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Towards an ultrafast 3D imaging scanning LiDAR system:a review

Photonics Research 2024年第8期12卷 1709-1729页

作者： ZHI LI YAQI HAN LICAN WU ZIHAN ZANG MAOLIN DAI SZE YUN SET SHINJI YAMASHITA QIAN LI H.Y.FU Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Electrical Engineering and Information Systems The University of TokyoTokyo 113-8656Japan Research Center for Advanced Science and Technology The University of TokyoTokyo 153-8904Japan School of Electronic and Computer Engineering Peking UniversityShenzhen 518055China

Light detection and ranging(LiDAR),as a hot imaging technology in both industry and academia,has undergone rapid innovation and *** current mainstream direction is towards system miniaturization and *** are many metrics that can be used to evaluate the performance of a LiDAR system,such as lateral resolution,ranging accuracy,stability,size,and *** recently,with the continuous enrichment of LiDAR application scenarios,the pursuit of imaging speed has attracted tremendous research ***,for autonomous vehicles running on motorways or industrial automation applications,the imaging speed of LiDAR systems is a critical *** this review,we will focus on discussing the upper speed limit of the LiDAR *** on the working mechanism,the limitation of optical parts on the maximum imaging speed is *** beam scanner has the greatest impact on imaging *** provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning ***,we highlight the spectral scanning LiDAR as a new paradigm of ultrafast ***,to further improve the imaging speed,we then review the parallel detection methods,which include multiple-detector schemes and multiplexing ***,we summarize the LiDAR systems with the fastest point acquisition rate reported *** the outlook,we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

关键词： LiDAR scanner system

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Empowering Patient Care: A Novel Patient Drug Recommendation System Using Artificial Intelligence with Modified Learning Strategy 1

Empowering Patient Care: A Novel Patient Drug Recommendation...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Samuthira Pandi, V. Kavitha, V.R. Shinev, S. Arumai Roopa Chandrika, R. Valarmathy, A.S. Glory, E. Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India S A Engineering College Department of CSE Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of CSE TamilNadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha University Department of Biosciences Chennai India

ISBN: (纸本)9798350365337

In the era of personalized medicine, the demand for efficient and accurate drug recommendation systems has escalated. This paper introduces a pioneering approach to enhance patient care through a novel Patient Drug Recommendation System (PDRS). Leveraging the power of machine learning (ML), specifically Cascaded Multi-Layer Perceptrons (MLP), our system aims to provide precise and personalized drug recommendations based on an extensive Drug Review Dataset. The proposed PDRS capitalizes on the inherent complexities of drug reviews, extracting valuable insights and patterns from patient feedback. The cascaded MLP architecture is designed to sequentially process and analyze diverse aspects of drug reviews, capturing nuanced information and correlations that may be overlooked by traditional recommendation systems. Our methodology involves preprocessing the drug review dataset to address noise and ensure data quality. Feature extraction techniques are applied to distill meaningful information, and the cascaded MLP model is trained using a robust set of labeled data. The system's performance is evaluated on various metrics, with a remarkable accuracy of 97%, signifying its efficacy in predicting suitable drug recommendations for patients. The cascaded MLP architecture demonstrates superior adaptability, allowing the system to comprehend intricate relationships between patients, drugs, and their respective effects. The achieved accuracy underscores the potential of our PDRS to revolutionize clinical decision-making, fostering a more personalized and efficient approach to prescribing medications. © 2024 IEEE.

关键词： Feature extraction

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Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrates

Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrate...

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2024 IEEE Silicon Photonics Conference, SiPhotonics 2024

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui The University of British Columbia Department Of Materials Engineering Vancouver Canada Fraunhofer Institute For Solar Energy Systems Heidenhofstrasse Freiburg Germany The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada National Taiwan University Graduate Institute Of Electronics Engineering Taipei Taiwan National Taiwan University Graduate Institute Of Photonics And Optoelectronics Taipei Taiwan National Taiwan University Graduate School Of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350394047

This work successfully developed an independent Ge-VCSEL epitaxy and fabrication technology route. This is the second successful Ge-VCSEL technology reported worldwide, and the first Ge-VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process etc. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-VCSEL wafer has a 40% lower surface root-mean square roughness and 72% lower average bow-warp. Additionally, the Ge-VCSEL has a 10% lower threshold current density and a 19% higher maximum slope efficiency than the GaAs-VCSEL. © 2024 IEEE.

