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Electronic Circuit in the Two-Scroll Hyperchaotic System with a Unique Saddle-Point Rest Point and its Synchronization via Integral Sliding Mode Control

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 452-464页

作者： Mujiarto Ayob, Ahmad Faisal Mohamad Vaidyanathan, Sundarapandian Sambas, Aceng Benkouider, Khaled Nawawi, Mohd Kamal Mohd Khalid, Kamal Madi, Elissa Nadia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Mechanical Engineering Universiti Malaysia Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Research and Development Centre Vel Tech University Avadi Tamil Nadu Chennai600062 India Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala TerengganuGong Badak Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Electronics Faculty of technology Badji-Mokhtar University Annaba23000 Algeria Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia

A new 4-D two-scroll hyperchaotic system with three quadratic nonlinearities is detailed in this research paper. It is shown that the new hyperchaotic system has a unique saddle-point rest point at the origin and it exhibits self-excited attractor. A detailed stability analysis is carried out for the new hyperchaotic *** is also proved that the new hyperchaotic system has multistability with coexisting attractors. Next, we invoke integral sliding mode control for the global self-synchronization of the new hyperchaotic system taken as leader-follower *** electronic circuit design of the new two-scroll hyperchaotic system is developed in MultiSim, which is beneficial for engineering applications. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Sliding mode control

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M2SILENT: Enabling Multi-user Silent Speech Interactions via Multi-directional Speakers in Shared Spaces 25

M2SILENT: Enabling Multi-user Silent Speech Interactions via...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Zhou, Juntao Ding, Dian Li, Yijie Lu, Yu Wang, Yida Zhang, Yongzhao Chen, Yi-Chao Xue, Guangtao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computing National University of Singapore Singapore Singapore Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400713941

We introduce M2Silent, which enables multi-user silent speech interactions in shared spaces using multi-directional speakers. Ensuring privacy during interactions with voice-controlled systems presents significant challenges, particularly in environments with multiple individuals, such as libraries, offices, or vehicles. M2Silent addresses this by allowing users to communicate silently, without producing audible speech, using acoustic sensing integrated into directional speakers. We leverage FMCW signals as audio carriers, simultaneously playing audio and sensing the user's silent speech. To handle the challenge of multiple users interacting simultaneously, we propose time-shifted FMCW signals and blind source separation algorithms, which help isolate and accurately recognize the speech features of each user. We also present a deep-learning model for real-time silent speech recognition. M2Silent achieves Word Error Rate (WER) of and Sequence Error Rate (SER) of in multi-user silent speech recognition while maintaining high audio quality, offering a novel solution for privacy-preserving, multi-user silent interactions in shared spaces. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blind source separation

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Eye Contact During Live Video Conferencing Using External Video Camera

Eye Contact During Live Video Conferencing Using External Vi...

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Computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Vrishab Prasanth Davey Akilandeswari. P Rohit Kumar Chandrashekhar S Byadgi Kalaiyarasan S G. K. Sandhia Mandeep Guleria Vijeth B School of Computing SRM Institute of Science and Technology KTR Dept. of Electrical Engineering National Institute of Technology Uttarakhand Samsung R&D Bangalore

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

Video conferencing and other forms of live communication methods enjoy widespread popularity. However, the absence of interpersonal, psychological, or tactile feedback diminishes engagement compared to in-person communication. A notable challenge arising from the distance between the webcam and display of the device lies in the absence of contact of eye during live video conferences. Addressing this issue involves manipulating video frames to alter the direction of the gaze, creating the illusion of sustained eye contact. This research introduces a technique for preserving eye contact, entailing the rotation of a user's eyeball prior to transmitting the frame of video to the distant place. The implementation leverages WebRTC and TFLite within a application optimized for mobile devices. Convolutional neural networks based on warping play a pivotal role in superimposing the corrected eye onto the detected eye position during the eye position correction process. The employed loss functions contribute to maintaining the geometry of the eye structures while mitigating color shifts around the borders of the eye regions. The technology's real-time eye gaze rectification capability on consumer-oriented mobile devices is demonstrated, highlighting its remarkable speed and resolution. This approach to gaze correction, achieved through postprocessing, proves to be a practical solution for addressing the challenge of lost contact of eye during real time video communication. The model's integration into an application facilitates its implementation.

