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Through-polymer,via technology-enabled,flexible,lightweight,and integrated devices for implantable neural probes

Microsystems & Nanoengineering 2024年第2期10卷 279-291页

作者： Cunkai Zhou Ye Tian Gen Li Yifei Ye Lusha Gao Jiazhi Li Ziwei Liu Haoyang Su Yunxiao Lu Meng Li Zhitao Zhou Xiaoling Wei Lunming Qin Tiger H.Tao Liuyang Sun college of Electronics and information Engineering Shanghai University of Electric PowerShanghaiChina 2020 X-Lab Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina School of Graduate Study University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Transducer technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina School of Physical Science and technology ShanghaiTech UniversityShanghaiChina Center for Excellence in Brain Science and Intelligence technology Chinese Academy of SciencesShanghaiChina Neuroxess co. Ltd.(Jiangxi)NanchangJiangxiChina Guangdong Institute of Intelligence Science and technology HengqinZhuhaiGuangdongChina Tianqiao and Chrissy Chen Institute for Translational Research ShanghaiChina

In implantable electrophysiological recording systems,the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing.While advancements in MEMS and CMOS technology have significantly improved these components,their interconnection still relies on conventional printed circuit boards and sophisticated adapters.This conventional approach adds considerable weight and volume to the package,especially for high channel count systems.To address this issue,we developed a through-polymer via(TPV)method inspired by the through-silicon via(TSV)technique in advanced three-dimensional packaging.This innovation enables the vertical integration of flexible probes,amplifier chips,and PCBs,realizing a flexible,lightweight,and integrated device(FLID).The total weight of the FLIDis only 25%that of its conventional counterparts relying on adapters,which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants.Furthermore,by incorporating a platinum-iridium alloy as the top layer material for electrical contact,the FLID realizes exceptional electrical performance,enabling in vivo measurements of both local field potentials and individual neuron action potentials.These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

关键词： alloy neural recording

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Service Function Chain Migration in LEO Satellite Networks

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China communications 2024年第3期21卷 247-259页

作者： Geng Yuhui Wang Niwei Chen Xi Xu Xiaofan Zhou Changsheng Yang Junyi Xiao Zhenyu Cao Xianbin School of Electronic and information Engineering Beihang UniversityBeijing 100191China Key Laboratory of Advanced technology of Near Space information System Ministry of Industry and Information Technology of ChinaBeijing 100191China shanghai Key Laboratory of Satellite Network Shanghai 201204China shanghai Satellite Network Research Institute co. Ltd.Shanghai 201204China

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

关键词： network function virtualization(NFV) resource allocation satellite networks service function chain(SFC) SFC migration SFC placement soft-ware defined network(SDN)

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Research on condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

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computer Modeling in Engineering & Sciences 2024年第4期139卷 679-698页

作者： Hong Qian Haixin Wang Guangji Wang Qingyun Yan college of Automation Engineering Shanghai University of Electric PowerShanghai200090China shanghai Key Laboratory of Power Station Automation technology Shanghai200072China Empower information technology(shanghai)co. Ltd.Shanghai201508China China Nuclear Power Engineering Design co. Ltd.Shenzhen518052China

This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosion,which lead to increased loss of cold resources.The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction.This methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall performance.The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel.The mathematical model is constructed quantitatively using the improved Mahalanobis distance.Furthermore,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction.This model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature characteristics.This model facilitates the objective examination and prediction of the progression of equipment deterioration trends.The present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this research.These models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.

关键词： condenser health degree improved Mahalanobis distance GC-Informer model Markov error correction

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Modified dynamic event-triggered scaled formation control for multi-agent systems via a sparrow search algorithm based co-design algorithm

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Frontiers of information technology & Electronic Engineering 2024年第2期25卷 197-213页

作者： Yanping YANG Siyu MA Dawei LI Jinghui SUO college of information Science and technology Donghua UniversityShanghai201620China Engineering Research Center of Digitized Textile&Apparel technology Donghua UniversityShanghai201620China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Information Science and TechnologyDonghua UniversityShanghai201620China

This paper is concerned with the scaled formation control problem for multi-agent systems(MASs)over fixed and switching topologies.First,a modified resilient dynamic event-triggered(DET)mechanism involving an auxiliary dynamic variable(ADV)based on sampled data is proposed.In the proposed DET mechanism,a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks.Meanwhile,the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error.Second,a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies.The scaled formation error system is modeled as a time-varying delay system.Then,several sufficient stability criteria are derived by constructing appropriate Lyapunov-Krasovskii functionals(LKFs).A co-design algorithm based on the sparrow search algorithm(SSA)is presented to design the control gains and triggering parameters jointly.Finally,numerical simulations of multiple unmanned aerial vehicles(UAVs)are presented to validate the designed control method.

