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A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse. 代理标识符的马尔可夫链替换策略:在保持文本重用的同时最小化重新识别风险。
IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-10-01 Epub Date: 2025-10-06 DOI: 10.3390/electronics14193945
John D Osborne, Andrew Trotter, Tobias O'Leary, Chris Coffee, Micah D Cochran, Luis Mansilla-Gonzalez, Akhil Nadimpalli, Alex McAnnally, Abdulateef I Almudaifer, Jeffrey R Curtis, Salma M Aly, Richard E Kennedy

"Hiding in Plain Sight" (HIPS) strategies for Personal Health Information (PHI) replace PHI with surrogate values to hinder re-identification attempts. We evaluate three different HIPS strategies for PHI replacement, a standard Consistent replacement strategy, a Random replacement strategy, and a novel Markov model strategy. We evaluate the privacy-preserving benefits and relative utility for information extraction of these strategies on both a simulated PHI distribution and real clinical corpora from two different institutions using a range of false negative error rates (FNER). The Markov strategy consistently outperformed the Consistent and Random substitution strategies on both real data and in statistical simulations. Using FNER ranging from 0.1% to 5%, PHI leakage at the document level could be reduced from 27.1% to 0.1% and from 94.2% to 57.7% with the Markov strategy versus the standard Consistent substitution strategy, at 0.1% and 0.5% FNER, respectively. Additionally, we assessed the generated corpora containing synthetic PHI for reuse using a variety of information extraction methods. Results indicate that modern deep learning methods have similar performance on all strategies, but older machine learning techniques can suffer from the change in context. Overall, a Markov surrogate generation strategy substantially reduces the chance of inadvertent PHI release.

个人健康信息(PHI)的“隐藏”(HIPS)策略用替代值替换PHI,以阻止重新识别尝试。我们评估了三种不同的HIPS策略,即标准的一致替换策略,随机替换策略和一种新的马尔可夫模型策略。我们使用一系列假阴性错误率(FNER)来评估这些策略在模拟PHI分布和来自两个不同机构的真实临床语料库上的信息提取的隐私保护效益和相对效用。在实际数据和统计模拟中,马尔可夫策略始终优于一致替代策略和随机替代策略。在FNER范围为0.1%至5%的情况下,与标准一致性替代策略相比,马尔可夫策略可以将文档级别的PHI泄漏从27.1%降低到0.1%,从94.2%降低到57.7%,FNER分别为0.1%和0.5%。此外,我们使用各种信息提取方法评估了包含合成PHI的生成语料库的重用情况。结果表明,现代深度学习方法在所有策略上都具有相似的性能,但旧的机器学习技术可能会受到上下文变化的影响。总的来说,Markov代理生成策略大大减少了无意中释放PHI的机会。
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引用次数: 0
Wet Etching-Based WO3 Patterning for High-Performance Neuromorphic Electrochemical Transistors 基于湿蚀刻的高性能神经形态电化学晶体管WO3图像化
3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-18 DOI: 10.3390/electronics14061183
Liwei Zhang, Sixing Chen, Shaoming Fu, Songjia Han, Li Zhang, Yu Zhang, Mengye Wang, Chuan Liu, Xiaoci Liang
WO3-based electrochemical transistors (ECTs) are recognized as candidates for three-terminal memristors due to their high on–off ratio, long retention time, and rapid switching speed. However, their patterned fabrication often relies on complex vacuum systems or extreme processing conditions, hindering cost-effective scalability. Here, we developed a novel wet etching technique integrated with sol–gel-derived WO3 channels, enabling ambient-air fabrication of Nafion-WO3 ECTs. The wet-etched devices achieve an on–off ratio of ~105, surpassing unetched and dry-etched counterparts by orders of magnitude. Furthermore, they exhibit exceptional paired-pulse facilitation and long-term stability, maintaining 12 distinct conductance states for 103 s, and an on–off ratio of ~102 over 25 read–write cycles. XPS result shows higher W5+ content and M-O-H bond proportion for wet-etched devices, revealing an optimized interface, with enhanced H+ injection efficiency. The simulated artificial neural network using this wet-etched ECT shows ~97% recognition accuracy for handwritten numerals. This approach offers a novel patterning strategy for developing cost-effective, high-performance neuromorphic devices.
