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Guest Editorial Special Issue on Neuromorphic Devices and Circuits for Next-Generation Flexible Electronics in IEEE Journal on Flexible Electronics

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第1期3卷 2-3页

作者： Liu, Fengyuan Goswami, Sreetosh Pan, Lijia Wang, Yanzhi Fondazione Bruno Kessler Microsystems Technology Unit Center for Sensors and Devices Trento38123 Italy Northeastern University Bendable Electronics and Sustainable Technologies Group Electrical and Computer Engineering Department BostonMA02120 United States Indian Institute of Science Centre for Nano Science and Engineering Bengaluru560012 India Nanjing University School of Electronic Engineering Nanjing210023 China Northeastern University Electrical and Computer Engineering Department BostonMA02120 United States

It is fascinating how our body receives an immense amount of sensory information through numerous receptors distributed throughout the body and efficiently integrates it to make decisions for daily activities, while maintaining extremely low energy consumption and cognitive load. This bioinspired sensory information processing paradigm offers unparalleled advantages over traditional von Neumann architectures due to its exceptional energy efficiency, fault tolerance, and adaptability. While previous efforts in this area have mainly focused on the development of biomimetic sensors, it is equally important to create a computing architecture that can process sensory data locally before transmitting it to a higher level. The strategy of offloading computation at the edge can significantly reduce data latency, saving transmission bandwidth and relieving the burden of computation at a higher level, just like the way our peripheral nervous system complements the central nervous system.

关键词： Special issues and sections Neuromorphic engineering Receptor (biochemistry) Flexible electronics

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Identification Over Compound Multiple-Input Multiple-Output Broadcast Channels

Identification Over Compound Multiple-Input Multiple-Output ...

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作者： Rosenberger, Johannes Pereg, Uzi Deppe, Christian Technical University of Munich Chair of Communications Engineering Department of Computer Engineering School of Computation Information and Technology Munich80333 Germany Institute for Communications Engineering Technical University of Munich Munich80333 Germany Munich80799 Germany Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering The Technion Hellen Diller Quantum Center Haifa3200003 Israel

The identification capacity region of the compound broadcast channel is determined under an average error criterion, where the sender has no channel state information. We give single-letter identification capacity formulas for discrete channels and multiple-input multiple-output Gaussian channels under an average input constraint. The capacity theorems apply to general discrete memoryless broadcast channels. This is in contrast to the transmission setting, where the capacity is only known for special cases, notably the degraded broadcast channel and the multipleinput multiple-output broadcast channel with private messages. Furthermore, the identification capacity region of the compound multiple-input multiple-output broadcast channel can be larger than the transmission capacity region. This is a departure from the single-user behavior of identification, since the identification capacity of a single-user channel equals the transmission capacity. © 1963-2012 IEEE.

关键词： Codes (symbols)

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Optimal Parameter Estimation of CAPN Model for Li-ion Battery

Optimal Parameter Estimation of CAPN Model for Li-ion Batter...

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2023 International Conference on computer, Electronics and electrical engineering and their Applications, IC2E3 2023

作者： Bharti, Soumya Saini, Vikash Kumar Kumar Yadav, Anshul Kumar, Rajesh Al-Sumaiti, Ameena S. Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Malaviya National Institute of Technology Center for Energy and Environment Jaipur India Swami Keshvanand Institute of Technology Department of Electrical Engineering Jaipur India Khalifa University Advanced Power and Energy Center Electrical Engineering and Computer Science United Arab Emirates

ISBN: (纸本)9798350338003

Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model. © 2023 IEEE.

关键词： Lithium-ion batteries

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Adaptive Active Fault-Tolerant Attitude Control for Spacecraft with Adaptive Prescribed Performance 6

Adaptive Active Fault-Tolerant Attitude Control for Spacecra...

