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6th International Conference on Computational Intelligence and Applications, ICCIA 2021

作者： Yan, Chenyang Todo, Yuki Tang, Zheng Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan University of Toyama Department of Intelligence Information Systems Toyama Japan

ISBN: (纸本)9781665439336

Motion direction detection is an important part of the visual system. In the past few decades, research on the directional selectivity of motion detection is of great progress. However, they are all concentrated on the cellular level and lack a full understanding in systemic level. To understand motion direction detection, we present a novel motion detection mechanism using the Hassenstein-Reichardt (HR) model. We mimic a subunit of the HR model, propose a local motion direction detective neuron which can detect a specific direction of motion by performing a multiplication operation on one photoreceptor input with a fixed temporal delay and its adjacent photoreceptor input without delay. The proposed neuron can be extended to a two-dimensional vision, for ease of understanding, 8 neurons are implemented in detecting 8 directions of motion. Furthermore, we assume that each photoreceptor in the receptive field has its own local motion direction detective neurons and therefore we can obtain the global motion direction by accumulating the number of activations of all local motion direction detective neurons. The computer simulations have validated that the mechanism we proposed is reliable. We believe it may provide a strong support in understanding motion direction detection and other more complex processing in the visual system. © 2021 IEEE.

关键词： Motion analysis

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Co-Design of Network Topology and Qubit Allocation for Distributed Quantum Computing

Co-Design of Network Topology and Qubit Allocation for Distr...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia USA Dept. of Engineering Technology University of Houston Houston USA Dept. of Electrical and Computer Engineering University of Houston Houston USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.

关键词： Costs Network topology Computational modeling Qubit Topology Computational efficiency Resource management Quantum circuit Integrated circuit modeling Optimization

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Effect of Spectrogram Preprocessing and Enhancement on Speaker Recognition Performance

Effect of Spectrogram Preprocessing and Enhancement on Speak...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Segun I. Popoola Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria Department of Engineering Manchester Metropolitan University Manchester United Kingdom

Preprocessing in biometrics is the process of fine-tuning input data or features extracted from data by applying varying techniques to improve recognition performance. In most speaker recognition literature, researchers are silent about how the decision for such parameters used was chosen. This work systematically arrived at such decisions by carrying out several experiments on preprocessing and enhancement of spectrograms to see the effect on speaker recognition performance using a Convolutional Neural Network. First, different preprocessing experiments were carried out, one preprocessing parameter, in turn, was varied while the others were kept constant and eventually, the best parameters for all the preprocessing methods were combined to produce a spectrogram that yielded the best accuracy. The second part consists of enhancement experiments, where a series of image improvement techniques were applied to the spectrograms to further improve the initial accuracy. With the right parametric combination of the preprocessing techniques, speaker recognition improved by 25%. Spectrogram enhancements improved performance by a further 1%. Experimental results revealed that the dimensionality of a spectrogram can be significantly reduced with a very negligible drop in the overall performance which can enhance storage and computational requirements.

关键词： Training Deep learning Systematics Quantization (signal) Data preprocessing Feature extraction Speaker recognition

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Evaluation of Centralized, Distributed and Federated Learning for IoT Intrusion Detection systems

Evaluation of Centralized, Distributed and Federated Learnin...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Omar Elnakib Eman Shaaban Mohamed Mahmoud Karim Emara Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Electrical and Computer Engineering Tennessee Technological University Cookeville TN USA

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. Distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. The three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. This result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy.

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Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

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Energy and AI 2023年第4期14卷 294-303页

作者： Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar Chikodili Ugwuishiwu Centre for Space Transport and Propulsion (CSTP)Epe1 LASU Epe driveLagos106101Nigeria Department of Electrical&Electronics Engineering University of LagosAkoka101017LagosNigeria Centre for Intelligent Systems(CIS) School of Engineering and TechnologyCQ UniversityNorman Gardens4701RockhamptonAustralia Strategic Centre for Cyber Security Research Institute(CSRI) School of Information TechnologyDeakin UniversityGeelong3216MelbourneAustralia Department of Computer Science University of NigeriaNsukka410001EnuguNigeria

Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the *** of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the *** proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic *** input parameter used in the models is easily measured with basic power *** model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing ***(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden *** results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.

关键词： Total harmonics distortion(THD) Power quality(PQ) Artificial neural network(ANN)

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Nyctale: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction

Nyctale: Neuro-Evidence Transformer for Adaptive and Persona...

