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Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

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Bioelectronic Medicine 2023年第1期9卷 1-34页

作者： Wang, Chan He, Tianyiyi Zhou, Hong Zhang, Zixuan Lee, Chengkuo Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117576 Singapore National University of Singapore 5 Engineering Drive 1 Singapore117608 Singapore Suzhou Industrial Park Suzhou215123 China NUS Graduate School for Integrative Science and Engineering National University of Singapore Singapore117456 Singapore

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears;implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring;soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generati

关键词： Volatile organic compounds

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Iterative Learning Control - Algorithms, Applications and Future Research Directions 63

Iterative Learning Control - Algorithms, Applications and Fu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rogers, Eric Chu, Bing Moore, Kevin Oomen, Tom Tan, Ying University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom Colorado School of Mines Department of Electrical Engineering United States Eindhoven University of Technology Department of Mechanical Engineering Netherlands Delft University of Technology Department of Mechanical Engineering Netherlands University of Melbourne Department of Mechanical Engineering Australia

ISBN: (纸本)9798350316339

This paper gives a tutorial on iterative learning control nearly five decades after what is widely regarded as the first substantive paper in the literature. The focus is on algorithm development under a number of general headings (linear, optimization, frequency domain, and nonlinear), together with supporting experimental validation/industrial applications and also applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

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Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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Causes and Consequences of IPD Mismatch in XR Devices

Causes and Consequences of IPD Mismatch in XR Devices

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Wilcox, Laurie M. Allison, Robert S. Department of Psychology York University TorontoM3J1P3 Canada Dept. of Electrical Engineering and Computer Science York University TorontoM3J1P3 Canada

Binocular 3D display systems, including virtual, augmented and mixed reality (collectively referred to as XR) devices support stereoscopic depth perception by presenting dichoptic views separately to the two eyes rendered according to their vantage points. A common human factors issue is a mismatch between the observer’s physical interpupillary distance (IPD) and 1) the separation between the device optics and 2) the rendered/captured content. This paper provides a theoretical review of the importance and consequences for user experience of accurately estimating IPD and incorporating it into HMD adjustments, calibration and rendering. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Stereocenters

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Lightweight secure image encryption: a tent map chaos theory approach

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Multimedia Tools and Applications 2025年 1-20页

作者： Odeh, Ammar Taleb, Anas Abu Alhajahjeh, Tareq Navarro, Francisco Ayesh, Aladdin Department of Computer Science Princess Sumaya University of Technology Amman1196 Jordan Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom Faculty of Computing University of Aberdeen King’s College AberdeenAB24 3 FX United Kingdom

In the digital era, securing image data against unauthorized access is a critical challenge, particularly in applications such as cloud storage, IoT security, and medical imaging. This study presents a lightweight and secure image encryption algorithm based on the Tent Map chaotic system, which enhances encryption strength while maintaining computational efficiency. The proposed approach effectively obfuscates image content, achieving a uniform histogram distribution indicative of robust encryption. Comparative analysis with existing methods demonstrates its superiority, particularly in security performance metrics, including entropy (7.996), NPCR (99.65%), and UACI (33.4724%). Additionally, image quality assessments using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) confirm that the algorithm preserves image integrity while ensuring strong encryption. The algorithm’s lightweight nature makes it suitable for large-scale applications, such as real-time encryption in cloud environments, IoT security, and multimedia protection. These results highlight the effectiveness of the proposed method in balancing security, efficiency, and image quality, making it a practical solution for modern image encryption challenges. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Signal to noise ratio

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Microwave characterization of two Ba_(0.6)Sr_(0.4)TiO_(3) dielectric thin films with out-of-plane and in-plane electrode structures

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Journal of Advanced Ceramics 2023年第8期12卷 1521-1532页

作者： Hanchi Ruan Theo Graves Saunders Henry Giddens Hangfeng Zhang Achintha Avin Ihalage Jonas Florentin Kolb Matthew Blunt Sajad Haq Haixue Yan Yang Hao School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK Department of Chemistry University College LondonLondon WC1H 0AJUK Advanced Services and Products QinetiQFarnborough GU140LXUK

