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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Individual Alpha Frequency tACS modifies the detection of space-time optical illusion

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Brain Stimulation 2025年第1期18卷 360-360页

作者： Scoccia, Adriano Neri, Francesco Catrambone, Vincenzo Cinti, Alessandra Benelli, Alberto Romanella, Sara Grabot, Laetitia Valenza, Gaetano Smeralda, Carmelo Luca Santarnecchi, Emiliano van Wassenhove, Virginie Rossi, Simone Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab) Department of Medicine Surgery and Neuroscience Neurology and Clinical Neurophysiology Section University of Siena Italy Oto-Neuro-Tech Conjoined Lab Policlinico Le Scotte University of Siena Italy Department of Information Engineering University of Pisa Bioengineering & Robotics Research Center “E. Piaggio” Pisa Italy Precision Neuroscience & Neuromodulation Program Massachusetts General Hospital Harvard Medical School Boston MA USA Cognitive Neuroimaging Unit CEA DRF/Joliot NeuroSpin INSERM Université Paris-Saclay 91191 Gif-sur-Yvette France Department for Cognitive Neuroscience Faculty of Biology & Cognitive Interaction Technology Center of Excellence Bielefeld University Bielefeld Germany

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On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator

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Scientific reports 2023年第1期13卷 17527页

作者： Farzad Soltanian Mehdi Nosrati Saleh Mobayen Chuan-Chun Li Telung Pan Ming-Ta Ke Paweł Skruch Department of Electrical Engineering University of Alberta Edmonton Canada. Department of Electrical Engineering Manhattan College New York USA. Department of Electrical Engineering University of Zanjan Zanjan Iran. mobayens@yuntech.edu.tw. Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou 640301 Yunlin Taiwan. mobayens@yuntech.edu.tw. National Taiwan University Hospital Yunlin Branch Yunlin Taiwan. a***@***. Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Yunlin 64002 Taiwan. Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou 640301 Yunlin Taiwan. Department of Automatic Control and Robotics AGH University of Science and Technology 30-059 Kraków Poland.

High-figure of merit (FoM) plasmonic microwave resonator is researched as a non-invasive on-body sensor to monitor the human body's blood glucose variation rate in adults for biomedical applications, e.g., diabetic patients. The resonance frequencies of the proposed sensor are measured to be around [Formula: see text] GHz and [Formula: see text] GHz over the frequency band of DC to 6GHz which are suitable for monitoring interstitial fluid (ISF) changing rate. The [Formula: see text] sensor is experimentally wrapped on the human body arm to monitor the blood glucose changing rate via amplitude and frequency variations of the sensor. Amplitude variation and frequency shift are measured to be around 7 dB and 30 MHz, respectively. The measured results demonstrate the high precision of the proposed approach to depict a valid diagram for glucose changing rate due to good impedance matching of the designed microwave sensor and human body. The sensor is shown to enhance the sensitivity by a factor of 5 compared to the conventional ones.

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Super Resolution for Improved Positioning of an MRI-Guided Spinal Cellular Injection Robot

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Journal of Medical robotics Research 2021年第1-2期6卷

作者： Martinez, Daniel Enrique Meinhold, Waiman Oshinski, John Hu, Ai-Ping Ueda, Jun Robotics PhD Program Georgia Institute of Technology North Ave NW Atlanta 30332 GA United States Department of Radiology Emory University 201 Dowman Dr Atlanta 30322 GA United States Intelligent Sustainable Technologies Division Georgia Tech Research Institute 640 Strong Street Atlanta 30318 GA United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology North Ave NW Atlanta 30332 GA United States

This paper presents the development of a magnetic resonance imaging (MRI)-conditional needle positioning robot designed for spinal cellular injection. High-accuracy targeting performance is achieved by the combination of a high precision, parallel-plane, needle-orientation mechanism utilizing linear piezoelectric actuators with an iterative super-resolution (SR) visual navigation algorithm using multi-planar MR imaging. In previous work, the authors have developed an MRI conditional robot with positioning performance exceeding the standard resolution of MRI, rendering the MRI resolution the limit for navigation. This paper further explores the application of SR to images for robot guidance, evaluating positioning performance through simulations and experimentally in benchtop and MRI experiments. © 2021 World Scientific Publishing Company.

关键词： cellular therapeutics magnetic resonance imaging needle steering spinal cord Super resolution surgical robots visual robot guidance

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Frequency dependent reduction of cyber-sickness nausea in Virtual Reality by transcranial oscillatory stimulation of the vestibular cortex

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Brain Stimulation 2023年第1期16卷 260-260页

作者： Benelli, Alberto Neri, Francesco Cinti, Alessandra Romanella, Sara M. Giannotta, Alessandro De Monte, David Mandalà, Marco Smeralda, Carmelo Prattichizzo, Domenico Rossi, Simone Santarnecchi, Emiliano Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab) Unit of Neurology and Clinical Neurophysiology Department of Medicine Surgery and Neuroscience University of Siena Italy Oto-Neuro-Tech Conjoined Lab Policlinico Le Scotte University of Siena Italy Precision Neuroscience & Neuromodulation Program Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Otolaryngology Department of Medicine Surgery and Neuroscience Neurology and Clinical Neurophysiology Section University of Siena Italy Siena Robotics and Systems (SiRS) Lab Department of Information Engineering and Mathematics University of Siena Siena Italy

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Which ads to show? Advertisement image assessment with auxiliary information via multi-step modality fusion

arXiv

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arXiv 2019年

作者： Park, Kyung-Wha Lee, JungHoon Kwon, Sunyoung Ha, Jung-Woo Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Statistics and Actuarial Science Soongsil University Clova AI Research NAVER Corp Department of Computer Science and Engineering Seoul National University Surromind Robotics

Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertisements. Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference. Existing studies mainly focus on images and thus are less useful for advertisement scenarios where rich auxiliary data are available. Here we propose a modality fusion-based neural network that evaluates the aesthetic preference of images with auxiliary information. Our method fully utilizes auxiliary data by introducing multi-step modality fusion using both conditional batch normalization-based low-level and attention-based high-level fusion mechanisms, inspired by the findings from statistical analyses on real advertisement data. Our approach achieved state-of-the-art performance on the AVA dataset, a widely used dataset for aesthetic assessment. Besides, the proposed method is evaluated on large-scale real-world advertisement image data with rich auxiliary attributes, providing promising preference prediction results. Through extensive experiments, we investigate how image and auxiliary information together influence click-through rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Image fusion

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Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information 15

Decision-Level Fusion Method for Emotion Recognition using M...

