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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Zhou, Yanlin Tan, Kai Shen, Xinyu He, Zheng Zheng, Haotian Johns Hopkins University Computer Science MD United States University Of Washington Electrical & Computer Engineering SeattleWA United States Biostatistics Columbia University FriscoTX United States Applied Analytics Columbia University New York United States New York University Electrical & Computer Engineering New YorkNY United States

ISBN: (纸本)9798350361445

Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization. © 2024 IEEE.

关键词： Proteins

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High-Performance Rejection Sampling Hardware Circuit Design for Kyber 15

High-Performance Rejection Sampling Hardware Circuit Design ...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Wang, Yang Zhang, Huihong Zhang, Yuejun Wei, Hongshuai Wang, Pengjun Yuan, Tengfei Wang, Chengjie Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

CRYSTALS-Kyber is a powerful Post-Quantum Cryptography(PQC) with high resistance to quantum computer attacks. In order to optimize the core sampling structure of Kyber so as to improve the hardware execution efficiency, this paper proposes a high-performance rejection sampling hardware circuit design scheme for Kyber. The scheme first divides the input random numbers by width converter, and then combines four parallel sampling cores to shorten the sampling time period;then on the basis of the high parallelization of sampling cores, this work use the reorganization of random numbers and the bit-weighted value characteristics of binary numbers to design high-efficiency comparator to reduce the rejection rate, and reduce the sampling time and overhead;and finally, splice the valid inputs and set up a buffer to store the generated samples. The scheme is verified for its performance by testing under Taiwan Semiconductor Manufacturing Company(TSMC) 65nm process and FPGA implementation under Vivado platform. The experimental results demonstrate that the optimized sampling circuit reduces the rejection rate from 18.73% to 11.33% and shortens the sampling clock period by 80.84% compared to the basic implementation;comparing with the related work, it reduces the area by 67.77% while controlling the sampling time, and the consumption of hardware resources is greatly reduced, which improves the efficiency of the hardware execution of the Kyber rejection sampling. © 2023 IEEE.

关键词： CRYSTALS-Kyber high-efficiency comparator Post-Quantum Cryptography rejection sampling width converter

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Scaling Distributed Collaboration in Mixed Reality

Scaling Distributed Collaboration in Mixed Reality

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Genay, Adélaïde Syiem, Brandon Victor Wong, Emily Feuchtner, Tiare Knibbe, Jarrod Grønbæk, Jens Emil Sloth Velloso, Eduardo School of Computer Science University of Sydney SydneyNSW Australia School of Computing and Information Systems The University of Melbourne MelbourneVIC Australia HCI Group University of Konstanz Konstanz Germany Department of Computer Science Aarhus University Aarhus Denmark School of Electrical Engineering and Computer Science The University of Queensland St. LuciaQLD Australia

ISBN: (纸本)9798400713958

Distributed collaboration in Mixed Reality (MR) promises to revolutionise how people connect across different physical environments, offering experiences akin to face-to-face interactions. However, previous work has mostly focused on enabling this vision in overly simplified settings such as with only two users interacting in identical distributed environments. Scaling current systems to work with large groups and for common real-life scenarios is a persistent challenge that requires addressing multiple tensions. We identified six challenges: 1) supporting locally congruent actions from heterogeneous remote spaces, 2) communicating accurate user behaviours through virtual representation instead of physical bodies, 3) facilitating organic group interactions within limited physical space, 4) maintaining conversational dynamics even in asynchronous exchanges, 5) providing equal access to physical objects for all participants, and 6) enabling efficient task switching within a complex ecology of applications, devices, and accessibility needs. This workshop aims to gather researchers and practitioners to explore actionable strategies for resolving these challenges. Through a mix of presentations, hands-on activities, and group discussions, participants will generate new ideas and develop a research agenda to articulate the future of MR collaboration systems. The workshop outcomes will include a list of concrete next steps for the community to bring distributed MR collaboration at scale. © 2025 Copyright held by the owner/author(s).

