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2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Abdullah, Muhammad Rosli Harun, Noor Hasmiza Wahab, Azimah Abdul Ibrahim, S Noorjannah Jamilan, Mohd Azerulazree Rahim, Nur Aina Syuhada Universiti Kuala Lumpur British Malaysian Institute Medical Engineering Technology Section Selangor Malaysia Universiti Kuala Lumpur Institute of Medical Science Technology Selangor Malaysia Engineering International Islamic University Department Of Electrical & Computer Gombak Selangor 53100 Malaysia Ministry of Health Malaysia NMCRC Institute for Medical Research Selangor Shah Alam40170 Malaysia

ISBN: (纸本)9798350304107

A plasmonic tracker had been developed for detecting a plasmonic energy at Total Internal Reflectance angle. It is crucial to determine thin film thicknesses, resonance angle and refractive index of a dielectric material. However, a low cost and flexible plasmonic tracker could offers a precise control for accurate measurement system. The main objective is to develop manual, automated and smart control of the plasmonic tracker at high resolution via a PC based system. The plasmonic tracker uses two 1.8° bipolar stepper motor as a prism and silicon photodiode stages. Both of the stepper motors were operated in microstepping that generate 3200 steps at 0.1125° pulse per step. A stable microstepping operation was observed at distance, period, phase angle, torque, RPM and power respectively at 1250 mm/ sec, 90 μS, 3.4°, 0.22 Nm, 200 RPM and 4.64 W. A plasmonic curve and resonance angle were plotted and displayed from a customized software interface which provides high sensitivity, throughput, performance and efficiency. © 2023 IEEE.

关键词： Plasmonics

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DentAssignNet: Assignment Network for Dental Cast Labeling in the Presence of Dental Abnormalities

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Dascalu, Tudor Ramezanzade, Shaqayeq Bakhshandeh, Azam Bjorndal, Lars Iurcov, Raluca Vrtovec, Tomaz Ibragimov, Bulat University of Copenhagen Department of Computer Science Copenhagen Denmark University of Copenhagen Cariology and Endodontics Section of Clinical Oral Microbiology Department of Odontology Faculty of Health and Medical Sciences Copenhagen Denmark University of Oradea Department of Dentistry Oradea Romania University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia

This study focuses on the challenging problem of labeling a collection of objects with inherent morphological and positional dependencies, where instances may be missing or duplicated. We integrate principles of assignment theory in the design of a convolutional neural network to find the optimal label set given pairwise geometrical features extracted from the candidate objects. The objective function aims to minimize the distance between the one-hot encoded labels of the objects and the scores produced by the model, with added emphasis on the scores corresponding to the optimal assignment plan. We tested our solution in the dental domain on the task of finding the teeth labels given a set of candidate instances. The study database included 1200 dental casts of upper and lower jaws from 600 patients. The model reached identification accuracies of 0.952 and 0.968 for the lower and upper jaws, respectively. Moreover, we presented a solution for generating teeth candidates using a multi-step pipeline consisting of coarse and fine segmentations. The algorithm was tested on a database consisting of 600 dental casts, reaching an F1 score of 0.968. © 2013 IEEE.

关键词： Convolutional neural networks

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Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models

Biological Signals for Diagnosing Sleep Stages Using Machine...

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International CSI computer Conference, CSICC

作者： Sahar Hassanzadeh Mostafaei Jafar Tanha Amir Sharafkhaneh Ritwick Agrawal Zohair Hassanzadeh Mostafaei Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Medicine Section of Pulmonary Critical Care and Sleep Medicine Baylor College of Medicine Houston Texas USA Department of Computer Engineering Islamic Azad University of Tabriz Branch Tabriz Iran

Sleep quality is important for health and can prevent diseases. Polysomnography is a standard scientific and clinical tool to evaluate sleep. Sleep staging is one of the main tasks in the sleep study that characterizes sleep cycles. In recent years, many studies are conducted using machine learning approaches to classify sleep stages. These studies can improve the accuracy and speed of the classification task, but most of them have not considered the performance of minority sleep classes. Since the number of samples in the sleep classes varies greatly, it can be considered as imbalanced data. In this study, we propose an ensemble method to handle class imbalance problem in the sleep staging task. For this purpose, we select nine biomedical signals including two EEG channels, two EOG channels, EMG, ECG, Abdominal, Thorax, and Airflow from the Sleep Heart Health Study (SHHS1) dataset. Then we use an ensemble of data-level resampling methods to rebalance the data space of sleep classes. Finally, we employ different machine learning algorithms to classify sleep stages. To evaluate the performance of the proposed method, in addition to general metrics, we use various measures such as Geometric mean (G-mean) and Matthew's Correlation Coefficient (MCC), which are proper metrics for the imbalanced data classification. The results of the developed method show that it achieves high accuracies of 0.9727, 0.9410, 0.8816, and 0.8725 for two, three, five, and six sleep classes, respectively.

