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Investigation of the long-term adhesion and barrier properties of a PDMS-Parylene stack with PECVD ceramic interlayers for the conformal encapsulation of neural implants 23

Investigation of the long-term adhesion and barrier properti...

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23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021

作者： Babaroud, Nasim Bakhshaee Dekker, Ronald Holk, Ole Tiringer, Ursa Taheri, Peyman Horvath, Domonkos Nanasi, Tibor Ulbert, Istvan Serdijn, Wouter Giagka, Vasiliki Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Micro-Integration IZM Berlin Germany Delft University of Technology Delft Netherlands Institute of Cognitive Neuroscience and Psychology Research Center for Natural Science Budapest Hungary Philips Research Eindhoven Netherlands Rotterdam Netherlands

ISBN: (纸本)9780956808677

In this paper, we investigate the long-term adhesion strength and barrier property of our recently proposed encapsulation stack that includes PDMS-Parylene C and PECVD interlayers (SiO2 and SiC) for adhesion improvement. To evaluate the adhesion strength of our proposed stack, the sample preparation consisted in depositing approximately 25 nm of SiC and 25 nm of SiO2 on half wafers, previously coated with Parylene C. Next, 50 µm PDMS was spin-coated on top. Finally, the samples were detached from the Si wafer and soaked in a PBS solution at 67 °C to accelerate the aging process. Two samples were also implanted, subcutaneously, on the left and right subscapular regions of a rat. The optical inspection and peel tests performed after two months confirmed our preliminary findings and showed a significant improvement of the adhesion in our proposed encapsulation stack compared to the case of PDMS on Parylene C alone. In addition, the X-ray photoelectron spectroscopy (XPS) analysis at the interface between SiC and Parylene C showed different peaks for the interface compared to the reference spectra, which could be an indication of a chemical bond. Finally, water vapor transmission rate (WVTR) tests were performed to investigate the barrier property of our proposed encapsulation stack against water vapor transmission. The results demonstrated that the proposed stack acts as a significantly (two orders of magnitude) higher barrier against moisture compared to only Parylene C and PDMS encapsulation layers. The proposed method yields a fully transparent encapsulation stack over a broad wavelength spectrum that can be used for the conformal encapsulation of flexible devices and thus, making them compatible with techniques such as optical imaging and optogenetics. © 2021 2021 23rd European Microelectronics and Packaging Conference and Exhibition, EMPC 2021. All rights reserved.

关键词： Adhesion

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RASSS: A hijack-resistant confidential information management scheme for distributed systems

RASSS: A hijack-resistant confidential information managemen...

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作者： Bu, Lake Isakov, Mihailo Kinsy, Michel A. Adaptive and Secure Computing System Laboratory Department of Electrical and Computer Engineering Boston University Boston United States

In distributed systems there is often a need to store and share sensitive information (e.g., encryption keys, digital signatures, login credentials etc.) among the devices. It is also generally the case that this piece of information cannot be entrusted to any individual device since the malfunction or compromising of one node could jeopardize the security of the entire system. Even if the information is split among the devices, there is still a risk when an attacker can compromise a group of them. Therefore we have designed and implemented a secure and robust secret sharing scheme to enable a more resilient sharing of confidential information. This solution provides three important features: (i) it uses threshold secret sharing to split the information into shares to be kept by all devices in the system;so the information can only be retrieved collaboratively by groups of devices;(ii) it guarantees the privacy of the confidential information under a certain number of passive hijacking attacks;and (iii) it ensures the integrity of the confidential information against any number of hijackers who actively and collusively attack the devices. It is able to identify all the compromised devices, while still keeping the secret unforgeable to attackers. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Information management

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Multi-granularity ensemble sample selection and label correction for classification with noisy labels

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Applied Soft Computing 2025年 180卷

作者： Kecan Cai Hongyun Zhang Witold Pedrycz Duoqian Miao Chaofan Chen Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkiye

Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.

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The Phenology and Water Level Time-Series Mangrove Index for Improved Mangrove Monitoring

SSRN

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SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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Modelling and Simulation of Induction Machine Under DC Current Injection Braking

Modelling and Simulation of Induction Machine Under DC Curre...

