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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Melnyk, Bohdan Stakhiv, Petro Melnyk, Nataliya Franko, Yuriy Seniv, Bohdan Martsenyuk, Yevgeniya Ivan Franko National University of Lviv Department of Information Systems of Management Lviv Ukraine Lviv Polytechnic National University Department of Theoretical and General Electrical Engineering Lviv Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine Banking Business Department of Financial Technologies Ternopil Ukraine Ukrainian National University Department of Computer Science West Ternopil Ukraine

ISBN: (纸本)9798350350036

The article is devoted to the development of software for the implementation of the stochastic optimization method. The classical method of the directed cone and its modification are considered. Its shortcomings in the process of searching for the global minimum are pointed out. To improve the search, it is proposed to use algorithms with adaptation of optimization parameters. The architecture of software implementation of optimization algorithms of the directed cone method is proposed. Appropriate software was developed. It was tested on the example of optimization of the structured cable system of the telecommunications network. For this, a corresponding optimization problem is formulated. The results of its solution and application of optimization algorithms are analyzed. © 2024 IEEE.

关键词： Optimization algorithms

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NFChain: A Practical Fingerprinting Scheme for NFC Tag Authentication 42

NFChain: A Practical Fingerprinting Scheme for NFC Tag Authe...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Yang, Yanni Cao, Jiannong An, Zhenlin Wang, Yanwen Hu, Pengfei Zhang, Guoming Shandong University School of Computer Science and Technology Qingdao China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Hunan University College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350334142

NFC tag authentication is highly demanded to avoid tag abuse. Recent fingerprinting methods employ the physical-layer signal, which embeds the tag hardware imperfections for authentication. However, existing NFC fingerprinting methods suffer from either low scalability for a large number of tags or incompatibility with NFC protocols, impeding the practical application of NFC authentication systems. To fill this gap, we propose NFChain, a new NFC fingerprinting scheme that excavates the tag hardware uniqueness from the protocol-agnostic tag response signal. Specifically, we harness an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies, which significantly improves fingerprint scalability. However, extracting the desired fingerprint encounters two practical challenges: (1) fingerprint inconsistency under different NFC reader and tag configurations and (2) fingerprint variations across multiple measurements of the same tag due to the signal noise in generic readers. To tackle these challenges, we first design an effective nulling method to eliminate the effect of device configurations. Second, we employ contrastive learning to reduce fingerprint variations for accurate authentication. Extensive experiments show we can achieve as low as 3.7% FRR and 4.1% FAR for over 600 tags. © 2023 IEEE.

关键词： Authentication

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Robustness of Optimal Circadian Rhythm Entrainment under Model Perturbation

Robustness of Optimal Circadian Rhythm Entrainment under Mod...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Zidi Tao Agung Julius John T. Wen Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

The optimal circadian rhythm entrainment problem has been studied based on mathematical models, e.g. the Kronauer model. In this paper, we study the robustness of light-based circadian rhythm entrainment under model parameter perturbations as well as propose a feedback control law to improve the robustness of the entrainment strategy. Our study finds the model parameter whose perturbations affect the entrainment the most. We also find that feedback control reduces the sensivitity of the entrainment process to model parameter *** relevance— Circadian misalignment has negative impacts on health, such as higher risks of cardiovascular disease and cancer. We present a numerical study of how well optimized circadian rhythm entrainment plans that are derived from a generic mathematical model work on personalized cases.

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Counterfeit Chip Detection using Scattering Parameter Analysis

arXiv

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arXiv 2023年

作者： Safa, Maryam Saadat Mosavirik, Tahoura Tajik, Shahin Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

The increase in the number of counterfeit and recycled microelectronic chips in recent years has created significant security and safety concerns in various applications. Hence, detecting such counterfeit chips in electronic systems is critical before deployment in the field. Unfortunately, the conventional verification tools using physical inspection and side-channel methods are costly, unscalable, error-prone, and often incompatible with legacy systems. This paper introduces a generic non-invasive and low-cost counterfeit chip detection based on characterizing the impedance of the system's power delivery network (PDN). Our method relies on the fact that the impedance of the counterfeit and recycled chips differs from the genuine ones. To sense such impedance variations confidently, we deploy scattering parameters, frequently used for impedance characterization of RF/microwave circuits. Our proposed approach can directly be applied to soldered chips on the system's PCB and does not require any modifications on the legacy systems. To validate our claims, we perform extensive measurements on genuine and aged samples from two families of STMicroelectronics chips to assess the effectiveness of the proposed approach. © 2023, CC BY.

