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2nd International Conference on computer Engineering, Network, and Intelligent Multimedia, CENIM 2019

作者： Dewantara, Bima Sena Bayu Miura, Jun Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Toyohashi University of Technology Dept. of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9781728129655

Tracking a target person is a vital job in human-robot interaction. The robot must always notice a particular person as the interaction target partner. However, it is sometimes very hard to distinguish the target person because there are many other persons around the target. In this paper, we propose a target person detection and tracking system by combining person's frontal photometric features such as face and clothing color, and coordinate of the person's location in the real world. We apply an illumination invariant face recognition method named OptiFuzz. Hue-Saturation histogram (HS-histogram) is used to obtain the clothing color feature, and a location of the person is acquired from a calibrated single camera view. All these features are then fed into an algorithm of Naive Bayes to discriminate between the target person and others. Our experimental results indicate a successful outcome as it is always possible to detect and track the target person. © 2019 IEEE.

关键词： Human robot interaction

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Radiogenomics of glioblastoma: Identification of radiomics associated with molecular subtypes

arXiv

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arXiv 2020年

作者： Wijethilake, Navodini Islam, Mobarakol Meedeniya, Dulani Chitraranjan, Charith Perera, Indika Ren, Hongliang Dept. of Computer Science Engineering University of Moratuwa Sri Lanka Dept. of Biomedical Engineering National University of Singapore Singapore

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture features from the tumor subregions to identify the correlations with gene expression features and overall survival. Consequently, we utilize the radiomics to find associations with the subtypes of glioblastoma. Accordingly, the fractal dimensions of the whole tumor, tumor core, and necrosis regions show a significant difference between the Proneural, Classical and Mesenchymal subtypes. Additionally, the subtypes of GBM are predicted with an average accuracy of 79% utilizing radiomics and accuracy over 90% utilizing gene expression profiles. Copyright © 2020, The Authors. All rights reserved.

关键词： Tumors

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Non-Linear Age of Information: An Energy Efficient Receiver-Centric Approach

arXiv

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arXiv 2021年

作者： Miridakis, Nikolaos I. Tsiftsis, Theodoros A. Yang, Guanghua The Institute of Physical Internet School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China The Dept. of Informatics and Computer Engineering University of West Attica Aegaleo 12243 Greece

The age of information (AoI) performance metric for point-to-point wireless communication systems is analytically studied under Rician-faded channels and when the receiver is equipped with multiple antennas. The general scenario of a non-linear AoI function is considered, which includes the conventional linear AoI as a special case. The stop-and-wait transmission policy is adopted, where the source node samples and then transmits new data only upon the successful reception of previous data. This approach can serve as a performance benchmark for any queuing system used in practice. New analytical and closed-form expressions are derived with respect to the average AoI and average peak AoI for the considered system configuration. We particularly focus on the energy efficiency of the said mode of operation, whereas some useful engineering insights are provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Learning to explicitate connectives with Seq2Seq network for implicit discourse relation classification 13

Learning to explicitate connectives with Seq2Seq network for...

引用

13th International Conference on Computational Semantics, IWCS 2019

作者： Shi, Wei Demberg, Vera Dept. of Language Science and Technology Saarland Informatics Campus Saarbrücken66123 Germany Dept. of Mathematics and Computer Science Saarland University Saarland Informatics Campus Saarbrücken66123 Germany

ISBN: (纸本)9781950737192

Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation. © 2019 IWCS 2019 - Proceedings of the 13th International Conference on Computational Semantics - Long Papers. All rights reserved.

关键词： Semantics

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LETHE: A Digital Intervention for Cognitive Decline*

LETHE: A Digital Intervention for Cognitive Decline*

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios S. Loukas Thomas Kassiotis Ignacio Lamata Martinez Lefteris Koumakis Jeroen Bruinsma Roberto Pasciuti Monica Balatresi Ville Tenhunen Adam Fiakkas Lelia Ataliani Georgia S. Karanasiou Manolis Tsiknakis Hannes Hilberger Markus Bödenler Bianca Schnalzer Simone Huber Mattia Pirani Matteo Colombo Sten Hanke Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute FORTH-BRI Ioannina Greece Computational BioMedicine Laboratory Institute of Computer Science FORTH-ICS Heraklion Greece EGI Foundation Amsterdam The Netherlands Dept. of Health Promotion Care and Public Health Research Institute Maastricht University Maastricht The Netherlands ExtraRed Innovations Pisa Italy Infotrend Innovations Co. Ltd Nicosia Cyprus eHealth Institute FH Joanneum University of Applied Sciences Graz Austria Innovation2Grow I2G Bologna Italy

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform *** Relevance — Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

关键词：

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Demo: PH Watch - Leveraging pulse oximeters in existing wearables for reusable, real-time monitoring of pH in sweat 17

Demo: PH Watch - Leveraging pulse oximeters in existing wear...

