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2nd International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022

作者： Souza, Adriana Freitas, Diamantino Bahia Federal Institute of Education Science and Technology Bahia Brazil Doctoral Program in Digital Media Faculty of Engineering of the University of Porto Porto Portugal Faculty of Engineering of the University of Porto Speech Processing Laboratory Electrical and Computer Engineering Department Porto Portugal

ISBN: (数字)9781665470162

ISBN: (纸本)9781665470162

Being one essential part of the solutions we are developing to provide accessibility for blind persons, synthesized speech of mathematical content, although having evolved in naturalness in recent years, still keeps a characteristic of generating very long outputs, which makes it difficult for a person who uses a screen reader to memorize. Eye-gaze tracking has been useful for studying cognitive processes and student's effort in performing tasks therefore it seems attractive as a means to understand how people analyze, process, and break down extensive math expressions into smaller ones. This understanding can help to build a similar model of auditory rendering of MathML capable of avoiding cognitive overload. Therefore, we carried out an experiment to do a gaze-based analysis of reading and memorizing mathematical expressions. Our sample consisted of fourteen participants, and seven mathematical expressions were used in the experiment. The results showed that the computed complexity of the expressions and the average duration of eye-gaze fixations have a strong and significant relationship. Therefore, we conclude that the complexity variable must always be considered in the speech synthesis process of mathematical expressions. However, when considering why participants requested the repetition of an expression with not larger complexity during the memorization process, a tentative interpretation based on the number of MathML tree nodes and the computed complexity of the expressions was not enough to explain it, indicating that other factors need to be investigated. Although some answers to the research questions were not yet conclusive, we consider this research relevant because it points to directions for the work in order to minimize the cognitive processing and mental load of the synthesized speech rendering of mathematical expressions. © 2022 IEEE.

关键词： Eye tracking

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Content-Aware Opportunistic Packet Scheduling with Dynamic Routing Algorithm for Multi-path Video Streaming over Mesh Networks*

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Chinese Journal of Electronics 2023年第2期21卷 270-276页

作者： Yongfer Zhang Yunsheng Zhang Chun Yuan Shiyin Qin Zhihai He Digital Media Laboratory School of Computer Science and Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China The Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Electrical and Computer Engineering University of Missouri Columbia MO U.S.A

Multi-path video streaming over networks emerges as an important application in multimedia communication. Since the efficiency of multi-path video streaming is closely tied to the packet scheduling strategies, in this paper, we develop a content-aware opportunistic packet scheduling scheme with dynamic routing algorithm, which aims to maximize the reconstructed video quality by considering the content diversity of each video packet. To handle the problem of increased reordering delay, a sliding-window-based policy is then developed to find a balance between the reconstructed video quality and the reordering delay, throughout and packet loss. Our extensive experimental results demonstrate that the proposed packet scheduling scheme significantly outperforms EDPF and achieves a good tradeoff between the reconstructed video quality and the reordering time at the receiver side.

关键词： Scheduling algorithms Heuristic algorithms Packet loss Receivers Streaming media Routing Dynamic scheduling

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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A discussion regarding the application of the hertz contact theory on biological samples in afm nanoindentation experiments

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Micro and Nanosystems 2021年第1期13卷 42-48页

作者： Kontomaris, Stylianos Vasileios Stylianou, A. Nikita, K.S. Malamou, A. Mobile Radio Communications Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Faculty of Architecture Engineering & Built Environment Athens Metropolitan College Athens Greece Cancer Biophysics Laboratory Department of Mechanical and Manufacturing Engineering Faculty of Engineering University of Cyprus Nicosia Cyprus Radar Systems and Remote Sensing Lab of School Electrical & Computer Engineering National Technical University of Athens Athens Greece