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IEEE International Conference on Image Processing

作者： Sadaf Khademi Anastasia Oikonomou Konstantinos Plataniotis Arash Mohammadi Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Medical Imaging Sunnybrook Health Sciences Centre Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Drawing inspiration from the primate brain’s intriguing evidence accumulation process, and guided by models from cognitive psychology and neuroscience, the paper introduces the NYCTALE framework, a neuro-inspired and evidence accumulation-based Transformer architecture. The proposed neuro-inspired NYCTALE offers a novel pathway in the domain of Personalized Medicine (PM) for lung cancer diagnosis. In nature, Nyctales are small owls known for their nocturnal behavior, hunting primarily during the darkness of night. The NYCTALE operates in a similarly vigilant manner, i.e., processing data in an evidence-based fashion and making predictions dynamically/adaptively. Distinct from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the NYCTALE performs predictions only when sufficient amount of evidence is accumulated. In other words, instead of processing all or a pre-defined subset of CT slices, for each person, slices are provided one at a time. The NYCTALE framework then computes an evidence vector associated with contribution of each new CT image. A decision is made once the total accumulated evidence surpasses a specific threshold. Preliminary experimental analyses conducted using a challenging in-house dataset comprising 114 subjects. The results are noteworthy, suggesting that NYCTALE outperforms the benchmark accuracy even with approximately $60 \%$ less training data on this demanding and small dataset.

关键词： Analytical models Adaptation models Accuracy Computed tomography Computational modeling Lung Lung cancer

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Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning

Adaptive Resource Allocation in Quantum Key Distribution (QK...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Rakpong Kaewpuang Minrui Xu Dusit Niyato Han Yu Zehui Xiong Xuemin Sherman Shen School of Computer Science and Engineering Nanyang Technological University (NTU) Pillar of Information Systems Technology and Design Singapore University of Technology and Design (SUTD) Department of Electrical and Computer Engineering University of Waterloo

Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without exposing their local datasets. To facilitate QKD-FL, the architectural design and routing management framework are essential. However, effective implementation is still lacking. To this end, we propose a hierarchical architecture for QKD-FL systems in which QKD resources (i.e., wavelengths) and routing are jointly optimized for FL applications. In particular, we focus on adaptive QKD resource allocation and routing for FL workers to minimize the deployment cost of QKD nodes under various uncertainties, including security requirements. The experimental results show that the proposed architecture and the resource allocation and routing model can reduce the deployment cost by 7.72% compared to the CO-QBN algorithm.

关键词： Adaptation models Costs Uncertainty Adaptive systems Federated learning computer architecture Routing

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Advancing Arabic Inner Speech Recognition with Machine Learning and Deep Learning

Advancing Arabic Inner Speech Recognition with Machine Learn...

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electrical engineering (ICEENG), International Conference on

作者： Aya E. Ahmad Habiba Hafrag Ziad A. Meligy Heba Ali Abdelbary Sahar Selim Systems and Biomedical Engineering Cairo University Giza Egypt Biomedical and Bioinformatics Engineering School of Electrical Communication and Computer Engineering Egypt-Japan University of Science and Technology Center for Informatics Science (CIS) School of Information Technology Nile University Sheikh Zayed Egypt

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Individuals with motor and communication impairments struggle to access communication tools and fully participate in society. Advances in Brain-computer Interface (BCI) technology enable direct brain communication via noninvasive EEG signals from inner speech. However, accessible applications for Arabic-speaking individuals remain limited. Our research explores various feature extraction and classification methods on the publicly available Arabic EEG dataset, "ArEEG." Using EEGNet on raw EEG data, we improved cross-validation accuracy by 2%, reaching 27.1% per subject. Additionally, we propose a novel K-Nearest Neighbors (KNN) approach with Relative Wavelet Energy (RWE) and Gabor Transform features, selected via ANOVA, achieving 31.03% accuracy in subject-dependent analysis.

关键词： Wavelet transforms electrical engineering Accuracy Speech recognition Nearest neighbor methods Wavelet analysis Motors Feature extraction Electroencephalography Convolutional neural networks

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Enhancing Power Quality Event Classification with AI Transformer Models

Enhancing Power Quality Event Classification with AI Transfo...

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IEEE General Meeting Power& Energy Society

作者： Ahmad M. Saber Amr Youssef Davor Svetinovic Hatem Zeineldin Deepa Kundur Ehab El-Saadany EECS Department Advanced Power and Energy Center Khalifa University Abu Dhabi UAE Department of Electrical and Computer Engineering University of Toronto Toronto Canada Concordia Institute for Information Systems Engineering Montreal Canada

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal’s amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%–91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

关键词： Voltage measurement Noise Power quality Transformers Harmonic analysis Frequency measurement Numerical models Noise measurement Transient analysis Signal to noise ratio

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