关键词： Training Heart Machine learning algorithms Sleep Transforms Feature extraction Thorax

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An Ensemble Model for Sleep Stages Classification

An Ensemble Model for Sleep Stages Classification

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Iranian Conference on Electrical engineering (ICEE)

作者： Sahar Hassanzadeh Mostafaei Jafar Tanha Amir Sharafkhaneh Zohair Hassanzadeh Mostafaei Mohammed Hussein Ali Al-Jaf Alireza Fakhim Babaei Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Medicine Section of Pulmonary Critical Care and Sleep Medicine Baylor College of Medicine Houston Texas USA Department of Computer Engineering Islamic Azad University of Tabriz Branch Tabriz Iran

One of the main parts of health is the quality of sleep. Sleep disorders can be diagnosed using a standard sleep test called polysomnography. Sleep staging is a task in the field of sleep study that determines sleep cycles. In recent years, machine learning techniques are used to classify sleep stages using biological signals derived from PSGs. In this study, we propose an ensemble machine learning model for classifying sleep stage. We use nine biological signals from the SHHSI dataset, including two-channel EEG, two-channel EOG, ECG, EMG, abdominal, thorax, and airflow signals. Then we extract different features such as RRI and RPE from the ECG signals and frequency features from EEG signals. Finally, we develop an ensemble model using Light Gradient Boost (LGB) and eXtreme Gradient Boost (XGB) algorithms. In the end, we evaluate the proposed ensemble method using different metrics and compare its performance with other state-of-the-art techniques. The results of the proposed method show that it achieves an overall accuracy of 0.8951 in the five-class classification of sleep stages including Wake, Nl, N2, N3, and REM.

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Cryptanalysis and improvement of quantum secure multi-party summation using single photons

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Physica Scripta 2024年第3期99卷 035114-035114页

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 section 3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 section 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

A previous study [Entropy 25.4 (2023): 590] proposed a quantum secure multi-party summation protocol wherein n participants could obtain the modulo-2 summation result using single photons and single-particle operations. Owing to the absence of an entanglement state, this protocol was more practical than other quantum summation protocols that used entanglement properties. Thus, this study investigated the claim that this proposed protocol prevented the participants’ secret inputs from being disclosed. It was found that the previously proposed protocol was vulnerable to a quantum Trojan horse attack owing to the circular qubit transmission mode. Using a security loophole, this study proposed an attack strategy that allowed an outside attacker to steal any participant’s secret input without being detected by a third party and participants. In addition, a simulation experiment was conducted to demonstrate the feasibility of the proposed attack and evaluate the number of quantum Trojan horse probes that the attacker must inject into regular qubit sequences. Finally, a solution was proposed by modifying the protocol to securely prevent quantum Trojan horse attacks. © 2024 IOP Publishing Ltd.

关键词： Photons

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Data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Abu Bakar, Azlinda Nizar Hamidon, Mohd Yaacob, Mohd Hanif Paiman, Suriati Jaafar, Haslina Wan Jusoh, Wan Nursheila Universiti Putra Malaysia Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Science Department of Physics Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Selangor Serdang Malaysia Universiti Kuala Lumpur Section of Aerospace Malaysians Institute of Aviation Technology Selangor Dengkil Malaysia

ISBN: (纸本)9798350333312

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO2-MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100-1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 - 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work. © 2023 IEEE.

关键词： Thick films

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Optimizing resource allocation in flex-grid entanglement distribution networks

Optimizing resource allocation in flex-grid entanglement dis...