关键词： Grid computing

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WikiTND24: A Chinese Text Normalization Database 27

WikiTND24: A Chinese Text Normalization Database

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Li, Wu-Hao Chiang, Chen-Yu College of Electrical Engineering and Computer Science National Taipei University Taiwan Dept. of Communication Engineering National Taipei University New Taipei City Taiwan

ISBN: (纸本)9798331506032

Labeling large datasets for constructing a text normalization system is cumbersome and time-consuming. Although some self-supervised learning models can reduce the amount of training data needed, labeling sufficient data still requires significant labor and time. To address this issue, we used a self-developed rule-based text normalization system to nor-malize Chinese Wikipedia texts, generating a large amount of training data to develop a non-standard word recognition model. Besides verifying the performance of our model with the FlatTN database test set, we also demonstrated that it can help detect errors in the rule-based system and accelerate the development of a data-driven or machine-learning-based text normalization system. Additionally, we are making this database, WikiTND24, publicly available for everyone to use. © 2024 IEEE.

关键词： Self-supervised learning

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Physics Informed Neural Networks Predicting Junction Temperature of Power Devices for PV-Based DC Microgrids 50

Physics Informed Neural Networks Predicting Junction Tempera...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Sharma, Shrivatsal Sharma, Utkarsh Bhattacharya, Subhashish Electrical and Computer Engineering Department North Carolina State University RaleighNC27606 United States Computer Science Department North Carolina State University RaleighNC27606 United States

ISBN: (纸本)9781665464543

This paper proposes a physics-informed neural network model to predict the junction temperature of power devices utilized in the converter that integrates photo voltaic (PV) to the DC microgrid. The power converter operates at maximum power tracking point mode. The model considers solar irradiance, ambient temperature, output voltage of PV, and switching frequency as inputs. The model is trained using a wide range of real-world operating conditions from the National Renewable Energy Laboratory (NREL) database. The importance and utility of the proposed model are presented using two different scenarios. In one of the scenarios, the model is used to predict the duration of different overload conditions that the device can sustain. Such a prediction can help to develop prognostic methods to ensure a device operates within the allowed temperature limits. The other scenario demonstrates the modification in the switching frequency of the converter if the prediction temperature of a device exceeds the allowed limit. This scenario eliminates the need for temperature sensors and again ensures the safe operation of a device. © 2024 IEEE.

关键词： Microgrids

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Deep Learning Assisted Blind Modulation Classifier for Blockage-Infused Sparse Visible Light Communications

Deep Learning Assisted Blind Modulation Classifier for Block...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Shaik, Parvez Kececi, Cihat Garg, Kamal K. Ismail, Muhammad Serpedin, Erchin Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX United States Pandit Deendayal Energy University School of Technology Gujarat Gandhinagar India Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798350376715

This study investigates the performance of a deep learning-assisted convolutional neural network (CNN) based modulation classifier for blockage-infused sparse visible light communications (VLCs). A single VLC access point-based office setup is considered with human blockages obstructing the receiver node (user). System performance is assessed by a CNN-based neural network to distinguish the received noisy modulated symbol at the receiver without having prior knowledge of the transmitted symbols. Modulation schemes such as binary phase shift keying, quadrature amplitude modulation, and on-off shift keying are considered. Performance metrics illustrate that the blockages cause a significant impact on signal classification. computer simulations corroborate the correctness of the obtained analytical expressions. © 2024 IEEE.

关键词： Convolutional neural networks

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A Highly Selective Dual Bandpass Filter using Couple Line Resonator for Modern Wireless Communication Systems 4

A Highly Selective Dual Bandpass Filter using Couple Line Re...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Mushtaq, Bilal Rehman, Muhammad Abdul Hussain, Aqib Abbass, Muhammad Jamshed Riphah International University Department of Electrical Engineering Islamabad Pakistan Wroclaw University of Science and Technology Department of Computer Engineering Poland Wroclaw University of Science and Technolog Department of Electrical Engineering Poland

