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15th IFIP WG 11.10 International Conference on Critical Infrastructure Protection, ICCIP 2021

作者： Mellish, Robert Graham, Scott Dunlap, Stephen Sweeney, Patrick Computer Engineering Air Force Institute of Technology Wright-Patterson Air Force Base OH United States Cyber Security Research Engineer Air Force Institute of Technology Wright-Patterson Air Force Base OH United States

ISBN: (纸本)9783030935108

Cyber-physical systems incorporate powerful devices that are used to monitor and control physical processes. These devices along with collectable statistics can be leveraged as sensors for network-based and host-based anomaly detection. Host-based anomaly detection can be used in a defense-in-depth strategy to complement traditional network-based anomaly detection systems as well in systems for which network-based options are infeasible due to their operating environments. This chapter discusses the development of an anomaly detection system for a SEL-3505 RTAC programmable logic controller using the recommended IEC 61131 programming tools. The required device statistics are harvested by creating a Modbus server on the test system and polling the server to retrieve data. The collected data is used to create a representative fingerprint for the associated task. When the measured behavior differs from the fingerprint, an anomaly is detected and an alarm is raised. This approach is flexible and easily implemented in existing installations. The performance of the anomaly detection system is evaluated against several network-based attacks across multiple firmware revisions and project types. Recommendations are made to improve anomaly detection performance. © 2022, This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply.

关键词： Controllers

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A robust subband adaptive filter algorithm for sparse and block-sparse systems identification

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Journal of Systems engineering and Electronics 2021年第2期32卷 487-497页

作者： ZAHRA Habibi HADI Zayyani MOHAMMAD Shams Esfand Abadi Research Institute for Information and Communications Technologies Academic Center for EducationCulture and ResearchTehran ***Iran Department of Electrical and Computer Engineering Qom University of TechnologyQom 371951519Iran Faculty of Electrical Engineering Shahid Rajaee Teacher Training UniversityTehran ***Iran

This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)*** proposed algorithm uses a GCMPN cost function to combat the impul-sive *** further accelerate the convergence rate in the sparse and the block-sparse system identification processes,the proportionate versions of the proposed algorithm,the L0-norm GCMPN-SAF(L0-GCMPN-SAF)and the block-sparse GCMPN-SAF(BSGCMPN-SAF)algorithms are also ***,the convergence analysis of the proposed algorithm is *** results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

关键词： subband adaptive filter(SAF) generalized continuous mixed p-norm(GCMPN) sparse system block-sparse system impulsive interference

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Development of AI Tool for Caries Detection from Dental Radiograph 12

Development of AI Tool for Caries Detection from Dental Radi...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Ramadasan, Swaetha Manju, T. Vijayakumar, K. Prabha, S. Kumar Dutta, Pushan Kumar Verma, Deepak Senior Business Intelligence Engineer Perma Technologies Atlanta United States St. Joseph's Institute of Technology Department of CSE Chennai India SIMATS Center for Research and Innovation Saveetha School of Engineering Department of CSE Chennai602105 India Amity University School of Engineering and Technology Kolkata India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

Dental caries (DC), sometimes called tooth decay, is a bacterial illness that demineralizes and destroys the tooth's complex structures, leaving cavities or holes in the teeth. DC may result in abscesses and tooth loss if left untreated. It is strongly advised that DC be diagnosed and treated as soon as possible. Clinical evaluations usually involve a dentist's ocular assessment and then radiograph-supported screening. This project aims to create an AI-supported system for radiograph-based dental caries detection. The suggested method uses a Convolutional Neural Network (CNN) architecture to find the DC region in digital radiographs. The work combines CNN segmentation with picture pre-processing to increase detection accuracy. The suggested tool's efficacy is assessed on a chosen database, and the experimental findings verify that the scheme can successfully identify DC using VGG-UNet with an accuracy of over 98%, surpassing the performance of UNet, SegNet, and UNet+. © 2024 IEEE.

关键词： CLAHE Dental caries Radiograph Segmentation VGG-UNet

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An Improved Hierarchical Control Strategy for Power Management in Islanded Hybrid Microgrids 2

An Improved Hierarchical Control Strategy for Power Manageme...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jeba Singh, O. Yamini, K. Anish Pon Rajesh Sharma, R. Mahapathra, Sheila Alliance University Centre for Academic Research Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798350388916

Power management and control through a hierarchical control strategy in a Hybrid Micro Grid (HMG) consisting of multiple AC and DC small Micro Grids (MG) with different voltage ratings are focussed of this research. The ultimate aim of the proposed power management strategy is to limit the continuous operation of Interlink (IL)in different load conditions. Hence, the power loss at the IL converters are reduced, orelse it may affect the operational stability of the HMG. The hierarchical control strategy for power management and coordination for islanded mode of HMG is implemented and analysed. The designed system consists of primary and secondary control levels, that provides an accurate power transfer among the small MGs. The new methodology ensure a continuous power flow within the small MGs with minimal operation of IL converter. © 2024 IEEE.

关键词： Hybrid power

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement

A Recurrent Neural Network for Image Deblocking Detection an...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： R Venkatesan A. Pandiaraj M Selvakumar Department of Information Technology Bannari Amman Institute of Technology–Sathyamangalam Department of Computer Science and Engineering School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation.

关键词： Visualization Image segmentation Recurrent neural networks Transform coding Wounds Media Image representation

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Deep-Learning and Geometric Mean Optimizer Combined Scheme for Brain MRI Classification 12

Deep-Learning and Geometric Mean Optimizer Combined Scheme f...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Ramadasan, Swaetha Saranya, S. Vijayakumar, K. Prabha, S. Kumar Dutta, Pushan Kumar Singh, Sushil Perma Technologies Senior Business Intelligence Engineer Atlanta United States St. Joseph's Institute of Technology Department of CSE Chennai India Center for Research and Innovation Saveetha School of Engineering SIMATS Department of CSE Tamil Nadu Chennai India Amity University School of Engineering and Technology Kolkata India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

Disease in brain is one of the medical emergencies and appropriate diagnoses and treatment is necessary. Medical imaging-assisted Brain Condition Monitoring (BCM) is one of the common clinical procedures, and Magnetic Resonance Imaging (MRI) is a standard modality for BCM. This research presents a unique BCM scheme utilizing the deep-learning procedure. Several stages of this arrangement includes the following;MRI slice resizing, feature extraction using deep-learning procedure, feature optimization with Geometric Mean Optimizer (GMO), feature fusion and binary classification. In this work, brain tumour detection is presented. This work is implemented using individual, fused and ensemble of features. The investigational outcome authorizes that this arrangement works fine on the brain tumour detection task using the benchmark image datasets. The proposed approach helped to accomplish a BT recognition accuracy of 100% with the Support Vector Machine (SVM) using ensemble features. This confirms that the proposed MRI-supported BCM arrangement worked well on the chosen image database. © 2024 IEEE.

关键词： Support vector machines

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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

MSEMG: Surface Electromyography Denoising with a Mamba-based...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

ISBN: (纸本)9798350368741

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2025 IEEE.

关键词： Deep neural network ECG interference removal State space model Surface electromyography

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof 2

Multi-chain Storage Scheme Based on Hash Calendar and Recurs...

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2nd International Conference on computer Science and Blockchain, CCSB 2022

作者： Wang, Chao Yao, Zhongyuan Zhang, Yong Guo, Shangkun Zhao, Lindong Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution;and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain. © 2022 IEEE.

关键词： Blockchain

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