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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(Science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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A Single Phase Five Level Switched Capacitor Inverter With Common Ground Configuration

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IEEE Access 2024年 12卷 101814-101828页

作者： Al Shaheer, Abdullah Mohammad Hammad, Syed Sarwar, Adil Zaid, Mohammad Ahmad, Shafiq Liu, Hwa-Dong ZHCET Aligarh Muslim University Department of Electrical Engineering King Saud University College of Engineering Industrial Engineering Department National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering

In the energy sector, the application of renewable energy sources especially solar photovoltaics (PV), is expanding exponentially. Inverters find application in converting DC power from solar PV generating arrays to AC power and feeding it to grid. This study proposes a 5-level switched-capacitor multilevel inverter (SCMLI) that can be used for solar PV applications. The problem of leakage current was mitigated with the use of common ground configuration. Based on various performance parameters a comparison with recently proposed SCMLI's has been carried out. The proposed SCMLI achieves an efficiency of 95.03% under a 2-kW load. It utilizes one input DC power source, 7 switches, and 2 capacitors only. The experimental results of the proposed inverter show better harmonic profile with reduced Total Harmonic Distortion (THD). The performance of the proposed MLI topology is validated through MATLAB/SIMULINK. Moreover, loss analysis has been carried out on PLECS platform. The hardware implementation is done on a power converter test bed. © 2013 IEEE.

关键词： common ground multilevel inverter Single phase switched capacitor

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications

Modified Isolated SEPIC Converter for EV Charging and High V...

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2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Ahmad, Javed Lin, Chang-Hua Khan, Mohammad Suhail Liu, Hwa-Dong Fahad, Mohammad National Taiwan University of Science and Technology College of Electrical Engineering and Computer Science Department of Electrical Engineering Taipei Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei Taiwan

ISBN: (纸本)9798350399769

This research article presents a modified isolated SEPIC converter suitable for high-power applications. It uses a transformer instead of coupled inductor for isolation between the input power source and the output load. The steady-state analysis is explained in detail. Thermal loss analysis is performed by using PLECS software. Further, a hardware prototype is developed to verify the performance aspects of the proposed converter. © 2023 IEEE.

关键词： High frequency transformers

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes 59

Using Random Forest and Linear SVM for Dyslexia Classificati...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Kaushik, Padmanabh Yu, Yih-Choung Gabel, Lisa Lafayette College Department of Electrical and Computer Engineering EastonPA18042 United States Lafayette College Department of Psychology and Neuroscience Program EastonPA18042 United States

ISBN: (纸本)9798331513269

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia;however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments. © 2025 IEEE.

关键词： Adversarial machine learning

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Wigner non-negative states that verify the Wigner entropy conjecture

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Physical Review A 2024年第1期110卷 012228-012228页

作者： Qipeng Qian Christos N. Gagatsos Program in Applied Mathematics The Department of Electrical and Computer Engineering The Wyant College of Optical Sciences The

We present further progress, in the form of analytical results, on the Wigner entropy conjecture set forth by Van Herstraeten and Cerf [Phys. Rev. A 104, 042211 (2021)] and Hertz et al. [J. Phys. A: Math. Theor. 50, 385301 (2017)]. Said conjecture asserts that the differential entropy defined for non-negative, yet physical, Wigner functions is minimized by pure Gaussian states while the minimum entropy is equal to 1+lnπ. We prove this conjecture for the qubits formed by Fock states |0〉 and |1〉 that correspond to non-negative Wigner functions. In particular, we derive an explicit form of the Wigner entropy for those states lying on the boundary of the set of Wigner non-negative qubits. We then consider general mixed states and derive a sufficient condition for the conjecture's validity. Lastly, we elaborate on the states which are in accordance with our condition.

关键词： Quantum foundations Quantum information theory

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Optimum classical beam-position sensing

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Physical Review Applied 2024年第4期22卷 L041004页

作者： Wenhua He Christos N. Gagatsos Dalziel J. Wilson Saikat Guha Wyant College of Optical Sciences Department of Electrical and Computer Engineering Program in Applied Mathematics

Beam-displacement measurements are widely used in optical sensing and communications; however, their performance is affected by numerous intrinsic and extrinsic factors, including beam profile, propagation loss, and receiver architecture. Here we present a framework for designing a classically optimal beam-displacement transceiver, using quantum estimation theory. We consider the canonical task of estimating the position of a diffraction-limited laser beam after passing through an apertured volume characterized by Fresnel-number product DF. As a rule of thumb, higher-order Gaussian modes provide more information about beam displacement, but are more sensitive to loss. Applying quantum Fisher information, we design mode combinations that optimally leverage this trade-off, and show that a greater than tenfold improvement in precision is possible, relative to the fundamental mode, for a practically relevant DF=100. We also show that this improvement is realizable with a variety of practical receiver architectures. Our findings extend previous works on lossless transceivers, may have immediate impact on applications, such as atomic force microscopy and near-field optical communication, and pave the way towards globally optimal transceivers using nonclassical laser fields.

关键词： Classical optics Metrology Quantum Fisher information Quantum optics Quantum parameter estimation Devices Wireless communication networks Atomic force microscopy

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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Mobility-Aware Federated Learning-Based Proactive UAVs Placement in Emerging Cellular Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1305-1318页

作者： Manzoor, Sanaullah Shakir, Muhammad Zeeshan Hasna, Mazen O. Qaraqe, Khalid A. University of the West of Scotland School of Computing Engineering and Physical Sciences PaisleyG72 0LH United Kingdom Qatar University College of Engineering Doha Qatar Texas A and M University Electrical and Computer Engineering Program Doha Qatar

With the vast proliferation of smart mobile devices, there is an ever-increasing demand for higher data rates and seamless connectivity throughout. Current 5th generation and beyond (B5G) cellular networks struggle to eradicate outage zones and ensure seamless connectivity. One promising solution to this problem is the use of unmanned aerial vehicles (UAVs) to assist the traditional ground network and provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disasters such as flooding. In this paper, we propose a novel users' mobility-aware & users' demand-aware federated learning-based proactive UAV placement (MFPUP) framework to assist the existing ground communication network and minimise overall network outages. Our MFPUP framework utilises the federated learning-based mobility prediction model that recommends the potential outage areas to deploy UAVs using user-UAV association techniques such as the optimum association approach (OAP) and the greedy association approach (GAP). In order to validate the performance of the proposed MFPUP scheme we carried out extensive simulations. The proposed LSTM-based mobility model outperforms the DNN model with 92.88% prediction accuracy. Further, our results show that the proposed MFPUP framework associates the optimal number of users to UAVs while also improving 1.25 times users' downlink rates as compared other UAVs placement schemes. © 2023 CCBY.

关键词： Aircraft accidents

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