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15th International Conference on Ubiquitous Robots, UR 2018

作者： Song, Kyu-Seob Nho, Young-Hoon Seo, Ju-Hwan Kwon, Dong-Soo Department of Robotics Program Korea Advanced Institute of Science and Technology Human-Robot Interaction Research Center Korea Republic of Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Human-Robot Interaction Research Center Korea Republic of

ISBN: (纸本)9781538663349

Human emotion recognition is an important factor for social robots. In previous research, emotion recognizers with many modalities have been studied, but there are several problems that make recognition rates lower when a recognizer is applied to a robot. This paper proposes a decision level fusion method that takes the outputs of each recognizer as an input and confirms which combination of features achieves the highest accuracy. We used EdNet, which was developed in KAIST based Convolutional Neural Networks (CNNs), as a facial expression recognizer and a speech analytics engine developed for speech emotion recognition. Finally, we confirmed a higher accuracy 43.40% using an artificial neural network (ANN) or the k-Nearest Neighbor (k-NN) algorithm for classification of combinations of features from EdN et and the speech analytics engine. © 2018 IEEE.

关键词： Engines

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Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

arXiv

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arXiv 2018年

作者： Liu, Xuan Fu, Jie Department of Electrical and Computer Engineering Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States

In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

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Approximate dynamic programming with probabilistic temporal logic constraints

arXiv

引用

arXiv 2018年

作者： Li, Lening Fu, Jie Robotics Engineering Program Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we develop approximate dynamic programming methods for stochastic systems modeled as Markov Decision Processes, given both soft performance criteria and hard constraints in a class of probabilistic temporal logic called Probabilistic Computation Tree Logic (PCTL). Our approach consists of two steps: First, we show how to transform a class of PCTL formulas into chance constraints that can be enforced during planning in stochastic systems. Second, by integrating randomized optimization and entropy-regulated dynamic programming, we devise a novel trajectory sampling-based approximate value iteration method to iteratively solve for an upper bound on the value function while ensuring the constraints that PCTL specifications are satisfied. Particularly, we show that by the on-policy sampling of the trajectories, a tight bound can be achieved between the upper bound given by the approximation and the true value function. The correctness and efficiency of the method are demonstrated using robotic motion planning examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Association Between Air Pollution and Obstructive Sleep Apnea in Terms of Respiratory Event Frequency and Lung-to-Finger Circulation Time

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Atmospheric Pollution Research 2025年

作者： Rachel Chien Zhihe Chen Peizheng Wang Wen-Te Liu Ying-Ying Chen Yen-Ling Chen Arnab Majumdar Jiunn-Horng Kang Kang-Yun Lee Wun-Hao Cheng Yi-Chih Lin Cheng-Jung Wu Yi-Chun Kuan Hsin-Chien Lee Cheng-Yu Tsai Research Center of Sleep Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Civil and Environmental Engineering Imperial College London London SW7 2AZ United Kingdom School of Respiratory Therapy College of Medicine Taipei Medical University Taipei Taiwan Research Center of Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City 235041 Taiwan Sleep Center Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Biomedical Engineering Taipei Medical University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei 110301 Taiwan Department of Otolaryngology Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Neuroscience Institute Taipei Medical University Taipei Taiwan Dementia Center Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Neurology Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Institute of Medical Humanities Taipei Medical University Taipei Taiwan Department of Psychiatry Taipei Medical University Hospital Taipei Taiwan

Air pollutant exposure has been shown to exacerbate obstructive sleep apnea (OSA) manifestations, such as increased respiratory episodes and intermittent oxygen desaturation. However, most studies have focused on episode frequency rather than duration. This study investigated the associations between air pollution and OSA manifestations using both frequency-based indices and the duration-based marker lung-to-finger circulation time (LFCT). Polysomnography data from 820 individuals with suspected OSA (mild-to-moderate: n=224; severe: n=596) were analyzed. Sleep disorder indices included the apnea–hypopnea index (AHI), oxygen desaturation index (ODI), arousal index, and mean LFCT. A novel composite metric, respiratory event response time area (RERTA), defined as the square root of the product of LFCT and AHI, was introduced to evaluate the interplay between frequency and duration. Air pollutant exposure was estimated using data from monitoring stations within 4 km of the registered residences of the study cohort. Multivariable Poisson regression models were employed to assess short-term (1-month) and long-term (1-year) exposure effects. Short-term PM 2.5 and NO 2 exposure were significantly associated with increased AHI, ODI, arousal index, and reduced LFCT. Long-term exposure to PM 2.5 , PM 10 , and NO 2 was associated with increased AHI and ODI, while PM 2.5 and PM 10 were linked to reduced LFCT. Both short- and long-term O 3 exposure were associated with increased AHI and ODI. PM 2.5 and O 3 (short-term) were positively associated with RERTA. These findings suggest that air pollution exacerbates OSA by increasing episode frequency and accelerating compensatory responses, thereby elevating cardiopulmonary burden as reflected by RERTA.

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