wo3基电化学晶体管(ECTs)因其高通断比、长保持时间和快速开关速度而被认为是三端忆阻器的候选材料。然而,它们的图案制造通常依赖于复杂的真空系统或极端的加工条件,阻碍了成本效益的可扩展性。在这里,我们开发了一种新的湿法蚀刻技术,集成了溶胶-凝胶衍生的WO3通道,使Nafion-WO3 ECTs能够在环境空气中制造。湿蚀刻器件实现了~105的通断比,超过了未蚀刻和干蚀刻器件的数量级。此外,它们还表现出优异的对脉冲促进和长期稳定性,在103秒内保持12种不同的电导状态,在25个读写周期内保持~102的通断比。XPS结果表明,湿蚀刻器件的W5+含量和M-O-H键比例更高,界面优化,H+注入效率提高。利用这种湿蚀刻ECT模拟的人工神经网络对手写数字的识别准确率达到97%。这种方法为开发具有成本效益的高性能神经形态设备提供了一种新的模式策略。
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引用次数: 2
A Flexible Multi-Ion Detection System Based on Organic Electrochemical Transistors for Physiological Monitoring 基于有机电化学晶体管的柔性多离子生理监测系统
3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2025-03-04 DOI: 10.3390/electronics14051023
Chenglin Li, Sixing Chen, Chuan Liu, Hui‐Jiuan Chen, Songjia Han
The continuous and real-time monitoring of physiological indicators is essential for early disease detection, prevention, and clinical diagnosis. In response to the growing demand for precise physiological parameter assessment, this study presents a flexible, organic electrochemical transistor (OECT)-based multi-ion sensing system designed to monitor key electrolyte concentrations—sodium (Na+), potassium (K+), and calcium (Ca2+)—in human biofluids. The system features a highly adaptable sensor array with a detection range tailored to physiological conditions, ensuring high selectivity and stability in complex biological environments. Our sensor demonstrated a sensitivity exceeding 1 mA/decade. To enhance measurement accuracy and mitigate cross-interference among ions, we integrate advanced machine learning algorithms, which optimize signal processing and significantly improve the system’s reliability. Additionally, we have developed a fully integrated hardware–software platform comprising customized signal acquisition circuitry and dedicated data analysis software, specifically tailored for OECT-based sensing applications. This comprehensive framework not only refines real-time ion detection but also paves the way for the broader clinical translation of OECT technology. The proposed system holds great promise for real-time physiological monitoring and point-of-care diagnostics, offering a potential paradigm shift in non-invasive, on-demand health assessment.
生理指标的连续、实时监测对于疾病的早期发现、预防和临床诊断至关重要。为了满足日益增长的对精确生理参数评估的需求,本研究提出了一种灵活的、基于有机电化学晶体管(OECT)的多离子传感系统,旨在监测人体生物体液中的关键电解质浓度——钠(Na+)、钾(K+)和钙(Ca2+)。该系统具有高度适应性的传感器阵列,可根据生理条件定制检测范围,确保在复杂的生物环境中具有高选择性和稳定性。我们的传感器显示灵敏度超过1ma /decade。为了提高测量精度和减轻离子之间的交叉干扰,我们集成了先进的机器学习算法,优化了信号处理,显著提高了系统的可靠性。此外,我们还开发了一个完全集成的硬件软件平台,包括定制的信号采集电路和专用的数据分析软件,专门为基于oect的传感应用量身定制。这个全面的框架不仅改进了实时离子检测,而且为OECT技术的更广泛的临床应用铺平了道路。该系统在实时生理监测和即时诊断方面有着巨大的前景,为无创、按需健康评估提供了潜在的范式转变。
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引用次数: 3
