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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Vijayalakshmi, V.J. Ojha, Varun Arunadevi, R. Chaudhary, Anisha Manu, S.E. Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Quantum University Research Center Quantum University India School of Engineering and Technology Civil Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

this paper investigates techniques for ensuring the robustness of video watermarking algorithms. Many watermarking strategies awareness of performance measures like protection and imperceptibility;however frequently need to remember robustness, which is a vital element in creating a robust watermark that can preserve diverse types of signal noise and intentional attacks. The authors proposed an adaptive technique for the usage of movement estimation-primarily based watermark encoding and interpreting to assure robustness towards diverse noise and alteration assaults, along with white noise, contrast less noise, image filtering, JPEG compression, and video cropping. The authors in comparison the proposed algorithm with existing methods in phrases of height sign-to-noise ratio (PSNR), normalized correlation (NC), and robustness evaluation with four reference points (RP4). Effects showed that the proposed set of rules carried out higher in terms of PSNR, NC, and RP4, providing robust evidence of its robustness in comparison to present algorithms. Usual, the consequences from this paper recommended that the proposed adaptive method is a promising solution for robust video watermarking. The algorithm presents a strong overall performance in phrases of security, imperceptibility, and robustness towards various sorts of noise and intentional attacks. Future studies could discover the utility of this method to a much wider variety of video watermarking situations. © 2024 IEEE.

关键词： White noise

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Design and Applications of Low-Coherence Semiconductor Light Emitters

Design and Applications of Low-Coherence Semiconductor Light...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Boon S. Ooi Omar Alkhazragi Hang Lu Photonics Laboratory Electrical and Computer Engineering Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

Despite its numerous advantages, high coherence degrades the performance of many applications due to unintentional interference effects. We review the recent advances in low-coherence semiconductor light emitters and ... 详细信息

ISBN: (纸本)9798350369311

关键词： Semiconductor lasers Reviews Interference Coherence Electro-optic effects Light emitting diodes Laser applications

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An Auto-ANN for EMG Classification for the Motion Recognition of the Human Hand

An Auto-ANN for EMG Classification for the Motion Recognitio...

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2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022

作者： Briouza, Sami Gritli, Hassene Khraief, Nahla Belghith, Safya Singh, Dilbag Tunis1002 Tunisia Tunis1164 Tunisia School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9781665477017

Electromyography (EMG) signals can contain useful information related to the user's intentions. The usage of these signals for motion recognition has been widely studied in the last few years and can be used to control exoskeletons for rehabilitation or assistance. The aim of this study is to identify different motions using EMG signals, by using an automatic Artificial Neural Network (ANN). We apply our proposed architecture to the Ninapro DB2 dataset. This dataset contains information about 40 different subjects and 49 different motions divided into three types of exercises. The dataset is divided into 70% training, 15% validation, and 15% testing. The accuracy found for the model is 81.18% for exercise of type B, 77.76% for exercise of type C, and 91.70% for exercise of type D, over 10 different subjects. © 2022 IEEE.

关键词： Neural networks

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Attack Tree Refinements Analysis and Verification by applying Coloured Petri Nets 48

Attack Tree Refinements Analysis and Verification by applyin...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pasandideh, Shabnam Pereira, Pedro Gomes, Luis Nova School of Science and Technology-Nova University Lisbon Center of Technology and Systems-UNINOVA Dept. Electrical and Computer Engineering Caparica Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Tree-based models). It is applied for evaluating each component's interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cyber-security analysis. © 2022 IEEE.

关键词： Cybersecurity

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Improved Whale Optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat 3

Improved Whale Optimization Algorithm and Optimized Long Sho...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Polimetla, Kiran Velpula, Srikanth Kumar Pareek, Piyush Sontakke, Dipti School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India Capgemini Inc United States

ISBN: (纸本)9798350318609

The Distributed Denial of Service (DDoS) attacks is a critical cyber security threat that blocks the services for users and leads to important damage for reputation and effective customers. The existing methods provides low classification accuracy due to irrelevant features in classification performance. The Improved Whale Optimization Algorithm (IWOA) is proposed for the selecting of relevant features which improved the classification performance. The selected features are detected and classified by Optimized Long Short-Term Memory (OLSTM) that provided high detection rate of DDoS attacks. The one-hot encoding and min-max normalization techniques are used in the data pre-processing stage to improve the performance of classification. The CIC-DDoS 2019 dataset are used for evaluating the proposed method. The IWOA-OLSTM method attained highest accuracy 97.12%, precision 96.74%, recall 96.27%. f1-score 96.54% and DR 93.26% on CIC-DDoS 2019 dataset which is effective than Convolutional Neural Network (CNN) - Bidirectional Long Short-Term Memory (BiLSTM) and CNN-LSTM. © 2024 IEEE.

