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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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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年第2期9卷 621-634页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (EERC), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that EERC is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that EERC achieves over 98% optimality for small-sized networks. More importantly, we evaluate EERC in realistic scenarios using a 3-D ray-tracing simulator and confirm that EERC performs very well even in urban terrain environments with buildings. © 2017 IEEE.

关键词： Convex optimization

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Development of Self-Powered e-Gear Selector Based on Single-Electrode Triboelectric Nanogenerator (TENG) Technology

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 20-29页

作者： Ali, Sam Maddipatla, Dinesh Bazuin, Bradley J. Atashbar, Massood Z. Western Michigan University Department of Electrical and Computer Engineering KalamazooMI49008 United States

A novel e-Gear selector single-electrode-based triboelectric nanogenerator (TENG) was successfully designed, fabricated, and tested on flexible substrates. The proposed device consists of four TENG sensors representing the traditional gear shift selector (P, R, N, and D). Leather, cotton, paper, and thermoplastic polyurethane (TPU) were selected as bottom triboelectric layers for TENGs 1–4, respectively. Silver (Ag) ink was screen-printed on the backside of each layer representing the single electrode. Human skin was chosen as the top triboelectric layer. Based on the selection of the materials, each TENG device generates different output voltages, enabling the selection of the intended gear mode without the need for a complex control algorithm. The TENG device was characterized in terms of open-circuit voltage (Voc) and short-circuit current (Isc). The repeatability and reproducibility of the TENG device were evaluated by fabricating three samples of each TENG device and by testing each TENG device three times. An average Voc of 4.27 ± 0.36, 7 ± 0.31, 9.05 ± 0.21, and 12.38 ± 0.34 Vpp, resulted in a power density of 0.37 ± 0.05, 0.72 ± 0.09, 0.95 ± 0.07, and 1.74 ± 0.10 μW/cm2, was measured for TENGs 1–4, respectively. The performance of the e-Gear selector showcased the potential of the TENG energy harvesting method for integration into the automotive industry, aligning with the rapid advancements in electric vehicle (EV) applications. This technology is particularly promising for future gear selector designs, as it eliminates the need for external power supplies, reduces cost, minimizes wiring, and offers greater flexibility in positioning within the vehicle. © 2022 IEEE.

关键词： Leather

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Deep learning for object recognition: A comprehensive review of models and algorithms

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International Journal of Cognitive Computing in engineering 2025年 6卷 298-312页

作者： Tsirtsakis, Paschalis Zacharis, Georgios Maraslidis, George S. Fragulis, George F. Department of Electrical and Computer Engineering University of Western Macedonia ZEP Campus Kozani50100 Greece

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks. © 2025 The Authors

关键词： Contrastive Learning

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Learning From Mistakes: A Multilevel Optimization Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1651-1663页

作者： Zhang, Li Garg, Bhanu Sridhara, Pradyumna Hosseini, Ramtin Xie, Pengtao University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multilevel optimization framework, learning from mistakes (LFM), for machine learning. We formulate LFM as a threestage optimization problem: 1) the learner learns, 2) the learner relearns based on the mistakes made before, and 3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data reweighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. © 2020 IEEE.

关键词： Adversarial machine learning

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Fundamentals of LEO Based Localization

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IEEE Transactions on Information Theory 2025年第7期71卷 5277-5311页

作者： Emenonye, Don-Roberts Dhillon, Harpreet S. Buehrer, R. Michael Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

In this paper, we derive the fundamental limits of low earth orbit (LEO) enabled localization by analyzing the available information in signals from multiple LEOs during different transmission time slots received on a multiple antennas and evaluate the utility of these signals for 9D localization (3D position, 3D orientation, and 3D velocity estimation). We start by deriving the Fisher Information Matrix (FIM) for the channel parameters that are present in the signals received from LEOs in the same or multiple constellations during multiple transmission time slots. To accomplish this, we define a system model that captures i) time offset between LEOs caused by having relatively cheap clocks, ii) frequency offset between LEOs, iii) the unknown Doppler rate caused by high mobility LEOs, and iv) multiple transmission time slots from a particular LEO. We transform the FIM for the channel parameters to the FIM for the location parameters and determine the required conditions for localization. To do this, we start with the 3D localization cases: i) 3D positioning with known velocity and orientation, ii) 3D orientation estimation with known position and velocity, and iii) 3D velocity estimation with known position and orientation. Subsequently, we derive the FIM for the full 9D localization case (3D position, 3D orientation, and 3D velocity estimation) in terms of the FIM for the 3D localization. Using these results, we determine the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas that allow for different levels of location estimation. We then provide insights into the interaction between the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas. One key result is that in the presence of time and frequency offsets and Doppler rate, it is possible to perform 9D localization (3D position, 3D velocity, and 3D orientation estimation) of a receiver by

