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arXiv 2022年

作者： de Souza, Renato William R. de Oliveira, João Vitor Chaves Passos, Leandro A. Ding, Weiping Papa, João P. de Albuquerque, Victor Hugo C. The Graduate Program in Applied Informatics University of Fortaleza CE Fortaleza Brazil The Pontifical Catholic University of Rio de Janeiro Brazil São Paulo State University Brazil The School of Information Science and Technology Nantong University Nantong China

In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for both supervised, semi-supervised, and unsupervised learning named Optimum-Path Forest (OPF) was proposed with competitive results in several applications, besides comprising a low computational burden. In this paper, we propose the Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier that learns the samples’ membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over twelve public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst-case scenarios. © 2022, CC BY.

关键词： Fuzzy logic

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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International Electron Devices Meeting (IEDM)

作者： Zefeng Xu Chun-Kuei Chen Hong-Lin Lin Yuan Gao Wei Ke Baochang Xu Pavel Dmitriev Carlan Arbiz Evgeny Zamburg Steven Touzard Xinlun Cai James Lee Suresh Venkatesan Aaron Danner Aaron Voon-Yew Thean Department of Electrical and Computer Engineering National University of Singapore (NUS) Integrative Sciences and Engineering Program NUS Graduate School Singapore Next-Generation Hybrid μ-Electronics Center (SHINE) Singapore School of Electronics and Information Technology Sun Yat-Sen University China 510275 Singapore Centre for Quantum Technologies NUS POET Technologies

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf 0.5 Zr 0.5 O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 10 9 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies’ 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device.

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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A Multi-objective Physiological Control for Continuous Flow Left Ventricular Assist Devices: Comparison of Estimator versus Sensor-based Feedback *

A Multi-objective Physiological Control for Continuous Flow ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Bruno Santos Idágene Cestari Biomedical Engineering Graduate Program Polythecnic School of Engineering University of Sao Paulo Av. Prof. Luciano Gualberto Sao Paulo Brazil Department of Electrical in Federal Institute of Sao Paulo and Department of Information Technology Universidade São Judas Tadeu Sao Paulo Brazil Bioengineering Laboratory Instituto do Coração InCor Hospital das Clínicas HCFMUSP Sao Paulo Brazil Biomedical Engineering Graduate Program Polytechnic School of Engineering University of Sao Paulo Sao Paulo Brazil

Left Ventricular Assist Devices have been successfully used for the treatment of Congestive Heart failure in patients who are not eligible for heart transplantation. This paper describes the implementation and comparison of the performance of a pressure sensor-based feedback controller. The strategies were tested on a mock loop of the systemic circulation. The results show that the use of pressure sensors generated a more accurate response of the controller compared to the use of *** Relevance— The study describes the integration of a LVAD physiological controller to a dynamic wireless monitoring system.

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A Deep Learning Model of Amyloid-${\boldsymbol{\beta }}$ Diffusion Simulation

A Deep Learning Model of Amyloid-${\boldsymbol{\beta }}$ Dif...

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International Conference on Electronics, information and Communications (ICEIC)

作者： ByeongChang Jeong Daegyeom Kim Hyun-Ghang Jeong Cheol E. Han Dept. of Electronic and Information Engineering The Alzheimer's Disease Neuroimaging Initiative Korea University Sejong Republic of Korea The Alzheimer's Disease Neuroimaging Initiative Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea Dept. of Psychiatry The Alzheimer's Disease Neuroimaging Initiative Korea University College of Medicine Seoul Republic of Korea

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid- $\beta$ accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid- $\beta$ diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid- $\beta$ diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid- $\beta$ with a high accuracy.

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Mathematical representation of emotion using multimodal recognition model with deep multitask learning

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2020年第12期140卷 1343-1351页

作者： Harata, Seiichi Sakuma, Takuto Kato, Shohei Computer Science Program Department of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya-city Aichi466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya-city Aichi466-8555 Japan

To emulate human emotions in robots, the mathematical representation of emotion is important for each component of affective computing, such as emotion recognition, generation, and expression. In a method that learns DNN from unimodality and represents emotions by vectors of continuous values (Emotional Space), the acquired Emotional Space is likely to depend on the modality, and considering the number of dimensions of Emotional Space is necessary. In this study, we aim at the acquisition of modality independent Emotional Space. We propose a method of acquiring Emotional Space by integrating multimodalities on a DNN and combining the emotion recognition task and unification task of Emotional Space of each modality. Through the experiments with audio-visual data, we confirmed in various dimensions of Emotional Space that there are differences in Emotional Space acquired from unimodality, and the proposed method can acquire a modality independent Emotional Space. We also investigated the compatibility of the recognition and the unification score by changing the number of dimensions of Emotional Space. Finally, we indicate the proposed method can acquire sufficient Emotional Space with a small number of dimensions, such as five or six dimensions, under this paper's experimental conditions. © 2020 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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Investigation of a Single-Channel Frequency-Domain Speech Enhancement Network to Improve End-to-End Bengali Automatic Speech Recognition Under Unseen Noisy Conditions

Investigation of a Single-Channel Frequency-Domain Speech En...

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Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Md Mahbub E Noor Yen-Ju Lu Syu-Siang Wang Supratip Ghose Chia-Yu Chang Ryandhimas E. Zezario Shafique Ahmed Wei-Ho Chung Yu Tsao Hsin-Min Wang Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taiwan National Tsing Hua University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan University of Information Technology & Sciences Bangladesh

ISBN: (纸本)9781665408714

Due to the presence of distortion, most of the single-channel frequency-domain speech enhancement (SE) approaches are still challenging for downstream automatic speech recognition (ASR) tasks, even with satisfactory improvements in enhancing speech quality and intelligibility. Recently, transformer-based models have shown better performance in speech processing tasks. Therefore, we intend to explore a transformer-based SE model, which is fine-tuned through a two-stage training scheme. Pre-training is performed using a feature-level optimization criterion through SE loss, and then a pre-trained end-to-end ASR model is used to fine-tune the SE model using an ASR-oriented optimization criterion through SE and ASR losses. We evaluate the proposed approach on low-resourced Bengali language, which has not received as much attention as resource-rich English or Mandarin languages in both SE and ASR fields. Experimental results show that it can improve the performance of SE and ASR under severe unseen noisy conditions and its performance is reasonably good compared with other state-of-the-art SE methods.

关键词： Training Frequency-domain analysis Training data Speech enhancement Transformers Robustness Noise measurement

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Mobile application to track people in covid19 monitoring and patients under covid19 supervision 2

Mobile application to track people in covid19 monitoring and...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Chrysler, A. Warnars, H.L.H.S. Utomo, W.H. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients. © Published under licence by IOP Publishing Ltd.

关键词： Mobile computing

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Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

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Sensors 2023年第22期23卷 9199-9199页

作者： Chen, Shuo-Tsung Ye, Ren-Jie Wu, Tsung-Hsien Cheng, Chun-Wen Zhan, Po-You Chen, Kuan-Ming Zhong, Wan-Yu Department of Medical Informatics Chung Shan Medical University Taichung40201 Taiwan Department of Information Center Chung Shan Medical University Hospital Taichung40201 Taiwan Graduate School of Applied Chinese Studies National Yunlin University of Science and Technology Yunlin 64002 Taiwan Bachelor’s Program in Business Management Fu Jen Catholic University New Taipei City242062 Taiwan

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. © 2023 by the authors.

关键词： Electrocardiograms

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