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Flexible,high-density,laminated ECoG electrode array for high spatiotemporal resolution foci diagnostic localization of refractory epilepsy

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Bio-Design and Manufacturing 2024年第4期7卷 388-398页

作者： Yafeng Liu Zhouheng Wang Yang Jiao Ying Chen Guangyuan Xu Yinji Ma Xue Feng Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Laboratory of Flexible electronics Technology Tsinghua UniversityBeijing 100084China College of Artificial Intelligence Southwest UniversityChongqing 400715China Institute of Flexible electronics Technology of THU Jiaxing 314000China Qiantang Science and Technology Innovation Center Hangzhou 310016China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

High spatiotemporal resolution brain electrical signals are critical for basic neuroscience research and high-precision focus diagnostic localization,as the spatial scale of some pathologic signals is at the submillimeter or micrometer *** entails connecting hundreds or thousands of electrode wires on a limited *** study reported a class of flexible,ultrathin,highdensity electrocorticogram(ECoG)electrode *** challenge of a large number of wiring arrangements was overcome by a laminated structure design and processing technology *** flexible,ultrathin,high-density ECoG electrode array was conformably attached to the cortex for reliable,high spatial resolution electrophysiologic *** minimum spacing between electrodes was 15μm,comparable to the diameter of a single *** hundred electrodes were prepared with an electrode density of 4444 mm^(-2).In focal epilepsy surgery,the flexible,high-density,laminated ECoG electrode array with 36 electrodes was applied to collect epileptic spike waves inrabbits,improving the positioning accuracy of epilepsy lesions from the centimeter to the submillimeter *** flexible,high-density,laminated ECoG electrode array has potential clinical applications in intractable epilepsy and other neurologic diseases requiring high-precision electroencephalogram acquisition.

关键词： Electrocorticogram(ECoG)electrode Epilepsy High density High resolution Laminated structure

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A thermo-responsive rewritable plasmonic bio-memory by regulating single core-satellite gold nanocluster dissociation

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Nano Research 2024年第8期17卷 7275-7282页

作者： Wen Zhang Yi Wang Yamin Wang Xiaomei Lu Weibing Wu Quli Fan Lei Zhang State Key Laboratory of Organic electronics and Information Displays&Institute of Advanced Materials(IAM) Nanjing University of Posts&TelecommunicationsNanjing 210023China Jiangsu Co-Innovation Center for Efficient Processing and Utilization of Forest Resources College of Light Industry and Food EngineeringNanjing Forestry UniversityNanjing 210037China Zhengzhou Institute of Biomedical Engineering and Technology Zhengzhou 450001China Key Laboratory of Flexible electronics(KLOFE) Institute of Advanced Materials(IAM)&School of Flexible Electronics(Future Technologies)Nanjing Tech University(Nanjing Tech)Nanjing 211816China

A thermo-responsive rewritable plasmonic bio-memory chip has been successfully fabricated on an indium tin oxide(ITO)glass slide by assembling core-satellite gold nanoclusters with different size of gold nanoparticles(AuNPs)using double-strand DNA(dsDNA)*** the prepared 70@DNA20@13 gold nanoclusters(AuNCs)exhibited more stable and greater photothermal conversion *** short time irradiation by 633 nm microbeam laser,every individual AuNCs could be excited and remove the satellite AuNPs on its ***,in the dissociation process of AuNCs with 3−5 satellite,its color would change from yellow to green,which showed more significant reduction in the red channel of the dark-field microscopy(DFM)images and could be defined to state“0”and“1”***,this plasmonic nano bio-memory could transform cyclically its state between 0 and 1 which exhibited excellent rewritable ability.