Data-Driven Maturity Level Evaluation for Cardiomyocytes Derived from Human Pluripotent Stem Cells (Invited Paper). 人类多能干细胞衍生心肌细胞的数据驱动成熟度评估(特邀论文)。
IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-02 Epub Date: 2024-12-18 DOI: 10.3390/electronics13244985
Yan Hong, Xueqing Huang, Fang Li, Siqi Huang, Qibiao Weng, Diego Fraidenraich, Ioana Voiculescu

Cardiovascular disease is a leading cause of death worldwide. The differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes offers significant potential for disease modeling and cell-based cardiac therapies. However, hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting their experimental and clinical applications. A critical challenge in current in vitro culture systems is the absence of standardized metrics to quantify maturity. This study presents a data-driven pipeline to quantify hPSC-CM maturity using gene expression data across various stages of cardiac development. We determined that culture time serves as a feasible proxy for maturity. To improve prediction accuracy, machine learning algorithms were employed to identify heart-related genes whose expression strongly correlates with culture time. Our results reduced the average discrepancy between predicted and observed culture time to 4.461 days and CASQ2 (Calsequestrin 2), a gene involved in calcium ion storage and transport, was identified as the most critical cardiac gene associated with culture duration. This novel framework for maturity assessment moves beyond traditional qualitative methods, providing deeper insights into hPSC-CM maturation dynamics. It establishes a foundation for developing advanced lab-on-chip devices capable of real-time maturity monitoring and adaptive stimulus selection, paving the way for improved maturation strategies and broader experimental/clinical applications.

心血管疾病是世界范围内死亡的主要原因。人类多能干细胞(hPSCs)向功能性心肌细胞的分化为疾病建模和基于细胞的心脏治疗提供了巨大的潜力。然而,hpsc来源的心肌细胞(hPSC-CMs)在很大程度上仍然不成熟,限制了它们的实验和临床应用。目前体外培养系统的一个关键挑战是缺乏标准化的指标来量化成熟度。本研究提出了一个数据驱动的管道,利用心脏发育各个阶段的基因表达数据来量化hPSC-CM成熟度。我们确定文化时间可以作为成熟度的可行代理。为了提高预测精度,使用机器学习算法来识别与培养时间密切相关的心脏相关基因。我们的研究结果将预测和观察到的培养时间之间的平均差异减少到4.461天,并且CASQ2 (Calsequestrin 2),一个参与钙离子储存和运输的基因,被确定为与培养时间相关的最关键的心脏基因。这种新的成熟度评估框架超越了传统的定性方法,为hspc - cm成熟度动力学提供了更深入的见解。它为开发能够实时成熟度监测和自适应刺激选择的先进芯片实验室设备奠定了基础,为改进成熟策略和更广泛的实验/临床应用铺平了道路。
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引用次数: 0
Accurate Medical Vial Identification Through Mixed Reality: A HoloLens 2 Implementation. 通过混合现实准确识别医用小瓶:HoloLens 2实现。
IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-02 Epub Date: 2024-11-11 DOI: 10.3390/electronics13224420
Bahar Uddin Mahmud, Guan Yue Hong, Afsana Sharmin, Zachary D Asher, John D Hoyle

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20-25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach.