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2022 IEEE Photonics Conference, IPC 2022

作者： Alnas, Jude Alshowkan, Muneer Nageswara Rao, S.V. Peters, Nicholas A. Lukens, Joseph M. University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL35401 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce a model for the distribution of frequency-polarization hyper-entangled photon pairs in a flexible-grid optical network. In order to optimize entanglement fidelity and entangled bit rate, we apply a geneti... 详细信息

ISBN: (数字)9781665434874

ISBN: (纸本)9781665434874

关键词： Genetic algorithms

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Prognostication of zooplankton-driven cholera pathoepidemiological Dynamics: Novel Bayesian-regularized deep NARX neuroarchitecture

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computers in Biology and Medicine 2025年 192卷 110197-110197页

作者： Raja, Muhammad Junaid Ali Asif Sultan, Adil Chang, Chuan-Yu Shu, Chi-Min Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan AI Center Yuan Ze University Taoyuan320 Taiwan

Background: Cholera outbreaks pose significant health concerns, particularly through freshwater contamination through zooplankton serving as reservoirs for Vibrio Cholerae. Understanding these complex interactions within the aquatic ecosystem through mathematical modeling regimes may help us predict and prevent the spread of Cholera disease spread in affected regions. Method: In this study, an innovative Bayesian regularized deep nonlinear autoregressive exogenous (BRDNARX) neural networks are employed to model the intricate dynamics of Zooplankton-Driven Cholera Disease Transmission (ZDCDT) system. The cholera epidemic propagation through freshwater contamination is uncovered with analysis on densities of phytoplankton, vibrio cholerae carrying zooplankton, human population vector and microbial pathogen vector populous in the marine biosphere. Synthetic data for the ZDCDT is presented for diverse simulations using a modified Adams-Bashforth-Moulton predictor corrector numerical scheme. Subsequently, these temporal data sequences are preprocessed for the novel BRDNARX computing paradigm with an exhaustive assessment on mean square error iterative convergence plots, error histogram charts, regression index reports, input-error crosscorrelation charts, error autocorrelation charts, and time-series response dynamics. Results and conclusions: Comparative absolute error analysis with reference numerical solution adheres to diminutive disparities of range 10−3 to 10−9. Finally, BRDNARX neurostructures are reconfigured for predictive analysis of ZDCDT system in terms of single and multi-step ahead predictors with mean square error outcomes that range from 10−9 to 10−11. This establishes the efficacy of BRDNARX in correctly adhering to the intricacies of the zooplankton-driven cholera pathoepidemiological dynamics with precise forward prognostication. © 2025 Elsevier Ltd

关键词： Mean square error

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Novel polynomial Abet data augmentation algorithm with GRU paradigm for nuclear power prediction

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Annals of Nuclear Energy 2024年 201卷

作者： Khan, Saifullah Awan, Saeed Ehsan Muhammad, Yasir Jadoon, Ihtesham Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 0.3 Yunlin Douliou64002 Taiwan

In current study innovative approach is presented to improve the accuracy and efficiency of machine learning models on the dataset for each of the 31 US states, solely comprising a single power output column in Terra watt-hours (TWh) spanning from January 2013 to November 2020 by exploiting the knacks of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid GRU-LSTM, Polynomial Feature Transform (PFT) and Polynomial Abet (PA) data augmentation with GRU for efficacious prediction. Achieving sufficient accuracy, a small univariate dataset of 95 rows was augmented through the PFT method, resulting in a significant reduction in prediction errors. Encouraged by this improvement, a novel algorithm PA data augmentation with GRU is introduced for further enhancing the accuracy, correlation, and computational efficiency of the prediction model. This research not only demonstrates the superior performance of the proposed PA data augmentation method but also highlights the benefit of using 31 individual univariate datasets for each state, allowing comprehensive testing and validation of the novel algorithm, providing compelling evidence of its efficacy in nuclear power prediction. The results from the proposed algorithms are evaluated using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE). A quantitative overview of PA and PFT data augmentation techniques’ comparison show the result in terms of accuracy (PA's 0.464% RRMSE versus PFT's 1.088 % RRMSE), correlation (PA's −0.5877 versus PFT's 0.4892), and computational efficiency (PA's 14.02 % less computational time then PFT) for the nuclear power prediction model. The research framework introduced in this study, with its focus on utilizing state-specific data for nuclear energy prediction, holds significant implications for governmental decision-making policies. It provides a comprehensive overview of the trends in power generation and can be utilized for both renewable and thermal sources ac

关键词： Long short-term memory

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