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IEEE International Conference on Control and Robotics engineering (ICCRE)

作者： Pichai Aree Department of Electrical and Computer Engineering Power System and Electrical Machine Research Group Klong Luang Pathumthani Thailand

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

Three-phase Induction machines (IM) are widely used as industrial drive applications. Braking them through DC current injection is one of the interesting applications. In this paper, their mathematical models under DC injection are presented for simulating their braking performances under various connecting types of stator windings. The potential use of IM as the external brake unit to control the speed of another running motor for torque-speed curve testing is demonstrated using the computer simulation. The factors influencing the braking torque of IM are discussed.

关键词： Stator windings Torque Windings Rotors Mathematical model Induction motors

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Application of RC5 for IoT devices in smart transportation system 8

Application of RC5 for IoT devices in smart transportation s...

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8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019

作者： Alsaffar, Nawal Elmedany, Wael Ali, Hayat Department of Information System University of Bahrain Sukhir Bahrain Department of Computer Engineering University of Bahrain Sukhir Bahrain

ISBN: (纸本)9781538676844

Nowadays Internet of Thing (IoT) technology has accessed all the aspects of our life. One of the IoT applications is the smart transportation. Smart transportation gains a lot of attention, as it is part of the smart cities. The data transmission in smart transportation is done through the wireless communication and especially through the Vehicular ad hoc networks (VANETs) which made the data vulnerable to many issues concerning security. Therefore, security techniques should be applied to secure vehicle's user data. In this paper, the RC5 encryption algorithm will be used as a security technique to protect users' privacy in smart transportation. The simulation of RC5 will be implemented with FPGA using Quartus Prime Lite Edition version 18. The purpose is to check hardware resources of RC5 and make sure it is suitable for smart transportation. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Shadow detection of moving objects in traffic monitoring video 9

Shadow detection of moving objects in traffic monitoring vid...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Mingrui Zhao, Wenbing Li, Xiying Wang, Dan Beijing University of Technology College of Computer Science and Technology Beijing China Sun Yat-sen University Intelligent Transportation Research Center School of Intelligent System Engineering Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe. © 2020 IEEE.

关键词： Security systems

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 915卷 v-vii页

作者： Marriwala, Nikhil Kumar Dhingra, Sunil Jain, Shruti Kumar, Dinesh Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical and Computer System Engineering RMIT University MelbourneVIC Australia

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Instance-based Deep Transfer Learning on Cross-domain Image Captioning 21

Instance-based Deep Transfer Learning on Cross-domain Image ...

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21st International Electronics Symposium, IES 2019

作者： Perdana, Rizal Setya Ishida, Yoshiteru Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan Department of Information System Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781728144498

Image captioning task achieves imposing result in generating text description of image by training on the large image and sentence pairs dataset (e.g., MSCOCO). Applied the large publicly available datasets to the specific new task will lead to the problem known as domain shifting due to the different probability distribution. In this research, we propose a Multimodal Instance-based Deep Transfer Learning (MIBTL) for cross-domain image captioning. Generally, transferring knowledge from the source domain into the target domain is pertinent due to the limited number of the dataset from the target domain. The limited number of the dataset also lead to a problem called overfitting. The instance-based strategy intuitively takes into account the influence of data by selecting the most representative data from source domain as the supplement for training on the target domain. We employ deep hash binary code representation of image and text pair to determine the distance between two data points. The experiment conducted by two types of transfer condition includes a slight shift (from MSCOCO to Flickr30k) and significant shift (from MSCOCO to CUB-200 and Oxford-102). The result shows that MIBTL can outperform other baselines to achieve state-of-the-art performance in cross-domain image captioning transfer learning. © 2019 IEEE.

关键词： Probability distributions

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Modeling heterogeneous edges to represent networks with graph auto-encoder

arXiv

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arXiv 2021年

作者： Wang, Lu Song, Yu Huang, Hong Ye, Fanghua Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Wuhan China Service Computing Technology and Systems Laboratory Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science University College London London United Kingdom

In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks into multiple views by using each view to describe a specific type of relationship between nodes, so that we can leverage the collaboration of multiple views to learn the representation of networks with heterogeneous edges. Given this, we propose a regularized graph auto-encoders (RGAE) model, committed to utilizing abundant information in multiple views to learn robust network representations. More specifically, RGAE designs shared and private graph auto-encoders as main components to capture high-order nonlinear structure information of the networks. Besides, two loss functions serve as regularization to extract consistent and unique information, respectively. Concrete experimental results on realistic datasets indicate that our model outperforms state-of-the-art baselines in practical applications. © 2021, CC BY.

关键词： Deep learning

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