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3rd International Conference on Signal Processing and Information Security, ICSPIS 2020

作者： Alkhodari, Mohanad Hassanin, Omnia Dhou, Salam Biomed. Eng. Graduate Program American University of Sharjah Sharjah United Arab Emirates Dept. of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781728189987

Brain tumor segmentation from magnetic resonance (MR) images can have a great impact on improving diagnostics, growth rate prediction, and treatment planning. In this paper, we provide a comparative study of four well-known segmentation algorithms, namely k-means clustering, histogram thresholding (Otsu), fuzzy c-means thresholding, and region growing. For the region growing algorithm, the seed selection process is automated and enhanced by preprocessing the images and approximating the tumor regions using initial clustering and/or thresholding approaches. The evaluation and comparison of the algorithms is conducted using a data-set of T1-Weighted Contrast-Enhanced magnetic resonance imaging (MRI) brain images. Ground truth tumor images were provided by three experienced radiologists and are used in the evaluation process. Results showed that the enhanced region growing method had the highest mean dice similarity coefficient with a score of 0.87, and the lowest under-segmentation rate (17.46%). The fuzzy c-means thresholding method had the lowest over-segmentation rate (0.03%). This study serves as a baseline for other advanced tumor segmentation studies such as the ones using the emergent machine learning approaches. © 2020 IEEE.

关键词： Image segmentation

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Utilizing Educational Robotics for Learning About Sustainability Issues

Utilizing Educational Robotics for Learning About Sustainabi...

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IEEE Latin American Robotics Symposium, LARS

作者： Andre Carlos Teixeira de Vasconcelos Almir Silva Silveira Raphael Melo de Oliveira Lucas Tejedor da Silva Giselle Medeiros Mesiara João Roberto de Toledo Quadros Master's Degree Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Department of Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Sense & Learn (SELEA)21 Copenhagen University Denmark Department of Electronic Engineering/Pos-Graduate Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Nowadays, schools are trying to insert knowledge and learning about sustainability issues and environmental conservation in the academic context from an early age. It is also observed the use of information technology with new didactic and ludic resources that can encourage elementary and high school students to obtain knowledge about sustainability. To this end, this research presents some results using Educational Robotics, Gestalt, and information technology, which students received from a Brazilian public elementary school. In this study, after attending workshops on sustainability, the students developed robotic resources to help them with sustainability-related knowledge. The resources were tested according to their objectives: a mobile robot that recognizes the food and shows its footprint and a box with gas sensors to identify if food is in consumable condition or should be recycled. The experiment was very successful and is in a continuous application phase.

关键词： Conferences Education Robot sensing systems Object recognition Mobile robots Sustainable development Information technology

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error 46

Guiding Brain-to-Vocalization Decoder Design Using Structure...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, Jingya Tostado-Marcos, Pablo Narasimha, Sowmya Manojna Patel, Aashish N. Arneodo, Ezequiel. M. Gentner, Timothy Q. Mishne, Gal Gilja, Vikash University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92092 United States University of California San Diego Department of Bioengineering La Jolla CA92092 United States University of California San Diego Institute for Neural Computation La Jolla CA92092 United States University of California San Diego Neuroscience Graduate Program La Jolla CA92092 United States University of California San Diego Department of Physics La Jolla CA92092 United States University of California San Diego Department of Psychology La Jolla CA92092 United States University of California San Diego Halicioǧlu Data Science Institute La Jolla CA92092 United States

ISBN: (纸本)9798350371499

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics. © 2024 IEEE.

关键词： Errors

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Recurrence plots and convolutional neural networks applied to nonintrusive load monitoring

Recurrence plots and convolutional neural networks applied t...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Cavalca, Diego L. Fernandes, Ricardo A. S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil Federal University of São Carlos Electrical Engineering Department São Carlos Brazil

ISBN: (纸本)9781728155081

The need to increasingly promote the efficient use of electricity for residential consumers motivated some researches to obtain solutions that allow the identification of loads for use in demand response programs and in the energy consumption disaggregation. Recently, the use of Convolutional Neural Networks for load identification has been common. However, it is necessary to transform the temporal signal into an image. In this sense, the present paper proposes a novel feature engineering stage, based on Recurrence Plots, to obtain the images for Nonintrusive Load Monitoring. In order to prove its effectiveness, the Recurrence Plots were compared to the Gramian Angular Difference Fields, which had already been applied in this context. For this purpose, it was used the REDD dataset, in which only the low frequency data measured in the main distribution panel were considered. Individual load measurements were only used for the labelling of the 90-seconds windows. The Convolutional Neural Network was trained and validated, considering two different strategies (multi-label and binary models), obtaining an F1-score of 66.05% and 72.65%, respectively. © 2020 IEEE.

