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Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2025

作者： Yang, Yanjun Cui, Jiaheng Kumar, Amit Luo, Dan Murray, Jackelyn Jones, Les Chen, Xianyan Hülck, Sebastian Tripp, Ralph A. Zhao, Yiping Department of Physics and Astronomy Franklin College of Arts and Sciences The University of Georgia AthensGA30602 United States School of Electrical and Computer Engineering College of Engineering The University of Georgia AthensGA30602 United States Department of Statistics Franklin College of Arts and Sciences The University of Georgia AthensGA30602 United States Department of Infectious Diseases College of Veterinary Medicine The University of Georgia AthensGA30602 United States Department of Epidemiology & Biostatistics College of Public Health The University of Georgia AthensGA30602 United States Tec5USA Inc. PlainviewNY11803 United States

ISBN: (纸本)9781510683440

Concurrent or sequential infections by multiple respiratory viruses pose significant challenges for diagnosis, treatment, and disease management, necessitating accurate and rapid identification methods. Here, we introduce a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) with deep learning to enable rapid, quantitative detection of respiratory virus co-infections. Utilizing highly sensitive silica-coated silver nanorod array substrates, we collected over 1.2 million SERS spectra from 11 viruses, nine two-virus mixtures, and four three-virus mixtures at varying concentrations in saliva. A novel deep learning model, MultiplexCR, was developed to simultaneously identify virus species and quantify their concentrations from the SERS spectra. The model achieved an exceptional classification accuracy of 98.6% for virus co-infections and a mean absolute error of 0.028 for concentration regression. In blind testing, MultiplexCR consistently demonstrated high accuracy and precise concentration predictions. This SERS-MultiplexCR platform performs the entire detection process within 15 minutes, highlighting its potential for rapid, point-of-care diagnostics in infection detection, as well as broader applications in food safety and environmental monitoring. © 2025 SPIE.

关键词： Nanorods

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Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system

arXiv

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arXiv 2024年

作者： Iskarous, Mark M. Chaudhry, Zan Li, Fangjie Bello, Samuel Sankar, Sriramana Slepyan, Ariel Chugh, Natasha Hunt, Christopher L. Greene, Rebecca J. Thakor, Nitish V. Department of Biomedical Engineering Johns Hopkins School of Medicine BaltimoreMD21205 United States Department of Organismal Biology and Anatomy University of Chicago ChicagoIL60637 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20814 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37325 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Public Health Johns Hopkins University BaltimoreMD21218 United States Texas College of Osteopathic Medicine The University of North Texas Health Science Center Fort WorthTX76107 United States Infinite Biomedical Technologies LLC. BaltimoreMD21202 United States

Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The neuromorphic encoding process transforms analog sensor readings into speed and force invariant spiking representations in three sequential stages: the force invariance module (in the analog domain), the spiking activity encoding module (transforms from analog to spiking domain), and the speed invariance module (in the spiking domain). The algorithms were tested on a tactile texture dataset collected in 15 speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to a real-time human-operated texture classification system. Similarly, the invariant representations improved classification accuracy, computational efficiency, and capability to identify textures explored in novel conditions. The invariant representation is even more crucial in this context due to human imprecision which seems to the classification system as a novel condition. These results demonstrate that invariant neuromorphic representations enable better performing neurorobotic tactile sensing systems. Furthermore, because the neuromorphic representations are based on biological processing, this work can be used in the future as the basis for naturalistic sensory feedback for upper limb amputees. © 2024, CC BY-NC-ND.

关键词： Sensory feedback

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Compensation Mechanisms for Double Auctions in Peer-to-Peer Local Energy Markets

引用

Current Sustainable/Renewable Energy Reports 2020年第4期7卷 165-175页

作者： Gerwin, Christoph Mieth, Robert Dvorkin, Yury Workgroup for Infrastructure Policy Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany Tandon School of Engineering Department of Electrical and Computer Engineering NYU BrooklynNY11201 United States Control Systems Group Technische Universität Berlin Einsteinufer 17 Berlin10587 Germany