关键词： Scaled consensus Formation control Dynamic event-triggered scheme Switching topology

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Mechanism of action, benefits, and research gap in fermented soybean meal utilization as a high-quality protein source for livestock and poultry

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Animal Nutrition 2024年第1期16卷 130-146页

作者： Modinat T.Lambo Haokai Ma Haosheng Zhang Peng Song Hongxiang Mao Guowen Cui Baisheng Dai Yang Li Yonggen Zhang college of Animal Science and technology Northeast Agricultural University Wilmar (shanghai) Biotechnology Research and Development Center co. Ltd college of Electrical Engineering and information Northeast Agricultural University

Animal nutritionists have incessantly worked towards providing livestock with high-quality plant protein feed resources.Soybean meal (SBM) has been an essential and predominantly adopted vegetable protein source in livestock feeding for a long time;however,several SBM antinutrients could potentially impair the animal's performance and growth,limiting its use.Several processing methods have been employed to remove SBM antinutrients,including fermentation with fungal or bacterial microorganisms.According to the literature,fermentation,a traditional food processing method,could improve SBM's nutritional and functional properties,making it more suitable and beneficial to livestock.The current interest in health-promoting functional feed,which can enhance the growth of animals,improve their immune system,and promote physiological benefits more than conventional feed,coupled with the ban on the use of antimicrobial growth promoters,has caused a renewed interest in the use of fermented SBM (FSBM) in livestock diets.This review details the mechanism of SBM fermentation and its impacts on animal health and discusses the recent trend in the application and emerging advantages to livestock while shedding light on the research gap that needs to be critically addressed in future studies.FSBM appears to be a multifunctional high-quality plant protein source for animals.Besides removing soybean antinutrients,beneficial bioactive peptides and digestive enzymes are produced during fermentation,providing probiotics,antioxidants,and immunomodulatory effects.Critical aspects regarding FSBM feeding to animals remain uncharted,such as the duration of fermentation,the influence of feeding on digestive tissue development,choice of microbial strain,and possible environmental impact.

关键词： Soybean meal Protein feed Fermentation Microorganism Livestock

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of computer Science and technology Harbin University of Science and TechnologyHarbin150080China shanghai Futures information technology co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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End-to-End Paired Ambisonic-Binaural Audio Rendering

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 502-513页

作者： Yin Zhu Qiuqiang Kong Junjie Shi Shilei Liu Xuzhou Ye Ju-Chiang Wang Hongming Shan Junping Zhang shanghai Key Laboratory of Intelligent information Processing School of Computer ScienceFudan UniversityShanghai 200433China Beijing ByteDance technology co.Ltd. Shanghai 201102China. Chinese University of Hong Kong Hong KongChina IEEE Institute of Science and technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 shanghai Center for Brain Science and Brain-Inspired technology Shanghai 200031China

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

关键词： Ambisonic attention binaural rendering neural network

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C-coRE:Clustering by code Representation to Prioritize Test Cases in compiler Testing

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computer Modeling in Engineering & Sciences 2024年第5期139卷 2069-2093页

作者： Wei Zhou Xincong Jiang Chuan Qin School of Electronic information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai200240China Hikvision Research Institute Hangzhou Hikvision Digital Technology Co.Ltd.Hangzhou310051China

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-coRE(short for Clustering by code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-coRE significantly enhances testing efficiency.Across four datasets,C-coR

关键词： compiler testing test case prioritization code representation

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Differential interference contrast phase edging net:an all-optical learning system for edge detection of phase objects

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Chinese Optics Letters 2024年第1期22卷 21-27页

作者：李一鸣李然陈泉栾海涛卢海军杨晖顾敏张启明 Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China School of Optical-Electrical and computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Nokia shanghai Bell co. Ltd.Shanghai 201206China college of Medical Instruments Shanghai University of Medicine and Health SciencesShanghai 201318China

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.

关键词： diffractive neural network edge detection phase objects

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Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

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Chinese Physics B 2024年第3期33卷 677-689页

作者：霍良安武兵杰 Business School University of Shanghai for Science and TechnologyShanghai 200093China School of Intelligent Emergency Management University of Shanghai for Science and TechnologyShanghai 200093China

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by glob

关键词： information diffusion vaccination behavior disease transmission multilayer networks local and global effect

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