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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Jhun, Ga San Hong, Pyo Min Lee, Keun Ahn, Jong Hyun Kim, Young Seon Kang, Dongwoo Lee, Youn Kyu Hongik University Department of Computer Engineering Seoul Korea Republic of Hongik University Department of Software and Communications Engineering Seoul Korea Republic of Hongik University Department of Electronic & Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

To address the security threats posed by highly realistic deepfakes, various deepfake detection methods have been proposed. In particular, texture features resulting from the post-processing steps in deepfake generation, such as color correction and edge smoothing have been widely utilized in deepfake detection methods. However their detection method is limited against advanced image synthesis mechanisms. In this paper, we propose a novel deepfake detection method based on texture features, an Image Waveform. Our proposed method converts the pixel values of an image into an Image Waveform to enhance texture features and utilizes two-stream CNN to maximize deepfake detection performance by effectively leveraging the features of the original image. Experimental results of real-world deepfake dataset, confirmed that our proposed method achieves higher detection accuracy compared to existing texture feature-based detection methods. © 2024 IEEE.

关键词： Image texture

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Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1108-1139页

作者： Farghaly, Samar I. Ismail, Mohamed I. Fouda, Mostafa M. Alwakeel, Ahmed S. Tanta University Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta31111 Egypt Idaho State University Department of Electrical and Computer Engineering College of Science and Engineering PocatelloID83209 United States Modern Academy for Engineering Department of Electronic Engineering and Communication Technology Cairo Governorate 4411602 Egypt Center for Advanced Energy Studies Idaho FallsID83401 United States Suez University Department of Electrical Engineering Faculty of Engineering Suez43221 Egypt

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks. © 2020 IEEE.

关键词： Channel state information

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Two-Stage Fine-Tuning For Low-resource Englishbased Creole with Pre-Trained LLMs

Two-Stage Fine-Tuning For Low-resource Englishbased Creole w...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Amanda Tan, Shu Hui Aung, Eint Sandi Yamana, Hayato Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350341072

Sentiment analysis adopts natural language processing (NLP) techniques to determine the emotional tone of the text. Sentiment analysis research has predominantly been conducted on commonly spoken languages, such as English and Mandarin Chinese. However, less widely spoken languages, such as Englishbased Creoles, have received limited attention due to the need for large-scale labeled datasets. English-based Creoles are derived from English, resulting in many similarities with English. However, English-based Creoles differ from English in some aspects, making the Creole unintelligible to other English speakers. In sentiment analysis research conducted on Singapore English-based Creole (Singlish), most existing research does not leverage the capabilities of large-language models, such as pretrained BERT. To our knowledge, only Gotera et al.'s work relates to a large-language model;however, their contribution is creating a new pre-trained model for Singlish. However, creating a new pre-trained model is computationally expensive, and pre-training a less widely spoken language will result in ineffective because of the small dataset. Hence, we propose a new two-stage fine-tuning framework for pre-trained models, targeting a low-resource English-based Creole, Singlish. Our proposed framework initially clusters the dataset into two based on each data point's percentage of English words. Then, two-stage fine-tuning is performed by transferring the pre-trained BERT model onto the clustered dataset with the higher English percentage in the first stage. The model is further transferred onto the clustered dataset with the lower English percentage in the second stage. Our proposed framework outperforms the traditional fine-tuning framework, achieving a weighted F1 score of 0.8344 for sentiment analysis. © 2023 IEEE.

