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Adaptive nonsingular integral-type dynamic terminal sliding mode synchronizer for disturbed nonlinear systems and its application to secure communication systems

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JVC/Journal of Vibration and Control 2023年第11-12期29卷 2566-2578页

作者： Vaseghi, Behrouz Mobayen, Saleh Din, Sami ud Hashemi, Seyedeh Somayeh Vu, Mai The Department of Electrical and Computer Engineering Abhar Branch Islamic Azad University Abhar Iran Department of Electrical Engineering University of Zanjan Zanjan Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Electrical Engineering Namal University Mianwali Mianwali Pakistan School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of

This study proposes an adaptive nonsingular integral dynamic terminal sliding mode tracker/synchronizer for disturbed nonlinear systems along with its usage in safe communication systems. The convergence of the closed-loop structure under unknown uncertainty and disturbances is guaranteed via Lyapunov analysis. Furthermore, a parameter-tuning method is planned to approximate the upper bound of uncertainty and disturbance terms, since this latter is typically unknown in practice. The proposed approach is used to design a digital secure transmission scheme according to the chaotic systems. The effectiveness of the suggested approach is validated using computer simulations on a benchmark example of chaotic system. The obtained outcomes clearly confirm the ability of the planned control approach enables to attain the desired tracking/synchronizing performance despite the disturbances. Additionally, when implemented to the data encryption of a communication system, the proposed control and secure communication techniques enabled the complete and secure retrieval of the original digital sequences. © The Author(s) 2022.

关键词： Chaotic systems

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Determination of Suitable Heats ink Layout for a High-Density Integrated Motor Drive Through an Effective Comparison Method

Determination of Suitable Heats ink Layout for a High-Densit...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Venkata Raghavendra, I. Lyu, Yanda Wang, Fred Chatterjee, Debanjan Cairoli, Pietro The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Abb Inc Us Corporate Research Center Raleigh United States

ISBN: (纸本)9798350376067

Integrated Motor Drives (IMDs), play a pivotal role in the ever-evolving landscape of technological advancements in fields such as electric transportation and industrial environments. IMDs face challenges managing heat dissipation from drive-switching devices, especially due to confined spaces and dynamic loads, intensified by elevated temperatures near motor heat sources. To overcome thermal challenges, Wideband gap devices are crucial for minimizing losses and enabling diverse heatsink layout configurations within natural cooling. This paper introduces a thermal design approach that employs an analytical thermal model to select extruded heatsinks. The heatsink lengths are then fine-tuned to limit device junction temperatures under dynamic load variations across various heatsink layout configurations. The validation of the analytical and CFD models is then demonstrated through experimental setup. Subsequently, for a 2-level VSI device placement, a comparative analysis is conducted on the heatsink designs selected across various layout configurations, with endeavours focused on identifying the layout configuration that gives out the highest power density. © 2024 IEEE.

关键词： Thermal modeling

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Sustainable Solutions for Emerging Issues in Wireless Technology

Sustainable Solutions for Emerging Issues in Wireless Techno...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Alnawayseh, Saif. E. A. Ihsan, Anaum Irfan, Muhammad Kousar, Shagufta Ali, Dilawer Abbas, Tahir College of Engineering Department of Computer Engineering Mutah University Jordan College of Engineering Department of Electrical Engineering Mutah University Al-Karak Jordan Lahore Pakistan Multan Pakistan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan

ISBN: (纸本)9798350369106

The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and environmental concerns must be addressed for sustainable growth. This paper provides an overview of these issues, including the impact of regulatory barriers and increased data demand on network infrastructure. It also suggests measures to support the development of effective wireless communication policies, ensuring sustainable progress as technologies advance towards 5G, MIMO, and IoT. The goal is to identify improvements for more robust wireless communication systems. © 2024 IEEE.

关键词： 5G Accessibility Communication IOT Network Infrastructure Security Wireless technology

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset

M3ED: Multi-Robot, Multi-Sensor, Multi-Environment Event Dat...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Chaney, Kenneth Cladera, Fernando Wang, Ziyun Bisulco, Anthony Hsieh, M. Ani Korpela, Christopher Kumar, Vijay Taylor, Camillo J. Daniilidis, Kostas University of Pennsylvania GRASP Laboratory PA19104 United States Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

ISBN: (纸本)9798350302493

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and performing aggressive flight maneuvers. Our dataset also covers demanding operational scenarios for event cameras, such as scenes with high egomotion and multiple independently moving objects. The sensor suite used to collect M3ED includes high-resolution stereo event cameras (1280×720), grayscale imagers, an RGB imager, a high-quality IMU, a 64-beam LiDAR, and RTK localization. This dataset aims to accelerate the development of event-based algorithms and methods for edge cases encountered by autonomous systems in dynamic environments. © 2023 IEEE.

关键词： Cameras

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A Robust Pilot Decontamination Scheme in Massive MIMO Systems: Integrating Rateless Orthogonal STBC, Weighted Graph Coloring, and Channel Estimation

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IEEE Access 2025年 13卷 94453-94463页

作者： Habib M. Hussien Zelalem A. Kelem Department of Electrical and Computer Engineering Artificial Intelligence and Robotics Center of Excellence Addis Ababa Science and Technology University Addis Ababa Ethiopia

Even though Multiple Input Multiple Output (MIMO) systems are one of the greatest innovations in wireless cellular network technology, they still face many challenges as we progress to 6G, hindering their ability to achieve the best throughput. In cellular networks, channel estimations are made using pilot sequences. Deploying these orthogonal time-frequency limited resources leads to pilot contamination, causing incorrect channel estimation due to mixed signals from neighboring cells and vice versa. Previous research has proven that this significantly decreases system performance. To reduce the impact and improve the output of Multiuser MIMO systems, this paper proposes a hybrid model combining Rateless Orthogonal Space-Time Block Codes (ROSTBC), Weighted Graph Coloring Pilot Allocation (WGCPA), and Maximum Likelihood Estimation (MLE). While ROSTBC has the ability to adapt to channel conditions, allowing the system to perform well despite pilot contamination, WGCPA and MLE address the problems of pilot contamination and the accuracy of channel estimation, respectively. The results showed that this hybrid scheme has proven to perform better compared to previously proposed schemes, providing a valuable contribution to reliable Massive MIMO wireless communication.

关键词： Channel estimation Contamination Interference Maximum likelihood estimation Resource management Spectral efficiency Accuracy Signal to noise ratio Massive MIMO Encoding

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Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

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China Communications 2023年第1期20卷 125-139页

作者： Wenqian Zhang Wenya Fan Guanglin Zhang Shiwen Mao College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849-5201USA

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among *** addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of *** this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain *** formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)*** address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)*** simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

关键词： cooperative mobile-edge computing blockchain workload offloading service caching load balancing deep reinforcement learning(DRL)

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory

Characterizing Memory Access Patterns of Various Convolution...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Jang, Jihoon Kim, Hyun Lee, Hyokeun Research Center for Electrical and Information Technology Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul Korea Republic of Inter-University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources. © 2023 IEEE.

关键词： Convolutional Neural Networks Memory access pattern Near data processing Network training Processing-in-Memory

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