关键词： Training Webcams Neural networks Tactile sensors Psychology Streaming media Network architecture Mobile handsets Videoconferences WebRTC

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technology Usage For Education: Polytechnic Deaf or Hard-of-Hearing Student

Technology Usage For Education: Polytechnic Deaf or Hard-of-...

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IEEE Symposium on Computers & Informatics (ISCI)

作者： Rini Hafzah Abdul Rahim Dinna Nina Mohd Nizam Nur Faraha Mohd Naim Aslina Baharum User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Computing and Information System School of Engineering and Technology Sunway University Selangor Malaysia

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

This article investigates the application of technology to enhance education for polytechnic deaf or hard-of-hearing (DHH) students. Polytechnic institutions play a crucial role in preparing students for diverse careers through hands-on, practical education. However, DHH students face unique challenges in accessing and fully participating in polytechnic programs. To provide a diverse approach, the technology focusing on visual technology, such as Augmented Reality and Virtual Reality, incorporates crucial components suitable for DHH visual learning modalities. The study uses a mixed-method approach, integrating questionnaires, interviews, and observational data to discover more about DHH lecturers’ and students’ preferred learning methods. 77 DHH Sijil Khas Operasi Katering student from selected three Polytechnic and Community Colleges gave their feedback from questionnaires, and five lecturers were interviewed to get a clearer picture of the current usage of technology among DHH students at polytechnic and community college. Results of the lecturer’s interview suggest that DHH students need visual aids, communication tools, and focus-enhancing resources to help them learn effectively, especially in cooking classes where understanding kitchen equipment and safety is crucial to prevent injuries. Meanwhile, results from DHH students’ questionnaires show students’ interest and prefer to use technology, with 88% agreeing to use technology such as Games, AR, VR and AR Games for their teaching and learning process and to make their learning process more interesting, fun and enjoyable. Integrating the use of technology and information technology as part of the approach of teaching aids to deaf students is hoped to help them to understand better, focus and have more fun in learning.

关键词： Learning systems Visualization Education Focusing Games Safety Interviews

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Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

arXiv

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arXiv 2024年

作者： Rao, Akash K. Menon, Vishnu K. Uttrani, Shashank Dixit, Ayushman Verma, Dipanshu Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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BADROBOT: JAILBREAKING EMBODIED LLMS IN THE PHYSICAL WORLD

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Zhu, Chenyu Wang, Xianlong Zhou, Ziqi Yin, Changgan Li, Minghui Xue, Lulu Wang, Yichen Hu, Shengshan Liu, Aishan Guo, Peijin Zhang, Leo Yu National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Beihang University China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BADROBOT, a novel attack paradigm aiming to make embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge’s flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BADROBOT’s attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BADROBOT. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Sentiment Analysis on 10-K Financial Reports using Machine Learning Approaches

Sentiment Analysis on 10-K Financial Reports using Machine L...

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11th IEEE International Conference on System engineering and technology, ICSET 2021

作者： Soong, Gim Hoy Tan, Chye Cheah School of Computing Asia Pacific University of Technology and Innovation Bukit Jalil Malaysia Asia Pacific University of Technology and Innovation Faculty of Computing Engineering and Technology Bukit Jalil Malaysia

ISBN: (纸本)9781665437660

10K Financial reports are submitted by public listed companies to the Security Exchange Commission (SEC) yearly or quarterly. It allows investors to understand strategic planning and directions of the business organization. Although true facts are required to be presented in the reports, it does not prevent companies from using confusing explanations to beautify the organizations current state. Hence, an automated approach to filter out sentiments from the reports is crucial to assist investors in evaluating financial reports. This research paper explores machine learning approaches to conduct sentiment analysis on 10K financial reports. Two different datasets were intended to be used for training the model but only the financial phrase bank dataset was used to produce the final machine learning models. Four machine learning models including fastText, Naïve Bayes Support Vector Machine (NBSVM), Bidirectional Gated Recurrent Units (BiGRU), and Bidirectional Encoder Representations from Transformers (BERT) are trained based on the financial phrase bank dataset. It is discovered that the BERT model performed with the best accuracy while testing the models while the fastText model provided the fastest loading and training time. Conclusion of this research paper shows that different machine learning models in sentiment analysis possess respective advantages and disadvantages and further research can be done with the combination of textual and numerical data in financial reports. © 2021 IEEE.

