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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Mammo, Andualem Fantahun Kassahun, Habitamu Endalamaew Gandhi, Smiley Gupta, Shashi Kant Salau, Ayodeji Olalekan Onibonoje, Moses Oluwafemi Kishore Kanna, R. University of Gondar Electrical and Computer Engineering Gondar Ethiopia Galgotias University Greater Noida Uttar Pradesh Greater Noida201310 India Eudoxia Research University Department of Computer Science and Engineering United States Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350364699

Cloud computing has grown in popularity as a storage platform that makes use of the internet to provide users with large amounts of data storage. The cloud's client base has grown throughout time, and access for consumers and businesses has expanded. At this time, security concerns develop among the cloud community. Different harmful assaults occur on well-known cloud services by exploiting weak password providers, causing them to lose confidentiality. Cryptography has evolved into an autonomous data encryption method, which allows users to manually cipher/decipher information. Secure keys can be generated using biometric key generation, eliminating the need for remembering. However, usability concerns persist since biometric credentials are required every time a file is encrypted or decrypted. This study focuses on multimodal biometrics, which will create a non-intrusive key for cryptography algorithms to encrypt/decrypt the file uploaded to the cloud by the client. To achieve this, this paper presents a convolutional neural networks approach which extract features from face and fingerprint biometrics and trains them to understand the patterns that are to be classified. Multi-label categorization features were employed as output labels, which were hashed to 512 bits from each of the multimodal biometrics and could be combined to yield 1024 bits for RSA cryptography encryption and decryption. False acceptance and rejection rates were utilized to assess the whole system. As a result, the face exhibits 2.13% FAR and the fingerprint 2.71% FAR on average. © 2024 IEEE.

关键词： Cloud platforms

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Pole Slipping in Droop-Based Grid-Forming Inverters

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IEEE Access 2025年 13卷 100338-100352页

作者： Negar Karimipour Hansi Wijayalath Arachchilage Amir Abiri Jahromi Behrooz Bahrani Mohammadreza F. M. Arani Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia School of Electronic and Electrical Engineering University of Leeds Leeds U.K.

Integration of grid-forming inverter-based resources is considered as a viable solution to address the challenges associated with the dynamics of power systems with high penetration levels of inverter-based resources. Yet, various aspects of dynamic behaviour of grid-forming inverters during disturbances have remained unknown. This paper for the first time introduces the concept of pole slipping in grid forming inverters, referring to the loss of synchronism where the inverter’s internal phase angle deviates beyond 180° with respect to the grid, similar to synchronous generators losing step. It is revealed that this event in grid forming inverters can be stable or unstable. The differences between stable and unstable pole slipping are highlighted. The determining factors for the occurrence of stable and unstable pole slipping in grid forming inverters and equipment-level implications of pole slipping are identified and studied. A solution is proposed to mitigate stable and unstable pole slipping in grid-forming inverters by adjusting the operating reference power during the disturbance. The validity of the proposed concept of pole slipping in grid forming inverters and the proposed solution is tested and verified through extensive time-domain simulations and experimental results. The results verify that pole slipping is more likely to happen in high short circuit ratios (SCRs). It is revealed that unstable pole slipping does not occur at SCRs lower than 3.5. Simulation and experimental results demonstrate that a phase jump as small as 10° may lead to instability in a grid with SCRs equal or higher than 10. This is while in lower SCRs such phase jumps result in long fault recovery without causing pole slipping.

关键词： Inverters Synchronous generators Power system stability Grid forming Voltage Rotors Circuit stability Transient analysis Current limiters Stability criteria

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Stability Assessment of Ultramicroelectrode Arrays in Neural Stimulation: An Electrochemical Impedance Spectroscopy Analysis 46

Stability Assessment of Ultramicroelectrode Arrays in Neural...

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

作者： Dong, Qiwei Ezeh, Cynthia Wu, Yupeng Hetke, Jamille F. Cogan, Stuart Orazem, Mark E. Otto, Kevin J. University of Florida Department of Electrical & Computer Engineering United States University of Florida Department of Chemical Engineering United States The University of Texas Department of Bioengineering Department of Material Sciences & Engineering Dallas United States NeuroNexus United States University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering United States