Purpose of Review: This paper compares different double-auction pricing mechanisms in the context of peer-to-peer trading in local electricity markets. Benchmarking against the traditional system marginal price (SMP) approach, we analyze the outcomes of the Vickrey-Clarke-Groves (VCG) and Pay-as-bid (PAB) approaches, which lead to a revenue imbalance in the market. To mitigate this imbalance, we propose a set of mechanisms that impose trading fees or subsidies with different fairness policies and discuss their impact on the behavior of market participants. Recent Findings: Proliferation of small-scale and distributed energy resources in low-voltage distribution systems has spurred the development of platforms for peer-to-peer local electricity trading. While recent studies prove that local electricity trading can be beneficial to both the peers and system, implementation of local energy markets is still in the early stages and there is no consensus on an optimal market design. Summary: This paper provides a detailed description of market properties for a competitive LEM that is cleared using the SMP, VCG, and PAB approaches. We show that the proposed compensation mechanisms effectively enforce predefined fairness policies and derive exact payments for each peer depending on market results and individual peer characteristics. The case study uses real-world data to illustrate that different compensation mechanisms can be used to incentivize desirable peer behavior, e.g., installation of new distributed energy resources, investment in grid supportive generation, or flexible energy usage. © 2020, Springer Nature Switzerland AG.

关键词： Power markets

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Development of Microcontroller-Based Manipulator Arm: A Trilemma Problem-Solving Framework for Robotics and STEM Education in Nigerian Schools

Development of Microcontroller-Based Manipulator Arm: A Tril...

引用

Technologies Applied to Electronics Teaching (TAEE)

作者： Mbadiwe S. Benyeogor Tobore L. Igbigbi Olusegun I. Lawal Udoh T. Akpan Andrew O. Benyeogor Nnaemeka M. Mbachu Kosisochukwu P. Nnoli Muhammad S. Aliero Adeboye Olatunbosun Institute of Physics University of Münster Münster Germany Department of Public Utilities City of Cleveland Ohio State USA Advanced Aerospace Engines Lab. Nat. Space Research & Dev. Agency Oka Akoko Ondo State Nigeria Dept. Mechatronics Engineering Lagos State Univ. of Sc. & Tech. Ikorodu Lagos State Nigeria CTE Computer Science & Robotics International Leadership of Texas Aggieland Texas USA Dept. Comp. Sc. & Electrical Engineering Constructor University Bremen Germany ICT Department Kebbi State Univ. of Sc. & Tech Aliero Nigeria Dept. Electrical & Electronics Engineering Ajayi Crowther University Oyo Town Oyo State Nigeria

ISBN: (数字)9798350348675

ISBN: (纸本)9798350348682

Advances in microprocessor technology and software engineering have resulted in the development of sophisticated robots that are used in modern society as assistive automata for production, security, and farming, among other things. To sustain the development of robots, robotics education should be included in school curricula (i.e., teaching and learning process). However, schools in many developing nations, such as Nigeria, are unable to adapt to this trend due to personnel, technological, infrastructural, and fiscal limitations. To address this problem, a low-cost robotic manipulator arm with real-time control algorithms and a human-robot interface that allows it to be controlled by a microcomputer is developed. This includes its functional model, geometry of link movement, mechanical design, control circuitries, and a Teraterm-based human-robot interface (HRI). Furthermore, an encryption method was developed that maps selected control keys to actuation functions in manipulator chains. We also explored how this robotic manipulator may be used as a teaching tool, particularly for teaching three major industrial technologies: machine-making, electronics, and computing. Our manipulator arm has undergone both unit and system tests and has been used for classroom teaching. Results of its applicability assessment show that our system could aid the teaching of robotics and STEM subjects in schools, as a way to solve the trilemma of harmonizing the concept of machines, electronic control, and computation in multidisciplinary domains.

关键词： Service robots Educational robots Education Manipulators Real-time systems Production facilities Security

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Impossibility theorems involving weakenings of expansion consistency and resoluteness in voting

arXiv

引用

arXiv 2022年

作者： Holliday, Wesley H. Norman, Chase Pacuit, Eric Zahedian, Saam Department of Philosophy and Group in Logic and the Methodology of Science University of California Berkeley United States Department of Electrical Engineering and Computer Science University of California Berkeley United States Department of Philosophy University of Maryland United States Stanford Institute for Economic Policy Research Stanford University United States

A fundamental principle of individual rational choice is Sen's γ axiom, also known as expansion consistency, stating that any alternative chosen from each of two menus must be chosen from the union of the menus. Expansion consistency can also be formulated in the setting of social choice. In voting theory, it states that any candidate chosen from two fields of candidates must be chosen from the combined field of candidates. An important special case of the axiom is binary expansion consistency, which states that any candidate chosen from an initial field of candidates and chosen in a head-to-head match with a new candidate must also be chosen when the new candidate is added to the field, thereby ruling out spoiler effects. In this paper, we study the tension between this weakening of expansion consistency and weakenings of resoluteness, an axiom demanding the choice of a single candidate in any election. As is well known, resoluteness is inconsistent with basic fairness conditions on social choice, namely anonymity and neutrality. Here we prove that even significant weakenings of resoluteness, which are consistent with anonymity and neutrality, are inconsistent with binary expansion consistency. The proofs make use of SAT solving, with the correctness of a SAT encoding formally verified in the Lean Theorem Prover, as well as a strategy for generalizing impossibility theorems obtained for special types of voting methods (namely majoritarian and pairwise voting methods) to impossibility theorems for arbitrary voting methods. This proof strategy may be of independent interest for its potential applicability to other impossibility theorems in social *** Codes 91B12, 91B14 Copyright © 2022, The Authors. All rights reserved.

