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4th International Conference on Digital Medicine and Image Processing, DMIP 2021

作者： Ali, Haytham A. Kudo, Hiroyuki Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tennoudai 1-1-1 Tsukuba305-8573 Japan Department of Mathematics Faculty of Science Sohag University Sohag82524 Egypt

ISBN: (纸本)9781450386487

The recovery of shapes from a few numbers of their projections is very important in Computed tomography. In this paper, we propose a novel scheme based on a collocation set of Gaussian functions to represent any object by using a limited number of projections. This approach provides a continuous representation of both the implicit function and its zero level set. We show that the appropriate choice of a basis function to represent the parametric level-set leads to an optimization problem with a modest number of parameters, which exceeds many difficulties with traditional level set methods, such as regularization, re-initialization, and use of signed distance function. For the purposes of this paper, we used a dictionary of Gaussian function to provide flexibility in the representation of shapes with few terms as a basis function located at lattice points to parameterize the level set function. We propose a convex program to recover the dictionary coefficients successfully so it works stably with only four projections by overcoming the issue of local-minimum of the cost function. Finally, the performance of the proposed approach in three examples of inverse problems shows that our method compares favorably to Sparse Shape Composition (SSC), Total Variation, and Dual Problem. © 2021 ACM.

关键词： Convex optimization

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SSVEPPoolformer: An Improved Poolformer Model With the Adaptive Denoising Algorithm for SSVEP-EEG Signal Classification

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Chunquan Liao, Zhiyuan Cheng, Yuxin Wang, Zitao Wu, Junyun Liu, Ruijun Liu, Peter X. Nanchang University School of Information Engineering Nanchang330031 China Nanchang University School of Mathematics and Computer Science Nanchang330031 China Beihang University School of Software Beijing100191 China Carleton University School of Department of Systems and Computer Engineering OttawaK1S 5B6 Canada

Most EEG classification algorithms based on steady-state visual evoked potentials (SSVEP-EEG) require filtering for denoising. However, manually set thresholds may inadvertently remove useful information, leading to a loss of significant signal features. Additionally, most deep learning-based SSVEP-EEG classification models have limited global feature extraction capabilities, and the self-attention mechanism in Transformers increases computational costs. To address these challenges, this paper proposes a novel SSVEP-EEG classification algorithm, SSVEPPoolformer. SSVEPPoolformer integrates an adaptive denoising algorithm with an improved Poolformer algorithm, enhancing both denoising performance and classification accuracy. The adaptive denoising algorithm dynamically adjusts the threshold using a compensation and adaptive adjustment mechanism, effectively filtering noise while retaining critical signal features. The improved Poolformer algorithm replaces the self-attention mechanism with an average pooling operation, reducing computational costs while maintaining performance. It also uses adaptive average pooling to integrate cross-channel feature information and extract global fine-grained features, improving global feature extraction. The SSVEPPoolformer model's efficacy was validated on two public datasets. Experimental results demonstrate that compared with other state-of-the-art methods, SSVEPPoolformer has higher classification accuracy and information Transfer Rate (ITR) in both intra-class and inter-class recognition scenarios, and has lower computational cost. © 1975-2011 IEEE.

关键词： Electroencephalography

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Deep learning for brain age estimation: A systematic review

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information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Guest Editorial: Sentiment Analysis as a Multidisciplinary Research Area

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 638-641页

作者： Cambria, Erik Xing, Frank Thelwall, Mike Welsch, Roy Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore National University of Singapore Department of Information Systems and Analytics School of Computing 117418 Singapore University of Wolverhampton School of Mathematics and Computing WolverhamptonWV1 1LY United Kingdom Massachusetts Institute of Technology Sloan School of Management CambridgeMA02139 United States

The papers in this special section focus on bringing multidisciplinary knowledge into sentiment analysis. The last two decades have witnessed an enormous amount of research works on sentiment analysis and significant progress has been made. In terms of depth, finer-grained semantic schemas are defined, such as aspect, target, and category. In terms of broadness, multimodal sentiment analysis touches audio and video channels. Conversational sentiment analysis considers contextual and time-dependent relations and domain-specific sentiment analysis settings tackles real-life challenges, including but not limited to user profiling, financial prediction, abusive language detection, and mental health.

关键词： Special Issues And Sections Sentiment Analysis Computational Modeling Semantics Neural Networks Market Research Recommender Systems Knowledge Based Systems Fuzzy Logic

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Testing Physical Unclonable Functions Implemented on Commercial Off-the-Shelf NAND Flash Memories Using Programming Disturbances 12

Testing Physical Unclonable Functions Implemented on Commerc...