Atomic Force Microscopy (AFM) Nanoindentation procedure regarding biological samples poses significant challenges with respect to the accuracy of the provided results. These challenges are related to the inhomogeneity of biological samples, various uncertainties in experimental methods and certain approximations regarding the theoretical analysis. The most commonly used theoretical model for data processing at the linear elastic regime regarding biological samples is the Hertz model. This paper focuses on the investigation of the resulting errors of the basic equation of the Hertz theory that depends on the ratio, indentation depth/indenter’s radius regarding the Young’s modulus calculation. Several examples in the literature that do not take into account the value of the ratio indentation depth/indenter’s radius are reported and the related errors are presented and discussed. In addition, an extended new equation is derived which takes into account the influence of the aforementioned ratio on the calculation of the Young’s modulus and can be easily used for calculations. Moreover, a rational explanation, regarding the extended differences of the Young’s modulus calculations using the same experimental results when these are processed using the Hertz model and the Oliver & Pharr analysis (which is the general model that applies for any axisymmetric indenter) is provided. In conclusion, the derived equation is further combined with equations which take into account the shape of the sample in order to provide a complete and reliable theoretical tool which can be generally applied in order to reduce the errors produced by the current methodology. © 2021 Bentham Science Publishers.

关键词： Nanoindentation

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Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2023年第4期10卷 1-30页

作者： Mavromatis∗, Ioannis Spyridopoulos, Theodoros Carnelli, Pietro Chin, Woon Hau Khalil, Ahmed Chakravarty, Jennifer Kun, Lucia Cipolina Piechocki, Robert J. Robbins, Colin Cunnington, Daniel Chase, Leigh Chiazor, Lamogha Preston, Chris Rahul Khan, Aftab Digital Catapult London United Kingdom Bristol Research & Innovation Laboratory Toshiba Europe Ltd. Bristol United Kingdom School of Computer Science & Informatics Cardiff University Cardiff United Kingdom Department of Electrical and Electronic Engineering University of Bristol Bristol United Kingdom Nexor Ltd. Nottingham United Kingdom IBM Research Winchester United Kingdom Ltd. Reading United Kingdom

The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper presents an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster research, testing, and evaluation of the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs. © (2023) I. Mavromatis et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://***/licenses/by/4.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： Intelligent systems

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Swarm Intelligence in Internet of Medical Things 10

Swarm Intelligence in Internet of Medical Things

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Roshanzamir, Mohamad Darbandy, Mohammad Tayarani Roshanzamir, Mahdi Alizadehsani, Roohallah Shoeibi, Afshin Ahmadian, Davood Fasa University Faculty of Engineering Department of Computer Engineering Fasa74617-81189 Iran Islamic Azad University Taft School of Architecture Taft*** Iran University of Tabriz Department of Electrical and Computer Engineering Tabriz Iran VIC Australia K. N. Toosi University of Technology FPGA Laboratory Faculty of Electrical Engineering Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Sciences Tabriz Iran

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Advancement in information and communication technology leads to different communication channels. One of them is the Internet of Things (IoT) which plays an important role in the new world. The Internet of Medical Things (IoMT) is the sub-branch of IoT which focuses on the communication between medical devices to share their data and information. It helps the healthcare industry to present better services for physicians and patients. It improves the quality of life by increasing the quality and decreasing the time and cost of services. An important challenge in IoMT is optimization in its different sub-problems and Swarm Intelligence (SI) algorithms are the best solution for them. In this paper, we tried to investigate the status of using SI in IoMT. Its applications and their corresponding performance were analyzed and investigated. The reported results show that using SI can efficiently solve the optimization problems in IoMT. © 2022 IEEE.

