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Intelligent Technologies (ACOIT), Asian Conference on

作者： Ramakrishnan Raman Deepa Dixit Jonardan Koner Dhaval Rabadiya Avinash Purandare Shailesh Rastogi Symbiosis International (Deemed University) Prin L N Welingkar Institute of Management Development and Research NICMAR University Pune Computer science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In the context of burgeoning smart cities, seamless data transmission plays a pivotal role in enhancing urban mobility management. This paper introduces a novel approach to Routing-Based Distributed Mobility Management (RBDMM) designed to support robust and seamless data communication. By integrating advanced routing protocols with distributed architectures, our proposed system addresses critical challenges of scalability, reliability, and latency in urban networks. The methodology involves the deployment of multiple distributed nodes across urban landscapes, each independently managing local traffic to facilitate quicker responses and reduced network latency. Key algorithms include a Path Selection Algorithm, Load Balancing Algorithm, and Data Integrity Algorithm, optimized through mathematical models to evaluate network latency and throughput. Simulations conducted using NS3 demonstrate significant improvements in data transmission efficiency and reductions in packet loss compared to conventional centralized mobility management solutions. The results show that the RBDMM system achieves a 41.67% reduction in average latency, a 37.5% increase in throughput, and an 8% improvement in packet delivery ratio. These findings indicate the system’s potential to significantly enhance data transmission processes in smart cities, ensuring more reliable, efficient, and scalable network management. In conclusion, the RBDMM system sets a foundation for future advancements in urban mobility management, promoting enhanced smart city operations and paving the way for further innovations in data management and urban planning.

关键词： Adaptation models Adaptive systems Smart cities Scalability Throughput Routing protocols Mathematical models Data communication Reliability Internet of Things

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Dual-Use Secure Key Distribution Protocol Based on FEC-Aided Unidirectional BER Estimation

Dual-Use Secure Key Distribution Protocol Based on FEC-Aided...

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OptoElectronics and Communications Conference, OECC

作者： Di Wu Lei Deng Qi Yang Zhijun Yan Xiaoxiao Dai Xiaojing Gao Mengfan Cheng National Engineering Research Center for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology (HUST) Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

We propose a unidirectional dual-use secure key distribution protocol based on the forward-error-correction-aided bit error rate estimation. Hybrid automatic repeat request and adaptive coding techniques are integrated to enhance the reliability of communication section. A simulation is conducted in a turbulent channel to verify the feasibility of the proposed protocol.

关键词： Protocols Bit error rate Estimation Parity check codes Reliability Automatic repeat request Optical devices Adaptive coding

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Expression of Concern for: Glaucoma Detection Using Deep Learning Approach in research Detection

Expression of Concern for: Glaucoma Detection Using Deep Lea...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Anil Pandurang Shinde Ramandeep Singh Renuka Shantappa Anami Noor Mohammed R. Selvameena Ashwini Jotiram Patil Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Maharashtra Department of Computer Science Guru Nanak Khalsa College Daroli Kalan Jalandhar Jalandhar Punjab Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttrakhand Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai TamilNadu India

ISBN: (数字)9798350315905

ISBN: (纸本)9798350315912

An persistent eye infection called glaucoma causes irreversible vision loss. A crucial problem in the clinical field is glaucoma identification. There have been very few studies designed to detect glaucoma in its early stages. However, using current techniques to carry out glaucoma infection detection was ineffective. Moreover, the conventional glaucoma infection diagnosis method required additional time. Glaucoma identification in various fundus images is a challenging task that necessitates mastery and extensive training. Glaucoma is an ocular neuropathy characterized by mild retinal ganglion cell degeneration. There are several different types of glaucoma, each having a unique a etiology and set of pathogenic factors, but they all have alterations in the structure and function of the optic nerve that are essentially universal. Convolution organisations, a type of deep learning computation, have swiftly emerged as a method for making decisions while analyzing clinical images. A chronic eye condition called glaucoma causes irreversible vision loss. In this section, we discuss two state-of-the-art glaucoma detection methods in the light of deep learning. The glaucoma dataset is used in this deep learning computation proposed for glaucoma photo analysis. This finding contrasts with convolutional brain network grouping techniques in the context of deep learning. Each rating indicates that the newly proposed worldview is more favorable than the one currently in use.

