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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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Harmonizing open banking in the European Union: an analysis of PSD2 compliance and interrelation with cybersecurity frameworks and standards

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International Cybersecurity Law Review 2024年第1期5卷 79-120页

作者： Gounari, Marianna Stergiopoulos, George Pipyros, Kosmas Gritzalis, Dimitris Dept. of Information & Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science Philips University Nicosia Cyprus Dept. of Informatics Athens University of Economics & Business Athens Greece

This paper focuses on the security protocols enacted in banking transactions across the European Economic Area (EEA), as stipulated by the Second or Revised Payment Service Directive (commonly referred to as ‘PSD2’ or simply ‘the Directive’). The study aims to comprehensively analyse the implementation and efficacy of these security measures within the specified jurisdiction. The Directive incorporates fundamental rights and obligations that all stakeholders are compelled to adhere to and delineates specific security measures and standards that both traditional banking institutions and third-party providers (TPP) are mandated to implement. In particular, one of the cardinal mandates for banking and financial institutions under PSD2 is the obligation to facilitate third-party access to customer data via open application programming interfaces (API). While this open banking paradigm and the consequent proliferation of data sharing unquestionably bring about various advantages, such as enhanced consumer choice and market competition, they concurrently expose the financial ecosystem to a slew of potential security vulnerabilities and privacy risks. Upon conducting a comprehensive review of the security requirements and measures stipulated under PSD2 and a comparative analysis with essential cybersecurity frameworks and standards (NIS2, Cybersecurity Act, GDPR, ISO 27001:22 and PCI DSS), we have ascertained a discernible lack of harmonisation and clarity concerning the technical security specifications for its effective implementation. This lacuna substantiates the challenges banks face in fully grasping the extensive spectrum of compliance obligations mandated by PSD2. The aim of this research is to offer a valuable contribution to both the comprehension and the pragmatic deployment of security standards in the context of banking transactions, as regulated by the PSD2. The paper serves as a valuable resource for traditional banking institut

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Hyperspectral Image Classification using Few Shot Domain Adaptation and Active Learning

Hyperspectral Image Classification using Few Shot Domain Ada...

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National Conference on Communications (NCC)

作者： Animesh Shree Rakesh Kumar Sanodiya dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City Andhra Pradesh India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Hyperspectral images (HSIs) are pivotal in remote sensing, providing rich spectral and spatial information for applications such as agriculture, environmental monitoring, and mineral exploration. Unlike traditional RGB images, HSIs capture data across hundreds of contiguous spectral bands, enabling precise material classification based on unique spectral signatures. However, the high dimensionality and the need for pixel-level labeling pose significant challenges, as manual annotation is labor-intensive and time-consuming. To address this, we leverage Domain Adaptation, a transfer learning technique that facilitates knowledge transfer from a well-labeled source domain to a sparsely labeled or unlabeled target domain. In this paper, we focus on few-shot Domain Adaptation for HSI classification, where only a limited number of labeled target samples are available. Our approach integrates Active Learning to iteratively fine-tune a classifier by selecting the most informative target samples, thus reducing domain shift and enhancing model performance. We utilize a framework that generates domain-invariant embeddings by aligning the spectral and spatial features of HSIs from the source and target domains. This method significantly reduces the need for manual labeling while maintaining high classification accuracy. Our experimental results demonstrate the effectiveness of combining few-shot Domain Adaptation with Active Learning, achieving a remarkable 98 % accuracy in hyperspectral image classification with limited labeled data.

关键词： Accuracy Uncertainty Scalability Active learning Transfer learning Manuals Minerals Labeling Hyperspectral imaging Image classification

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Doctoral Consortium AIxIA 2023

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CEUR Workshop Proceedings 2024年 3670卷

作者： Poggioni, Valentina Rossi, Silvia University of Perugia Dept. of Mathematics and Computer Science Perugia Italy University of Naples"Federico II" Dept. of Electronic Engineering and Information Technology Naples Italy

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Lightweight Deep Convolution Neural Network for Green Coffee Bean Defects Detection 6

Lightweight Deep Convolution Neural Network for Green Coffee...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Ke, Liang-Ying Chiang, Po-Han Hsia, Chih-Hsien Chen, Shih-Lun National Ilan University Department of Computer Science and Information Engineering Yilan Taiwan Kang Chiao International School New Taipei Taiwan Chung Yuan Christian University Dept. of Electronic Engineering Taoyuan Taiwan

ISBN: (纸本)9798350323535

The demand for high-quality coffee in the global market has increased. However, large labor resources are still required for sorting beans in coffee production due to different manual screening standards. Thus, maintaining consistency in the quality of coffee beans poses a challenge. However, computer vision (CV) based on lightweight deep convolutional neural networks (DCNNs) was used to detect coffee bean defects and evaluate the proposed model's structure. The proposed method was trained with the public coffee bean database. The experiment result showed a high recognition performance even with a small number of parameters achieving 96.00% of the F1-Score. © 2023 IEEE.