关键词： Sentiment analysis

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Motion Enhanced Model Based on High-Level Spatial Features

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computers, Materials & Continua 2022年第12期73卷 5911-5924页

作者： Yang Wu Lei Guo Xiaodong Dai Bin Zhang Dong-Won Park Ming Ma College of Computer Science and Engineering Inner Mongolia UniversityHohhot010021China Department of Information and Communications PaiChai UniversityDaejeon35345Korea

Action recognition has become a current research hotspot in computer *** to other deep learning methods,Two-stream convolutional network structure achieves better performance in action recognition,which divides the network into spatial and temporal streams,using video frame images as well as dense optical streams in the network,respectively,to obtain the category ***,the two-stream network has some drawbacks,i.e.,using dense optical flow as the input of the temporal stream,which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time *** this paper,instead of the dense optical flow,the Motion Vectors(MVs)are used and extracted from the compressed domain as temporal features,which greatly reduces the extraction ***,the motion pattern that MVs contain is coarser,which leads to low *** this paper,we propose two strategies to improve the accuracy:firstly,an accumulated strategy is used to enhance the motion information and continuity of MVs;secondly,knowledge distillation is used to fuse the spatial information into the temporal stream so that more information(e.g.,motion details,colors,etc.)is *** results show that the accuracy of MV can be greatly improved by the strategies proposed in this paper and the final recognition for human actions accuracy is guaranteed without using optical flow.

关键词： Action recognition motion vectors two-stream knowledge distillation accumulate strategy

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Beamforming headphone ANC for targeted noise attenuation 154

Beamforming headphone ANC for targeted noise attenuation

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AES Europe 2023: 154th Audio engineering Society Convention

作者： Zachos, Panagiotis Mourjopoulos, John Audio and Acoustic Technology Group Wire Communications Laboratory Department of Electrical Computer Engineering University of Patras Greece

ISBN: (纸本)9781713877783

A novel Targeted through Beamforming Active Noise Control (TBANC) approach for headphones is presented which achieves an improved noise reduction via targeting of a specific, potentially disturbing noise source in the presence of the diffuse field noise interference which is addressed by current ANC methods. The method utilizes the signals captured by two reference microphones, one on each headphone earcup, in a multi-reference ANC scheme while a phased array is formed by these two microphones that commonly reside on top of the headphone earcups. By employing a simple Time-Delay Beamforming approach to target the primary, undesired noise source, the proposed ANC successfully attenuates this source while also achieving a significant reduction of the diffuse noise. © 2023 AES Europe. All Rights Reserved.

关键词： Beamforming

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Double Deep Q Networks Reinforcement Learning based Controller for Grid-Connected 7-Level PUC Inverter 4

Double Deep Q Networks Reinforcement Learning based Controll...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Alquennah, Alamera Nouran Chida, Melanie Zamzam, Tassneem Trabelsi, Mohamed Ghrayeb, Ali Khatri, Sunil Texas A&M University Department of Electrical and Computer Engineering College StationTX United States Texas A&M University at Qatar Qatar Foundation Department of Electrical and Computer Engineering Doha Qatar Kuwait College of Science and Technology Electronics and Communications Engineering Department Kuwait City Kuwait

ISBN: (纸本)9798350306262

The Packed-U-Cell (PUC) topology has been widely employed as a single DC-source multilevel inverter (MLI) in various applications, with different control techniques. In grid-connected applications, the two main control objectives are the minimization of the total harmonic distortion (THD%) of the generated current and the regulation of the flying capacitor voltage at its reference value. This paper proposes a reinforcement learning-based control (RLC) using the off-policy value function-based double deep Q networks (DDQN) reinforcement learning algorithm for a grid-tied single-phase 7-level PUC inverter. The agent consists of two DQNs that estimate the value function and determine the control action at each time step. The reinforcement learning-based control using DDQN (RLC-DDQN) is trained by interacting with the PUC7 system on three different pairs of current and voltage references. In the testing phase, the trained agent was tested on different operating conditions to validate its performance. The presented results show that the current THD has been kept below 3% for different reference signals from 4A to 11A, whereas the maximum deviation of the capacitor voltage from its volts reference value is 0.5V. The trained RLC-DDQN algorithm shows a high dynamic response under disturbed operating conditions (variations in the reference signals including the grid current, the DC-source voltage, and the voltage-current phase-shift). Moreover, the robustness of the trained agent when applied on systems with different component design (capacitance and inductance variations) is verified. © 2024 IEEE.