关键词： Cybersecurity

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Temperature measurement with compact Fabry-Perot Interferometer employing polymer-filled Hollow Core Fiber

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Optik 2025年 327卷

作者： Lashari, Ghulam Abbas Mumtaz, Farhan Aziz, Abdul Ali, Mumtaz Department of Engineering and Technology Sukkur IBA University Sukkur65200 Pakistan Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

This research proposes and experimentally demonstrates a polymer-filled Hollow Core Fiber (HCF) based compact Fabry-Perot Interferometer (FPI) for temperature sensing. The two beams: one reflectd at the interface between multimode fiber and the hollow core fiber and the other beam reflected at the interface between the hollow core fiber and the thermosensitive polymer form the Fabry-Perot cavity. The thermosensitive material filled in the core of the HCF acts as the sensing element. The HCF core is partially filled with transparent photopolymer, which has high Thermo-Optic Coefficient (TOC) and Thermo-Expansion Coefficient (TEC) as compared to silica, therefore it can provide high temperature sensitivity. The temperature sensitivity of the proposed FPI sensor can reach up to 53.5 pm/℃. The results indicate that the polymer-filled hollow-core fiber based FPI offers a promising approach for achieving high temperature sensing than pure silica based sensors. The proposed device consists of compact design with easy and low-cost fabrication. Moreover, the temperature measuring device provides stable and linear output response and brings numerous prospects for ocean temperature measurements, food, medical and chemical sensing applications. © 2025 Elsevier GmbH

关键词： Fabry-Perot interferometers

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Robust Anomaly Detection in Network Traffic using Deep Learning Models 3

Robust Anomaly Detection in Network Traffic using Deep Learn...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Nema, Ashish Tomar, Raghvendra Singh Mani, Anand Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electrical & Electronics Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

The exact detection of anomalies in computer network traffic is crucial to network protection. This study presents a novel approach to achieving the stated goal: the use of deep learning models. To correctly capture the temporal, geographic, and probabilistic aspects of network data, the recently developed approach integrates deep autoencoders (DAE), variable autoencoders (VAE), and long short-term memory (LSTM) networks. The proposed strategy surpassed six industry-standard solutions in terms of accuracy, recall, F1-score, AUC-ROC, false positive rate (FPR), and false negative rate (FNR). This was demonstrated via performance reviews. Furthermore, the proposed technique makes optimum use of currently available resources. This study improves network security by employing more robust anomaly detection algorithms. © 2023 IEEE.

关键词： Long short-term memory

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Intelligent Low-Bandwidth Frequency Controller for VSGs at Economic Dispatch in Islanded Microgrid 14

Intelligent Low-Bandwidth Frequency Controller for VSGs at E...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Sati, Shraf Eldin Al-Durra, Ahmed Zeineldin, Hatem H. El-Fouly, Tarek H. M. El-Saadany, Ehab F. Khalifa University of Science and Technology Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331516116

This paper proposes a distributed low-bandwidth intelligent controller (DLBIC) to restore islanded microgrid (IMG) frequency and maintain economic dispatch (ED) among inverter-based distributed generators (IBDGs). The DLBIC utilizes local measurements and receives a single variable from the nearest DG, thereby reducing communication link bandwidth and network infrastructure costs. Notably, the DLBIC is model-free, operating without prior knowledge of the IMG model and parameters. It outperforms other approaches by effectively handling the nonlinearity and complexity of IBDGs without extensive training. Leveraging reinforcement learning principles, the DLBIC can learn autonomously and efficiently manage uncertainties and disturbances within the IMG. The effectiveness of the DLBIC is demonstrated through tests against load connection and disconnection scenarios using the IEEE 38-node benchmark. Results indicate that the proposed intelligent controller successfully restores IMG frequency, mitigates frequency nadir/overshoot, eliminates oscillations, and achieves optimal ED. © 2025 IEEE.

关键词： Reinforcement learning

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An optimal on-demand scrubbing solution for read disturbance errors in phase-change memory

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IEIE Transactions on Smart Processing and Computing 2021年第1期10卷 55-60页

作者： Kim, Moonsoo Lee, Juhan Kim, Hyun Lee, Hyuk-Jae Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Republic of

Phase-change memory is a promising technology due to its attractive properties. However, phase-change memory is difficult to commercialize because of its reliability issues. A read disturbance error, which is the main cause of reliability issues, occurs when a cell is repeatedly read. A conventional solution for read disturbance errors is periodically scrubbing the cells. However, this method requires read counters to count the number of reads per word. This paper proposes an on-demand scrubbing solution that does not require read counters, which significantly reduces resource overhead. The proposed method observes the number of errors in a word using error-correcting code. If the number of errors is larger than a pre-defined threshold, scrubbing is performed to fix the errors. The proposed method removes nearly 1GB of hardware overhead required by read counters, and fixes more than 99.99% of read disturbance errors. © 2021 The Institute of Electronics and Information Engineers

关键词： Phase change memory

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Distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizant

Distributed Robust Optimization Method for AC/MTDC Hybrid Po...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Haixiao Li Aleksandra Lekić Faculty of Electrical Engineering Mathematics & Computer Science Delft University of Technology Delft The Netherlands School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing China

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

AC/multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint, enabling DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the constructed AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed optimization method.

关键词： Renewable energy sources Uncertainty Network topology Computational modeling Optimization methods Linear approximation Hybrid power systems Topology Numerical models Load flow

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