ISBN: (纸本)9798350371499

The overall focus of this research program is to investigate the viability and safety of microelectrode arrays (MEAs) and ultramicroelectrode arrays (UMEAs) for microstimulation (μStim) in the brain. We aim to assess their potential to deliver adequate electrical stimulation for neural activation without causing electrode or tissue damage. To this end, here we conducted galvanostatic Electrochemical Impedance Spectroscopy (EIS) to assess the behavior of electrodes of various diameters both pre- and post-stimulation in a rat model. Our first-generation electrode arrays (G1) were designed with electrode diameters spanning 3 orders of magnitude and including subcellular dimensions. Using these devices, we delivered 0.4 nC/phase stimulation in vivo. We conducted in vitro EIS measurements before and after the current pulsing. Our findings show that these electrode arrays, including UMEAs, exhibit stable electrochemical behavior following a 0.4 nC/phase current pulse. To validate the EIS frequency range used for the G1 measurements, we designed a second-generation electrode array (G2) that included a terminating lead and a looping lead. This array provided the ability to assess the accuracy contour plot of our EIS system and electrode array platform. This exploratory research contributes to the ongoing knowledge of UMEAs in μStim applications. These developments may result in high-fidelity, non-damaging multichannel μStim, and improved neuromodulation technology for the treatment of neural diseases and injuries. © 2024 IEEE.

关键词： Electrochemical impedance spectroscopy

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Automated Deep Learning Pipeline for Accurate Segmentation of Aortic Lumen and Branches in Abdominal Aortic Aneurysm: A Two-Step Approach 2

Automated Deep Learning Pipeline for Accurate Segmentation o...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Hassan, Fatma N. Mahmoud, Ahmed M. Elnokrashy, Ahmed F. Hassan, Ashraf Y. Benha University Benha Faculty of Engineering Department of Electrical Engineering Benha Egypt Cairo University Department of Biomedical Engineering and Systems Giza Egypt Astute Imaging KirklandWA98033 United States Nile University Department of Computer Science Technology and Computer Science Giza Egypt Benha University Benha Egypt

ISBN: (纸本)9798350362633

Abdominal Aortic Aneurysm (AAA) is a serious medical condition characterized by the abnormal enlargement of the abdominal aorta. If left untreated, AAA can have life-threatening consequences. Accurate segmentation of the aorta in Computed Tomography Angiography (CTA) images plays a vital role in treatment planning for AAA. However, manual and semi-automatic segmentation methods suffer from limitations in terms of time and accuracy. This study presents a deep learning pipeline that aims to fully automate the precise and efficient segmentation of the aorta and its branches within CTA images. A two-step approach is proposed for the segmentation of the aorta and its branches in contrast-enhanced CTA scans. The first step involves utilizing a UNet++ model to perform aortic segmentation across the entire set of CTA axial slices. In the second step, a post processing algorithm is employed to track the continuity of the segmented aorta while effectively discarding false positive (FP) objects. The assessment of the fully automated method revealed remarkable outcomes, with a mean Dice coefficient of 0.941 on a test set consisting of 10 CTA scans. The automated segmentation results are utilized to create a comprehensive 3D model. The study results indicate that the utilization of the proposed deep learning-based pipeline is highly effective in achieving accurate segmentation of the aortic lumen and its branches. The practical implications of this approach extend to pre-operative planning. This highlights the valuable contribution of the proposed method in improving the management and treatment of patients diagnosed with aortic aneurysm. © 2024 IEEE.

关键词： computerized tomography

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Fractional Brownian motion with fluctuating diffusivities

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Physical Review E 2024年第1期110卷 014105-014105页

作者： Adrian Pacheco-Pozo Diego Krapf Department of Electrical and Computer Engineering and School of Biomedical Engineering <a href="https://***/03k1gpj17">Colorado State University</a> Fort Collins Colorado 80523 USA.

Despite the success of fractional Brownian motion (fBm) in modeling systems that exhibit anomalous diffusion due to temporal correlations, recent experimental and theoretical studies highlight the necessity for a more comprehensive approach of a generalization that incorporates heterogeneities in either the tracers or the environment. This work presents a modification of Lévy's representation of fBm for the case in which the generalized diffusion coefficient is a stochastic process. We derive analytical expressions for the autocovariance function and both ensemble- and time-averaged mean squared displacements. Further, we validate the efficacy of the developed framework in two-state systems, comparing analytical asymptotic expressions with numerical simulations.

关键词： Anomalous diffusion Fractional Brownian motion Disordered systems Statistical methods

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A Comparative Study on Physical and Perceptual Features for Deepfake Audio Detection 1

A Comparative Study on Physical and Perceptual Features for ...

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1st International Workshop on Deepfake Detection for Audio Multimedia, DDAM 2022

作者： Li, Menglu Ahmadiadli, Yasaman Zhang, Xiao-Ping Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9781450394963

Audio content synthesis has stepped into a new era and brought a great threat to daily life since the development of deep learning techniques. The ASVSpoof Challenge and the ADD Challenge have been launched to motivate the development of Deepfake audio detection algorithms. Currently, the detection models, which consist of front-end feature extractors and back-end classifiers, utilize the physical features mainly, rather than the perceptual features that relate to natural emotions or breathiness. Therefore, we provide a comprehensive study on 16 physical and perceptual features and evaluate their effectiveness in both Track 1 and Track 2 of the ADD Challenge. Based on results, PLP, as a perceptual feature, outperforms the rest of the features in Track 1, while CQCC has the best performance in Track 2. Our experiments demonstrate the significance of perceptual features in detecting Deepfake audios. We also seek to explore the underlying characteristics of features that can distinguish Deepfake audio from a real one. We perform statistical analysis on each feature to show its distribution differences on real and synthesized audios. This paper will provide a potential direction in selecting appropriate feature extraction methods for the future implementation of detection models. © 2022 Association for Computing Machinery.

