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IEEE Sensors Letters 2024年第9期8卷 1页

作者： Singh, Kanishk Chen, Chao-Hung Tai, Li-Chia Huang, Wei-Chen Pan, Tung-Ming Chang Gung University Department of Electronics Engineering Taoyuan33302 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30093 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30093 Taiwan Chang Gung Memorial Hospital Division of Urology Taoyuan33305 Taiwan

In this research work, we present a flexible iridium oxide (IrOx) extended-gate field-effect transistor (EGFET) biosensor for label-free detection of the epidermal growth factor receptor (EGFR) biomarker. IrOx was employed as the sensing membrane material due to its excellent electrochemical properties, high conductivity, stability, biocompatibility, and ability to facilitate redox reactions. The IrOx film was deposited on a flexible polyimide (PI) substrate through a sol-gel process and characterized using atomic force microscopy and X-ray photoelectron spectroscopy. As a pH sensor, the IrOx EGFET exhibited a super-Nernstian sensitivity of 68.54 mV/pH with high linearity, a low hysteresis of ∼ 6 mV, and a drift rate of 0.50 mV/h at pH 7 over 12 h. For EGFR detection, the IrOx surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde to immobilize anti-EGFR antibodies. The EGFR sensor demonstrated a wide linear detection range from 1 to 1000 ng/mL with a sensitivity of 11.78 mV/log(ng/mL) and a linearity of 99.8%, making it suitable for clinical EGFR monitoring in cancer patients. The flexible IrOx EGFET shows promise for reliable label-free detection of EGFR. © 2017 IEEE.

关键词： Substrates

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Rule Based Maliciousness Detection in computer Network using Machine Learning in Fog Node

Rule Based Maliciousness Detection in Computer Network using...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Farooque Hassan Kumbhar Raza Nathani Kamran Ali Memon Wessam Ali Mesbah Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Computer Science Department National University of Computer and Emerging Sciences Pakistan Electrical Engineering Department & Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The internet of things (IoT) enables heterogeneous devices to independently participate in global communications; however, it exposes the low power devices with minimum capabilities to vulnerability. This has led to the rise of attackers who leverage their attacks on low power devices to collect private data, without the user knowledge. In this paper, we look at the possibility of creating a supervised machine learning mechanism that autonomously detects packet being sent over the systems before it has reached the internet. The proposed model is trained to identify distributed denial of service (DDoS) attacks for outgoing packets, and subsequently inform and send the detected data to a monitoring node. Considering the low power devices, the proposed solution enables a rule-based system where packets can be detected with binary decisions. However, the rules and detection requires decision tree model training with appropriate datasets. Our evaluations show that the proposed mechanism can detect malicious packets without incurring additional delays in the communication by forwarding all packets to intermediate routers or fog nodes for inspection.

关键词： Training Wireless communication Computational modeling Machine learning Denial-of-service attack Data models Internet of Things

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Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical systems

arXiv

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arXiv 2023年

作者： Beintema, Gerben I. Schoukens, Maarten Toth, Roland Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions. In this paper, we introduce a novel identification method of such systems, which results in a dynamical model that is able to produce the time-varying output distribution accurately without taking restrictive assumptions on the data-generating process. The method is formulated by first deriving a novel and exact representation of a wide class of nonlinear stochastic systems in a so-called meta-state-space form, where the meta-state can be interpreted as a parameter vector of a state probability function space parameterization. As the resulting representation of the meta-state dynamics is deterministic, we can capture the stochastic system based on a deterministic model, which is highly attractive for identification. The meta-state-space representation often involves unknown and heavily nonlinear functions, hence, we propose an artificial neural network (ANN)-based identification method capable of efficiently learning nonlinear meta-state-space models. We demonstrate that the proposed identification method can obtain models with a log-likelihood close to the theoretical limit even for highly nonlinear, highly stochastic systems. © 2023, CC BY.

关键词： Stochastic systems

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APPROXIMATE DYNAMIC PROGRAMMING FOR A MEAN-FIELD GAME OF TRAFFIC FLOW: EXISTENCE AND UNIQUENESS

arXiv

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arXiv 2023年

作者： Tirumalai, Amoolya Baras, John S. Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland 8223 Paint Branch Dr College ParkMD20740 United States

Highway vehicular traffic is an inherently multi-agent problem. Traffic jams can appear and disappear mysteriously. We develop a method for traffic flow control that is applied at the vehicular level via mean-field games. We begin this work with a microscopic model of vehicles subject to control input, disturbances, noise, and a speed limit. We formulate a discounted-cost infinite-horizon robust mean-field game on the vehicles, and obtain the associated dynamic programming (DP) PDE system. We then perform approximate dynamic programming (ADP) using these equations to obtain a sub-optimal control for the traffic density adaptively. The sub-optimal controls are subject to an ODE-PDE system. We show that the ADP ODE-PDE system has a unique weak solution in a suitable Hilbert space using semigroup and successive approximation methods. We additionally give a numerical simulation, and interpret the results. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Observer-Based Control of Second-Order Multi-vehicle systems in Bearing-Persistently Exciting Formations

Observer-Based Control of Second-Order Multi-vehicle Systems...

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IEEE Conference on Decision and Control

作者： Zhiqi Tang Baris Fidan Karl H. Johansson Jonas Mårtensson Tarek Hamel Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechanical and Mechatronics Engineering Department University of Waterloo ON Canada I3S-UCA CNRS Université Côte d’Azur & Institut Universitaire de France (IUF) France

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles’ relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers.

