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11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Bahadur, Rabya Ur Rehman, Saeed Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering Islamabad Pakistan Department of Electrical Engineering COMSATS Institute of Information Technology Abbottabad Pakistan

ISBN: (纸本)9789897582790

Detection of Muscle Activity Interval plays a pivotal role in the design and implementation of real-time My-oelectric controlled devices and their applications. This paper presents an algorithm for real-time detection of onset/offset points in the muscles activity by employing adaptive threshold technique on the Correlation Coefficient of Taeger Kaiser Energy Operator using low cost hardware. Performance of the algorithm has also been evaluated through real-time tests carried out under various constrained scenarios and different signal to noise ratios, revealing very promising results with a maximum accuracy of 99.9% using medium or no external forces. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Adaptive algorithms

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Semi-supervised Learning for Epileptic Focus Localization Using Deep Convolutional Autoencoder

Semi-supervised Learning for Epileptic Focus Localization Us...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Hisham Daoud Magdy Bayoumi The Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Dept. of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA

Epileptic focus localization is an important process for successful epileptic surgery. The intracranial Electroencephalogram (iEEG) recording helps the neurosurgeons to take the right decision in patients undergoing epilepsy surgical procedure by delineating the epileptogenic area. This requires accurate classification of the EEG signals from different brain areas into focal and non-focal groups. In this paper, we propose a semi-supervised learning based system utilizing a deep neural network to accurately analyze the non-stationary and nonlinear EEG signals. The presented method is based on training a deep convolutional autoencoder neural network in an unsupervised manner and then using the pre-trained encoder along with multi-layer perceptron for classification of the EEG signals into focal and non-focal. EEG feature extraction and classification are performed in a single automated system rather than extracting handcrafted features as in the previous work. The proposed approach leverages the convolutional autoencoder by reducing the features' dimension and extracting the discriminative spatio-temporal features from the EEG signals. Experimental results show the ability of the proposed method to localize the epileptogenic area with an average accuracy of 93.2% using ten-fold cross validation strategy which is the highest among the state of the art.

关键词： Feature extraction Electroencephalography Training Brain modeling Convolution Classification algorithms Machine learning

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Deep learning based automated extraction of intra-retinal layers for analyzing retinal abnormalities 20

Deep learning based automated extraction of intra-retinal la...

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20th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2018

作者： Hassan, Taimur Usman, Anam Akram, M. Usman Furqan Masood, M. Yasin, Ubaidullah Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering Islamabad Pakistan Department of Ophthalmology Armed Forces Institute of Ophthalmology Rawalpindi Pakistan

ISBN: (纸本)9781538642948

Extraction of retinal layers from optical coherence tomography (OCT) scans is critical for analyzing retinal anomalies and manual segmentation of these retinal layers is a very cumbersome task. Recently, deep learning has gained much popularity in medical image analysis due to its underlying precision and robustness. Many researchers have utilized deep learning for extracting retinal layers from OCT images. However, to the best of our knowledge, there is no literature available that presents a robust segmentation framework that is able to extract retinal layers from OCT scans having different retinal pathological syndromes. Therefore, this paper presents a deep convolutional neural network and structure tensor-based segmentation framework (CNN-STSF) for the fully automated segmentation of up to eight retinal layers from normal as well as diseased OCT scans. First of all, the proposed framework computes coherent tensor from the candidate scan through which retinal layers are extracted. Afterwards, the pixels representing the layers are further classified using cloud based deep convolutional neural network (CNN) model trained on 1,200 retinal layers patches. CNN model in the proposed framework computes the probability of each layer pixels and assign it to be part of that layer for which it has the highest probability. The proposed framework was tested and validated on more than 39,000 retinal OCT scans from different publicly available datasets and from local Armed Forces Institute of Ophthalmology (AFIO) dataset where it outperformed all the existing solutions by achieving the overall layer segmentation accuracy of 0.9375. © 2018 IEEE.

关键词： Optical tomography

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Torque Ripple Minimization of Four-phase Switched Reluctance Motor using Direct Torque Control with an Innovative Switching Sequence Scheme

Torque Ripple Minimization of Four-phase Switched Reluctance...

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IEEE Energy Conversion Congress and Exposition

作者： Krishna Reddy Pittam Deepak Ronanki Parthiban Perumal Abdul R. Beig Sheldon S. Williamson Dept. of Electrical and Electronics Engineering National Institute of Technology Surathkal Karnataka India Dept. of Electrical and Computer Engineering University of Ontario Institute of Technology Ontario Canada Advanced Power and Energy Center Dept. of Electrical and Computer Engineering Khalifa University Abu Dhabi UAE

Direct torque control (DTC) technique is the prominent control strategy, used to control the switched reluctance motor (SRM) with a reduced torque ripple in comparison to the traditional current control techniques. However, it draws higher phase current in order to maintain the required electromagnetic torque during phase commutation, thus reduces torque per ampere. To circumvent this issue, a new DTC method with an innovative switching sequence is introduced in this paper, which minimizes torque ripple as well as power loss. The efficacy of the proposed scheme is validated for four-phase SRM through detailed simulation studies and compared with the conventional DTC scheme. The results show that the proposed scheme exhibits an improved steady-state as well as dynamic performance under various operating conditions.

