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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

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Robust Imagined Speech Production from Electrocorticography ...

International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： Data mining

Low-power DC energy conversion system based on a novel single-switch current-fed series-parallel resonant converter

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Low-power DC energy conversion system based on a novel singl...

2017 IEEE International Conference on Industrial Technology, ICIT 2017

作者： Chuang, Ying-Chun Chuang, Hung-Shiang Yang, Chun-Hsiang Chen, Tien-Chi Department of Electrical Engineering Kun Shan University No. 195 Kun-Da Road Yong-Kang District Tainan City71003 Taiwan Guangdong-Taiwan College of Industrial Science and Technology Dongguan University of Technology No. 251 Xueyuan Road Guancheng District Dongguan Guangdong Province China Mechanical and System Research Laboratories Industrial Technology Research Institute No. 195 Sec. 4 Chung Hsing Road Chutung Hsinchu31040 Taiwan Department of Computer and Communication Kun Shan University No. 195 Kun-Da Road Yong-Kang District Tainan City71003 Taiwan

ISBN: (纸本)9781509053209

This work develops a novel single-switch current-fed series-parallel resonant converter for use in direct current (DC) energy conversion systems. The converter comprises a class-E inverter and a series-parallel resonant tank with a bridge rectifier. It uses a single active power switch to reduce the cost of the energy conversion system and control circuits. With a simple circuit configuration and few components, a highly efficient circuit is realized. Experimental data were obtained using a laboratory prototype with an input voltage of 30 V, an output voltage of 16.5 V, an output power of 15.13 W, and a switching frequency of 60 kHz. The measured prototype efficiency was 93.3 % under a full load. Accordingly, the novel converter has great potential for use in low-power DC energy conversion systems. © 2017 IEEE.

关键词： Energy conversion

Salience-Default Mode Functional Network Connectivity Linked to Positive and Negative Symptoms of Schizophrenia (vol 45, pg 892, 2018)

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SCHIZOPHRENIA BULLETIN 2019年第6期45卷 1382-1382页

作者： Hare, Stephanie M. Neuroscience Institute Georgia State University Atlanta GA To whom correspondence should be addressed Neuroscience Institute Georgia State University PO Box 5030 Atlanta GA 30302-5030 USA tel: 262-364-7427 fax: 404-413-5446 e-mail: share1@student.gsu.edu Psychiatry Service San Francisco VA Medical Center San Francisco CA Department of Psychiatry University of California San Francisco CA Psychiatry Service San Francisco VA Medical Center San Francisco CA Department of Psychiatry University of California San Francisco CA The Mind Research Network Albuquerque NM Department of Psychiatry and Behavioral Sciences The University of New Mexico Albuquerque NM Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill NC Division of Computer Science and Engineering CAIIT Chonbuk National University Jeonju Republic of Korea Department of Psychiatry University of Minnesota Minneapolis MN Department of Psychiatry University of Minnesota Minneapolis MN Geriatric Research Education and Clinical Center (GRECC) Minneapolis VA Health Care System Minneapolis MN Department of Psychiatry School of Medicine University of California San Diego San Diego CA Department of Psychiatry and Human Behavior University of California Irvine Irvine CA Department of Psychiatry and Human Behavior University of California Irvine Irvine CA Department of Psychiatry and Human Behavior University of California Irvine Irvine CA The Mind Research Network Albuquerque NM Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM Neuroscience Institute Georgia State University Atlanta GA The Mind Research Network Albuquerque NM Department of Psychology Georgia State University Atlanta GA

