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Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

Intelligent Automation & Soft Computing 2023年第3期35卷 2821-2835页

作者： Riaz Muhammad Ahmed Ali M.Abid Anwar Toufique Ahmed Soomro Omar AlShorman Adel Alshahrani Mahmoud Masadeh Ghulam Md Ashraf Naif H.Ali Muhammad Irfan Athanasios Alexiou Department of Electrical Engineering Sukkur IBA University65200SukkurPakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience and TechnologyLarkana77150Pakistan Faculty of Engineering and AlShrouk Trading Company Najran UniversityNajran61441Saudi Arabia Department of Medical Rehabilitation Sciences-Physiotherapy Program College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Computer Engineering Department Yarmouk UniversityIrbid21163Jordan Pre-Clinical Research Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Internal Medicine Medical CollegeNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Novel Global Community Educational Foundation HebershamNSW 2770AustraliaAFNP Med AustriaWien2770Austria

Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the *** EEG headset is a wearable device that records electrophysiological data from the *** paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV *** electrode placement locations are modiﬁed under a 10–20 standard *** fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and ***,the data is recorded from 20 subjects who used the EEG Headset,and signals were ***,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide *** raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,theseﬁndings have been compared to previously reported *** concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 *** also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.

关键词： Brain-computer interface EEG consumer-grade accessibility headset

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Shubhi Srivastava Bhargav H K Vijayakumar Gurani Arpana Chaturvedi Ankita Gandhi Yogesh Dharangutti Department of Information Science and Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Tumakuru India Department of Computer Science Karnatak University Dharwad India Department of IT/AI/ML New Delhi Institute of Management New Delhi India Department of Computer science and Engineering Parul Institute of Engineering and Technology Gujarat India Faculty of Law Symbiosis Law School Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

This paper presents a novel AI-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios.

关键词： Interpolation Visualization PSNR Superresolution Streaming media Mathematical models Real-time systems Hybrid power systems Kernel Faces

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Danish Javed Usama Arshad Shuhrabeel Peerzada Muhammad Ramiz Saud Nisar Ali Raja Hashim Ali Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi Pakistan Faculty of Engineering and Applied Science University of Regina Regina Canada Department of Business University of Europe for Applied Sciences Potsdam Germany

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through real-time detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety.

关键词： Deep learning YOLO Accuracy Law enforcement Streaming media Motion pictures Real-time systems Public security Security Artificial intelligence

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Assessing Insider Threats in Ai-Enhanced Cyber Security for Digital Medical Platforms

SSRN

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SSRN 2025年

作者： Manasa, R. Jayanthiladevi, A. Rasmi, A. Murugan, Sivaram Faculty of Computer Applications Marwadi University Gujarat Rajkot India Marwadi University Gujarat Rajkot India Information Science and Engineering RR Institute of Technology Bangalore India Department of Computer Engineering Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas58000 Turkey

Technology-fuelled medical premises equipped using AI-promoted suggestions have slowly changed clinical touch through movement-based examination, clinician improvement, and practical efficiency, and they bring all around land mega one-of-a-kind cyber observation, particularly inward threats due to ambitions or inadvertent leads individuals as getting jumbo access needed. Patient privacy, patient record integrity, and system utility can all be compromised by insider threats, which are issues created by people with some knowledge or access to the system. We outline aspects of insider threats, risk factors, and automation defenses against insider risks in AI-augmented cyber, defined within digital healthcare systems, focusing on the interplay between human and AI systems. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Traffic Sign Recognition System

Traffic Sign Recognition System

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IT and Industrial Technologies (ICIT), International Conference on

作者： Maqsood Ahmed Raja Hashim Ali Nisar Ali Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi Pakistan Department of Business University of Europe for Applied Sciences Potsdam Germany Faculty of Engineering and Applied Science University of Regina Regina Canada

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities.

关键词： Deep learning Adaptation models Accuracy Biological system modeling Road safety Real-time systems Convolutional neural networks Autonomous vehicles Tuning Vehicles

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Graph-Structured Swin-Transformer for Learned Image Compression

Graph-Structured Swin-Transformer for Learned Image Compress...

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Data Compression Conference (DCC)

作者： Lilong Wang Yunhui Shi Jin Wang Baocai Yin Nam Ling Faculty of Information Technology Beijing University of Technology Department of Computer Science and Engineering Santa Clara University

ISBN: (数字)9798350385878

ISBN: (纸本)9798350385885

End-to-end learned image compression exploits the expressive power of nonlinear transform modules to de-correlate the spatial redundancies of image contents. Due to its long-range attention scheme, transformer-based transforms can explore more global features for better reconstruction. However, transformer modules bring in indispensable computational costs, and the coarse utilization of transformer in learned image compression cannot meet the coding efficiency. In this paper, we propose a novel graph-structured swin-transformer for learned image compression, shown in Figure 1 . We assume that the global receptive field of attention map should be sparse not dense, while the local neighboring correlations must be strong.

