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24th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2024

作者： Lukač, Niko Kavran, Domen Ovčjak, Monika Ferk Bizjak, Marko Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

ISBN: (纸本)9798350353433

This paper presents a novel method for optimizing meteorological sensor placement over large geographic areas, leveraging genetic algorithm (GA) and classified Light Detection And Ranging (LiDAR) data. The airborne LiDAR data provides detailed surface information necessary for finding the optimal locations for sensor placement. By considering semantic segmentation of surface, relative Sky View Factor (SVF), and surface slope, the method significantly reduces the search space to find suitable locations for sensors placement. The considered GA then uniformly and optimally positions a user-specified number of sensors, guided by optimization constraints within the fitness function. This approach enhances dense meteorological data collection efficiency, ensuring better data quality and coverage in areas such as weather forecasting, climate studies, and pollution monitoring. It reduces redundancy and deployment costs while maximizing sensors exposure to environmental physical processes such as solar radiation and wind. © 2024 IEEE.

关键词： Semantic Segmentation

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Ternary Memory Composed of Ferroelectric Field-Effect Transistor Based on Dynamic Logic 3

Ternary Memory Composed of Ferroelectric Field-Effect Transi...

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Dong, Zhichao Xia, Yinshui Li, Yang Yang, Zhaohui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350369151

Ternary Content Addressable Memory (TCAM) is widely used in development areas such as network routers and machine learning, which are receiving significant attention. Ferroelectric field-effect transistors (FeFETs) are among the most promising devices currently being researched in the field of advanced complementary metal-oxide-semiconductor (CMOS) technology. FeFETs also possess a unique hysteresis in the Ids-Vgs characteristics, which allows the device to function as both a switch and a non-volatile (NV) memory element. Traditional TCAM has the problems of VTH drop or search disturbance. We leverage FeFET property to construct a novel TCAM based on dynamic logic that can be integrated as efficient and compact logic elements into various levels of the memory hierarchy. Due to the slow trend of performance scaling based on Moore's Law and the fact that many computational tasks are now more data-centric than compute-centric, it is highly necessary to design efficient and compact logic elements based on emerging technologies to enhance performance and conserve energy consumption. © 2024 IEEE.

关键词： MOS devices

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A Ripple Controller for Single-Inductor Multiple-Output Boost Converters with Non-Cross-Regulation 3

A Ripple Controller for Single-Inductor Multiple-Output Boos...

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Lei, Jingyu Xia, Yinshui Miao, Xinkai Yan, Deping Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350369151

single-inductor multiple-output (SIMO) boost converter faces an issuce of mutual interference and cross-regulation among output voltages. This paper proposes a ripple-based non-cross-regulation controller suitable for SIMO boost converters. The proposed ripple controller effectively resolves the problems of mutual interference and cross-regulation between outputs. © 2024 IEEE.

关键词： Boost converter

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A Power Balanced Against Side Channel Attack SM4 Algorithm Based on RRAM/CMOS Hybrid Technology 9

A Power Balanced Against Side Channel Attack SM4 Algorithm B...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Wang, Pengju Zhang, Yuejun Wei, Hongshuai Zhang, Huihong Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331509453

The SM4 algorithm is now widely used to ensure the security of data transmission, but its physical implementation is still vulnerable to side channel attack. By studying the structure of SM4 algorithm, this paper proposes a scheme of SM4 algorithm against side-channel attack based on RRAM/CMOS hybrid power balancing. The scheme first studies the dynamic power balancing mechanism under the cooperative operation mode of RRAM cell and CMOS, and designs the complementary output RRAM/CMOS hybrid power balancing cell circuit by utilizing the RRAM programmable resistor states and the efficient control of CMOS logic gates;then, the leakage analysis reveals that there is a vulnerability of SM4 algorithm hardware implementation in the wheel output. To address this issue, the power balancing cell circuit is applied to the wheel output position of SM4 algorithm to decouple the correlation between power consumption and data in the output stage;finally, the power consumption curve is extracted by Spectre simulation under TSMC 65nm process and verified against correlation power analysis (CPA) attack. The experimental results demonstrate that, compared with the traditional implementation, the circuit proposed in this paper can still defend against CPA attack with 1200 power consumption curves, with more than two times higher hardware security than the traditional SM4 implementation. © 2024 IEEE.

关键词： CMOS integrated circuits

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Power Minimization Beamforming Design for Active RIS-Assisted ISAC System 16

Power Minimization Beamforming Design for Active RIS-Assiste...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yin, Lei Jin, Ming Li, Yaming Liu, Yinhong Chen, Chaodi Zhang, Huifang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

This paper investigates the active reconfigurable intelligent surfaces (RIS)-assisted integrated sensing and communication (ISAC) system, in which a dual-functional base station (BS) simultaneously transmits communication information and senses radar targets. Specifically, we consider a challenging practical scenario where the channel state information (CSI) and the radar reflection coefficient are not fully available. Under this limitation, the communication outage probability and target angle estimation failure probability constraints are introduced to describe the robust beamforming design. We aim to minimize the system's total power consumption by jointly optimizing the BS transmit beamformers and the active RIS phase-shifting. To address the non-convex optimization problem, we use the BernsteinType Inequality to safely approximate probability constraints. Then, we use the alternate optimization (AO) algorithm and semidefinite relaxation (SDR) technique to solve subproblems iteratively. Numerical results verify the active RIS-assisted ISAC system can significantly alleviate 'multiplicative fading' and reduce the power consumption compared to benchmark schemes. © 2024 IEEE.

关键词： Convex optimization

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

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computers, Materials & Continua 2024年第6期79卷 4449-4469页

作者： Weiming Huang Baisong Liu Zhaoliang Wang Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315211China Inner Mongolia Metal Material Research Institute Baotou014000China

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries to combine nodes’textual content for *** still do not,however,directly simulate many interactions in network *** order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling ***,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation ***,the Commuting Matrix for massive node pair paths is used to improve computational ***,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson *** addition,we also consider solving the model’s parameters by applying variational *** results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational *** on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

关键词： Collaborative filtering citation networks variational inference poisson factorization tag recommendation

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Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection 26

Enhanced Anomaly Detection in Automotive Systems Using SAAD:...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Goina, Dacian Hogea, Eduard Maties, George West University of Timisoara Faculty of Mathematics and Computer Science Timisoara Romania Gheorghe Asachi Technical University of Iasi Faculty of Electrical Engineering Iasi Romania

ISBN: (纸本)9798331532833

This paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD). The SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through validation on real sensor data from a Hardware-in-the-Loop (HIL) environment within the automotive domain, where physical components of the vehicle are interfaced with simulation models. The HIL setup provides a controlled, real-time testing framework that replicates actual driving conditions, allowing for the validation of the vehicle's control systems under various operational scenarios without the need for a physical vehicle prototype. The key innovation of SAAD lies in its ability to significantly enhance the accuracy and robustness of anomaly detection when combined with Fully Connected Networks (FCNs) augmented by dropout layers. Comprehensive experimental evaluations indicate that the standalone statistical method achieves an accuracy of 72.1%, whereas the deep learning model alone attains an accuracy of 71.5%. In contrast, the aggregated method achieves a superior accuracy of 88.3% and an F1 score of 0.921, thereby outperforming the individual models. These results underscore the effectiveness of SAAD, demonstrating its potential for broad application in various domains, including automotive systems. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

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