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Thin film ferroelectric photonic-electronic memory

Light(Science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Resilient generation planning considering long periods of low-RES output

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Environment Systems and Decisions 2025年第2期45卷 1-13页

作者： Stasinos, Ektor-Ioannis Panteli, Mathaios Hatziargyriou, Nikos School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

The growing concerns over mitigating climate change effects resulted in power system planning and generation expansion strategies that aim in increasing penetration of intermittent renewable energy sources (RES) to fulfill the national energy and climate plans (NECPs) of countries worldwide. However, the variable and intermittent nature of RES output, compounded by uncertainties arising from climatic and weather changes, poses significant challenges. These challenges necessitate careful consideration in generation planning and in determining the operational reserves of RES-rich power systems to ensure resilience. This paper employs a generation capacity operational planning method based on the Screening Curve Method (SCM), assuming various levels of large RES penetration and diverse spinning generation reserve policies, necessary to cope with various levels of RES generation deficits, to guarantee power system resilience. The proposed approach can be used to assess the overall reduction of capacity and decommissioning of thermal units, while ensuring resilient operation against various levels of annual RES generation deficits. Furthermore, an approximation of the total annual system operation cost is also provided, considering the impact of the employed spinning reserve policies. © The Author(s) 2025.

关键词： Decommissioning of thermal units Low-renewable energy sources generation Power system resilient generation planning Screening curve method

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Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches

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IEEE Transactions on Information Theory 2025年第7期71卷 4976-5012页

作者： Kim, Junghoon Kim, Taejoon Das, Anindya Bijoy Hosseinalipour, Seyyedali Love, David J. Brinton, Christopher G. Motorola Mobility ChicagoIL60654 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ25281 United States The University at Akron Department of Electrical and Computer Engineering OH United States University at Buffalo-SUNY Department of Electrical Engineering NY14260 United States Purdue University Department of Electrical and Computer Engineering West LafayetteIN47907 United States

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different twoway coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain. © 1963-2012 IEEE.

关键词： Gaussian distribution

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A Comprehensive Analysis of Fetal Electrocardiogram Signals 5

A Comprehensive Analysis of Fetal Electrocardiogram Signals

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Camacho, Brandon Ullah, Muhammad Sana Florida Polytechnic University Electrical and Computer Engineering Department Lakeland United States

ISBN: (纸本)9798331519957

This paper reviews different feature extraction and analysis methods for FECG signals. The FECG signals are very critical in monitoring the health of the fetuses;hence, most of the time, they are masked by maternal ECG and noise and introducing significant challenges during the analysis of the same signal. In this paper, several feature-extraction techniques are reviewed and compared. Techniques included, but were not limited to, time-domain, frequency-domain, and time-frequency domain methods such as PCA and ICA. We also review some newer approaches for machine learning, including deep learning methods that could be applied further to enhance the performance of signal separation and classification performance. Then, each method is tested on synthetic and realworld datasets, emphasizing computational efficiency and clinical applicability. By elaborating on the strengths and weaknesses of each approach, this paper summarize the results that give insight into optimum strategies to improve FECG signal processing for better diagnosis regarding fetal health. © 2025 IEEE.

关键词： Frequency domain analysis

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Xf-Calc: Towards a Fair Comparison between Approaches for Fuzzy Numbers Arithmetic

Xf-Calc: Towards a Fair Comparison between Approaches for Fu...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pisco-Jordan, Juan Ramos-Pozo, Luis Townsend-Hinostroza, Danae Tapia-Rosero, Ana ESPOL Polytechnic University Department of Electrical and Computer Engineering Guayaquil Ecuador

ISBN: (纸本)9798350374575

Since arithmetic operations that involve uncertain or imprecise linguistic expressions are often mentally solved, multiple approaches have been developed to emulate them with fuzzy numbers. Each approach may provide different results, and some may depend on a specific context. It is common to compare and analyze these approaches to determine which one is the best in different contexts. These comparisons can be laborious and prone to errors. In this paper a context-free calculator for fuzzy arithmetic is proposed. To facilitate the analysis of comparisons, this calculator is capable of solving an arithmetic problem using multiple approaches while providing reports. The first report shows the solution of each approach while the second report shows their respective graphical representation. The calculator has nine different approaches for addition, subtraction and multiplication with the flexibility to incorporate new approaches for further study. © 2024 IEEE.

关键词： Open source software

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Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 9-20页

作者： Yuan, Liangqi Wei, Yuan Li, Jia Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This article introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and 13 dynamic activities inspired by Nintendo Switch Ring Fit Adventure are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies in both applications, demonstrating the high accuracy and generalizability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties. © 2024 The Authors.

关键词： Deep neural networks

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Deep multi-scale convolutional neural networks for automated classification of multi-class leaf diseases in tomatoes

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Neural Computing and Applications 2024年第2期36卷 803-822页

作者： Elfatimi, Elhoucine Eryiğit, Recep Elfatimi, Lahcen Department of Computer Engineering Ankara University Ankara06830 Turkey Computer Engineering Department Polytechnic School of Montreal 2500 MontrealH3T 1J Canada

Deep learning techniques have gained immense popularity recently because of their remarkable capacity to learn complex patterns and features from large datasets. These techniques have revolutionized many fields by achieving advanced performance in various tasks. The availability of large datasets and the advancement of computing resources have enabled deep learning models to perform well in solving challenging problems. As a result, they have become an essential tool in many industries, including agriculture. The application of deep learning in agriculture has great potential for increasing productivity, reducing costs, and improving sustainability by aiding in the early identification and prevention of plant leaf diseases, optimizing crop yields, and facilitating precision agriculture. This paper suggests using a novel approach to automatically classify multi-class leaf diseases in tomatoes using a deep multi-scale convolutional neural network (DMCNN). The proposed DMCNN architecture consists of parallel streams of convolutional neural networks at different scales, which get merged at the end to form a single output. The images of tomato leaves are preprocessed using data augmentation techniques and fed into the DMCNN model to classify disease. The proposed approach is evaluated on a dataset of tomato plant images containing 10 distinct classes of diseases and compared with different existing models. The research results reveal that the suggested DMCNN model performs better than other models in terms of accuracy, precision, recall, and F1 score. Furthermore, the proposed model reported an overall accuracy of 99.1%, which is higher than the accuracy of existing models tested on the same dataset. The study demonstrates the potential of deep learning techniques for automated disease classification in agriculture, which can aid in early disease detection and prevent crop loss. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Spring

关键词： Convolutional neural networks

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Design of a Low-Cost Vector Based Muometric Navigation System

Design of a Low-Cost Vector Based Muometric Navigation Syste...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Lowy, Maxx A. Ullah, Muhammad Sana Florida Polytechnic University Department of Electrical and Computer Engineering Lakeland United States

ISBN: (纸本)9798331504847

This paper presents a low-cost vector based Muometric navigation system for precision location finding applications where GPS does not work. The proposed system obtains the amplitude information from a SiPM photodetector pulse using low-cost techniques. The system then obtains the location of the device using the data from a reference detector. © 2025 IEEE.

关键词： Global positioning system

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Ali Islam, Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSSs) face significant challenges in urban and suburban areas due to nonline-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly nonlinear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PCs). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and suburban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that the proposed PC-DeepNet achieves superior accuracy than existing model- and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2001-2012 IEEE.

关键词： Global positioning system

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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