关键词： Fisher information matrix

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IC Backside Tamper Detection using Impedance Sensing

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IEEE Access 2025年 13卷 90706-90725页

作者： Mosavirik, Tahoura Tajik, Shahin Worcester Polytechnic Institute Electrical and Computer Engineering Department WorcesterMA01609 United States

The expansion of flip-chip technologies and lack of backside protection made integrated circuits (ICs) vulnerable to certain classes of physical attacks mounted from the IC’s backside, including laser-assisted probing, electromagnetic side-channel analysis, body biasing injection (BBI), and focused ion beam (FIB) editing. Existing countermeasures are expensive, incompatible with standard IC fabrication, and unsuitable for legacy systems such as field-programmable gate arrays (FPGAs), which are widely used in industrial and defense systems. In this paper, we demonstrate how impedance monitoring of the printed circuit board (PCB) and the IC package’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect IC backside tampering. Our method is based on the fact that any attempt to expose the backside silicon substrate, such as the removal of the fan and heatsinks, or polishing it, leads to changes in the equivalent impedance of the package’s PDN, and hence scanning the package impedance will reveal if the package integrity has been violated. To validate our claims, we deployed an on-FPGA network analyzer on an AMD Zynq UltraScale+ MPSoC (16 nm), which is part of a multi-PCB *** performed a series of backside tamper attacks, including the removal of cooling components and IC backside silicon milling/polishing, across different temperatures, and evaluated detection effectiveness leveraging the difference of means as the statistical metric. © 2013 IEEE.

关键词： Focused ion beams

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A Highly-Parallel and Scalable Hardware Accelerator for the NTest Othello Game Engine

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IEEE Transactions on Parallel and Distributed Systems 2025年第8期36卷 1620-1633页

作者： Popa, Stefan Petric, Vlad Ivanovici, Mihai Transilvania University of Brasov Electronics and Computers Department Faculty of Electrical Engineering and Computer Science Romania

Othello is a two-player combinatorial game with 1E+28 legal positions and 1E+58 game tree complexity. We propose a HIghly PArallel, Scalable and configurable hardware accelerator for evaluating the middle and endgame Othello positions. We base HIPAS on NTest - a leading software Othello engine that uses the minimax algorithm with a quality pattern-based evaluation function, alpha-beta pruning, and heuristic mobility sorting. We describe its architecture and Field Programmable Gate Array implementation, measure its performance, and compare it with prior solutions. HIPAS achieves the highest quality evaluation, the highest performance with speed-ups up to several hundreds, and the best energy efficiency. The main novelty is the algorithm implementation as a circular pipeline and a Finite State Machine with pseudo-parallel processing. Although Othello was recently claimed to be weakly solved, the game remains unsolved in a stronger sense. A weak solution only shows how to force a draw. It does not guarantee a win if the opponent makes a mistake. HIPAS can validate the weak solution faster and more efficiently. A multi-threaded NTest software component evaluating the beginning and part of the middle game, combined with one or more instances of HIPAS for handling the remainder can provide a stronger solution. © 1990-2012 IEEE.

关键词： Middleware

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electrical System Planning of Large-scale Offshore Wind Farm Based on N+ Design Considering Optimization of Upper Power Limits of Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1784-1794页

作者： Shurong Wei Hao Wang Yang Fu Fangxing Li Lingling Huang IEEE Department of Electrical Power Engineering Shanghai University of Electric PowerShanghai 200090China Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

electrical system planning of the large-scale offshore wind farm is usually based on N-1 security for equipment lectotype. However, in this method, owing to the aggregation effect in large-scale offshore wind farms, offshore electrical equipment operates under low load for long periods, thus wasting resources. In this paper, we propose a method for electrical system planning of the large-scale offshore wind farm based on the N+ design. A planning model based on the power-limited operation of wind turbines under the N+ design is constructed, and a solution is derived with the optimization of the upper power limits of wind turbines. A comprehensive evaluation and game analysis of the economy, risk of wind abandonment, and environmental sustainability of the planned offshore electrical systems have been conducted. Moreover, the planning of an infield collector system, substation, and transmission system of an offshore electrical system based on the N+ design is integrated. For a domestic offshore wind farm, evaluation results show that the proposed planning method can improve the efficiency of wind energy utilization while greatly reducing the investment cost of the electrical system.

关键词： electrical system N+design offshore wind farm planning optimization

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