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Achieving Effective Artifact Subspace Reconstruction in EEG Using Real-Time Video-based Artifact Identification

Achieving Effective Artifact Subspace Reconstruction in EEG ...

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2023 IEEE international conference on systems, Man, and Cybernetics, SMC 2023

作者： Kang, Sunghyun Won, Kyungho Kim, Heegyu Baek, Jihoon Ahn, Minkyu Jun, Sung Chan Gwangju Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Gwangju Korea Republic of

ISBN: (纸本)9798350337020

Identifying and minimizing physiological artifacts in EEG is challenging because these artifacts may corrupt the underlying brain activity severely. In this work, we proposed a hybrid approach to detect/reduce EEG artifacts by combining the MediaPipe face mesh model and artifact subspace reconstruction (ASR). Four types of artifacts, eye blinking, horizontal/vertical eye movements, and jaw movement during EEG measurement were generated to test our approach. We observed that real-time video-based artifact identification achieved over 95% accuracy in detecting eye blinking, horizontal eye movement, and jaw movement. Moreover, the targeted noise reduction was effective in analyzing the signal-to-noise ratio (SNR) for each specific artifact. This work may contribute to improving the reliability and accuracy of EEG data analysis in the real-world and online scenarios by providing a practical and effective approach to identifying and reducing physiological artifacts in real-time. © 2023 IEEE.

关键词： artifact detection artifacts electroencephalography (EEG) image processing

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An image-based Fall Detection System for the Elderly using YOLOv5 2

An Image-based Fall Detection System for the Elderly using Y...

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2nd international conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Marshal, S. Raj, Samson Arun Jebaseeli, T. Jemima Niranjan, S. CSE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350340235

A system's capacity to identify whether a person has fallen is known as fall detection. People who fall have difficulty moving their muscles. Even without injuries, the elderly people are too exhausted to stand up. Currently, there is an increasing amount of focus on the protection of old people living alone. The fall detection system using wearable gadgets and environmental monitoring devices has been introduced. Yet, there are certain problems, such as high invasion, poor accuracy, and weak resilience, due to the usage of the internet of Things and early fall detection warnings. Elderly people frequently need assistance getting up after falling, and if that assistance is delayed, the consequences of falling could be highly serious. Despite extensive studies on the subject conducted over the past few years, creating non-invasive technologies for automatic fall detection that are trustworthy and acceptable to the people being monitored remains difficult. The geriatric population has continued to increase as a result of medical technological advancements. Because falls can result in fractures and have catastrophic repercussions have received a lot of attention. Therefore, it is crucial for both older individuals and those who care for them to identify fall accidents quickly. Some of the fall detection scenarios are people who are irresponsible and stumble, those who are afflicted with ailments like heart attacks or strokes, children playing on the ground, and senior citizens. © 2023 IEEE.

关键词： Convolutional Neural Network deep learning elderly fall detection image processing

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Various Algorithms and Techniques for Traffic Density Estimation 1

Various Algorithms and Techniques for Traffic Density Estima...

引用

1st international conference on Computational Science and technology, ICCST 2022

作者： Venkat, Sanjay Sarkar, Swagata Shreemadhi, B. Siri, Karanam Poorna Bavanika, M. Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665476553

Traffic has been a major problem in recent times. Traffic management is a must for safer and faster transportation. Automatic smart signal controlling systems respond to day-to-day world traffic densities to provide precedence and reduction in cross-road delays for travelers. According to the traffic density, the time between signals is adjusted so that the waiting time in traffic reduces. The ML model is capable of detecting accidents and immediately sends notifications or alert messages to nearby hospitals. The model tracks the GPS location of the ambulance and ensures no red signal in its pathway. Zebra crossing signals are also included, the signal timings are switched based on the crossing crowd. Automatic Smart signal Control is a complete Machine Learning based project with some applications of IoT and Video / image processing. Implementation of the model all over the major signals and junctions provides faster and safer transportation. © 2022 IEEE.

关键词： image processing

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FedCNNAvg: Federated Learning for Preserving-Privacy of Multi-clients Decentralized Medical image Classification

FedCNNAvg: Federated Learning for Preserving-Privacy of Mult...