关键词： Reinforcement learning

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Fault simulation Framework using PyUVM

Fault simulation Framework using PyUVM

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2023 International Conference on Microelectronics, ICM 2023

作者： Fayez, Mina Hanna Eladawy, Mohamed Ahmed Sahyon, Micheal Safwat Ahmed, Islam Osama El-Din, Omar Hossam Elshafie, Mohamed Ahmed Taha, Mohamed Ayman Talaat, Mohamed Gamal Cairo University Faculty of Engineering Electronics and Communications Department Cairo Egypt Ain Shams University Faculty of Engineering Computer Department Cairo Egypt

ISBN: (纸本)9798350380828

This paper presents a fault simulation environment developed using PyUVM to address challenges in simulating faults during the manufacturing process of integrated circuits (ICs). The two main challenges tackled are simulating pre-manufacturing fault behavior and overcoming performance issues. The contributions of this work include expanding the fault simulation capacity beyond existing fault simulation methodologies, improving simulation time through efficient algorithms, and enabling compatibility with Universal Verification Methodology (UVM) and SystemVerilog testbenches. The environment allows for injecting various types of faults into a design, reporting the resulting behavior, and facilitating comprehensive analysis under different fault scenarios. The proposed solution enhances fault simulation capabilities, reduces simulation time, and provides compatibility for seamless integration with existing designs and verification methodologies. © 2023 IEEE.

关键词： Integrated circuits

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Multi-robot Cooperative Transport Simulation System

Multi-robot Cooperative Transport Simulation System

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2023 IEEE/CIC International Conference on communications in China, ICCC Workshops 2023

作者： Li, Xiaodong Lin, Yangfei Du, Zhaoyang Yin, Rui Wu, Celimuge The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Hangzhou City University Hangzhou China

ISBN: (纸本)9798350345407

Multi-robot systems have gained significant attention in recent years due to their potential for collaborative handling and operations. Designing an effective simulation system for multi-robot environments is crucial for enabling research and development in this field, while minimizing risks and costs associated with real robot testing. This paper presents a Multi-Robot Collaborative Transport (MRCT) simulation system that integrates the Robot Operating System (ROSI) with the Gazebo software environment. The system employs the FastDDS communication framework for task allocation and control, enabling efficient distribution of tasks among robots. A dual-robot model is developed for collaborative handling, with two robots equipped with manipulators working together to accomplish transport tasks. The performance of the simulation system is evaluated by comparing the trajectory of transported objects with that of the dual-robot system. The results demonstrate the exceptional performance and accuracy of the MRCT system. © 2023 IEEE.

关键词： Robot Operating System

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Location Estimation of Moving Targets by Passive Sonobuoy and Recurrent Deep Neural Network

Location Estimation of Moving Targets by Passive Sonobuoy an...

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2023 International Symposium on Networks, computers and communications, ISNCC 2023

作者： Abbaspour, Hooshang Najafi, Ghazal Master of Electrical and Communications Tehran Iran Shahre Rey Branch Electricl and Computer Engineering Department Tehran Iran

ISBN: (纸本)9798350335590

Today, one of the biggest challenges for military experts is to identifying and track submarines. concealment, is one of the most critical features of the Navy, which leads to limited active signal transmission. On the other hand, environmental factors such as salinity changes, temperature, the harsh and inhomogeneous environment of the sea etc. Affect the sound signals and their propagation. Currently, sonobuoys are an affordable and practical resource for tracking submarines. Passive sonobuoy is necessary for some operational conditions and special missions, especially the military. In this paper, the deep neural network is trained by a particle filter to the location estimation by passive sonobuoy in two dimensions. Also, simple dynamic equations without pre-processing are considered for sonobuoy and can be processed by a deep neural network, which has a high ability to estimate highly complex functions. The ratio of the average power of the primary signal to the noise of the sonobuoys is assumed in the SNR= [0,10]dB is assumed, and the simulation results demonstrate that the proposed method achieve high accuracy compared to the gradient descent training algorithms. Due to the use of a neural network, the computational load is significantly reduced and real-time calculations. © 2023 IEEE.

关键词： Submarines

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