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Focusing Intracranial Aneurysm Lesion Segmentation by Graph Mask2Former with Local Refinement in DSA Images

Focusing Intracranial Aneurysm Lesion Segmentation by Graph ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yancheng Mo Yanlin Chen Yan Hu Jiongfu Xu Hao Wang Limeng Dai Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen People’s Hospital Shenzhen China

Intracranial Aneurysm (IA) lesion segmentation is significant for IA treatment, which is one of the high death rate and deformity cerebrovascular diseases. Segmenting the IA lesions accurately is still challenging in digital subtraction angiography (DSA) images due to blurred boundaries, imaging noise, and intracranial vascular morphologies. In this paper, we are the first time to propose a novel instance segmentation network architecture, Graph Mask2Former, to segment IA lesions automatically based on DSA images. Specifically, we apply a graph convolution module to reassign label information, aiming to adjust the confidence weight of error instances adaptively. Furthermore, we design a local refinement module to refine the coarse mask output. The extensive experiments on the clinical IA- DSA and LiTS datasets show that our method outperforms recent state-of-the-art methods. This paper also provides the visual analysis to explain the inherent behavior of our method.

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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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Near-eye light field display based on biological stereoscopic vision

Near-eye light field display based on biological stereoscopi...

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The 2023 International Conference on Display technology, (ICDT 2023)

作者： Zhao, Jian Xia, Jun Xu, Yue Jiang, Yujin School of Computer Engineering Nanjing Institute of technology No.1 Hongjing Road Jiangsu Nanjing211167 China Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering Southeast University Nanjing210018 China SuXin Vision Technologies Co Ltd No.2888 Dongwu Road Jiangsu Suzhou215000 China

Near-eye display technology can provide users with an immersive visual experience. However, there are still many challenges in this technology, the most urgent of which is visual comfort. The main reason is that the display device cannot render the correct depth cues to solve the conflict between the human eye's radial angle and adjustment, resulting in dizziness, nausea, binocular fatigue and other problems for users to use for a long time. In this paper, we will analyze and review the technical schemes which can provide accurate or near-accurate depth clues in the current near-eye display technology. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Stereo image processing

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Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images

arXiv

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arXiv 2023年

作者： Liu, Shouyue Hao, Jinkui Xu, Yanwu Fu, Huazhu Guo, Xinyu Liu, Jiang Zheng, Yalin Liu, Yonghuai Zhang, Jiong Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Cixi Biomedical Research Institute Wenzhou Medical University China School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Department of Computer Science Southern University of Science and Technology China Department of Eye and Vision Science University of Liverpool United Kingdom Department of Computer Science Edge Hill University United Kingdom

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Problems in NP Can Admit Double-Exponential Lower Bounds When Parameterized by Treewidth or Vertex Cover 51

Problems in NP Can Admit Double-Exponential Lower Bounds Whe...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Foucaud, Florent Galby, Esther Khazaliya, Liana Li, Shaohua Mc Inerney, Fionn Sharma, Roohani Tale, Prafullkumar Université Clermont Auvergne CNRS Mines Saint-Étienne Clermont Auvergne INP LIMOS Clermont-Ferrand63000 France Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden University of Gothenburg Sweden Technische Universität Wien Austria CISPA Helmholtz Center for Information Security Saarbrücken Germany University of Bergen Norway Indian Institute of Science Education and Research Pune India

