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Annual Conference of Industrial Electronics Society

作者： Arwa Hasnain Bharmal Manaal Khan Agha Dilawar Ahsan Ali Saad Umer Baig Ishtiyaq Ahmad Makda Ahmad Usman Haleema Qamar Electrical and Computer Engineering DSSE Habib University Karachi Pakistan

The paper presents the design of a controller for an Isolated DC-DC Boost Converter with voltage doubler (IBVD) topology, utilizing the Ziegler-Nichols (ZN) tuning method. The presence of load variations and disturbances in the power electronic converters used in Electric Vehicles (EV) necessitates the use of a controller to ensure stable operation. Moreover, achieving a smooth and safe driving experience requires improvements in the transient response. This work address these challenges by demonstrating an efficient controller design for voltage regulation. To validate its effectiveness, the controller is tested using FPGA-enabled hardware-in-loop (HIL) technique, where the controller is implemented on the FPGA while the converter operates in a simulated environment using MATLAB Simulink. The results demonstrate that the controller successfully achieves the desired steady state and transient output both for stable operation and load variations.

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Machine Learning for Improved Resource Block Detection in 4G LTE Cognitive Radio Networks

Machine Learning for Improved Resource Block Detection in 4G...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Ali, Yousuf Moiz Ahmed, Arsalan Mumtaz, Tariq Habib University Department of Electrical and Computer Engineering Pakistan

ISBN: (纸本)9781665412438

Accurate resource block detection through spectrum sensing is vital for cognitive radios in order to identify the free spaces in the licensed spectrum. The emerging field of machine learning is frequently being used to enhance the performance of spectrum sensing. Therefore, in this paper, a detailed study has been presented regarding the performance improvement of spectrum sensing through machine learning algorithms targeting the 4G LTE resource block detection. A novel spectrum sensing scheme has been proposed which achieves optimal performance at high as well as low Signal-to-Noise Ratio (SNR) conditions. The proposed scheme combines energy detection for high SNR conditions and matched filter detection with Naive Bayes algorithm for low SNR conditions. © 2021 IEEE.

关键词： Cognitive radio

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

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aXonica: A support package for MRI based Neuroimaging

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Biotechnology Notes 2024年第1期5卷 120-136页

作者： Wajid, Bilal Jamil, Momina Awan, Fahim Gohar Anwar, Faria Anwar, Ali Dhanani School of Science and Engineering Habib University Karachi Pakistan Muhammad Ibn Musa Al-Khwarizmi Research & Development Division Sabz-Qalam Lahore Pakistan Department of Electrical Engineering University of Engineering & Technology Lahore Pakistan Out Patient Department Mayo Hospital Lahore Pakistan Department of Computer Science University of Minnesota Minneapolis United States

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for ‘free for use’ tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with ‘aXonica,’ an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an

关键词： Bioimaging Magnetic resonance imaging (MRI) Neuroimaging Ubuntu

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Adversarial Environment Design via Regret-Guided Diffusion Models 38

Adversarial Environment Design via Regret-Guided Diffusion M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chung, Hojun Lee, Junseo Kim, Minsoo Kim, Dohyeong Oh, Songhwai Interdisciplinary Program in Artificial Intelligence ASRI Seoul National University Korea Republic of Department of Electrical and Computer Engineering ASRI Seoul National University Korea Republic of

Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environments tailored to the agent's capabilities. While prior works demonstrate that UED has the potential to learn a robust policy, their performance is constrained by the capabilities of the environment generation. To this end, we propose a novel UED algorithm, adversarial environment design via regret-guided diffusion models (ADD). The proposed method guides the diffusion-based environment generator with the regret of the agent to produce environments that the agent finds challenging but conducive to further improvement. By exploiting the representation power of diffusion models, ADD can directly generate adversarial environments while maintaining the diversity of training environments, enabling the agent to effectively learn a robust policy. Our experimental results demonstrate that the proposed method successfully generates an instructive curriculum of environments, outperforming UED baselines in zero-shot generalization across novel, out-of-distribution environments. Project page: https://***/projects/ADD. © 2024 Neural information processing systems foundation. All rights reserved.