引用

international conference on Data Science and Network Engineering, ICDSNE 2023

作者： Chanda, Charu Murmu, Anita Kumar, Piyush National Institute of Technology Patna Patna India

ISBN: (纸本)9789819967544

Federated Learning (FL) permits the cooperative training of a joint model for several medical facilities while maintaining the decentralization of the data owing to privacy considerations. However, Federated optimizations often struggle with the heterogeneity of data dissemination among medical facilities. Nowadays, the domains of medical image classification, compression, and privacy are particularly difficult for diagnosing disease. The transmission of these medical images through the internet for diagnostic reasons must be protected against cyberattacks. In this proposed method, a Federated Learning approach with a Convolutional Neural Network (FedCNN) and Federated Averaging (FedAVG) is employed for classification problems. This technique adjusts the contribution of each data sample to the local goal during optimization based on knowledge of the client’s label distribution, thereby minimizing the instability caused by data heterogeneity. The model utilizes a hybrid approach to ensure consistency in time-series data. The datasets, namely, COVIDx-19 X-ray and malaria that are freely accessible are the subject of our in-depth investigations. The experimental results have been analyzed by evaluation metrics, namely, accuracy (78.79 and 98.92), precision (73.72 and 95.73), and recall (71.91 and 93.91) for proper validation. The findings demonstrate that FedCNN achieves better convergence performance than the main FL optimization methods under comparison. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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A VQ-VAE based Recognition Algorithm for Terahertz Security Inspection images 14

A VQ-VAE Based Recognition Algorithm for Terahertz Security ...

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14th international conference on signal Processing systems, ICSPS 2022

作者： Zha, Xinyu Bai, Yechao School of Electronic Science and Engineering Nanjing University Nanjing210023 China

ISBN: (纸本)9798350336313

Terahertz imaging technology has been used in the field of security inspection due to its excellent perspective ability. However, the terahertz security inspection images have the problem of poor image quality and cannot cover all types of dangerous objects. In this paper, a terahertz image recognition algorithm based on Vector Quantized-Variational Autoencoder (VQ-VAE) is proposed. The algorithm realizes image data enhancement through dynamically learned prior, and discrete potential space is used to efficiently identify abnormal objects outside the training set. In order to improve the network recognition performance, the attention CBAM module is introduced to increase the attention ratio of the key target area. The algorithm embeds Center Loss metric learning module into the network frame to increase the inter-class distance and reduce the intra-class distance, thereby improving the clustering and generalization performance. The experimental verification is carried out on the actual collected terahertz security image data set. The experimental results show that the improved and optimized VQ-VAE image recognition network is significantly better than the convolutional neural network in recognition accuracy and has better generalization ability. © 2022 IEEE.

关键词： image recognition

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Experimental test and performance of RSSI-based indoor localization in LoRa Networks 17

Experimental test and performance of RSSI-based indoor local...

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17th international conference on Future Networks and Communications / 19th international conference on Mobile systems and Pervasive Computing / 12th international conference on Sustainable Energy Information technology, FNC/MobiSPC/SEIT 2022

作者： Aarif, L'houssaine Tabaa, Mohamed Hachimi, Hanaa Casablanca Morocco Systems Engineering Laboratory Sultan Moulay Slimane University Beni Mellal Morocco

LoRa (Long Range) is a wireless communication technology, which has established itself in recent years as one of the most widely used LPWAN (Low-Power Wide-Area Network) technologies in various IoT (internet of things) applications. It is based on Chirp spread spectrum modulation (CSS) in which the number of bits per symbol depends on the spreading factor used, that varies from 7 to 12. In this paper, we have considered the RSSI (Received signal Strength Indicator) parameter in the indoor environment. For LoRa, we use the LoRa transceiver based on two HTCC AB01 modules for their ability to modify the spreading factor (SF). In our work, we transmit in the 868MHz frequency band, and we use a bandwidth 125 KHz and three spreading factors SF7, 9 and 12. Subsequently, we tried to measure RSSI in an indoor environment with a short distance under LOS and NLOS conditions for three spreading factors. We sent the temperature measured by the DHT11 sensor from the transmitter to the receiver to measure the RSSI for each distance and for each spreading factor. The experimental results show that LoRa can be used in an indoor context, and that the spreading factor improve the performance of this technology in terms of RSSI. Moreover, the power losses of the received signal are more important in the NLOS conditions compared to those observed in the LOS conditions. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the conference Program Chairs.

关键词： internet of things

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Remote Monitoring and Early Warning System of Subway Construction based on Wireless Sensor Network technology

Remote Monitoring and Early Warning System of Subway Constru...