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12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022

作者： Anagnostopoulos, Nikolaos Athanasios Fan, Yufan Saleem, Muhammad Umair Mexis, Nico Gelóczi, Emiliia Klement, Felix Frank, Florian Schaller, André Arul, Tolga Katzenbeisser, Stefan University of Passau Faculty of Computer Science and Mathematics Innstraße 43 Passau94032 Germany Technical University of Darmstadt Computer Science Department Hochschulstraße 10 Darmstadt64289 Germany Technical University of Darmstadt Department of Electrical Engineering and Information Technology Merckstraße 25 Darmstadt64283 Germany EUMETSAT Allee 1 Darmstadt64295 Germany

In this work, we present a Physical Unclonable Function (PUF) implemented on a Commercial Off-The-Shelf (COTS) NAND Flash memory module using programming disturbances, and examine the robustness of its responses to environmental variations. In particular, we test a removable Flash memory module serving as a PUF, under nominal conditions, as well as under temperature and voltage variations. To determine its resilience to environmental variations, we utilise well-known PUF metrics, such as the Hamming weight and the intra-device Hamming distance. Our results prove that, in general, the tested Samsung K9F1G08U0E NAND Flash memory can be used to realise a lightweight, scalable, and flexible hardware security primitive, namely a PUF, that can be utilised in the context of smart homes, smart vehicles, and other smart applications, as well as to protect commercial devices and networks in general. However, voltage variations seem to pose a substantial threat to the adoption of this PUF in practice. This threat may be addressed by small-scale design improvements that should be implemented and tested in practice as part of future works. © 2022 IEEE.

关键词： Automation

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A High-frequency Pneumatic Oscillator for Soft Robotics

arXiv

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arXiv 2024年

作者： Li, Longchuan He, Shuqian Qi, Qiukai Cui, Ye Yan, Cong Jiang, Kaige Kang, Shuai Tokuda, Isao T. Wang, Zhongkui Ma, Shugen Liu, Huaping College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Engineering Mathematics University of Bristol Bristol United Kingdom College of Mechanical and Electrical Beijing University of Chemical Technology Beijing China Department of Mechanical Engineering Ritsumeikan University 1-1-1 Nojihigashi Shiga Kusatsu Japan College of Materials Science and Engineering Beijing University of Chemical Technology Beijing China Guangzhou China Department of Computer Science and Technology Beijing National Research Centre for Information Science and Technology Tsinghua University Beijing China

Soft robots, while highly adaptable to diverse environments through various actuation methods, still face significant performance boundary due to the inherent properties of materials. These limitations manifest in the challenge of guaranteeing rapid response and large-scale movements simultaneously, ultimately restricting the robots’ absolute speed and overall efficiency. In this paper, we introduce a high-frequency pneumatic oscillator (HIPO) to overcome these challenges. Through a collision-induced phase resetting mechanism, our HIPO leverages event-based nonlinearity to trigger self-oscillation of pneumatic actuator, which positively utilizes intrinsic characteristics of materials. This enables the system to spontaneously generate periodic control signals and directly produce motion responses, eliminating the need for incorporating external actuation components. By efficiently and rapidly converting internal energy of airflow into the kinetic energy of robots, HIPO achieves a frequency of up to 20 Hz. Furthermore, we demonstrate the versatility and high-performance capabilities of HIPO through bio-inspired robots: an insect-like fast-crawler (with speeds up to 50.27 cm/s), a high-frequency butterfly-like wing-flapper, and a maneuverable duck-like swimmer. By eliminating external components and seamlessly fusing signal generation, energy conversion, and motion output, HIPO unleashes rapid and efficient motion, unlocking potential for high-performance soft robotics. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

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A Novel and Advantageous Recovery Solution for Deadlock Problem of Flexible Manufacturing Systems Based on Petri Nets Modeling Theory 9

A Novel and Advantageous Recovery Solution for Deadlock Prob...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Sciences and Technology Department of Electronic Engineering Taiwan National Kaohsiung University of Sciences and Technology Department of Computer Science and Information Engineering No. 415 Jiangong Rd. Sanmin Dist. Kaohsiung City807618 Taiwan General Education Center Air Force Academy Mathematics and Physics Division No. Sisou 1 Jieshou W. Rd. Gangshan Dist. Kaohsiung City820009 Taiwan

ISBN: (纸本)9798350398380

Instead of the mass production industry in the past, over these decades, precision and automatic manufacturing have become more and more widely accepted in the production area. With highly variable productivity and flexibility, Flexible Manufacturing Systems (FMS) can decrease production costs and increase efficiency. Due to its resource sharing, unexpected system deadlock may occur in some situations. In research on system deadlock control, lots of existing literature use deadlock prevention as the primary control method, while it could block resources transporting and downscale the system reachability graph. This paper adopts a deadlock recovery policy as the direct control strategy based on control transition technology. This kind of control strategy and its benefit could be demonstrated through classical systems of simple sequential processes with resources (S3PR) nets and their Petri nets model. © 2023 IEEE.