ISBN: (纸本)9783959773225

Treewidth serves as an important parameter that, when bounded, yields tractability for a wide class of problems. For example, graph problems expressible in Monadic Second Order (MSO) logic and Quantified SAT or, more generally, Quantified CSP, are fixed-parameter tractable parameterized by the treewidth of the input’s (primal) graph plus the length of the MSO-formula [Courcelle, Information & Computation 1990] and the quantifier rank [Chen, ECAI 2004], respectively. The algorithms generated by these (meta-)results have running times whose dependence on treewidth is a tower of exponents. A conditional lower bound by Fichte, Hecher, and Pfandler [LICS 2020] shows that, for Quantified SAT, the height of this tower is equal to the number of quantifier alternations. These types of lower bounds, which show that at least double-exponential factors in the running time are necessary, exhibit the extraordinary level of computational hardness for such problems, and are rare in the current literature: there are only a handful of such lower bounds (for treewidth and vertex cover parameterizations) and all of them are for problems that are #NP-complete, Σp2-complete, Πp2-complete, or complete for even higher levels of the polynomial hierarchy. Our results demonstrate, for the first time, that it is not necessary to go higher up in the polynomial hierarchy to achieve double-exponential lower bounds: we derive double-exponential lower bounds in the treewidth (tw) and the vertex cover number (vc), for natural, important, and well-studied NP-complete graph problems. Specifically, we design a technique to obtain such lower bounds and show its versatility by applying it to three different problems: Metric Dimension, Strong Metric Dimension, and Geodetic Set. We prove that these problems do not admit 22o(tw) ·nO(1)-time algorithms, even on bounded diameter graphs, unless the ETH fails (here, n is the number of vertices in the graph). In fact, for Strong Metric Dimension, the double-expo

关键词： Parameterization

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Analysis Role of ML and Big Data Play in Driving Digital Marketing's Paradigm Shift

Analysis Role of ML and Big Data Play in Driving Digital Mar...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Ravisankar, A. Ponniah, M. Tripathi, Vikas Kulshreshtha, Kushagra Srivastava, Arun Pratap Singh, Vikram Erode Sengunthar Engineering College Department of Management Studies Erode India G.T.N Arts College Pg and Research Department of Commerce Dindigul India Graphic Era Deemed University Department of Computer Science and Engineering Uttarakhand Dehradun India Institute of Business Management Gla University Mathura India Lloyd Institute of Engineering and Technology Department of CSE Greater Noida India Quantum University Uttarakhand Roorkee India

ISBN: (纸本)9798350310061

Marketing strategies are being revolutionized by the development of user data and the expanding usability of Machine Learning (ML) as well as Big Data approaches. The wide variety of options that ML and Big Data applications provide in building and sustaining a competitive corporate edge are not fully understood by researchers and marketers. Based on a thorough analysis of academic and commercial literature, we offer a classification of ML and Big Data use cases in marketing in this article. In order to effectively employ ML and Big Data in marketing, we have discovered 11 recurrent use cases that are grouped into 4 homogenous families. These families are: fundamentals of the consumer, the consumer experience, decision-making, and financial impact. We go over the taxonomy's repeating patterns and offer a conceptual framework for understanding and extending it, emphasizing the practical ramifications for marketers and academics. © 2023 IEEE.

关键词： Big data

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An Efficient Object Detection and Classification from Restored Thermal Images based on Mask RCNN 6

An Efficient Object Detection and Classification from Restor...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Thenmozhi, E. Karunakaran, A. Arunkumar, J.R. Chinnammal, V. Kalaivanan, C. Anitha, G. Panimalar Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Electronics and Communication Engineering Chennai India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In recent years, thermal cameras are extensively employed in several industries, including biometrics, intelligent surveillance, and health monitoring. The thermal cameras' exorbitant price, meanwhile, makes them difficult to obtain. Furthermore, blurring brought on by camera movement, object movement, and focus settings is a problem with thermal photos. There haven't been many research on thermal image-centered picture restoration that focus on such issues. Additionally, it is critical to accelerate the processing capability of image treatment technologies in order to work in tandem with techniques like object tracking and activity detection that make use of temporal data from thermal recordings. Furthermore, no research has been done on the use of thermal pictures for super-resolution rebuilding and deblurring. Due to the inability to discern reflected on the soil surface or wall caused by the heat emitted by the item, previous research on object recognition using thermal imaging include inaccuracies. This paper suggests a deep learning-based technique for thermal image reconstruction that combines deblurring and super-resolution reconstruction in one step. Recent advances in deep learning have shown that approaches based on generative adversarial networks (GANs) perform well in image-to-image translation challenges because they can maintain texture features in pictures and produce finer, more convincing textures than traditional feed forward encoders. This research study suggests a deblur-SRRGAN for thermal image restoration in light of the benefits of GAN. Additionally, a MR-CNN is also recommended to perform object recognition in the thermal image reconstruction. © 2022 IEEE.

关键词： Image reconstruction

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