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Design and usability assessment of touchscreen interface in medical devices: a case study of a neonatal incubator

Research on Biomedical engineering 2025年第1期41卷 1-6页

作者： Viviani, Carlos Alessandro Bassi Elias, Leonardo Abdala Calil, Saide Jorge Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Center for Biomedical Engineering University of Campinas Campinas Brazil

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Secured IIoT against trust deficit - A flexi cryptic approach

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Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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Data-driven Component-level Design of Switching Power Converters Using Predictive Modeling

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IEEE Transactions on Power electronics 2025年第8期40卷 11126-11142页

作者： Reese, Skye Sauter, Bailey Maksimovic, Dragan University of Colorado Colorado Power Electronics Center Department of ElectricalComputer and Energy Engineering BoulderCO80309 United States

This paper shows how the design of switching power converters at the component level can be assisted by data-driven, automated, systematic methods. This work uses regression machine learning (ML) techniques, where predictive component models are trained using large amounts of component data and design-oriented parameters derived from the data. For a given converter topology and specification set, the ML-based component models are used to predict converter power loss, bill-of-material (BOM) cost, and footprint area. This allows for an algorithm to iterate over design parameters to optimize the objective function in terms of efficiency, size, cost, or a combination of these performance metrics while also meeting all design constraints. Once the algorithm converges, the optimized parameters are applied to filter the original dataset, identifying components that are most likely to be chosen for the specific design. The developed design automation approach allows for comparisons between topologies, device technologies, and operating modes, and reduces the design time by quickly providing component recommendations. To illustrate an application of the ML-based design approach, the tool is applied to the design of 48-to-12 V, 5 A synchronous buck dc-dc converters using Si or GaN power semiconductors, where the design objective is to minimize loss while meeting BOM cost and area constraints. © 1986-2012 IEEE.

关键词： Gallium nitride

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Classification of Common Stem-based Dragon Fruit Diseases using Convolutional Neural Network 19

Classification of Common Stem-based Dragon Fruit Diseases us...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Magahis, Carlos Andrei F. Ugale, Rionne Angelo Villaverde, Jocelyn F. School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798331507817

Dragon Fruit stem diseases significantly threaten crop yield and agricultural productivity. While multiple capable dragon fruit disease detection systems already exist, there is a scarcity of research investigating the application of ResNet-50 in classifying dragon fruit stem diseases. This study presents a portable device that can capture pictures of dragon fruit stems for early disease detection using a ResNet-50 convolutional neural network (CNN) model based on Raspberry Pi 3B+. Images of healthy and infected dragon fruit stems with visual symptoms of anthracnose, cactus virus x, and stem canker were collected using a standard webcam and preprocessed to train the model. The device was tested in a controlled environment and achieved a 91.67% accuracy in classifying between the three common stem diseases. The results demonstrate the potential of ResNet-50 for accurate and efficient dragon fruit stem disease identification. © 2025 IEEE.

关键词： Convolutional neural networks

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Two-dimensional confined topological modes in all dielectric guided mode resonance structure through surface features

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Optical and Quantum electronics 2025年第4期57卷 1-17页

作者： Ramachandra, Swathy Menezes, Maclean Paul Tumuluru, Vamsi Krishna Kulkarni, Raghavendra G. Sinha, Rajat K. Bhowmick, Kaustav Department of Electronics and Communication Engineering PES University Karnataka Bangalore India Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Guided mode resonance (GMR) structures offer simplicity and have spectral resonance capabilities such as narrowband resonance, high Q-factor, whereas topological surface features offer spatial light control. In this paper, we explore the integration of the GMR structures with topological photonics to achieve extreme two-dimensional (2D) spatial confinement of light. With this integration, we propose a novel design that leverages discontinuous surface features (crosscut) to excite Jackiw—Rebbi (JR) solution, which describes relationship between Dirac equation and topological insulators. Finite element modelling, considering various structural and material parameters, demonstrate a 40% improvement in light confinement compared to benchmark GMR structures, with a calculated full width half maximum (FWHM) of the confined optical mode measured to be approximately ~ 18 ± 3 nm. This FWHM value represent the spatial extent of the confined optical field, indicating the achieved field localization in the proposed structures. This significant enhancement showcases the potential of our approach for applications demanding ultra- precise spatial resolution. Furthermore, tolerance studies reveal good robustness to variations in the dimensions of the crosscut and angle of polarization, ensuring practical feasibility. The proposed structure exhibited good tolerance of up to ~ ± 10%. This work paves the way for highly confined 2D light manipulation within all- dielectric platforms, opening exciting avenues for nanophotonic devices and applications requiring light localization. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Topology

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Physical-layer security in MU-MISO downlink networks against potential eavesdroppers

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Digital Communications and Networks 2025年第2期11卷 424-431页

作者： Woong Son Minkyu Oh Heejung Yu Bang Chul Jung C4I R&D Lab. LIG Nex1 Co.Ltd. Department of Electronics Engineering Chungnam National University Department of Electronics and Information Engineering Korea University Department of Electrical and Computer Engineering Ajou University

Recently, wireless security has been highlighted as one of the most important techniques for 6G mobile communication systems. Many researchers have tried to improve the Physical-Layer Security(PLS) performance such as Secrecy Outage Probability(SOP) and Secrecy Energy-Efficiency(SEE). The SOP indicates the outage probability that the data transmission between legitimate devices does not guarantee a certain reliability level, and the SEE is defined as the ratio between the achievable secrecy-rate and the consumed transmit power. In this paper, we consider a Multi-User Multi-Input Single-Output(MU-MISO) downlink cellular network where a legitimate Base Station(BS) equipped with multiple transmit antennas sends secure information to multiple legitimate Mobile Stations(MSs), and multiple potential eavesdroppers(EVEs) equipped with a single receive antenna try to eavesdrop on this information. Each potential EVE tries to intercept the secure information, i.e., the private message, from the legitimate BS to legitimate MSs with a certain eavesdropping probability. To securely receive the private information, each legitimate MS feeds back its effective channel gain to the legitimate BS only when the effective channel gain is higher than a certain threshold, i.e., the legitimate MSs adopt an Opportunistic Feedback(OF) strategy. In such eavesdropping channels, both SOP and SEE are analyzed as performance measures of PLS and their closed-form expressions are derived mathematically. Based on the analytical results, it is shown that the SOP of the OF strategy approaches that of a Full Feedback(FF) strategy as the number of legitimate MSs or the number of antennas at the BS increases. Furthermore, the trade-off between SOP and SEE as a function of the channel feedback threshold in the OF strategy is investigated. The analytical results and related observations are verified by numerical simulations.

关键词： Physical-layer security Opportunistic feedback Potential eavesdroppers Secrecy outage probability Secrecy energy-efficiency

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Aircraft Simulation in Game Development Using Reinforcement Learning 4

Aircraft Simulation in Game Development Using Reinforcement ...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Redwan Talha, Md. Navid Hassan, Tonmoy Sabab, Al Mithila, Shaila Sharmin Ahmed, Syed Hasib Department of Electrical and Computer Engineering Plot # 15 Dhaka1229 Bangladesh Department of Electrical and Electronics Engineering Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331530938

The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj

关键词： Flight simulators

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Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings

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Optical and Quantum electronics 2025年第1期57卷 1-22页

作者： Oktay, Semih Duru, İremnur Bakır, Halit Tabaru, Timuçin Emre Department of Electrical-Electronics Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Department Sivas University of Science and Technology Sivas Turkey

This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), rand

关键词： Antireflection coatings

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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering CT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2025 IEEE.

关键词： Inductive power transmission

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