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17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019

作者： Balaji, Ananta Narayanan Yuan, Chen Wang, Bo Peh, Li-Shiuan Shao, Huilin Dept. of Electrical and Computer Engg National University of Singapore Singapore Dept. of Biomedical Engg National University of Singapore Singapore Dept. of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781450366618

Sweat is a readily accessible bodily fluid for detecting biomarkers such as pH, glucose etc., enabling continuous and non-invasive assessment of the well-being of individuals. Our proposed work aims at leveraging pulse oximeter chips in current-day fitness trackers for real-time continuous monitoring of pH in sweat. We achieve that by fabricating a highly responsive and long-term reusable pH sweat sensor on a flexible material to achieve skin conformity, targeting the sensor to work at the reflected infrared (880nm) and red (660nm) photoplethysmograph (PPG) signal intensities recorded by pulse oximeters. The sensor can be readily mounted atop any wearable with a pulse oximeter. We have successfully demonstrated a low-cost, low-power, highly-responsive and long-term reusable wrist-worn wearable prototype, pH Watch, for real-time continuous monitoring of pH value of sweat. We conducted on-body trials with 10 participants and pH Watch achieves an accuracy of $\approx$91%. We also showed that the integration of our sweat sensor does not hinder the pulse oximeter from measuring heart rate and SpO\textsubscript2, and users can continue with their daily activities with motion artifacts removed efficiently from PPG signals using the TROIKA framework, resulting in heart rate and SpO\textsubscript2 measurements with an accuracy of $\approx$95% and $\approx$96% respectively when validated against commercial finger pulse oximeter measurements. To the best of our knowledge, pH Watch is the first demonstration of a reusable sweat sensor that can be readily integrated into today's smart watches with pulse oximeters, paving the way for ubiquitous sensing of biomarkers. © 2019 Copyright held by the owner/author(s).

关键词： Watches

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Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

arXiv

引用

arXiv 2022年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Khosravi, Abbas Zare, Assef Gorriz, Juan M. Chale-Chale, Amir Hossein Khadem, Ali Rajendra Acharya, U. Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University Geelong Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy. © 2022, CC BY-NC-ND.

关键词： Support vector machines

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Energy-Aware Multi-Goal Motion Planning Guided by Monte Carlo Search

Energy-Aware Multi-Goal Motion Planning Guided by Monte Carl...

引用

IEEE International Conference on Automation science and Engineering (CASE)

作者： Yazz Warsame Stefan Edelkamp Erion Plaku Dept. of Informatics King’s College London London Dept. of Informatics University of Koblenz-Landau Mainz Germany Dept. of Electrical Engineering and Computer Science Catholic University of America Washington DC USA

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Autonomous robots need a reliable way to preserve their energy level while performing a persistent task such as inspection or surveillance. Toward this objective, this paper considers the multi-goal motion-planning problem with multiple recharging stations where a robot operating in a complex environment has to reach each goal while reducing the travel distance and the number of times it recharges. This paper develops an integrated approach that couples sampling-based motion planning with Monte-Carlo Tree Search (MCTS). The proposed MCTS searches over a discrete abstraction, which is obtained via a probabilistic roadmap, and uses a reward function to calculate when, where, and whether it is beneficial to recharge. This results in short tours that also reduce the number of recharges. Such tours are used to guide sampling-based motion planning as it expands a tree of collision-free and dynamically-feasible motions. Experiments with nonlinear dynamical robot models operating in obstacle-rich environments demonstrate the efficiency of the approach.

关键词： Robots Collision avoidance Planning Energy states Charging stations Mathematical model Monte Carlo methods

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Arduino based cost-effective design and development of a digital stethoscope 15

Arduino based cost-effective design and development of a dig...

引用

15th International Conference on Emerging Technologies, ICET 2019

作者： Waqar, Muhammad Inam, Shehla Ur Rehman, Muhammad Azeem Ishaq, Muhammad Afzal, Mafia Tariq, Nimra Amin, Faisal Qurat-Ul-Ain National University of Science and Technology Dept. of Biomedical Engineering and Sciences Pakistan Riphah International University Dept. of Biomedical Engineering Islamabad Pakistan Dept. of Electrical Engineering National University of Science and Technology Islamabad Pakistan Bahria University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781728154039

The medical device used for auscultation is known as a stethoscope. An ordinary acoustics-based stethoscope is limited in its ability to provide high-quality sound in noisy environments. To deal with this problem digital stethoscopes have been developed. A digital stethoscope enables the use of advanced noise cancellation techniques resulting in high- quality sound compared to a conventional stethoscope. Currently, conventional stethoscopes are commonly used by physicians due to their low cost compared to the digital ones. This paper presents an Arduino DUE based cost-effective digital stethoscope with real-time realization of cardiac sounds over a graphic display and logging capabilities for post-processing in powerful numerical software such as MATLAB. The proposed design is considerably low-cost compared to the commercially available digital stethoscopes while providing 2.375 \pm 0.222 dB better signal-to-noise ratio. © 2019 IEEE.

关键词： MATLAB

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Fast Spatial Autocorrelation

Fast Spatial Autocorrelation

引用

IEEE International Conference on Data Mining (ICDM)

作者： Anar Amgalan LR Mujica-Parodi Steven S. Skiena Dept. of Physics and Astronomy Stony Brook University Dept. of Biomedical Engineering Stony Brook University Dept. of Computer Science Stony Brook University

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's I and Geary's C are widely used to measure spatial autocorrelation, they are slow: all popular methods run in Ω(n 2 ) time, rendering them unusable for large data sets, or long time-courses with moderate numbers of points. We propose a new S A statistic based on the notion that the variance observed when merging pairs of nearby clusters should increase slowly for spatially autocorrelated variables. We give a linear-time algorithm to calculate S A for a variable with an input agglomeration order (available at https://***/aamgalan/spatial_autocorrelation). For a typical dataset of n ≈ 63,000 points, our S A autocorrelation measure can be computed in 1 second, versus 2 hours or more for Moran's I and Geary's C. Through simulation studies, we demonstrate that S A identifies spatial correlations in variables generated with spatially-dependent model half an order of magnitude earlier than either Moran's I or Geary's C. Finally, we prove several theoretical properties of S A : namely that it behaves as a true correlation statistic, and is invariant under addition or multiplication by a constant.

关键词： Correlation Computational modeling Time series analysis Transforms Time measurement Spatial databases Standards

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