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Microscopy-Guided 3-D Reconstruction of Nanodendrites in Biosensors

IEEE Sensors Journal 2023年第9期23卷 9025-9032页

作者： Fan, Sue-Yuan Huang, Yi-Pin Khuntia, Sucharita Chang, Jen-Wen Liou, Ci-Ruei Zhang, Bing Tai, Li-Chia Institute of Electrical and Control Engineering Department of Electrical and Computer Engineering Hsinchu30010 Taiwan Lam Research FremontCA94538 United States

Nanodendritic structures have gained increasing popularity in electrochemical sensors. However, it is still rare to generate a 3-D model in a short period of time to understand the structure-function relationship of the sensors. Here, we report the construction of a 3-D model for nanodendritic, metallic structures frequently grown on top of bioelectronics. This is achieved by merging two sources of 2-D dendritic information, which includes top-view images from scanning electron microscopy (SEM) and side-view visualization from Monte Carlo simulations. The microscopy images provide the boundary conditions to tune the Monte Carlo simulations to construct the 3-D dendritic morphology. We validated the 3-D model by comparing the dendritic area densities predicted via this model with those computed from microscopy images. In addition, tuning the simulation parameters in the 3-D model can be used to find the optimized dendritic density, which is an essential indicator for sensitivity enhancement. The success of this model provides a means to understand the sensitivity limits of bioelectronics through dendritic growth without the need for time-consuming sensor fabrication and testing. Furthermore, our SEM-guided Monte Carlo technique provides a dendritic model with a significant resemblance to experimental images. It possesses the potential for applications in 3-D morphological investigations for future biosensor design. © 2001-2012 IEEE.

关键词： Scanning electron microscopy

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DDoS Attack Detection in Smart Grid Networks Using Visual Features 2

DDoS Attack Detection in Smart Grid Networks Using Visual Fe...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali Naqvi, Sardar Shan Li, Yuancheng Uzair, Muhammad School Of Control And Computer Engineering North China Electric Power University Beijing102206 China Comsats University Islamabad Department Of Computer Engineering Wah cantt Pakistan

ISBN: (纸本)9798350394962

Smart grid networks play a key role in making electric power infrastructure more efficient and ensuring reliable energy distribution. However, the interconnected communication devices in these networks expose the system to several network threats, such as the distributed denial of service (DDoS) attacks. Timely DDoS attack detection and mitigation is an important but challenging task for maintaining service integrity. In this paper, we propose to leverage computer vision models to detect DDoS attacks in smart grid networks. After preprocessing the input network data and extracting features, we transform the features into two-dimensional images, employing them to train and fine-tune a vision MLP mixer model. Our approach offers the advantage of adaptability, as any pre-existing computer vision model can be utilized for fine-tuning purposes, providing flexibility in the absence of readily available pretrained models in the DDoS detection domain. Performance metrics, including accuracy, precision, recall, and F1 score obtained from our experiments, show promising results for DDoS attack detection in smart grid networks using the UNB-ISCX-IDS dataset, which contains real-world network traffic data containing different attack scenarios. Our approach will be beneficial for robust and secure smart grid operation in real-world scenarios. © 2024 IEEE.

关键词： Network security

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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Quadruped Stanford Pupper Type Robot Modeling and Simulation 18

Quadruped Stanford Pupper Type Robot Modeling and Simulation

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Mija, Nelu Leba, Monica Rus, Cosmin Muntean, Emanuel University of Petrosani UPET Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Nowadays there are many research efforts in the field of artificial intelligence applied in all the fields of robotics. There are developed and trained new models both supervised and unsupervised learning. ln order to be able to get a reliable final product, a very important step is modeling and simulation. This paper focuses on the analysis of a quadruped robot, Stanford Pupper type, for which the mathematical model for the locomotor system was developed in order to be then implemented in a simulator that allows the development of machine learning algorithms for training it. There can be implemented in the simulator various use cases, like: learning to walk both supervised and unsupervised scenarios, learning to navigate on a zone with uneven surface and with small obstacles, or learning to climb stairs or other big obstacles. © 2023 ITMA.