关键词： Formal verification

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Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects

arXiv

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arXiv 2022年

作者： Ghassami, AmirEmad Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Department of Computer Science Johns Hopkins University United States Department of Electrical Engineering Stanford University United States Department of Environmental and Occupational Health Program in Public Health University of California Irvine United States Department of Statistics and Data Science The Wharton School University of Pennsylvania United States

We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time;hence, long-term effects of treatment are unobserved but short-term effects will be observed. Therefore, data from neither domain alone suffices for causal inference about the effect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach, and then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment effect as well as the effect of treatment on the treated. Our first approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of an observed confounder such that the short-term and long-term potential outcome variables have the same partial additive association with that confounder. Our third approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. We propose influence function-based estimation strategies for each of our data fusion frameworks and study the robustness properties of the proposed estimators. © 2022, CC BY.

关键词： Data fusion

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Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes

arXiv

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arXiv 2024年

作者： Bandyopadhyay, Sabyasachi Zhang, Jiaqing Ison, Ronald L. Libon, David J. Tighe, Patrick Price, Catherine Rashidi, Parisa J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida GainesvilleFL United States Perioperative Cognitive Anesthesia NetworkSM UF Health University of Florida GainesvilleFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Department of Anesthesiology College of Medicine University of Florida GainesvilleFL United States Department of Geriatrics and Gerontology Department of Psychology New Jersey Institute for Successful Aging School of Osteopathic Medicine Rowan University GlassboroNJ United States Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida GainesvilleFL United States

The association between preoperative cognitive status and surgical outcomes is a critical, yet scarcely explored area of research. Linking intraoperative data with postoperative outcomes is a promising and low-cost way of evaluating long-term impacts of surgical interventions. In this study, we evaluated how preoperative cognitive status as measured by the clock drawing test contributed to predicting length of hospital stay, hospital charges, average pain experienced during follow-up, and 1-year mortality over and above intraoperative variables, demographics, preoperative physical status and comorbidities. We expanded our analysis to 6 specific surgical groups where sufficient data was available for cross-validation. The clock drawing images were represented by 10 constructional features discovered by a semi-supervised deep learning algorithm, previously validated to differentiate between dementia and non-dementia patients. Different machine learning models were trained using 5-fold cross-validation to classify postoperative outcomes in hold-out test sets. The models were compared with respect to their relative performance, time complexity, and interpretability. Shapley Additive Explanations (SHAP) analysis was used to find the most predictive features for classifying different outcomes in different surgical contexts. Relative classification performances achieved by different feature sets showed that the perioperative cognitive dataset which included clock drawing features in addition to intraoperative variables, demographics, and comorbidities served as the best dataset for 12 of 18 possible surgery-outcome combinations. Interpretability analysis showed that duration of surgery was the most significant predictor of adverse outcomes, followed by the mean alveolar concentration of isoflurane or sevoflurane used during anesthesia. Disruptions in baseline correlations between intraoperative variables in different surgeries and the relative directionality of these varia

关键词： Anesthetics

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GSLB: The Graph Structure Learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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Understanding policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address public Safety

arXiv

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arXiv 2023年

作者： Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bhaskararayuni, Sai Datta Ravindran, Arun Reid, Shannon Tabkhi, Hamed Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Electrical Engineering and Computer Systems University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Criminal Justice University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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A multi-channel ratio-of-ratios method for noncontact hand video based SpO2 monitoring using smartphone cameras

TechRxiv

引用

TechRxiv 2021年

作者： Tian, Xin Wong, Chau-Wai Ranadive, Sushant M. Wu, Min The Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States The Department of Electrical and Computer Engineering and the Forensic Sciences Cluster NC State University RaleighNC27695 United States The Department of Kinesiology School of Public Health University of Maryland College ParkMD20742 United States

Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone’s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%. © 2021, CC BY-NC-SA.

关键词： Smartphones

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