关键词： Sentiment analysis Analytical models Loading Bit error rate Machine learning Companies Predictive models

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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches

Improving Oil Spill Detection in Marine Environments Through...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： M Phani Shanmukh S Baghavathi Priya Tamilselvi Madeswaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Department of Information Technology University of Technology and Applied Sciences Nizwa Oman

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment.

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Impact of COVID-19 Variants Using Multi-Criteria Selection Approach

Impact of COVID-19 Variants Using Multi-Criteria Selection A...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Shrddha Sagar Avadhesh Kumar Sofia Singh Garima Shukla Diwakar Upadhyay School of Computer Science and Engineering Galgotias University Uttar Pradesh India Department of Artificial Intelligence Amity School of Engineering and Technology Amity University Noida Uttar Pradesh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Maharashtra School of Computing Graphic Era Hill University Haldwani India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The aim of the proposed work is related to the detailed study of various types of COVID-19 along with its symptoms. This study utilized a retrospective approach to the COVID data from 2019 to 2021 to assess the severity level of the various types of COVID-19. After a detailed analysis, we have identified seven symptoms which are as follows: breathing, chest pain, fever, body ache, sore throat and cough, tiredness, and loss of taste/smell. In this study, the prediction of the COVID-19 severity level is made using a fuzhzy multi-objective optimization based on ratio analysis (fuzzy MOORA) approach. The proposed model simultaneously predicts the severity level of COVID-19 with multiple criteria. The severity levels listed on government-approved websites for various types of COVID-19 are considered for validating the proposed methodology's result. The highest overall performance index value is used to decide. As a result, the overall performance index value has been achieved and moreover, we can predict the severity level of different forms of COVID-19 using this strategy. After the implementation of our proposed approach, we got the overall performance index value of delta T4 (0.433086). The Coronavirus Virus 2019 (COVID-19) pandemic is on the increase for over two years. The World Health Organization (WHO) statistics show that approximately 260 million confirmed cases have been documented, with over five million deaths. In our work, we have implemented a fuzzy MOORA approach for predicting the severity level of the different types of COVID-19. The overall performance index value of each COVID-19 is shown in the table. Table 1.2 demonstrates that Delta T4 is ranked 1 st , Gamma T3 is ranked 2 nd , Beta T2 is ranked 3 rd , Alpha T1 4th and Omicron T5 has the least rank.

关键词： COVID-19 Quantum computing Pandemics Pain Computer viruses Organizations Predictive models Performance analysis Artificial intelligence Optimization

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A Deep Learning Framework for the Classification of ECG Signals 8

A Deep Learning Framework for the Classification of ECG Sign...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Khan, Sadaf Bhatti, Wahab Haseeb Chaudhary, Fawad Shafique, Arsalan Irfan, Muhammad Teevno, Mansoor Ali Qureshi, Rizwan Army Public College of Management and Sciences Rawalpindi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Electrical Engineering Pakistan Mehran University of Engineering and Technology Jamshoro Pakistan National University of Computer and Emerging Sciences School of Computing Karachi Pakistan

ISBN: (纸本)9781665491068

The electrical activity of human heart is captured by an electrocardiogram (ECG) signal. The careful analysis of ECG signal can provide valuable insights into the cardiac activity, which can lead to timely therapeutic interventions. The rise of deep learning and the large amount of complex healthcare datasets provides an opportunity to develop intelligent data analysis systems, that can help identify disease patterns and clinical decision support. In this work, we propose a deep neural network model to classify real time ECG signals into normal, atrial fibrillation, and ventricular fibrillation. In the preprocessing stage, the signal is passed through various filters to remove different types of artifacts. The proposed model is trained on a public dataset, containing 30,450 training examples and validated across 6,525 examples and then tested with 6,525 examples. The model achieves testing accuracy of 0.9969%. © 2022 IEEE.

关键词： Deep neural networks

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