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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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MR2CPPIS: Accurate prediction of protein–protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

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computers in Biology and Medicine 2024年 176卷 108543-108543页

作者： Gong, Yinyin Li, Rui Liu, Yan Wang, Jilong Cao, Buwen Fu, Xiangzheng Li, Renfa Chen, Danny Z. College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518066 China College of Information and Electronic Engineering Hunan City University Yiyang413002 China Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

Proteins play a vital role in various biological processes and achieve their functions through protein–protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://***/YyinGong/MR2CPPIS. © 2024 Elsevier Ltd

关键词： Forecasting

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New Dynamic Bayesian Network for Time-to-Event Prediction

New Dynamic Bayesian Network for Time-to-Event Prediction

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ACS/IEEE International Conference on computer systems and Applications

作者： Walid Ben Abdallah Hasna Njah Salma Jamoussi National Engineering School of Gabes Gabes University Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax University Tunisia Higher institute of Computer sciences and multimedia of Gabes Gabes University Tunisia Higher institute of Computer sciences and multimedia of Sfax Sfax university Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Time-to-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-to-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-to-event processes.

关键词： Multiple sclerosis Accuracy Precision medicine Probabilistic logic Bayes methods Timing Medical diagnostic imaging Context modeling

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Adaptive energy-preserving algorithms for guiding center system

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Plasma Science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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AI/ML Based Detection and Categorization of Covert Communication in IPv6 Network

arXiv

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arXiv 2025年

作者： Rahman, Mohammad Wali Ur Lin, Yu-Zheng Weeks, Carter Ruddell, David Gabriellini, Jeff Hayes, Bill Hariri, Salim Ziegler, Edward V. Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Management Information Systems University of Arizona TucsonAZ85721 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States Laboratory for Advanced Cybersecurity Research National Security Agency United States

The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. In addition, we also proposed a Generative AI-assisted interpretation concept based on prompt engineering as a preliminary study of the role of Generative AI agents in covert communication. © 2025, CC BY-NC-ND.

关键词： Decision trees

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Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

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Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

Spatial adaptivity with boundary refinement for smoothed par...

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作者： Xu, Yanrui Song, Chongming Wang, Xiaokun Ban, Xiaojuan Wang, Jiamin Zhang, Yalan Chang, Jian School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing China National Centre for Computer Animation Bournemouth University Poole United Kingdom Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Fluid simulation is well-known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary-distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed-distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid–solid coupling details more efficiently and effectively capture dynamic effects beneath the surface. © 2023 The Authors. computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Hydrodynamics

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