关键词： Scattering parameters

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Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving

arXiv

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arXiv 2023年

作者： Huang, Zhanhong Zhang, Xiao Huang, Xinming The Department of Electrical & Computer Engineering Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception error may adversely impact the motion planning results. In this work, we propose a joint simulation framework with LiDAR-based perception and motion planning for real-time automated driving. Taking the sensor input from the CARLA simulator with additive noise, a LiDAR perception system is designed to detect and track all surrounding vehicles and to provide precise orientation and velocity information. Next, we introduce a new collision bound representation that relaxes the communication cost between the perception module and the motion planner. A novel collision checking algorithm is implemented using line intersection checking that is more efficient for long distance range in comparing to the traditional method of occupancy grid. We evaluate the joint simulation framework in CARLA for urban driving scenarios. Experiments show that our proposed automated driving system can execute at 25 Hz, which meets the real-time requirement. The LiDAR perception system has high accuracy within 20 meters when evaluated with the ground truth. The motion planning results in consistent safe distance keeping when tested in CARLA urban driving scenarios. © 2023, CC BY.

关键词： Motion planning

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Design and Implementation of a Low-Cost Malaria Diagnostic System Based on Convolutional Neural Network

SSRN

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SSRN 2025年

作者： Ekobo Akoa, Brice Mohamadou, Madina Jean, Ndoumbe Department of Electrical and Telecommunications Engineering National Advanced School of Engineering of Yaounde Yaounde Cameroon Department of Computer and Telecommunications Engineering National Higher Polytechnic School of Douala Douala Cameroon

This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the convolutional neural network. The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not;its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. © 2025, The Authors. All rights reserved.

关键词： Diseases

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Controllable Built-in Leakage Inductance for A Novel Matrix Integrated Transformer

Controllable Built-in Leakage Inductance for A Novel Matrix ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Feng Jin Zheqing Li Ahmed Nabih Qiang Li Bradley Department of Electrical and Computer Engineering Virginia Polytechnic and State University Blacksburg USA

Integrated transformer with built-in leakage inductors is a good approach for simplifying the whole system, as it integrates leakage inductors into the transformer with printed circuit board (PCB) windings for CLLC resonant converter. A novel matrix integrated transformer with adjustable built-in leakage inductance is proposed in this paper. The built-in leakage inductance can be controlled by adjusting the winding arrangement of primary and secondary windings or adjusting the reluctance of each core leg, which changes the coupling between primary and secondary windings. Inductance measurements of integrated transformers with different winding arrangements and air gaps of cores are implemented to verify the proposed two methods. The novel matrix integrated transformer was demonstrated on a 500kHz 11kW single-phase CLLC resonant converter for onboard charger application with 0.14% efficiency improvement at full load compared with the original design.

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Performance Evaluation of the DC Motor using Robust H-Infinity and H2 Controllers

Performance Evaluation of the DC Motor using Robust H-Infini...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Abubakar Umar Suleiman H. Haruna Aminu Y. Zubairu Shamsuddeen Yusuf Dahiru Musbahu Ibrahim Abdulwahab Computer Engineering Department ABU Zaria Federal Polytechnic N’yak Shandam Plateau State Mechanical Engineering Department ABU Zaria Electrical Engineering Department FUT Minna Niger State Electrical Engineering Department ABU Zaria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