关键词： Musculoskeletal system

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Optimization of synthetic oscillatory biological networks through Reinforcement Learning

Optimization of synthetic oscillatory biological networks th...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Giannantoni, Leonardo Savino, Alessandro Di Carlo, Stefano Politecnico di Torino Control and Computer Engineering Department Torino10129 Italy

ISBN: (纸本)9798350337488

In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RL's capabilities in navigating biological systems' inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms' computational speed. Despite these limitations, the study highlights RL's significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks. © 2023 IEEE.

关键词： bifurcation analysis biological oscillator computational biology optimization reinforcement learning synthetic biology

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Modeling and Simulation of Electric Vehicle Driveline 18

Modeling and Simulation of Electric Vehicle Driveline

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Rus, Cosmin Leba, Monica Risteiu, Marius Nicolae Marcus, Razvan University of Petrosani UPET Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Electric vehicle drivetrain modeling and simulation is a crucial aspect of electric vehicle design and development. A driveline is responsible for transferring power from the motor to the wheels, and the modeling and simulation process helps optimize the drivetrain design for efficiency, performance and safety. This paper presents in an extended manner the design and mathematical modeling of a dynamic transmission system of an electric vehicle. The mathematical model was simulated in Matlab Simulink and the obtained results were the basis of the implementation of the entire system in a small vehicle with offroad capabilities. This work is part of a larger project that has as its objective the creation of a complete electric vehicle starting from the design part and up to the testing on the roads. © 2023 ITMA.

关键词： Electric vehicles

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Memory Replay For Continual Learning With Spiking Neural Networks 33

Memory Replay For Continual Learning With Spiking Neural Net...

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Proietti, Michela Ragno, Alessio Capobianco, Roberto Sapienza University Department of Computer Control and Management Engineering Italy Sony Ai

ISBN: (纸本)9798350324112

Two of the most impressive features of biological neural networks are their high energy efficiency and their ability to continuously adapt to varying inputs. On the contrary, the amount of power required to train top-performing deep learning models rises as they become more complex. This is the main reason for the increasing research interest in spiking neural networks, which mimic the functioning of the human brain achieving similar performances to artificial neural networks, but with much lower energy costs. However, even this type of network is not provided with the ability to incrementally learn new tasks, with the main obstacle being catastrophic forgetting. This paper investigates memory replay as a strategy to mitigate catastrophic forgetting in spiking neural networks. Experiments are conducted on the MNIST-split dataset in both class-incremental learning and task-free continual learning scenarios. © 2023 IEEE.

关键词： Neural networks

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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

GRAIGH: Gene Regulation accessibility integrating GeneHancer...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Martini, Lorenzo Bardini, Roberta Savino, Alessandro Di Carlo, Stefano Politecnico di Torino Control and Computer Engineering Department Torino10129 Italy

ISBN: (纸本)9798350337488

Single-cell assays for transposase-accessible chromatin sequencing data represent a potent tool for exploring the epigenetic heterogeneity within cell populations. Despite their power, understanding the chromatin accessibility landscape poses challenges. This study introduces Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to interpreting genome accessibility by integrating information from the GeneHancer database, detailing genome-wide enhancer-to-gene associations. Initially, we outline the methods for integrating GeneHancer with scATAC-seq data. This involves creating a new matrix where GeneHancer element IDs replace traditional accessibility peaks as features. Subsequently, the paper assesses the method's ability to analyze data and detect cellular heterogeneity. Notably, our findings demonstrate the selective accessibility of GeneHancer elements for distinct cell types, with connected genes serving as precise marker genes. Furthermore, we explore the specificity of GeneHancer element accessibility, highlighting their high selectivity against gene activity. This investigation underscores the potential of Gene Regulation Accessibility Integrating GeneHancer in unraveling the complexities of chromatin accessibility, offering insights into the nuanced relationship between accessibility and cellular heterogeneity. © 2023 IEEE.

关键词： Bioinformatics Enhancers GeneHancer scATAC-seq

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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