in the usage of IoT, which has led to important security issues. This research presents a ground-breaking security solution known as the Self-Adaptive Blockchain-Based Intrusion Detection System (SAB-IDSI), which was developed particularly for Internet of Things (IoT) settings. In order to determine how well SAB-IDSI performs in contrast to traditional Intrusion Detection Systems (IDS), a series of experiments and assessments were carried out. The findings indicate that there was a significant increase in the accuracy of the intrusion detection. The outstanding capability of SAB-IDSI to reliably detect genuine incursions while limiting false alarms is demonstrated by its accuracy, which is 96.2%. False alerts are kept to a minimum. In addition to this, it has a very high recall rate of 94.8%, which ensures that it has a very high capture rate of genuine incursions. The fact that SAB-IDSI has a balanced F1-Score of 95.5% further validates its overall efficacy in the detection of intrusions. The average reaction time of 12.5 milliseconds for SAB-IDSI is clear evidence of its effectiveness in rapidly responding to threats, greatly surpassing the performance of traditional IDS. This highlights its potential for timely threat mitigation, which is essential in situations where time is of the essence. In addition to this, SAB-IDSI has a significantly lower energy consumption rate, which demonstrates its potential to increase the amount of time that Internet of Things devices may remain operational while also lowering total energy expenses.

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Enhancing The Felsenstein Method To Reinvigorate Phylogenetic Starting In The Big data

Enhancing The Felsenstein Method To Reinvigorate Phylogeneti...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ashendra Kumar Saxena Priyanka Vishwkarma Sandeep Kumar N. Thangarasu K. S. Bhuvaneshwari Bhandari Mahesh Ashok College of Computing Science and Information Technology Teerthankar Magaveer University Moradabad Uttar Pradesh India Department of Computer Science & Engineering Invertis University Bareilly Uttar Pradesh India School of Commerce and Management IIMT University Meerut Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Tamilnadu India Vishwakarma Institute of Information Technology Pune Maharashtra India

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic.

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Deterministic relation between optical polarization and lattice symmetry revealed in ion-doped single microcrystals

arXiv

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arXiv 2022年

作者： Li, Peng Guo, Yaxin Liu, Ao Yue, Xin Yuan, Taoli Zhu, Jingping Zhang, Yanpeng Li, Feng Key Laboratory for Physical Electronics and Devices The Ministry of Education School of Electronic Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'An710049 China School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi'An710021 China

Rare-earth ions doped crystals are of great significance for micro-sensing and quantum information, whilst the ions in the crystals emit light with spontaneous partial polarization, which is, though believed to be originated from the crystal lattice structure, still lacking a deterministic explanation that can be tested with quantitative accuracy. We report the experimental evidence showing the profound physical relation between the polarization degree of light emitted by the doped ion and the lattice symmetry, by demonstrating, with unprecedented precision, that the lattice constant ratio c/a directly quantifies the macroscopic effective polar angle of the electric and magnetic dipoles, which essentially determines the linear polarization degree of the emission. Based on this discovery, we further propose a pure optical technology to identify the three-dimensional orientation of a rod-shaped single microcrystal using the polarization-resolved micro-spectroscopy. Our results, revealing the physical origin of light polarization in ion-doped crystals, open the way towards on-demand polarization control with crystallography, and provide a versatile platform for polarization-based microscale sensing in dynamical systems. © 2022, CC BY.

关键词： Metal ions

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