关键词： Convolution

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： K Karthick Praveen Talari Nishadevi V R Krishna Prakash R. Asokan Velusamy A Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy Tamilnadu Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Hyderabad Telangana Dept. of Computer Science and Engineering Mahendra College of Engineering Salem Tamilnadu Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamilnadu

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Performance evaluation Roads Scalability Vehicular ad hoc networks Clustering algorithms Packet loss Routing

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Feasibility of Wide Area Network Traffic Distribution in Cloud Computing Enabled Wireless Networks

Feasibility of Wide Area Network Traffic Distribution in Clo...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mimani, Sushant Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Jiwani, Nasmin Kiruthiga, T. GoDaddy SnohomishWA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cloud computing enabled Wi-Fi networks provide an outstanding opportunity for corporations to distribute extensive area community (WAN) site visitors and advantage from reduced latency because of the proliferation of hyper connectivity. The feasibility of implementing a Wide Area Network (WAN) traffic distribution system in cloud computing enabled wireless networks was studied. The study aimed to determine the practicality and effectiveness of this approach in improving network performance. For the experiment, a sample network topology was designed to simulate real-world scenarios and subjected to diverse traffic loads. The results showed that the WAN traffic distribution system was indeed feasible and could effectively distribute and balance network traffic in cloud computing enabled wireless networks. Furthermore, key metrics such as network latency, packet loss, and throughput were significantly improved compared to traditional networks without the WAN distribution system. Further research and development in this area could potentially lead to improved network efficiency and enhanced user experience in the rapidly growing field of cloud computing. Those consequences reveal the feasibility of deploying WAN site visitors distribution in cloud computing-enabled wireless networks, and serves as a starting point for further study aiming to improve the performance of such networks. © 2024 IEEE.

关键词： Wide area networks

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Nazim Uddin Md Sajib Mia Mohammed Nasir Uddin Prince Mahmud Dept. of Information and Communication Technology Chandpur Science and Technology University Bangladesh Dept. of Computer Science and Engineering Sylhet Engineering College Bangladesh Dept. of Computer Science and Engineering Jagannath University Bangladesh Dept. of Computer Science and Engineering Chandpur Science and Technology University Bangladesh

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%.

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COVID-19 Diagnosis Algorithm Based on Convolutional Neural Networks

COVID-19 Diagnosis Algorithm Based on Convolutional Neural N...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Han, Xinxin Xiong, Xueguan Wang, Liuling Yang, Yuxin Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology School of National Defense Science Mianyang China Southwest University of Science and Technology School of Economics and Management Mianyang China Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

This study investigates an automated COVID-19 diagnosis system based on convolution networks, which aims to improve the effectiveness and accuracy of diagnosis. In a systematic analysis of 1709 chest X-rays, the research team used the ResNet model of deep learning for training and evaluation. Based on a detailed analysis of the confusion matrix generated by the model, the study found that the weighted average accuracy of the model and the recall rate reached 94,04% with an F1 value of up to 0,9403. This data shows better performance of convolutional neural networks in automated diagnosis. The results of the study show that neural network technology can effectively relieve the workload of doctors and provide diagnostic support for future public health events. © 2024 IEEE.

关键词： COVID-19

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A Broadband MIMO Antenna Array for WiFi-6E/7 in Laptop computer Applications

A Broadband MIMO Antenna Array for WiFi-6E/7 in Laptop Compu...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Jun-Hong Yang Hsin-Lung Su Dept. Computer Science and Information Engineering National Pingtung University Pingtung Taiwan Dept. Computer and Communication National Pingtung University Pingtung Taiwan

ISBN: (数字)9798350366976

ISBN: (纸本)9798350366983

This paper proposed a broadband 2×2 MIMO antenna array for a WiFi-6E/7 mobile communication system in laptop computer applications. The structure of the single antenna element is composed of a compact loop antenna with a straight parasitic metal line structure. The operating frequency band covers the WiFi-6E/7 band and the future possible WiFi-7 frequency band (∼9 GHz). The single antenna size is 20 mm × 4.0 mm × 4.0 mm. The 2×2 MIMO antenna array is on the edge of the top of a laptop screen. The overall size of the 2×2 MIMO antenna array is 44 mm × 4.0 mm × 4.0 mm. The antenna design does not use extra passive components for impedance matching or isolation technology to improve isolation. The isolation is more than 16.5 dB, the measured efficiency of the antenna is between 65–90%, and the value of gain is between 4.2–8.5 dBi.

关键词： Antenna measurements Portable computers Gain measurement Broadband communication Broadband antennas Antennas Wireless fidelity

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