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Assessing Distributed Energy Resource Hosting Capacity Under Different Grid-Tie Inverter Control Strategies 8

Assessing Distributed Energy Resource Hosting Capacity Under...

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8th Workshop on Communication Networks and Power Systems, WCNPS 2023

作者： Santos, Brenda L. M. Barros, Luciano S. Barbosa, Daniel Moreira, Fernando A. Federal University of Bahia Graduate Program in Electrical Engineering Salvador Brazil Federal University of Paraíba Computer Systems Department João Pessoa Brazil Federal University of Bahia Department of Electrical and Computer Engineering Salvador Brazil

ISBN: (纸本)9798350343571

Despite the potential benefits that the integration of distributed energy resources (DERs) can bring to the system, it may cause problems related to power quality constraints, such $as$ reverse power flow in substation and overvoltages. An effective approach to address these problems involves the adoption of reactive and active power control in grid-tie inverters associated with DER. Therefore, this paper assesses the impacts of grid-tie inverter control modes, including both Volt-Var and Volt-Watt strategies, on the DER hosting capacity. In order to improve the overall system operation, modifications in the Volt-Var and Volt-Watt curves were proposed. It is noteworthy that these control strategies can have adverse effects on certain distribution system performance indicators, such $as$ voltage deviation and power losses;for this reason, these indicators are also evaluated in this study. A stochastic approach was adopted to deal with the uncertainties associated with DERs and loads. Finally, from tests conducted in the IEEE 33-bus test system, it was concluded that the proper adjustment of the Volt-Var and Volt-Watt control curves significantly influences DER hosting capacity, $as$ well $as$ voltage deviation and power losses. © 2023 IEEE.

关键词： Reactive power

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

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Multimedia Tools and Applications 2024年第38期83卷 86059-86078页

作者： Mabrouk, Besma Bouattour, Nadia Mabrouki, Noura Sellami, Lamia Ben Hamida, Ahmed Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax Tunisia Department of Neurology University Hospital Habib Bourguiba Sfax Tunisia Department of Psychiatry University Hospital Taher Sfar Mahdia Tunisia Department IS College of Computer Science King Khaled Universiy ‘KKU’ Abha Kingdom of Saudi Arabia Saudi Arabia

Diagnosing Alzheimer’s disease (AD) in its prodromal stage is a significantly crucial area of research. Approximately 50% of individuals within the well-known Mild Cognitive Impairment (MCI) cohort are estimated to progress to AD, and the factors influencing conversion remain unknown. Gaining insights into the disease evolution can enhance support strategies and potentially slow down the pathology. Utilizing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, our objective is to construct a framework for distinguishing between Normal Controls (NC) and different stages of Alzheimer’s Disease (AD), encompassing Earlier Mild Cognitive Impairment (EMCI), Later Mild Cognitive Impairment (LMCI), and AD patients. In pursuit of this objective, we preprocessed Diffusion Tensor and Magnetic Resonance brain images from 237 subjects, generating corresponding brain connectivity maps. Notably, we introduce an innovative linearity assessment method that utilizes the Ordinary Least Squares (OLS) linear regression model to identify and select relevant features for classification. This approach effectively identifies features with strong linear relationships to the target variable. Our method’s superiority is demonstrated through a comparative analysis with the traditional SelectKBest approach. By integrating this feature selection strategy with a Logistic Regression model, our study achieves both efficient and highly accurate classification outcomes, highlighting the effectiveness of the proposed method. In a four-class classification scenario, the model attained an accuracy of 66%±0.06. In binary classification, the results were equally impressive, with an area under the curve of 0.68±0.10% for CN vs. EMCI discrimination, 99±0.02%for distinguishing LMCI from adjacent classes CN and EMCI, and 0.79%±0.08 for discriminating AD from healthy subjects. Additionally, the calculation of Pearson’s correlation coefficient has been employed to identify cortical regions affected b

关键词： Neurodegenerative diseases

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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