An evaluation studies for the position control of the Direct Current (DC) motor using two robust controllers have been presented. The primary objective of the DC motor is to control the motor’s speed on a particular reference when disturbances and uncertainties act on it. Regulating the angular speed through the adjustment of the power supply is complicated by uncertainties and disturbances which emphasized the need for a robust controller. The H ∞ and H 2 were used for the control of the DC motor. Their performances were evaluated, and simulation was done in MATLAB 2022b environment. From the simulated results, it showed that the two robust controllers performed satisfactorily when measured against robust stability and robust performance. The designed H ∞ controller had a better singular value plot of 40dB at the upper bound and -79.2dB at the lower bound. While it had a step response performance with regards to output one; rise time of 6.77 secs, settling time of 57.4 secs and steady state error of 0.515. Also, with regards to output two; it had a rise time of 72.3 secs, a settling time of 119 secs, with a steady state error of 0.756. The H 2 controller had a singular value plot of 37.4dB at the upper bound and -116dB at the lower bound. It also had a step response performance with regards to output one; with rise time of 6.94 secs, settling time of 63.9 secs and steady state error of 0.347. with regards to output two; it had a rise time of 91.2 secs, settling time of 154 secs and steady state error of 0.756. This shows good robust stability and robust performance for the DC motor’s position control. This demonstrates the effectiveness of the robust controllers on the DC motor.

关键词： Lower bound Upper bound Uncertainty Robust stability Power supplies Position control DC motors Stability analysis Steady-state MATLAB

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Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning

arXiv

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arXiv 2024年

作者： Kommu, Sindhura Wang, Yizhi Wang, Yue Wang, Xuan Department of Computer Science Virginia Polytechnic Institute and State University BlacksburgVA United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University ArlingtonVA United States

Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is a complex challenge that requires capturing the intricate relationships between genes and their regulatory interactions. In this study, we tackle this challenge by leveraging the single-cell BERTbased pre-trained transformer model (scBERT), trained on extensive unlabeled scRNA-seq data, to augment structured biological knowledge from existing GRNs. We introduce a novel joint graph learning approach scTransNet that combines the rich contextual representations learned by pretrained single-cell language models with the structured knowledge encoded in GRNs using graph neural networks (GNNs). By integrating these two modalities, our approach effectively reasons over both the gene expression level constraints provided by the scRNA-seq data and the structured biological knowledge inherent in GRNs. We evaluate scTransNet on human cell benchmark datasets from the BEELINE study with cell type-specific ground truth networks. The results demonstrate superior performance over current state-of-the-art baselines, offering a deeper understanding of cellular regulatory mechanisms. © 2024, CC BY.

关键词： Graph neural networks

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Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5166-5181页

作者： Yang, Cuixin Dong, Rongkang Xiao, Jun Zhang, Cong Lam, Kin-Man Zhou, Fei Qiu, Guoping The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong Shenzhen University College of Electronic and Information Engineering Shenzhen518060 China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen518060 China University of Nottingham NottinghamNG8 1BB United Kingdom School of Computer Science NingboNG8 1BB China

As virtual and augmented reality applications gain popularity, omnidirectional image (ODI) super-resolution has become increasingly important. Unlike 2D plain images that are formed on a plane, ODIs are projected onto spherical surfaces. Applying established image super-resolution methods to ODIs, therefore, requires performing equirectangular projection (ERP) to map the ODIs onto a plane. ODI super-resolution needs to take into account geometric distortion resulting from ERP. However, without considering such geometric distortion of ERP images, previous methods only utilize a limited range of pixels and may easily miss self-similar textures for reconstruction. In this paper, we introduce a novel Geometric Distortion Guided Transformer for Omnidirectional image Super-Resolution (GDGT-OSR). Specifically, a distortion modulated rectangle-window selfattention mechanism, integrated with deformable self-attention, is proposed to better perceive the distortion and thus involve more self-similar textures. Distortion modulation is achieved through a newly devised distortion guidance generator that produces guidance for the rectangular windows by exploiting the variability of distortion across latitudes. Furthermore, we propose a dynamic feature aggregation scheme to adaptively fuse the features from different self-attention modules. We present extensive experimental results on public datasets and show that the new GDGT-OSR outperforms methods in existing literature. © 1991-2012 IEEE.

关键词： Image resolution

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