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IEEE International Symposium on Biomedical Imaging

作者： Siwei Mai Ali E. Haddad Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University Department of Computer Engineering University of Basrah

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for under-standing how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain's functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high-confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes.

关键词： Hands Brain Heuristic algorithms Motors Electroencephalography Brain-computer interfaces Spatiotemporal phenomena Timing Biomedical imaging

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

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An Efficient Test Scheduling Method Based on Dynamic Pairing

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Kim, Heetae Yun, Hyojoon Yoon, Doohyun Kang, Sungho Yonsei University Computer Systems and Reliable SoC Laboratory Department of Electrical and Electronic Engineering Seoul03722 Korea Republic of

Test scheduling is a process that manages tests within a System-on-Chip (SoC) to minimize test time by allocating test resources and adjusting priorities. Efficient test scheduling offers cost saving opportunities by reducing test time without compromising test coverage. Since test scheduling is a NP-hard problem, conventional methods adopt optimization or heuristic algorithms that leverage metrics of each test. However, due to the interdependence of tests based on how limited resources are allocated, finding the optimal solution to minimize test time is challenging. In this paper, a dynamic pairing algorithm is proposed to consider the mutual influence of tests on each other in the test scheduling process. The proposed method identifies the available test resources for a specific pair and minimizes the test time of the paired modules under identified constraints. Additionally, the proposed algorithm employs a heuristic-based approach to test scheduling to reduce the CPU time needed for scheduling. The proposed heuristic algorithm sequentially schedules test target modules and determines the optimal test schedule through dynamic pairing. Experiments have been carried out on diverse benchmarks and under various constraint conditions. The results indicate that the proposed method achieves shorter test times on average in comparison to conventional methods. © 2025 IEEE. All rights reserved.

关键词： System-on-chip

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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Optimized Design of Sub-Harmonic Synchronous Machines

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IEEE Transactions on Magnetics 2025年

作者： Sajjad Hossain Rafin, M. Ali, Qasim Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States Sukkur IBA University Department of Electrical Engineering Sukkur Pakistan

This paper presents an optimized design approach for sub-harmonic synchronous machines (SHSMs) to minimize torque ripple and improve performance for high-power electric vehicle applications. The proposed design incorporates a novel rotor harmonic winding configuration and utilizes advanced skewing techniques to achieve optimal spatial distribution of magnetic forces. Using Ansys Maxwell software, finite element analysis (FEA) was conducted on 8-pole, 48-slot SHSMs with a rated output of 1.3 kW and 900 rpm. Compared to earlier designs, the optimized 2-layer and 3-layer SHSMs demonstrated significant reductions in torque ripple—83.94% and 86.84%, respectively. This improvement is attributed to the uniform distribution of harmonic windings and carefully calibrated rotor skewing, both of which enhance spatial efficiency and magnetic field uniformity across the rotor. These configurations achieve lower torque ripple and maintain high efficiency and competitive average torque output, making them suitable alternatives to conventional wound rotor synchronous machines (WRSMs) and permanent magnet synchronous motors (PMSMs). The findings validate the potential of the optimized SHSMs as high-performance, brushless solutions for electric vehicle propulsion, contributing to advancements in synchronous machine design and sustainable transportation technologies. © 1965-2012 IEEE.

关键词： Permanent magnets

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Artificial Intelligence Techniques With Digital Twin for Fault Diagnosis in Interconnected systems: A Review

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IEEE Access 2025年 13卷 91860-91874页

作者： Imen Nakti Majdi Mansouri Rami Al-Hmouz Atef Khedher Laboratory of Advanced Technology and Intelligent Systems ENISO University of Sousse Sousse Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman

As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments.

关键词： Artificial intelligence Real-time systems Sensors Fault detection Fault diagnosis Interconnected systems Intelligent sensors Smart grids Digital twins Adaptation models

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

Understanding the Impact of Unobservable Variables on the Pe...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Juarez Garcia, Ezequiel Stephens, Chad L. Napoli, Nicholas J. Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Crew Systems & Aviation Operations Branch and System-Wide Safety Project NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separ ability of class distributions. Such challenges result in unrepresentative training data, complex skewedor warped decision boundaries, and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions with in the feature space through partitioning. This enables the optimization of local models with inthe regions to overcome the impact of un observ ability and complex decision boundaries indifferent feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of un observability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobserv ability in predictive mo

关键词： Information fusion

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Data-Driven Adaptive Control for Frequency Perturbations in Power systems with PV-SmartParks

Data-Driven Adaptive Control for Frequency Perturbations in ...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Ali Arzani Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Seamless operation of PV plants during both transient and dynamic conditions is imperative for all stakeholders including energy control centers. Fast and dampened transient response, low steady-state error, and stability are sought for power system operation with PV plants. For PV power perturbation at point of interconnection (POI), this can be further supported by deploying auxiliary fast-acting non-synchronous generation such as SmartParks (SPs). SmartParks are parking lots with a large number of electric vehicles performing bidirectional power transfers, namely, vehicle-to-grid and grid-to-vehicle. In this study, a data-driven adaptive frequency controller (DD-AFC) was developed for SPs to compensate for frequency perturbations at PV-POI. The DD-AFC was incorporated into the controller of SPs to provide a fast response for frequency perturbations caused by PV-site power perturbations. The adaptiveness of the frequency droop comes from data-driven empirical characterization of the PV plant operation at the PV-site location using time-series data analytics. The DD-AFC supports fast-frequency control response by SPs for mitigating fast power perturbation at PV-POIs. The effectiveness of the DD-AFC was illustrated via real-time simulations of a bulk power system with a 210MW p PV plant and 100MW p of SmartParks in RTDS.

关键词： Automatic frequency control Time-frequency analysis Vehicle-to-grid Transient response Data analysis Perturbation methods Power system stability Adaptive control Transient analysis Vehicle dynamics

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