ISBN: (纸本)9798350335316

In this paper, a miniaturized dual-band bandpass filter (DB-BPF) based on a dual-path stub-loaded resonator is presented. A transversal filtering design is introduced in the proposed DB-BPF, which provides two signal propagation paths from input to output. In total, six transmission poles and four transmission zeros are obtained, which improves overall filter selectivity. The fractional bandwidth (FBW) of dual bands with center frequencies of 2.24 and 7.35 GHz is 36.4% and 31.2%, respectively. The physical size of a fabricated filter is (0.43 × 0.17)λg, where λg is the guided wavelength at 2.24 GHz. The DB-BPF prototype is fabricated using a Roger Duroid RT/5880 substrate to further validate the result. The full-wave EM simulation and measured results show good agreement. The proposed dual-band bandpass is applicable to a wide range of modern communication technologies, including Wi-Fi, satellite, cellular, radar, and broadcast communication. © 2023 IEEE.

关键词： Bandpass filters

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Model-free based Real-time Authentication Framework for Distributed Synchrophasors

Model-free based Real-time Authentication Framework for Dist...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zheng, Yao Qiu, Wei Yao, Wenxuan Li, Bing Duan, Junfeng Yin, He Hunan University Department of Electrical and Electronic Engineering Changsha China University of Tennessee Department of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9781665471640

Cyber-attacks have the potential to evade bad synchrophasor data detection in wide-area monitoring systems (WAMS), thus having a detrimental impact on situational awareness and synchrophasor-based applications. To tackle this challenge, this paper proposes a model-free synchrophasor data authentication framework to defend against false data injection. The principal components analysis and gramian angular field are first combined to extract the critical features from the synchrophasor data in the distributed power system. Considering the massive synchrophasor measurement data, a convolutional neural network is further presented to fulfil the data authentication based on the extracted features. The efficacy of the real-time authentication framework is confirmed through various experiments. The experimental results demonstrate an accuracy rate of 93.15% when utilizing field synchrophasor data, without the need for knowledge of system model parameters. © 2023 IEEE.

关键词： Cyber attacks gramian angular field principal components analysis synchrophasor data authentication

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Sparse channel estimation for MIMO OTFS/OTSM Systems using Finite-resolution ADCs

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IEEE Transactions on Communications 2024年第6期73卷 3971-3987页

作者： Mehrotra, Anand Srivastava, Suraj Reddy, Shanmughanadha Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Department of Electrical Engineering UP Kanpur208016 India Indian Institute of Technology Department of Electrical Engineering Jodhpur India Rakuten Mobile Tokyo Japan University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Variational Bayesian learning (VBL)-based sparse channel state information (CSI) estimation is conceived for multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) and for orthogonal time sequence multiplexing (OTSM)-based systems relying on low-resolution analog-to-digital convertors (ADCs). First, the CSI estimation model is developed for MIMO-OTFS systems considering quantized outputs. Then a novel VBL technique is developed for exploiting the inherent DD domain sparsity. Subsequently, an end-to-end system model is derived for MIMO-OTSM systems, once again, using only finite-resolution ADCs. Similar to OTFS systems, it is demonstrated that the channel is sparse in the delay-sequency (DS)-domain. Thus the sparse CSI estimation problem of the MIMO-OTSM system can also be solved using the VBL technique developed for its OTFS counterpart. A bespoke minimum mean square error (MMSE) receiver is developed for data detection, which unlike the conventional MMSE receiver also accounts for the quantization error. Finally, finite-resolution ADCs emerge as a solution, offering reduced costs and energy consumption amid the growing challenge posed by energy-intensive high-resolution ADCs in Next-Generation (NG) systems. The efficacy of the proposed techniques is validated by simulation results, surpassing the state-of-the-art and signalling a transition towards more sustainable communication technologies. © 1972-2012 IEEE.

关键词： Mean square error

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Advanced Air Mobility: Innovations, Applications, Challenges, and Future Potential

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Journal of Air Transportation 2025年第2期33卷 169-187页

作者： Raza, Waleed Renkhoff, Justus Ogirimah, Olayemi Bawa, Gurvir Kaur Stansbury, Richard S. Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Information Systems University of Maryland BaltimoreMD21250 United States

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