关键词： Surface emitting lasers

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Developing High Confinement and Low-loss Silicon Nitride Photonic Devices for Optical Coherence Tomography (Invited)

Developing High Confinement and Low-loss Silicon Nitride Pho...

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Novel Techniques in Microscopy, NTM 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Ji, Xingchen Chen, Junzhuo Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

There is a surging interest in developing integrated Optical Coherence Tomography (OCT) system. However, most components are based on silicon which cannot be used for wavelength below 1.2 µm. Here, we discuss the... 详细信息

ISBN: (纸本)9781957171210

关键词： Breast cancer imaging Frequency combs Optical coherence tomography Optical imaging Photonic devices Supercontinuum sources

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Non-Hermitian Band Structure of Photonic Temporal Crystals

Non-Hermitian Band Structure of Photonic Temporal Crystals

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Centerfor Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department ofElectrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally confirm the non-Hermitian band structures of a photonic temporal crystal in the microwave. These non-Hermitian band structures are shown to be two measurable distinct subsets of complex eigenfrequenc... 详细信息

ISBN: (纸本)9781957171258

关键词： Band structure

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Health-Conscious Model Predictive Control for Integrated Shipboard Power systems with Generator and Battery under High Ramp Rate Loads

Health-Conscious Model Predictive Control for Integrated Shi...

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European Control Conference (ECC)

作者： Mitchell Levi Satish Vedula Olugbenga Moses Anubi Heath Hofmann Jing Sun Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA Department of Electrical and Computer Engineering Center for Advanced Power systems Florida State University Tallahassee FL USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Energy storage on all-electric vessels can enable shipboard-integrated power systems to meet increasing pulse load requirements and reduce fuel consumption and greenhouse gas emissions. However, integrating energy storage into shipboard power systems imposes additional requirements on the power management systems. In this paper, a power management controller is developed for the integrated power system utilizing a model predictive control (MPC) scheme. The MPC formulations developed here aim to manage the competing requirements of minimizing component health degradation and the power tracking error when responding to high ramp rate power demands. Sensitivity analysis is performed to explore the direct tradeoff between generator health and battery health when meeting the load power demand is strictly enforced. The MPC solution is demonstrated for varying energy storage sizes to provide insight for the design and operation of an integrated shipboard power system.

关键词： Degradation Power demand Costs Sensitivity analysis Power system management Generators Batteries

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ChromaSense-Empowering Health, Empowering You

ChromaSense-Empowering Health, Empowering You

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Ravi Durbha Valencia Koomson Department of Electrical & Computer Engineering Advanced Integrated Circuits & Systems Lab Tufts University Medford MA USA

Wearable devices have transformed from novelties into indispensable companions for millions, finding applications in health and wellness, empowering individuals to proactively manage their well-being. Among these, pulse oximeters play a vital role in detecting health issues like hypoxemia. Yet, traditional oximeters struggle with accuracy in individuals with darker skin tones and lack multi-vital sign monitoring using Photoplethysmogram (PPG) signals. Addressing these challenges, we introduce ChromaSense, a Skintone Compensated Pulse Oximeter (SCPO) equipped with advanced sensors and intelligent algorithms. It aims to provide precise oxygen saturation readings for all skin tones, ensuring equitable access to quality healthcare. By addressing pulse oximetry disparities, ChromaSense seeks to enhance patient outcomes, promote inclusivity in healthcare, revolutionize medical diagnostics and treatment, and drive a patient-centered healthcare transformation.

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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad Customization for Streaming Media

Enhancing Viewer Engagement: Exploring Generative Long Short...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Troy University Montgomery Campus AL. Department of electrical engineering Masters in electrical engineering Sr. Principal Systems Engineer in C & RS Platform services DT Amtrak USA University of south alabama

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad Customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, “ad is something that happens with the viewer”. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization.

关键词： Supply chains Collaboration Companies Streaming media Data models History Advertising Long short term memory Monitoring Consumer electronics

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Explainable Symbolic Regression Model for Tool Wear Diagnosis 9

Explainable Symbolic Regression Model for Tool Wear Diagnosi...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mishra, Debasish Han, Seulki Pattipati, Krishna R. Bollas, George M. University of Connecticut Department of Electrical & Computer Engineering 371 Fairfield Way StorrsCT06269 United States Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut 159 Discovery Dr. StorrsCT06269 United States University of Connecticut Department of Chemical & Biomolecular Engineering 159 Discovery Dr. StorrsCT06269 United States

ISBN: (纸本)9798350311402

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an R2 of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model for tool wear estimation. © 2023 IEEE.

关键词： Regression analysis

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