关键词： Tracking Stability Simulation Dynamics Observers Position measurement Control theory Sensors Velocity measurement Vehicle dynamics

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Deduplication of Textual Data by NLP Approaches

Deduplication of Textual Data by NLP Approaches

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IEEE Conference on Vehicular Technology (VTC)

作者： Kiana Ghassabi Peyman Pahlevani Daniel E. Lucani Department of Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

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Giant-segNet: macular edema detection using Giant Trevally Optimizer deep seg network

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Australian Journal of electrical and Electronics engineering 2024年

作者： C, Anuradha. S, Christina Magneta. C, Chandru Vignesh. S, Mary Rexcy Asha. K, Vijayan. C, John Clement Singh. Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and communication engineering Sapthagiri NPS University Karnataka Bengaluru India Department of Electronics and Communication Engineering Kings Engineering College Tamil Nadu Chennai India

Macular edema is a primary cause of blindness and loss of vision in people with visual retinal disorders. An insufficient dataset may result in overfitting, reducing the model’s ability for generalize the diverse cases. In this paper a novel Giant-SegNet has been proposed for the early detection of macular edema. Initially the input Kaggle dataset images are gathered and pre-processed using gaussian adaptive bilateral filter for enhancing the quality of the images. The enhanced images are fed into SegNet to segment the macular region in retinal images. Then the segmented macular region is inputted into Shuffle Net for extracting the best features and Giant Trevally Optimizer for selecting the best features. Finally, a fully connected layer is designed to classify the macular and non-macular edema. The experimental findings use the specific measures of accuracy, recall, precision, and F1 score to demonstrate the durability of the proposed approach. The proposed Giant-SegNet achieves a detection accuracy of 99.44% for macular edema. Compared to Dense Net, Alex Net, and ResNet, SegNet achieves an accuracy rate of 96.72%, 92.89%, and 97.11%, respectively. Giant-SegNet improves overall accuracy by 2.44%, 5.04%, and 4.34% over CNN, faster R-CNN, and VGG-16 CNN, respectively. © 2024 Engineers Australia.

关键词： Optical coherence tomography

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Biomedical Image Quality Improvement with Spatial Domains 2

Biomedical Image Quality Improvement with Spatial Domains

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2nd International Conference on Innovative and Creative Information Technology, ICITech 2021

作者： Fernando, Erick Siagian, Pandapotan School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia

ISBN: (纸本)9781728197463

Digital image processing aims to improve the quality of an original image so that it can display an image that is relatively better than the original image, so as to obtain the detailed information needed for an analysis need. Quality degradation Biomedical images in computerization usually experience changes in brightness, blur, and contrast stretching. Because of the deterioration in the image quality, doctors and patients cannot obtain the information needed for analysis, therefore the need for biomedical image processing techniques with spatial domain methods to improve the quality and details of information in the image so that it helps doctors in diagnosing. Data analysis is biomedical images like X-ray images, CT-Scan (computer Tomographic Scan) images, and USG (Ultrasound Graphic) images. A focused study on improving image quality can be done with digital spatial domain image processing techniques. Digital domain image processing is digital image processing based on the manipulation of pixel values in the image directly, in the form of point processing and mask processing. Process testing for each image for Blur level is CT-scan images with a value of 0.98436, ultrasound images with a value of 0.9875, X-ray images with a value of 0.9836. This value is blur level value get near to 1 which means the image becomes clearer. The analysis results prove that the spatial domain method can clarify the object image in this study. The biomedical image quality processing model is proven to be able to improve the quality of the image that is declining due to the digitization process and assist the user in analyzing the biomedical image. © 2021 IEEE.

关键词： Image quality

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Dead-time Compensation Method for Bus-clamping Modulated Voltage Source Inverter

arXiv

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arXiv 2023年

作者： Ghaderloo, Reza Asrar Shen, Yidi Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

Bus-clamping Pulse Width Modulation (PWM) is an effective method to reduce the switching loss in a three-phase voltage source inverter (VSI). In bus-clamping PWM scheme, the phase legs are switched using high frequency PWM signals for two-third of the line cycle, while for the remaining duration of cycle, the pole voltage is clamped to either positive or negative rail of the DC bus. In PWM operation of a half bridge, a dead-time is applied between the gate signals of complementary switches to ensure safe and reliable operation. However, introduction of dead-time leads to poor power quality, increased Total Harmonic Distortion (THD) and variation in actual voltage compared to the intended pole voltage. Moreover, when the bus-clamping technique is used, the PWM has both high frequency switching region and clamped region in a line cycle, and consequently, the undesired effects of dead-time are further aggravated. Therefore, in order to enhance the quality of output voltage, this paper presents a dead-time compensation strategy for a VSI operating with bus-clamping PWM. The proposed method calculates the required compensation term to be added on the modulation signal considering wide range of operating conditions. Additionally, the compensation includes a new strategy for low current conditions near zero-crossing to avoid distortion. The proposed method is verified by simulation and experiments in a three-phase VSI with a switching frequency of 100 kHz and a fundamental frequency of 60Hz. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulse width modulation

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On constraints in first-order optimization: a view from non-smooth dynamical systems

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The Journal of Machine Learning Research 2022年第1期23卷 11681-11727页

作者： Michael Muehlebach Michael I. Jordan Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen Germany Department of Electrical Engineering and Computer Sciences Department of Statistics University of California Berkeley CA

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning.

关键词： convex optimization nonconvex optimization constrained optimization non-smooth dynamical systems gradient-based optimization convergence rate analysis

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