引用

international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Zhao, Liang Wang, Qu Fu, Meixia Gao, Jing Wei, Hanming Shanxi Information Industry Technology Research Institute Co. Ltd Taiyuan030012 China School of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819975396

To enhance the intelligence level of monitoring risk sources in the process of subway construction, this system deploys a variety of sensors in the target area using wireless sensor network technology. Relevant data from the target area is collected through these sensors, which serve as data reference for monitoring risks during the subway construction. The collected data is transmitted to the management terminal in real-time through wireless network and is analyzed and processed for decision-making assistance. This remote monitoring system enables administrators to keep track of the construction project at all times and provide timely feedback to adjust ongoing efforts. Ultimately, the analysis and processing of the collected data provide powerful support and assistance for decision-making. In summary, this subway construction monitoring system has a high intelligence level, which greatly improves the safety and efficiency of subway construction and allows administrators to be always informed of the target area's conditions for timely decision-making and adjustments, guaranteeing the smooth progress of subway construction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Real-Time Smart System for Marking Attendance that Uses image Processing in a SaaS Cloud Environment 1

引用

2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Nalinipriya, G. Venkata Seshasai, Akilla Srikanth, G.V. Saran, E. Zaid, Mohammed Department of Information Technology Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

A facial recognition system is a piece of technology that can match a human face from a database of images. It is often used for robust authentication. An Attendance Management System is a tool used by businesses to track employee time and participation information. Software as a service (SaaS) allows users to access the software via the internet as a service. Advanced and automated attendance management technologies are being used to address this issue. The problem of proxies and students being counted as present even if they are not is easily resolved with this architecture. This technology records attendance via a live video broadcast. This system's objective is to develop a facial recognition-based class attendance system because the manual technique of taking attendance currently in use is cumbersome and difficult to maintain. The creation of the database, face detection, face recognition, and attendance updating are the four stages of this system. Faces can be recognized in the live-streamed video from the classroom. The attendance will be recorded in a spreadsheet and saved to Google Drive at the end of the session. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Color Light Field image Encryption Algorithm based on DNA Coding and Self-Controlled Chaotic System

Color Light Field Image Encryption Algorithm Based on DNA Co...

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2021 international conference on signal Processing and Communication technology, SPCT 2021

作者： Zhang, Xinyue Bai, Enjian Jiang, Xue-Qin Wu, Yun School of Information Science & Technology Donghua University Shanghai China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

Color light field images encryption algorithm based on frequency domain DNA coding and integer wavelet transform (IWT) is introduced. First, the light field image is decomposed into sub-view images in the angular domain plane. Subsequently, a low-frequency coefficient matrix and three high-frequency coefficient matrices are obtained in the frequency domain using the two-dimensional IWT. The low-frequency coefficient matrix is encrypted using DNA coding and a self-controlled chaotic system based on PWLCM. The high-frequency coefficient matrix is scrambled, and then the reconstructed intermediate encrypted image is obtained by a two-dimensional integer wavelet inverse transform. Finally, in the spatial domain, a diffusion operation is performed to obtain the final encrypted image. The simulation shows that this algorithm has the advantages of large key space, high operation efficiency, good encryption security and strong resistance to various attacks. © 2022 SPIE.

关键词： Frequency domain analysis

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3D Lorenz chaotic map to 4D hyperchaos: A comparison based on image encryption

3D Lorenz chaotic map to 4D hyperchaos: A comparison based o...

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Emerging systems and Intelligent Computing (ESIC), international conference on

作者： Banhi Dutta Choudhuri Das Arijit Saha Somali Sikder Sanyal Department of ECE B.P Poddar Institute of Managementand Technology Kolkata India DumDum Motijheel Rabindra Mahavidyalaya Kolkata India

internet is playing a major role in the modern era of faster communication. Information communicated over internet must be secured with robust encryption scheme to assure confidentiality. Encryption schemes using chaotic maps provide high level of security to transmitted information. Robustness of cryptographic technique further improves with application of hyperchaos rather than lower dimensional chaotic maps. In this communication we have studied comparison between image encryption techniques developed based on 3D Lorenz chaotic system and 4D hyperchaos. Performance analysis of both the techniques are compared based on various parameters like key sensitivity analysis, key space analysis, histogram analysis, calculation of information entropy and correlation coefficients.

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