关键词： Machine learning

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Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

arXiv

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arXiv 2021年

作者： Zhou, Yuqi Zhu, Hao Hanasusanto, Grani A. The Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States The Graduate Program in Operations Research & Industrial Engineering The University of Texas at Austin AustinTX78712 United States

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based (∞-Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational efficiency

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Hybrid Artificial Intelligence and IoT in Healthcare 1

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丛书名： Intelligent Systems Reference Library

2021年

作者： Akash Kumar Bhoi Pradeep Kumar Mallick Mihir Narayana Mohanty Victor Hugo C. de Albuquerque

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In 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 State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Histogram specification by assignment of optimal unique values

arXiv

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arXiv 2021年

作者： Ramos, Vítor S. de Q. Silveira, Luiz Felipe de Q. Silveira Júnior, Luiz Gonzaga Electrical and Computer Engineering Graduate Program Federal University of Rio Grande do Norte Natal Brazil Computer Engineering and Automation Department Federal University of Rio Grande do Norte Natal Brazil Communications Engineering Department Federal University of Rio Grande do Norte Natal Brazil

In this paper, we propose two novel algorithms for histogram specification and quantile transformation of data without local information. These are core techniques that can serve as building blocks for applications that require specifying the sample distribution of a given set of data. Histogram specification is best known for its image enhancement applications, whereas quantile transformation is typically employed in data preprocessing for data normalization. In signal processing, methods often require temporal or spatial information;in data preprocessing, methods work by interpolation or by approximation, drawing from results in computational statistics, and have a trade-off between speed and quality. It is nontrivial to accommodate for cases that do not have local information (e.g., tabular data) while also providing a fast, exact solution. For that, we take up a concept in image processing called group mapping law and propose an extension. The proposed extension allows us to formulate a convex functional where we look for the best approximation between the output unique values and the reference histogram. Then, we apply the ordered assignment solution, a result in optimal transport, to reconstruct the output from the optimal unique values. Two sets of results show the effectiveness of the proposed algorithms when compared to traditional and state-of-the-art methods. The proposed algorithms are fast, exact, and least p-norm optimal. Further, we define the algorithms as generic data processing methods. Thus, contributions from this paper can be easily incorporated in applications spanning many disciplines, especially in applied data science. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Dementia Detection by Shopping Refugees Support Robot

Dementia Detection by Shopping Refugees Support Robot

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IEEE Global Conference on Life Sciences and Technologies (LifeTech)

作者： Yuta Kishimoto Kenichi Arai Tetsuo Imai Toru Kobayashi Computer and Information Science Program Graduate School of Engineering Nagasaki University Nagasaki Japan Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki University Nagasaki Japan

ISBN: (数字)9781728170633

ISBN: (纸本)9781728170640

We have developed the Shopping Refugees Support Robot that enables the elderly to order items through Social Networking Service (SNS) by voice conversation without relying on smartphones. This paper proposes a detection method of dementia signs using this Robot. Concretely, deterioration of cognitive function is detected by the evaluation of short-term memory utilizing order history of the Robot. We expect detection of dementia signs on a daily basis without making the elderly person conscious of having a dementia test, by information that only the person knows. We implemented a prototype and confirmed its functional validity.

关键词： Dementia SNS Comunication robot Internet of Things Artificial Intelligence Robot Sympathetic Nervous System Dementia Internet of things Artificial Intelligence Refugees inspection methods Shopping Home shopping aged person

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Neuromuscular fatigue detection by mechanomyography in people with complete spinal cord injury

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Research on Biomedical engineering 2020年第3期36卷 203-212页

作者： Krueger, Eddy Popović-Maneski, Lana Neto, Guilherme Nunes Nogueira Mendonça Scheeren, Eduardo Fiusa, Jessika Mehret Nohama, Percy Neural Engineering and Rehabilitation Laboratory Universidade Estadual de Londrina LondrinaPR Brazil Graduate Program in Rehabilitation Sciences UEL-UNOPAR Campus Universitário s/n zipcode LondrinaPR86051-990 Brazil Institute of Technical Sciences of the Serbian Academy of Sciences and Arts KnezMihailova 35/IV zipcode Belgrade11000 Serbia Graduate Program in Health Technology Pontifícia Universidade Católica do Paraná Rua Imaculada Conceição 1155 Prado Velho zipcode CuritibaPR80215-901 Brazil Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Av. Sete de Setembro 3165 zipcode CuritibaPR80230-901 Brazil

Purpose: Functional electrical stimulation (FES) is a method of activating paralyzed muscles. During FES application, fast muscle fatigue can occur (the inability of stimulated muscles to generate force). Therefore, it is beneficial to estimate the muscle fatigue for FES closed-loop control for walking to prevent unexpected muscle collapse and adapt the FES strategy in real time. Mechanomyography (MMG) is a noninvasive technique for registering myofiber vibrations, representing an ideal candidate for the provision of feedback. The hypothesis was that MMG signals could effectively detect muscle fatigue and, thus, provide feedback. Methods: We tested this hypothesis by analyzing the wavelet transform of signals from an MMG sensor positioned over the rectus femoris muscle during electrically evoked contractions in subjects with spinal cord injury (SCI). The signals were collected from a total of 24 lower limb muscles. We investigated both legs of 15 participants with spinal cord injury (male, YOA = 27.13 ± 5.05, M = 75.8 ± 10.35 kg, and H = 1.78 ± 0.07 m, American Spinal Injury Impairment Scale (AIS) A and B). All MMG signals were analyzed in 12 frequency bands from 5 to 53 Hz. Results: We found different trends in the magnitudes in different frequency bands. The magnitude decreased in 13, 16, 20, 25, and 35 Hz bands in correlation with fatigue. The greatest statistical difference was found at 20 Hz and 25 Hz. Conclusion: This result suggests that processed MMG signals indicate muscle fatigue and can, thus, be used as the feedback in FES systems. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Muscle

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