关键词： Deep learning

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Reward-Driven Clustered Federated Learning for DRL Based UAV-RIS Networks 8

Reward-Driven Clustered Federated Learning for DRL Based UAV...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Wong, Yi Jie Tham, Mau-Luen Kwan, Ban-Hoe Iqbal, Amjad Lee, Ying Loong Chang, Yoong Choon Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang Malaysia Universiti Tunku Abdul Rahman Department of Mechatronics and Biomedical Engineering Kajang Malaysia Carleton University Department of System and Computer Engineering Canada

ISBN: (纸本)9798350315516

Deep reinforcement learning (DRL) has empowered numerous reconfigurable intelligent surface (RIS) aided millimeter-wave unmanned aerial vehicle (UAV) communications. However, most existing works often employ single-Task DRL, that could only handle one specific environment (or task). This hurdles the large-scale adoption of DRL-based UAV-RIS networks, since the environment has infinite variations. In this paper, we aim to solve diverse UAV-RIS tasks with a minimum number of DRLs required. Specifically, we proposed a reward-driven clustering technique to autonomously cluster all 50 simulated tasks into 10 clusters. For each cluster, we train a multi-Task DRL using Federated Learning to handle all tasks in the cluster. Simulation shows that given 50 unique tasks, the proposed method could reduce the total agents required from 50 to 10, which is a 5-fold reduction. © 2023 IEEE.

关键词： Reinforcement learning

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes 59

Using Random Forest and Linear SVM for Dyslexia Classificati...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Kaushik, Padmanabh Yu, Yih-Choung Gabel, Lisa Lafayette College Department of Electrical and Computer Engineering EastonPA18042 United States Lafayette College Department of Psychology and Neuroscience Program EastonPA18042 United States

ISBN: (纸本)9798331513269

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia;however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimal Load Flow Analysis for Distributed Generation in Radial Distribution Systems Using Modified Newton Raphson Method

Optimal Load Flow Analysis for Distributed Generation in Rad...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Saravanakumar, R. Saranpriya, R. Singaram, Gopinath Hariprabhu, M. Meenatchi, K. Sharanyanivasini, J.S. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India Erode India Annasaheb Dange College of Engineering and Technology Department of Electrical Engineering Sangli Maharashtra Ashta416301 India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur639113 India K S R Institute for Engineering and Technology Department of Electrical and Electronics Engineering India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thudupathi India

ISBN: (纸本)9798350300857

The economic status of the country depends upon the power consumption, if the power consumption is more and then there is a chance of over loading in the transmission lines, which may result in more losses. According to one hypothesis. the voltage profile of a radial distribution system with distributed generation (DG) can be enhanced by adding more effective control mechanisms for both actual and reactive power. Because it imnroves system stability and successtully reduce power dissipation in radial distribution networks, distributed production systems outperform traditional approaches for limiting power loss. In order to undertake a comprehensive examination of load flow, the current study used a real-time distribution system in India comprised of 25 buses. The location of distribution generation also identified with trial and error method. The Wind Turbine Generator System (WTGS) is located in different buses to improve the voltage profile. The detailed analysis is carried out with the E-TAP system. © 2023 IEEE.

关键词： Electric power utilization

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GEODESIC PROPERTIES OF A GENERALIZED WASSERSTEIN EMBEDDING FOR TIME SERIES ANALYSIS

GEODESIC PROPERTIES OF A GENERALIZED WASSERSTEIN EMBEDDING F...

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ICML Workshop on Topology, Algebra, and Geometry in Machine Learning, TAG:ML 2022

作者： Li, Shiying Rubaiyat, Abu Hasnat Mohammad Rohde, Gustavo K. Department of Biomedical Engineering University of Virginia CharlottesvilleVA22908 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Department of Biomedical Engineering Department of Electrical and Computer Engineering CharlottesvilleVA22908 United States

Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present. In this paper, we study the geodesic properties of time series data with a generalized Wasserstein metric and the geometry related to their signed cumulative distribution transforms in the embedding space. Moreover, we show how understanding such geometric characteristics can provide added interpretability to certain time series classifiers, and be an inspiration for more robust classifiers. The appendix can be found at https://***/abs/2206.01984. © 2022 Proceedings of Machine Learning Research. All rights reserved.

关键词： Embeddings

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