准确识别药瓶对于紧急医疗服务至关重要,特别是对于那些彼此相似但标签、体积和浓度不同的药瓶。本研究介绍了一种利用微软HoloLens 2混合现实技术实时检测小瓶的方法。该系统还配备了一个SQL服务器来管理条形码和瓶信息。我们对HoloLens 2相机和外部扫描仪的条形码检测能力进行了比较分析。在正常照明条件下,HoloLens 2在20-25厘米外有效识别较大的条形码。然而,它在检测被外部扫描仪持续检测到的较小条形码时面临困难。帧率调查揭示了性能波动:在标准照明条件下平均每秒10.54帧(fps),在低光条件下下降到10.10帧/秒,在面对高条形码密度时进一步下降到10.05帧/秒。分辨率测试表明,1920 × 1080的屏幕分辨率产生了最佳的精度水平,准确率为98%。另一方面,1280 × 720的分辨率在93%的精度和速度之间取得了很好的平衡。HoloLens 2在理想情况下表现出令人满意的性能;然而,检测算法和相机分辨率的改进需要适应不同的环境。这种方法旨在帮助护理人员在危急情况下做出快速准确的决定,并解决依赖网络和人为错误等常见障碍。我们的新方法是一种混合方法,将外部蓝牙扫描仪与MR设备集成在一起,与仅使用扫描仪的方法相比,可以获得最佳结果。
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引用次数: 0
A Design Review for Biomedical Wireless Power Transfer Systems with a Three-Coil Inductive Link through a Case Study for NICU Applications. 基于NICU应用的生物医学无线电力传输系统的设计综述。
IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-10-01 Epub Date: 2024-10-07 DOI: 10.3390/electronics13193947
Amin Hazrati Marangalou, Miguel Gonzalez, Nathaniel Reppucci, Ulkuhan Guler

This paper outlines a design approach for biomedical wireless power transfer systems with a focus on three-coil inductive links for neonatal intensive care unit applications. The relevant literature has been explored to support the design approach, equations, simulation results, and the process of experimental analysis. The paper begins with a brief overview of various power amplifier classes, followed by an in-depth examination of the most common power amplifiers used in biomedical wireless power transfer systems. Among the traditional linear and switching amplifier classes, class-D and class-E switching amplifiers are highlighted for their enhanced efficiency and straightforward implementation in biomedical contexts. The impact of load variation on these systems is also discussed. This paper then explores the basic concepts and essential equations governing inductive links, comparing two-coil and multi-coil configurations. In the following, the paper discusses foundational coil parameters and provides theoretical and experimental analysis of both two-coil and multi-coil inductive links through step-by-step measurement techniques using lab equipment and addressing the relevant challenges. Finally, a case study for neonatal intensive care unit applications is presented, showcasing a wireless power transfer system operating at 13.56 MHz for powering a wearable device on a patient lying on a mattress. An inductive link with a transmitter coil embedded in a mattress is designed to supply power to a load at distances ranging from 4 cm to 12 cm, simulating the mattress-to-chest distance of an infant. the experimental results of a three-coil inductive link equipped with a Class-E power amplifier are reported, demonstrating power transfer efficiency ranging from 75% to 25% and power delivery to a 500 Ω-load varying from 340 mW to 25 mW over various distances.

本文概述了一种生物医学无线电力传输系统的设计方法,重点是新生儿重症监护病房应用的三线圈感应链路。通过查阅相关文献来支持设计方法、方程、仿真结果和实验分析过程。本文首先简要概述了各种功率放大器类别,然后深入研究了生物医学无线功率传输系统中最常见的功率放大器。在传统的线性和开关放大器类别中,d类和e类开关放大器因其提高效率和在生物医学环境中易于实现而备受关注。文中还讨论了负荷变化对这些系统的影响。然后,本文探讨了控制感应链路的基本概念和基本方程,比较了双线圈和多线圈结构。下面,本文讨论了基本线圈参数,并通过使用实验室设备逐步测量技术提供了双线圈和多线圈感应链路的理论和实验分析,并解决了相关挑战。最后,介绍了新生儿重症监护病房应用的案例研究,展示了一种工作在13.56 MHz的无线电力传输系统,用于为躺在床垫上的患者的可穿戴设备供电。在床垫中嵌入了一个带有发射器线圈的感应链路,用于向距离为4厘米至12厘米的负载供电,模拟婴儿床垫到胸部的距离。本文报道了配备e类功率放大器的三线圈感应链路的实验结果,表明功率传输效率从75%到25%不等,功率传输到500 Ω-load在不同距离上的功率传输从340 mW到25 mW不等。
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引用次数: 0
Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques 电子元件组装的进步:人工智能驱动的实时检测技术
IF 2.9 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-18 DOI: 10.3390/electronics13183707
Eyal Weiss
This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 milliseconds per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.
