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Optimization Effects of Rotating Transmitarray for Direction-of-Arrival Estimation with a Single Antenna

IEICE Communications Express 2025年第6期14卷 210-213页

作者： Ryusuke Sunagawa Hibiki Shiiba Makoto Sano Ryuji Kuse Takeshi Fukusako Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

This paper discusses the optimization effects of a transmitarray on a direction-of-arrival (DOA) estimation using the transmitarray and a single receiving antenna. The transmitarray consists of 1-bit elements with 0° and 180° transmission phases. The transmission phase distribution was optimized using a genetic algorithm and compared to a random distribution. The optimized transmitarray achieves a DOA estimation accuracy comparable to that of a conventional system using a 3-element linear array antenna. The root mean square error (RMSE) of the optimized system exhibits a decreasing trend as the signal-to-noise ratio (SNR) increases. Notably, at an SNR of 20 dB, the RMSE is 20° lower than that of the 3-element linear array antenna.

关键词： Estimation Direction-of-arrival estimation Linear antenna arrays Accuracy Antenna radiation patterns Transmitting antennas Optimization Signal to noise ratio Receiving antennas Vectors

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Face Mask Detection Using MobileNetV2 and VGG16 11th

Face Mask Detection Using MobileNetV2 and VGG16

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Kumar, Ujjwal Arora, Deepak Sharma, Puneet Department of Computer Science and Engineering Amity University Lucknow Campus Uttar Pradesh Lucknow India Dept. of CSE Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789811988240

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viruses

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A Secure and Efficient Group Authentication Framework for Blockchain-Enabled Vehicular Edge Computing systems 3

A Secure and Efficient Group Authentication Framework for Bl...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Meena, Pawan Dash, Suryansh Sachin Monika Rani, Poonam Netaji Subhas University of Technology Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798331519582

Maintaining privacy and trust within Vehicular Edge Computing (VEC) systems requires proper authentication methods that ensure safety and effectiveness in dynamic settings. Current authentication solutions face three main drawbacks including excessive computational costs together with complex identity management systems and large security exposure. The proposed lightweight Multi-Entity Authenticated Group Key Agreement protocol serves blockchain-based VEC networks according to this research. MAGKA enhances authentication through bulk certification removal and reduces reliance on identity authentication and provides seamless vehicle traffic across edge networks. The solution provides VEC authentication with robust security and scalability demonstrated through security analysis of its ability to withstand attacks. © 2025 IEEE.

关键词： Ad hoc networks

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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IEEE Radio and Wireless Symposium (RWS formerly RAWCON)

作者： Chandler J. Bauder Aly E. Fathy Dept. of Electrical Engineering & Computer Science University of Tennessee Knoxville Knoxville TN USA

ISBN: (数字)9798350377743

ISBN: (纸本)9798350377750

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-dept. metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases.

关键词： Estimation error Accuracy Shape Channel estimation Radar Electrocardiography MIMO Telemetry Heart rate variability Monitoring

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Real-Time Traffic Management system Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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A Machine Learning Framework & Development for Insider Cyber-crime Threats Detection

A Machine Learning Framework & Development for Insider Cyber...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Yousef, Rasheed Jazzar, Mahmmoud Eleyan, Amna Bejaoui, Tarek Palestine Technical University - Kadoorie Tulkarem College of It Dept. of Computer Science Palestine Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350302523

Many organizations face a significant challenge with insider threats. As conventional security measures like intrusion detection systems and firewalls aren't always effective in detecting and preventing such threats. Insider threats often come from trusted individuals who possess knowledge of and access to important organizational assets. This work explores the use of machine learning to classify insider threat behaviors, specifically focusing on three approaches such that supervised, unsupervised, and reinforcement learning. The paper describes the development of an unsupervised machine learning system that analyzes data from multiple technical sources to detect malicious insider activity. The system, which is designed to be simple and easy to assemble, was tested with existing machine learning algorithms and showed moderate success in detecting malicious insider activity during the training phase and negligible success during the testing *** results suggest that while machine learning can be a useful tool for detecting insider threats, it should not be solely relied upon for threat detection. To improve the current system's performance, it is necessary to include additional features, such as file names, email subjects and headers, and website types. Furthermore, physical security, cybersecurity, psychological, and organizational factors must be considered when addressing insider threats. Future research should focus on acquiring real datasets, collecting insider threat scenarios and use cases, and testing different machine learning approaches from both technical and non-technical sources. © 2023 IEEE.