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Yang, Ze Yang, Baoqing Ji, Ruihang Ma, Jie Harbin Institute of Technology Control and Simulation Center Harbin China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350316155

For spacecraft attitude control affected by environmental disturbance, parameter uncertainty and actuator fault, a novel composite active fault-tolerant scheme, combining a strong tracking Cubature Kalman filter (STCKF) with adaptive prescribed performance control (APPC), is investigated in this paper. The proposed STCKF is capable of estimating lumped fault rapidly but accurately, and it is robust to model uncertainty. An adaptive finite-time prescribed performance function (APPF) whose boundaries can be flexibly adjusted in the case of actuator faults is proposed. Then an active fault tolerant controller is designed using nonsingular terminal sliding mode control (NTSMC) in conjunction with APPF. Simulation experiments and comparisons show that the proposed strategy exhibits better fault tolerance, lower conservatism and better steady-state performance. © 2023 IEEE.

关键词： Attitude control

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Microgrid Energy Management with Energy Storage Systems:A Review

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 483-504页

作者： Xiong Liu Tianyang Zhao Hui Deng Peng Wang Jizhen Liu Frede Blaabjerg Energy Electricity Research Center Jinan UniversityZhuhai 519070China School of Electrical and Electronic Engineering Nanyang Technological University639798 Singapore School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology at Aalborg University Denmark

Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy ***,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased *** paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution ***,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy ***,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy *** programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation ***,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.

关键词： Architecture energy management energy storage systems microgrids optimization uncertainty models

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An Attention Mechanism Approach for Natural Color Retrieval for Plant Monitoring 20

An Attention Mechanism Approach for Natural Color Retrieval ...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chadavadh, Yanatorn Kasetkasem, Teerasit Patrapornnant, Teera Parittotakapron, Sirichai Isshiki, Tsuyoshi Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350310467

Even though agriculture practices have been continuously developed with the support of modern technologies, many more improvements can be made to enhance agricultural technologies and businesses. One such technology is the use of specific light color combinations to optimize the growth rate of plants. One obvious drawback is that plants' color will change according to the light color combinations. The light color can fool human eyes and may cause errors when monitoring for plant anomalies. Color correction methods should be applied to help restore the natural plant color with the white light source from the unnaturally colored plant images. Our color correction method uses an application of self-dot-product attention, multi-head attention, and channel attention combined with a U-Net-based model. This proposed method performs the color correction with the input image in the RGB color space in two steps. First, a global transformation network provides the global function that maps the input RGB color vectors from every pixel and produces the corrected RGB color vectors. The global mapping function is the same for all pixels in the image. Next, a local transformation network attempts to correct the local color distortions such as light the flickering of LED light due to the AC power supplier. © 2023 IEEE.

关键词： Deep learning

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 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： Time domain analysis

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Optimal Planning of Renewable Energy Resources Under Islanding Condition

Optimal Planning of Renewable Energy Resources Under Islandi...

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2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Sohel, Fardin Nushin, Shahriar Tarvir Uddin Ananda, Md. Monir Abid, Md. Shadman Ahshan, Razzaqul Daffodil International University Department of Electrical and Electronic Engineering Dhaka Bangladesh Islamic University of Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Nanotechnology Research Center Sultan Qaboos University Al Khoud123 Oman Sultan Qaboos University Department of Electrical and Computer Engineering Al Khoud123 Oman

ISBN: (纸本)9798350366051

The integration of renewable-based distributed generation (RDG) units instead of conventional fossil fuels is beneficial as it eliminates the negative impacts on the environment while ensuring an efficient operation of the power system. However, because of the irregular characteristics of renewable energy sources (RES), it is essential to ensure the optimum sizing and placement of the RDG units. Several challenges emerge when distributed generation (DG) units are disconnected from the grid, and an islanded system is formed. Ensuring system stability becomes a crucial task in such islanded systems. This paper presents a combined approach to determine the optimal load shedding amount along with the sizing and placement of RDG units by implementing Flood Optimizer. To address this multi-objective optimization (MOO) problem, an objective function is formed that incorporates the voltage stability index (VSI), the total load to be shed, and the overall system losses. The performance of the suggested method was evaluated in an islanding scenario involving two DG units within the IEEE 33 bus distribution system, utilizing MATLAB software. The results were analyzed through simulation, compiling values such as the remaining load on each bus, the size and position of RDG units, VSI, and total system loss. © 2024 IEEE.

关键词： Distributed energy

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