关键词： Internet of things

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Filling Deterministic Approximate Gap in Denoising Diffusion Probabilistic Models with Energy 38

Filling Deterministic Approximate Gap in Denoising Diffusion...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Kan, Ge Wang, Tian Liu, Deyuan Wang, Jian Fu, Yao Snoussi, Hichem Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Zhongguancun Laboratory Institute of Artificial Intelligence Sklsde Beijing100191 China Wuhan University School of Computer Science Wuhan430072 China Chinese Academy of Sciences Changchun Institute of Optics Fine Mechanics and Physics Changchun130033 China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303636

Diffusion probabilistic models (DPMs) have shown the best generative quality and admit impressive likelihood meanwhile. However, its variants take very long generative processes during sampling, because of the small noise scale assumption, say, sets the variance of the forward diffusion kernel to be very small. Under which, the generative denoising processes are of tractable Gaussian form. To shorten the generative process, we explore the non-Gaussian reverse kernel under the large noise scale assumption and analysis the deterministic approximate gap of Gaussian denoising process. To fill the gap, we enhance the standard DPM with conditional energy and introduce variantional sampler to construct tractable generative process. The new probabilistic model, termed denoising diffusion energy-based model (DDEBM), are trained with two stage adversarial optimization. Experiments show our models demonstrate competitive generative performance with only four steps. © 2023 IEEE.

关键词： Diffusion probabilistic model Energy-based model Generative model Image generation

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AST: Generalization of Deepfake Detection with Attention Siamese Training

AST: Generalization of Deepfake Detection with Attention Sia...

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2023 China Automation Congress, CAC 2023

作者： Peng, Taiying Wang, Tian Liu, Deyuan Wang, Jian Fu, Yao Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beihang University Zhongguancun Laboratory Institute of Artificial Intelligence SKLSDE Beijing100191 China School of Computer Science Wuhan University Wuhan430072 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303759

Recently deepfake detection research focused on distinguishing fake faces from real ones when they are evaluated on test datasets similar to the training set. However, these approaches are proved to fail once test sets are different from training sets, such as forgeries created from unseen generation methods. Due to the distributional differences brought about by various forgery methods. It is challenging for current deepfake detectors to have the ability to perform well on cross-domain forgeries. In this paper, we introduce a generalized method designed for the cross-domain deep fake detection task. Our key idea is modifying Efficient-Net with cross-attention block and Siamese training to improve the generalization of detectors in cross-domain datasets. We investigate how the triple loss function effect model's generalization ability on a theoretical level. The AST network can balance the model's generalizability across domains. The detection ability of the network is improved by an intra-class compact loss. © 2023 IEEE.

关键词： computer vision

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More task-balanced Class-Incremental Learning 38

More task-balanced Class-Incremental Learning

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Chen, Junzhi Wang, Tian Liu, Deyuan Wang, Jian Fu, Yao Soussi, Hichem Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Zhongguancun Laboratory Institute of Artificial Intelligence Sklsde Beijing100191 China Wuhan University School of Computer Science Wuhan430072 China Chinese Academy of Sciences Changchun Institute of Optics Fine Mechanics and Physics Changchun130033 China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303636

Although deep learning has achieved high performance on many tasks, neural networks suffer from catastrophic forgetting in incremental learning scenarios. Class-Incremental Learning focuses on the scenario where new category data continuously arrives. PASS (Prototype Augmentation and Self-Supervised) is a classic method that does not require storing old exemplars. Based on PASS, we propose three improvement measures from the perspective of making the new and old tasks more balanced. Firstly, we propose a balanced prototype replay (Balanced PR) strategy to make the image number of new and old categories that the model head encounters more balanced. Secondly, at the feature knowledge distillation (FKD) stage, we assign different weights to the incoming data based on the confidence of the old model, which is named Focal FKD. The goal is to minimize interference with learning new task data while preserving the representation capability of the model backbone on old data. Thirdly, we combine FKD with weight regularization on the model backbone. In the feature space, FKD is beneficial for preserving the positions of the old categories while weight regularization helps to separate the new and old categories well. Experiments on the benchmark indicate that there has been obvious improvement in the performance of PASS after our modifications. © 2023 IEEE.

关键词： Class-Incremental Learning focal task-balanced weight regularization

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System for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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