本研究提出了一种先进的方法,利用最先进的人工智能(AI)和深度学习技术,通过实时在线检测提高电子组装质量。其主要目标是确保符合严格的制造标准,特别是 IPC-A-610 和 IPC-J-STD-001。该系统利用拾放设备的现有基础设施,在装配过程中捕捉电子元件的高分辨率图像。这些图像由人工智能算法即时分析,能够检测出各种缺陷,包括损坏、腐蚀、伪造以及元件及其引线的结构异常。这种积极主动的方法改变了传统的被动质量保证方法,将实时缺陷检测和严格遵守行业标准整合到了组装流程中。该系统的准确率超过 99.5%,每个组件的处理速度约为 5 毫秒,使制造商能够及时发现并处理缺陷,从而显著提高制造质量和可靠性。该系统的实施利用了大数据分析技术,对超过十亿个组件进行分析,以完善检测算法,确保性能稳定。通过在缺陷升级之前预先防范和解决缺陷,该方法最大限度地减少了生产中断,提高了工作流程的效率,最终大大降低了成本。本文展示了多个组件缺陷案例研究,重点介绍了通过人工智能和深度学习识别出的各种类型的缺陷。这些案例与详细的性能指标相结合,为优化电子元件组装流程提供了见解,有助于提高生产效率和质量。
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引用次数: 0
Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids 基于信任的岛式交流微电网分布式合作控制中网络攻击的检测与缓解
IF 2.9 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-18 DOI: 10.3390/electronics13183692
Md Abu Taher, Mohd Tariq, Arif I. Sarwat
In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.
在本研究中,我们探讨了在孤岛式交流微电网的分布式协同控制中检测和缓解网络攻击的挑战,尤其侧重于检测虚假数据注入攻击(FDIAs),这是智能电网(SG)面临的一个重大威胁。智能电网将传统电力系统与通信网络整合在一起,形成了一个具有众多脆弱环节的复杂系统,使其成为网络攻击的首要目标。这些攻击可能导致私人数据泄露、控制网络故障甚至停电。基于机器学习的方法需要大量数据集和依赖于精确系统建模的数学模型,而我们的方法与之不同,不存在此类依赖关系。为了增强微电网抵御这些威胁的能力,我们在传统的合作控制算法中引入了一个新颖的可信度参数,从而提出了一种弹性控制算法。我们的方法基于分布式能源资源(DER)的电压测量和交换信息来评估其可信度,并利用库尔贝克-莱布勒(KL)发散来动态调整控制行动。我们在装有八个 DER 的 IEEE-34 总线馈电系统和装有二十二个 DER 的更大的微电网上进行了仿真,验证了我们的方法。结果表明,检测精度约为 100%,毫秒级的范围缓解时间,确保了系统的快速恢复。此外,在受到攻击的情况下,我们的方法几乎 100% 地提高了系统稳定性,展示了其在及时发现攻击和保持系统恢复能力方面的有效性。这些发现凸显了我们的方法在面对网络威胁时提高微电网系统安全性和稳定性的潜力。
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引用次数: 0
Attention-Enhanced Guided Multimodal and Semi-Supervised Networks for Visual Acuity (VA) Prediction after Anti-VEGF Therapy 用于预测抗血管内皮生长因子疗法后视力 (VA) 的注意力增强型多模态和半监督引导网络
IF 2.9 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-18 DOI: 10.3390/electronics13183701
Yizhen Wang , Yaqi Wang, Xianwen Liu, Weiwei Cui, Peng Jin, Yuxia Cheng, Gangyong Jia
The development of telemedicine technology has provided new avenues for the diagnosis and treatment of patients with DME, especially after anti-vascular endothelial growth factor (VEGF) therapy, and accurate prediction of patients’ visual acuity (VA) is important for optimizing follow-up treatment plans. However, current automated prediction methods often require human intervention and have poor interpretability, making it difficult to be widely applied in telemedicine scenarios. Therefore, an efficient, automated prediction model with good interpretability is urgently needed to improve the treatment outcomes of DME patients in telemedicine settings. In this study, we propose a multimodal algorithm based on a semi-supervised learning framework, which aims to combine optical coherence tomography (OCT) images and clinical data to automatically predict the VA values of patients after anti-VEGF treatment. Our approach first performs retinal segmentation of OCT images via a semi-supervised learning framework, which in turn extracts key biomarkers such as central retinal thickness (CST). Subsequently, these features are combined with the patient’s clinical data and fed into a multimodal learning algorithm for VA prediction. Our model performed well in the Asia Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition, earning fifth place in the overall score and third place in VA prediction accuracy. Retinal segmentation achieved an accuracy of 99.03 ± 0.19% on the HZO dataset. This multimodal algorithmic framework is important in the context of telemedicine, especially for the treatment of DME patients.