关键词： Petri nets

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Relative entropy inequality for capillary fluids with density dependent viscosity and applications

arXiv

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arXiv 2023年

作者： Caggio, Matteo Donatelli, Donatella Institute of Mathematics The Academy of Sciences of the Czech Republic Žitná 25 Praha 111567 Czech Republic Department of Information Engineering Computer Science and Mathematics University of L’Aquila via Vetoio Coppito L’Aquila67100 Italy

We derive a relative entropy inequality for capillary compressible fluids with density dependent viscosity. Applications in the context of weak-strong uniqueness analysis, pressureless fluids and high-Mach number flow... 详细信息

关键词： Viscosity

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A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 833-842页

作者： Hussain, Tassadaq Wang, Wei-Chien Gogate, Mandar Dashtipour, Kia Tsao, Yu Lu, Xugang Ahsan, Adeel Hussain, Amir Edinburgh Napier University School of Computing Edinburgh EH11 4BN United Kingdom National Cheng Kung University Institute of Computer and Communication Engineering Tainan 701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei 11529 Taiwan Chung Yuan Christian University Department of Electrical Engineering Taoyuan 32023 Taiwan National Institute of Information and Communications Technology Tokyo 187-0021 Japan University of Wolverhampton School of Mathematics and Computer Science Wolverhampton WV1 1LY United Kingdom

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms have been successfully applied in target signal enhancement in acoustic applications. As most target signals carry semantic information encoded in a hierarchal structure in short-and long-term contexts, noise may distort such structures nonuniformly. In most deep neural network-based algorithms, such local and global effects are not explicitly considered in a modeling architecture for signal enhancement. In this article, we propose a temporal attentive pooling (TAP) mechanism combined with a conventional convolutional recurrent neural network (CRNN) model, called TAP-CRNN, which explicitly considers both global and local information for acoustic signal enhancement (ASE). In the TAP-CRNN model, we first use a convolution layer to extract local information from acoustic signals and a recurrent neural network (RNN) architecture to characterize temporal contextual information. Second, we exploit a novel attention mechanism to contextually process salient regions of noisy signals. We evaluate the proposed ASE system using an infant cry dataset. The experimental results confirm the effectiveness of the proposed TAP-CRNN, compared with related deep neural network models, and demonstrate that the proposed TAP-CRNN can more effectively reduce noise components from infant cry signals with unseen background noises at different signal-to-noise levels. We further tested the TAP-CRNN ASE system on a downstream infant cry detection (ICD) system, which determines whether a sound segment is involved in an infant cry event. Experimental results show that TAP-CRNN ASE can effectively reduce the noise components, thereby improving the performance of ICD under noisy conditions. © 2020 IEEE.

关键词： Acoustic signal enhancement (ASE) bidirectional long-short term memory convolu-tional neural networks recurrent neural networks (RNN)

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Evolution equations on co-evolving graphs: long-time behaviour and the graph continuity equation

arXiv

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arXiv 2025年

作者： Carrillo, José Antonio Esposito, Antonio Mikolás, László Mathematical Institute University of Oxford Woodstock Road OxfordOX2 6GG United Kingdom Department of Information Engineering Computer Science and Mathematics Università degli Studi dell’Aquila Via Vetoio 1 Coppito L’Aquila67100 Italy

We focus on evolution equations on co-evolving, infinite, graphs and establish a rigorous link with a class of nonlinear continuity equations, whose vector fields depend on the graphs considered. More precisely, weak solutions of the so-called graph-continuity equation are shown to be the push-forward of their initial datum through the flow map solving the associated characteristics’ equation, which depends on the co-evolving graph considered. This connection can be used to prove contractions in a suitable distance, although the flow on the graphs requires a too limiting assumption on the overall flux. Therefore, we consider upwinding dynamics on graphs with pointwise and monotonic velocity and prove long-time convergence of the solutions towards the uniform mass *** Codes 35R02, 35A01, 35A02, 35A24, 35B40 Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear equations

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