Ferroelectric(FE)thin films have recently attracted renewed interest in research due to their great potential for designing novel tunable electromagnetic devices such as large intelligent surfaces(LISs).However,the mechanism of how a polar structure in the FE thin films contributes to desired tunable performance,especially within the microwave frequency range,which is the most widely used frequency range of electromagnetics,has not been illustrated *** this paper,we described several straightforward and cost-effective methods to fabricate and characterize Ba_(0.6)Sr_(0.4)TiO_(3)(BST)thin films at microwave *** prepared BST thin films here exhibit homogenous structures and great tunability(h)in a wide frequency and temperature range when the applied field is in the out-of-plane *** high tunability can be attributed to high concentration of polar *** response to the applied direct current(DC)field was directly visualized using a novel non-destructive near-field scanning microwave microscopy(NSMM)*** results have provided some intriguing insights into the application of the FE thin films for future programmable high-frequency devices and systems.

关键词： ferroelectric(FE) microwave tunability polarization thin film

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Development of a Current-Position Control Strategy for Motion Systems Utilizing Nonlinear Reluctance Actuators 63

Development of a Current-Position Control Strategy for Motio...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Al Saaideh, Mohammad Al-Rawashdeh, Yazan M. Alatawneh, Natheer Aljanaideh, Khaled Boker, Almuatazbellah Al Janaideh, Mohammad Memorial University St. John's Department of Electrical and Computer Engineering NLA1B 3X5 Canada University of Jordan Electrical Engineering Department at Al-Zaytoonah 130 Amman 11733 Jordan Memorial University St. John's Department of Mechanical and Mechatronics Engineering NLA1B 3X5 Canada Jordan University of Science and Technology Faculty of Engineering Department of Aeronautical Engineering Irbid22110 Jordan Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24060 United States University of Guelph School of Engineering GuelphON N1G 2W1 Canada

ISBN: (纸本)9798350316339

Reluctance actuators (RA) can replace the current Lorentz actuators in the next generation of positioning and scanning motion systems, such as the wafer stage in lithography machines. However, the nonlinear output force characteristic and the gap dependency of the RA are the main challenges in using the RA to drive motion systems. In this paper, we design a two-loop control approach for a reluctance actuator motion system (RAMS) to achieve tracking performance for a desired motion profile. First, the current control loop linearizes the RA under different conditions. Next, the position control loop is designed using a PID control based on an extended-high gain observer to achieve a desired motion profile considering unknown dynamics in the system. The simulation results show the efficiency of the proposed current control in linearizing the dynamic behavior under different desired forces and nominal air gaps and achieving a frequency response similar to the spring-mass-damper system. Moreover, the position control can achieve different long-stroke and short-stroke motion profiles with different amplitudes and ranges of motion. © 2024 IEEE.

关键词： Proportional control systems

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A Method to Classify Emotions Based on BERT 4

A Method to Classify Emotions Based on BERT

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4th International Signal Processing, Communications and engineering Management Conference, ISPCEM 2024

作者： Chen, Zeli Lin, Jian Jiang, Jie Jing, Di Department of Computer Science Lafayette College EastonPA United States School of Information Technology and Artificial Intelligence Zhejiang University of Finance and Economics Zhejiang Hangzhou China Department of Mathematical and Statistical Sciences University of Alberta EdmontonABT6G 2G1 Canada School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798331528676

Nowadays, as science and technology continue to advance, addressing people's emotional issues is of particular significance, and how to accurately and efficiently recognize emotions has become increasingly prominent. We explores sentiment recognition and classification methods, including rule-based approaches, traditional machine learning models, deep learning models, and multi-modal sentiment analysis. Through comparative analysis, we will evaluate the effectiveness of these methods in handling social media texts. Additionally, we has developed a BERT-based two-stage model that first determines sentiment polarity (positive or negative) and then performs detailed classification of negative emotions. This two-stage model significantly improves accuracy and detail recognition compared to traditional one-stage models and other baseline methods. The results show that our approach not only enhances sentiment analysis accuracy but also provides deeper insights into the emotional subtleties of social media texts. This advancement holds significant practical implications for real-time sentiment monitoring and more effective crisis management strategies. ©2024 IEEE.

关键词： Contrastive Learning

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