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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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Reusable free-standing hydrogel electronic tattoo sensors with superior performance

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npj Flexible Electronics 2024年第1期8卷 397-407页

作者： Shuyun Zhuo Alexandre Tessier Mina Arefi Anan Zhang Chris Williams Shideh Kabiri Ameri Department of Electrical and Computer Engineering Queen’s UniversityKingstonONCanada Centre for Neuroscience Studies Queen’s UniversityKingstonONCanada

Recently electronic tattoo sensors have attractedimmense interest for healthmonitoring mainly due to their higher sensing performance than conventional dry sensors,owing to the ultra-low thickness which results in their conformability to the ***,their performance is worse than wet ***,these electronic tattoo sensors are not durable and reusable when free-standing because of their low thickness and being too ***,we report a remarkably high-performance freestanding,reusable,ultrathin and ultra-soft electronic tattoo sensor made of parylene-hydrogel double layer system with high water retention over extended periods that can be used for the extended period of 6 *** hydrogel electronic tattoo(HET)sensors consist of electrically conductive self-adhesive hydrogel with a thickness of 20μm and Young’s modulus of only 31 kPa at 37℃,allowing for ultra-conformal contact to the skin microscopic *** HET sensors are fabricated using a scalable cost-effective method on ordinary tattoo papers and are laminated on the skin like temporary tattoos and were used for electrophysiological signals recording such as electrocardiography(ECG),electromyography(EMG),and skin hydration,temperature *** HET sensors,for the first time,show 234%lower sensor-skin interface impedance(SSII)and significantly lower susceptibility to motion than gold standard medical grade silver/silver chloride wet gel electrodes which are known to have the lowest SSII and susceptibility to ***,the low HET-skin interface impedance leads to a considerably larger signal amplitude and signal-to-noise ratio(SNR)of the electrophysiological signals recorded using HET sensors in comparison with those obtained using gold standard medical grade silver/silver chloridewet gel *** SNR of some types of electrophysiological signals such as EMG recorded using HET is up to 19 dB higher than gold standardmedical grade electrodes due to higher signal amplitude,s

关键词： silver electronic performance

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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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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(Science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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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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Flexible fibres take fabrics into the information age

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NATURE 2024年第7997期626卷 38-39页

作者： Jia, Xiaoting Parrott, Alex Xiaoting Jia is in the Bradley Department of Electrical and Computer Engineering the Department of Materials Science and Engineering and the School of Neuroscience Virginia Tech Blacksburg Virginia 24061 USA. Alex Parrott is in the Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg Virginia 24061 USA.

A technique for embedding fibres with semiconductor devices produces defect-free strands that are hundreds of metres long. Garments woven with these threads offer a tantalizing glimpse of the wearable electronics of t... 详细信息

关键词： engineering Materials science Technology

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Boost DC-DC converter with regenerative snubber

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Energy Reports 2024年 12卷 798-819页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typically include a front-end boost converter as the first stage and a second-stage isolated converter. In this paper, a new topology for a regenerative snubber in boost converters is proposed. Compared to traditional active or passive snubber topologies, the proposed snubber offers the advantages of having fewer components and occupying less space. Moreover, it does not require additional active devices and can be driven by widely-used commercial PWM control integrated circuits, achieving a higher level of cost-effectiveness and offering increased industrial applications. The final prototype of a power supply for a networking switch hub (as used in 5 G base stations) is fabricated to demonstrate the feasibility of the proposed method. © 2024 The Authors

关键词： Topology

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Recent advances in tip-enhanced Raman spectroscopy probe designs

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Nano Research 2023年第4期16卷 5555-5571页

作者： Da Xu Boqun Liang Yaodong Xu Ming Liu Department of Electrical and Computer Engineering University of California-RiversideRiversideCalifornia 92521USA Materials Science and Engineering program University of California-RiversideRiversideCalifornia 92521USA

Tip-enhanced Raman spectroscopy(TERS)imaging is a super-resolution imaging technique that features the merits of both surface-enhanced Raman spectroscopy(SERS)and scanning probe microscopy(SPM),such as the high chemical sensitivity from the former and the nanoscale spatial resolution from the *** advantages make TERS an essential nanospectroscopic characterization technique for chemical analysis,materials science,bio-sensing,*** probes,the most critical factor determining the TERS imaging quality,are expected to provide a highly confined electromagnetic hotspot with a minimized scattering background for the generation of Raman signals with high spatial *** two decades of development,numerous probe design concepts have been proposed and *** review provides a comprehensive overview of the state-of-the-art TERS probe designs,from the working mechanism to the practical *** start with reviewing the recent development of TERS configurations and the corresponding working mechanisms,including the SPM platforms,optical excitation/collection techniques,and probe preparation *** then review the emerging novel TERS probe designs,including the remote-excitation probes,the waveguide-based nanofocusing probes,the metal-coated nanofocusing probes,the nanowire-assisted selective-coupling probes,and the tapered metal-insulator-metal *** discussion focuses on a few critical aspects,including the surface-plasmon-polariton(SPP)hotspot excitation technique,conversion efficiency,working frequency,and *** the end,we review the latest TERS applications and give a perspective on the future of TERS.

关键词： tip-enhanced Raman spectroscopy plasmonics scanning probe microscopy nanoscale imaging nanophotonics nanofocusing

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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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