关键词： plasmonic assembly thermal-responsive single nanoparticle rewritable bio-memory

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Echo State Network Based on Improved Knowledge Distillation for Edge Intelligence

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Chinese Journal of electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu ZHAO Fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

Echo state network(ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： Knowledge engineering Training Performance evaluation Computational modeling Time series analysis Artificial neural networks Reservoirs

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

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Energy and AI 2024年第2期16卷 346-362页

作者： Mohd Herwan Sulaiman Zuriani Mustaffa faculty of Electrical&electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

关键词： Deep learning neural networks Evolutionary mating algorithm Feed forward neural networks Metaheuristic Optimizers Solar PV

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State of charge estimation for electric vehicles using random forest

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Green Energy and Intelligent Transportation 2024年第5期3卷 42-51页

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating *** electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery *** study applies a Random Forest(RF)machine learning algorithm to improve SOC ***,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving *** methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV *** contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world *** leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC *** unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving *** comparative analyses showcase the superiority of the RF over *** RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV *** results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW *** integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV ***,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a

关键词： Electric vehicles Extreme learning machine Machine learning Random Forest State of charge of battery

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长程能量传递提升上转换纳米颗粒的量子效率

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Science Bulletin 2024年第12期69卷 1809-1812页

作者：王文龙陈冰 College of Electronic and Optical Engineering and College of Flexible electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China

The integration of lanthanide-doped upconversion nanoparticles(UCNPs)into modern technology has been transformative,finding utility in a broad spectrum of applications ranging from super-resolution imaging and optogenetics to solar energy conversion and lasing[1–4].The core principle underlying these UCNPs involves the process of photon upconversion,which refers to the conversion of low-energy photons into high-energy photons,known as the anti-Stokes shift.

关键词：能量传递上转换纳米颗粒量子效率

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Advances of Electrochemical and Electrochemiluminescent Sensors Based on Covalent Organic Frameworks

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Nano-Micro Letters 2024年第2期16卷 395-422页

作者： Yue Cao Ru Wu Yan‑Yan Gao Yang Zhou Jun‑Jie Zhu Key Laboratory for Organic electronics and Information Displays(KLOEID)and Institute of Advanced Materials(IAM) Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210023People’s Republic of China State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210023People’s Republic of China

Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore *** the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy *** them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive *** this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and ***,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary ***,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.

关键词： Covalent organic frameworks Electrochemistry Electrochemiluminescence Sensors

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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Computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

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Computers, Materials & Continua 2024年第6期79卷 4825-4844页

作者： Ahmed H.Alhadethi Ikram Smaoui Ahmed Fakhfakh Saad M.Darwish Computer Science and School of electronics and telecommunications University of SfaxSfax3029Tunisia LETI Laboratory University of SfaxSfax3029Tunisia Digital Research Center of Sfax Technopark of SfaxSfax3029Tunisia Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt

The act of transmitting photos via the Internet has become a routine and significant *** the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further *** study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous *** main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image *** paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during *** employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious ***,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been *** applications involving the transmission and sharing of images across a *** suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture *** effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of *** scheme ensures a large reduction in computational and energy requirements while minimizing image quality *** approach employs multi-scale characteristics to improve the resistance of signatures against image *** proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

关键词： Image transmission image compression text hiding Bezier curve Histogram of Oriented Gradients(HOG) LSTM image enhancement Gaussian noise rotation

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Neural dynamics for improving optimiser in deep learning with noise considered

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 722-737页

作者： Dan Su Predrag S.Stanimirović Ling Bo Han Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhouChina faculty of Sciences and Mathematics University of NišNišSerbia School of electronics and Information Engineering Guangdong Ocean UniversityZhanjiangChina

As deep learning evolves,neural network structures become increasingly sophisticated,bringing a series of new optimisation *** example,deep neural networks(DNNs)are vulnerable to a variety of *** neural networks under privacy constraints is a method to alleviate privacy leakage,and one way to do this is to add noise to the ***,the existing optimisers suffer from weak convergence in the presence of increased noise during training,which leads to a low robustness of the *** stabilise and improve the convergence of DNNs,the authors propose a neural dynamics(ND)optimiser,which is inspired by the zeroing neural dynamics originated from zeroing neural *** authors first analyse the relationship be-tween DNNs and control ***,the authors construct the ND optimiser to update network ***,the proposed ND optimiser alleviates the non-convergence problem that may be suffered by adding noise to the gradient from different ***,experiments are conducted on different neural network structures,including ResNet18,ResNet34,Inception-v3,MobileNet,and long and short-term memory *** results using CIFAR,YouTube Faces,and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise-free and noise-polluted *** source code is publicly available at https://***/LongJin-lab/ND.

关键词： deep learning deep neural networks

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