关键词： Cybersecurity

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Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability

Research Square

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Research Square 2025年

作者： Osei, Isaac Aabaah, Iven Benjamin, Appiah Dept. Of Computer Science Ho Technical University Ho Ghana Dept. Of Information System and Technology C. K. Tedam University of Technology and Applied Science Kumasi Ghana

Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant privacy concerns. This project investigates the efficiency of Federated Learning (FL) frameworks in comparison to a conventional centralized AI model. We evaluated seven different state-of-the art Federated Learning frameworks to assess their performance in maintaining model accuracy and scalability. Among these, Per-FedAvg and FedOpt demonstrated efficiency compared to the cen-tralized framework. To further understand the performance of these models, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret their predictions, ensuring that they focus on medically relevant fea-tures. This project highlights that Federated Learning frameworks, specifically Per-FedAvg and FedOpt, offer promising alternatives to traditional AI methods by providing enhanced performance in lung cancer detection. © 2025, CC BY.

关键词： Federated learning

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms

Classification and Prediction of Stroke Disease Using Machin...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Podutur Lahari Akarsh Kumar D. Usha Nandini M. D. Anto Praveena Mercy Paul Selvan Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital" in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical.

关键词： Text mining Machine learning algorithms Accuracy Surveillance Stroke (medical condition) Tagging Syntactics

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Developing an Online Platform for Multimodal Lexical Learning for the Bengali Learners 3

Developing an Online Platform for Multimodal Lexical Learnin...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dash, Niladri Sekhar Deb, Srija Dutta, Nirnoy Majumder, Meghna Bhattacharyya, Amrita Mondal, Subhadip Pal, Alok Ranjan Linguistic Research Unit Indian Statistical Institute Kolkata India College of Engg. and Mgmt. Kolaghat Dept. of Computer Science and Engineering Kolaghat India

ISBN: (纸本)9781665494007

The usability of a lexical database of a natural language on an online platform is in great demand in this new era of online language education. An interface of this kind can primarily serve three different purposes: first, it plays an operational role in new frame of multimodal education system;second, it partly fulfills the necessity of online lexical resources which are in increasing need in research and application in the field of language technology;and third, it becomes a useful and omnipresent resource for native and foreign language users who want to learn meanings and usages of words of a natural language for different personal, academic, philanthropic and commercial purposes. Keeping these applications in view, in this paper, we describe the process that we apply to develop an online platform for the Bengali WordNet, which offers a unique opportunity for multimodal lexical learning to the Bengali learners. The proposed work, that we want to present in this paper, has the potentials to fulfill all the three needs stated above. While the WordNet provides different kinds of information directly associated with words, the digital platform has a very sensitive user-friendly interface for the access of the lexical resources stored in the WordNet. © 2023 IEEE.

关键词： Ontology

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Applying Vision Transformers for Herbal Medicine Classification: A Novel Approach to Plant Identification

Applying Vision Transformers for Herbal Medicine Classificat...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ramesh R Veeramalai Sankaradass Nishintha A HarshivKrishna M R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The medicinal plants are particularly important in traditional health care systems, yet the right identification of medicinal plants is one of the great challenges because of diversity and similarity among species. In this paper, we introduce a new approach to herbal medicine classification using Vision Transformers, or simply ViTs. This deep learning architecture is at the front edge of approaching complex patterns within plant images through leveraging self-attention mechanisms. Unlike the classical CNN, Vision Transformers are significantly better than their counterparts on tasks related to classification as they concentrate on global relationships between image patches. We apply and test our model on a comprehensive dataset of medicinal plants. It is demonstrated that Vision Transformer works better in accuracy and generalization than existing CNN-based approaches. In addition, the model demonstrates robustness under different environmental conditions related to lighting, variations of backgrounds, and also other probable challenges in identification with the real world. This study also brings out the possibility of directly integrating the classification system based on Vision Transformers into applications that may be used in mobile devices, which will now enable real-time identification in fieldwork settings for herbal medicines. Our findings show that the proposed Vision Transformers push medicinal plant classification in new directions in traditional and modern medical settings.

关键词： Medicinal plants computer vision Accuracy Biological system modeling Lighting Medical services Transformers Robustness Real-time systems Biomedical imaging

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