远程医疗技术的发展为眼底病患者的诊断和治疗提供了新的途径,尤其是在抗血管内皮生长因子(VEGF)治疗后,准确预测患者的视力(VA)对于优化后续治疗计划非常重要。然而,目前的自动预测方法往往需要人工干预,可解释性差,因此难以广泛应用于远程医疗场景。因此,迫切需要一种高效、可解释性强的自动预测模型,以改善远程医疗环境下 DME 患者的治疗效果。在本研究中,我们提出了一种基于半监督学习框架的多模态算法,旨在结合光学相干断层扫描(OCT)图像和临床数据,自动预测抗血管内皮生长因子治疗后患者的视力值。我们的方法首先通过半监督学习框架对 OCT 图像进行视网膜分割,进而提取视网膜中央厚度(CST)等关键生物标志物。随后,将这些特征与患者的临床数据相结合,并输入多模态学习算法,进行视网膜病变预测。我们的模型在亚太远程眼科协会(APTOS)大数据竞赛中表现出色,获得了总分第五名和视网膜缺损预测准确率第三名的好成绩。在 HZO 数据集上,视网膜分割的准确率达到 99.03 ± 0.19%。这种多模态算法框架在远程医疗方面非常重要,尤其是在治疗重度视网膜病变患者方面。
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引用次数: 0
A Low-Power, High-Resolution Analog Front-End Circuit for Carbon-Based SWIR Photodetector 用于碳基 SWIR 光电探测器的低功耗、高分辨率模拟前端电路
IF 2.9 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-09-18 DOI: 10.3390/electronics13183708
Yuyan Zhang, Zhifeng Chen, Wenli Liao, Weirong Xi, Chengying Chen, Jianhua Jiang
Carbon nanotube field-effect transistors (CNT-FETs) have shown great promise in infrared image detection due to their high mobility, low cost, and compatibility with silicon-based technologies. This paper presents the design and simulation of a column-level analog front-end (AFE) circuit tailored for carbon-based short-wave infrared (SWIR) photodetectors. The AFE integrates a Capacitor Trans-impedance Amplifier (CTIA) for current-to-voltage conversion, coupled with Correlated Double Sampling (CDS) for noise reduction and operational amplifier offset suppression. A 10-bit/125 kHz Successive Approximation analog-to-digital converter (SAR ADC) completes the signal processing chain, achieving rail-to-rail input/output with minimized component count. Fabricated using 0.18 μm CMOS technology, the AFE demonstrates a high signal-to-noise ratio (SNR) of 59.27 dB and an Effective Number of Bits (ENOB) of 9.35, with a detectable current range from 500 pA to 100.5 nA and a total power consumption of 7.5 mW. These results confirm the suitability of the proposed AFE for high-precision, low-power SWIR detection systems, with potential applications in medical imaging, night vision, and autonomous driving systems.
碳纳米管场效应晶体管(CNT-FET)因其高迁移率、低成本以及与硅基技术的兼容性,在红外图像检测领域大有可为。本文介绍了专为碳基短波红外(SWIR)光电探测器定制的列级模拟前端(AFE)电路的设计和仿真。该模拟前端电路集成了一个电容跨阻放大器(CTIA),用于电流到电压的转换,并采用相关双采样(CDS)技术降低噪声和抑制运算放大器偏移。10 位/125 kHz 逐次逼近模数转换器(SAR ADC)完善了信号处理链,以最少的元件数量实现了轨至轨输入/输出。AFE 采用 0.18 μm CMOS 技术制造,信噪比 (SNR) 高达 59.27 dB,有效位数 (ENOB) 为 9.35,检测电流范围为 500 pA 至 100.5 nA,总功耗为 7.5 mW。这些结果证实了所提出的 AFE 适用于高精度、低功耗的 SWIR 检测系统,有望应用于医疗成像、夜视和自动驾驶系统。
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