关键词： Image coding Correlation Redundancy Data compression Transforms Transformers Computational efficiency

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Enhanced Form-Based SPARQL Builder: Effortless Access to Temporal Medical Data for Users Without Technology Knowledge

Enhanced Form-Based SPARQL Builder: Effortless Access to Tem...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Petr Vcelak Ondrej Mika Martin Kryl Jana Kleckova Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Plzen Czech Republic

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Accessing complex medical data, especially temporal information, presents a significant challenge for non-technical users, including healthcare professionals not versed in technology or query languages like SPARQL. This paper introduces an enhanced form-based SPARQL query builder that offers effortless access to temporal medical data. The described system simplifies constructing SPARQL queries into an intuitive, form-driven interface that abstracts and hides the technical complexities. Novel enhancement is primarily the user's ability to filter and order data with column constraints. By focusing on usability, the system empowers users to retrieve temporal medical information without needing in-depth knowledge of database systems or SPARQL syntax. We evaluate the system's usability and performance by testing it with non-technical healthcare professionals. Results demonstrate that users can easily navigate the interface, significantly reduce the time spent querying the database, and effectively retrieve data. This approach benefits from FAIR data principles and enhances data reusability in healthcare settings for reaching accuracy and precision for follow-up analysis in evidence-based research, facilitating better decision-making that can improve future patient outcomes.

关键词： Knowledge engineering Decision making Focusing Medical services Syntactics Signal processing Ontologies Complexity theory Usability Medical diagnostic imaging

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DataDecon: Data Cleansing Tools for Large Language Model with Efficient Decontamination Techniques

DataDecon: Data Cleansing Tools for Large Language Model wit...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Sumeth Yuenyong Norapat Buppodom Koravich Sangkaew Konthee Boonmeeprakob Prachya Boonkwan Jillaphat Jaroenkantasima Pitikorn Khlaisamniang Anuruth Lertpiya Apivadee Piyatumrong Peerawat Rojratchadakorn Thaweewat Rugsujarit Teerapol Saengsukhiran Kriangkrai Saetan Isada Sukprapa Thanachot Thavornmongkol Nucharee Thongthungwong Patteera Triamamornwooth Chanon Utupon Kobkrit Viriyayudhakorn Phoochit Witchutanon Sadit Wongprayon Thepchai Supnithi Department of Computer Engineering Faculty of Engineering Mahidol University National Electronics and Computer Technology Center Pathumthani Thailand Artificial Intelligence Asssociation of Thailand Big Data Institute (Public Organization) Bangkok Department of Electrical Engineering Faculty of Engineering Kasetsart University Looloo Technology Faculty of Engineering Kasetsart University Kasikorn Labs Kasikorn Business Technology Group Faculty of Science King Mongkut’s Institute of Technology Ladkrabang School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Next AI Solutions Department of Biomedical Engineering Faculty of Engineering Mahidol University Department of Computer Science University of Liverpool Department of Mechanical Engineering King Mongkut’s University of Technology North Bangkok Artificial Intelligence Entrepreneur Association of Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

Large language models (LLMs) play an important role in modern NLP technology as they are versatile for a wide array of NLP tasks. However, constructing an LLM is challenging due to concealed construction pipelines, the lack of cleansed datasets, and hyperparameter settings, making it almost irreproducible. This paper presents an efficient pipeline for constructing an LLM tailored to a low-to-medium-sourced language with a high level of data contamination and tools to cleanse the dataset. Following our pipeline, we constructed OpenThaiGPT, an LLM for Thai, with only open-sourced datasets such as CC100, OSCAR, and mC4, and achieved the state-of-the-art accuracies on our downstream tasks. Here, we disclosed the data statistics and all hyperparameter settings for reproducibility.

关键词： Accuracy Large language models Decontamination Pipelines Data visualization Data models Natural language processing Reproducibility of results Arrays Contamination

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Vladimir Jordan - industrial water-hammer engineer and researcher 14

Vladimir Jordan - industrial water-hammer engineer and resea...

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14th International Conference on Pressure Surges 2023

作者： Bergant, A. Kramar, J. Tijsseling, A. Litostroj Power d.o.o. Ljubljana Slovenia Faculty of Mechanical Engineering University of Ljubljana Ljubljana Slovenia Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9789038657103

Vladimir Jordan was an adherent of post-WW2 hard working European engineering community. In 1947 he established and headed the design office of a newly founded manufacturer of turbine and industrial equipment: Litostroj. As a natural born artist, he 'fell in love' with the graphical method which he applied in conjunction with analytical methods for water-hammer analysis to a range of industrial problems. In particular, his original papers on the treatment of distributed vaporous cavitation along pipelines, shutoff valve induced hydraulic oscillations, and water-hammer effects in Kaplan turbine draft tubes, attracted wide interest in the seventies. Jordan's life and work are reviewed. Special attention is paid to a few highlights which may not sink into oblivion. © TU/e 2023 Pressure Surges 14.

关键词： Water hammer

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