Schizophrenia is a complex, debilitating mental disorder characterized by wide-ranging symptoms including delusions, hallucinations (so-called positive symptoms), and impaired motor and speech/language production (so-called negative symptoms). Salience-monitoring theorists propose that abnormal functional communication between the salience network (SN) and default mode network (DMN) begets positive and negative symptoms of schizophrenia, yet prior studies have predominately reported links between disrupted SN/DMN functional communication and positive symptoms. It remains unclear whether disrupted SN/DMN functional communication explains (1) solely positive symptoms or (2) both positive and negative symptoms of schizophrenia. To address this question, we incorporate time-lag-shifted functional network connectivity (FNC) analyses that explored coherence of the resting-state functional magnetic resonance imaging signal of 3 networks (anterior DMN, posterior DMN, and SN) with fixed time lags introduced between network time series (1 TR = 2 s; 2 TR = 4 s). Multivariate linear regression analysis revealed that severity of disordered thought and attentional deficits were negatively associated with 2 TR-shifted FNC between anterior DMN and posterior DMN. Meanwhile, severity of flat affect and bizarre behavior were positively associated with 1 TR-shifted FNC between anterior DMN and SN. These results provide support favoring the hypothesis that lagged SN/DMN functional communication is associated with both positive and negative symptoms of schizophrenia.

关键词： ICA salience network default mode network positive symptoms negative symptoms resting-state fMRI

Pmate: Pay More Attention to Edge Points in 3d Roof Wireframe Reconstruction

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SSRN

SSRN 2024年

作者： Hao, Qiaoqiao Liu, Yujun Huang, Shangfeng Han, Ting Zhu, Duxin Su, Jinhe Wu, Yundong Cai, Guorong Li, Jonathan School of Computer Engineering Jimei University Xiamen361000 China School of Geospatial Engineering and Science Sun Yat-Sen University Zhuhai519082 China School of Geospatial Engineering and Science University of Calgary CalgaryABT2N1N4 Canada The Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada Department of System Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The reconstruction of 3D Roof Wireframe from airborne LiDAR point clouds have attracted increasing attention in 3D vision and photogrammetry. Despite the advancements of deep learning-based methods in reconstructing roof wireframes, existing methods lead to subpar corner recall and incomplete results due to the difficulty of fully harness spatial structural information. An important structural characteristic of wireframes is that corner points are highly likely to be situated within the convex hull formed by the edge points. Meanwhile, the edges themselves are essentially confined within this convex hull. This structural information has not been fully exploited. In this paper, we propose to pays more attention to the edge points (Pmate) in 3D Roof Wireframe Reconstruction. Specifically, we utilize the Spatial Structure Generation module to generate an edge-weight attention for each point, representing the proximity of the point to the edge. Moreover, we develop a hybrid edge detection module based on edge point, which enhanced the accuracy of edge detection. Extensive experiments on the Building3D dataset demonstrate the effectiveness of Pmate, which improves edge recall by 10% compared to previous state-of-the-art methods with comparable edge precision. The source code of our approach are publicly available in https://***/onescholar/Pmate. © 2024, The Authors. All rights reserved.

关键词： Roofs

A new combination of message passing techniques for receiver design in MIMO-OFDM systems

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arXiv 2017年

作者： Zhang, Chuanzong Yuan, Zhengdao Wang, Zhongyong Guo, Qinghua School of Information Engineering Zhengzhou University Zhengzhou450001 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark National Digital Switching System Engineering and Technological Research and Development Center Zhengzhou Institute of Information Science and Technology Zhengzhou450001 China School of Information Engineering Zhengzhou University Zhengzhou450001 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia School of Electrical Electronic and Computer Engineering University of Western Australia CrawleyWA6009 Australia

In this paper, we propose a new combined message passing algorithm which allows belief propagation (BP) and mean filed (MF) applied on a same factor node, so that MF can be applied to hard constraint factors. Based on the proposed message passing algorithm, a iterative receiver is designed for MIMO-OFDM systems. Both BP and MF are exploited to deal with the hard constraint factor nodes involving the multiplication of channel coefficients and data symbols to reduce the complexity of the only BP used. The numerical results show that the BER performance of the proposed low complexity receiver closely approach that of the state-of-the-art receiver, where only BP is used to handled the hard constraint factors, in the high SNRs. Copyright © 2017, The Authors. All rights reserved.

关键词： Belief propagation

Preprocessing Techniques for Enhancing Vision-Based Detection system for Tire Sidewall Extraction

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Preprocessing Techniques for Enhancing Vision-Based Detectio...

International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US department of Transportation Cameras Tires Internet Data mining