关键词： Torque Torque measurement Reluctance motors Switches Mathematical model Torque control

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Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations

arXiv

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arXiv 2019年

作者： Chakravarthula, Sandeep Nallan Nasir, Md Tseng, Shao-Yen Li, Haoqi Park, Tae Jin Baucom, Brian Bryan, Craig J. Narayanan, Shrikanth Georgiou, Panayiotis Dept. of Electrical and Computer Engineering University of Southern California Dept. of Psychology University of Utah National Center for Veterans Studies Utah United States

Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, while helpful, are often constrained by access to clinical visits or therapist availability, especially when needed in a timely manner. There have hence been efforts on identifying whether communication patterns between couples at home can provide preliminary information about potential suicidal behaviors, prior to intervention. In this work, we investigate whether acoustic, lexical, behavior and turn-taking cues from military couples' conversations can provide meaningful markers of suicidal risk. We test their effectiveness in real-world noisy conditions by extracting these cues through an automatic diarization and speech recognition front-end. Evaluation is performed by classifying 3 degrees of suicidal risk: None, ideation, attempt. Our automatic system performs significantly better than chance in all classification scenarios and we find that behavior and turn-taking cues are the most informative ones. We also observe that conditioning on factors such as speaker gender and topic of discussion tends to improve classification performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech recognition

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Prediction of Diabetes Using Machine Learning Algorithms in Healthcare

Prediction of Diabetes Using Machine Learning Algorithms in ...

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International Conference on Automation and Computing (ICAC)

作者： Muhammad Azeem Sarwar Nasir Kamal Wajeeha Hamid Munam Ali Shah Department of Computer Science COMSATS Institute of Information technology Islamabad Pakistan Electrical Engineering School of Electrical Engineering and Computer Science Pakistan Center for Advanced Studies in Engineering Islamabad Pakistan

ISBN: (纸本)9781538648919

There are several machine learning techniques that are used to perform predictive analytics over big data in various fields. Predictive analytics in healthcare is a challenging task but ultimately can help practitioners make big data-informed timely decisions about patient's health and treatment. This paper discusses the predictive analytics in healthcare, six different machine learning algorithms are used in this research work. For experiment purpose, a dataset of patient's medical record is obtained and six different machine learning algorithms are applied on the dataset. Performance and accuracy of the applied algorithms is discussed and compared. Comparison of the different machine learning techniques used in this study reveals which algorithm is best suited for prediction of diabetes. This paper aims to help doctors and practitioners in early prediction of diabetes using machine learning techniques.

关键词： Machine learning algorithms Diseases Prediction algorithms Machine learning Diabetes Big Data

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Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities

Deep Learning Based Automated Extraction of Intra-Retinal La...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Taimur Hassan Anam Usman M. Usman Akram M. Furqan Masood Ubaidullah Yasin Department of Computer & Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Center for Advanced Studies in Engineering Islamabad Pakistan Department of Ophthalmology Armed Forces Institute of Ophthalmology Rawalpindi Pakistan

ISBN: (纸本)9781538642955

关键词： Retina Tensile stress Convolution Image segmentation Pathology Computational modeling Blindness

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A Simple Approach for Securing IoT Data Transmitted over Multi-RATs

A Simple Approach for Securing IoT Data Transmitted over Mul...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Rida Diba Elias Yaacoub Mohammed Al-Husseini Hassan Noura Khalid Abualsaud Tamer Khattab Mohsen Guizani Faculty of Computer Studies Arab Open University Beirut Lebanon Beirut Research and Innovation Center (BRIC) Beirut Lebanon Dept. of Computer Science & Engineering Qatar University Doha Qatar Dept. of Electrical Engineering College of Engineering Qatar University Doha Qatar Department of Electrical and Computer Engineering University of Idaho Moscow ID USA

In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud”. The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), e.g., using the long-term evolution (LTE) technology, or both (e.g., using multi- homing to transmit over multiple radio access technologies (Multi-RATs). Fast encryption or physical layer security techniques are needed to secure the data. In fact, during normal conditions, monitoring data can be transmitted using best effort transmission. However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams. In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed. The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time.

关键词： Rats Security Data communication Wireless fidelity Long Term Evolution Monitoring Receivers

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Cross-Domain Pseudo-Sensors in IBSM

Cross-Domain Pseudo-Sensors in IBSM

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International Conference on Information Fusion

作者： Kenneth J. Hintz Will Williamson Steven Brannon Jim Scrofani Dept. of Electrical and Computer Engineering George Mason University Fairfax VA USA Center for Multi-INT Studies Naval Postgraduate School Monterey CA USA

The applicable (sensor) function table (AFT) is one of the 6 major components of the Information Based Sensor Management (IBSM) approach to sensor management The AFT lists all available sensing actions from which the information instantiator (II) can downselect to an admissible set of sensor functions which are capable of satisfying an information request. The II further orders the admissible set by their expected (sensor) information value rate (EIVR-sen) for passing observation requests to the sensors. While the AFT was initially developed for hard sensors, it is becoming increasingly important to not only develop methods of characterizing soft sensors for representation in the AFT, but also for characterizing approximate contemporaneous measurements by both hard and soft sensors as AFT entries. Hard sensor entries in the AFT are reviewed. Suggestions are presented for AFT entries of several representative soft sensors. Hard and soft sensing functions are then combined where possible into cross-domain (hard/soft) pseudo-sensor entries. Additionally, brief examples are presented which demonstrate how the AFT entries may be represented in the Sensor Model Language (SML) format for improved automation of the sensor management process.

关键词： Applicable function table Information based sensor management Orchestrated resource management Cross-domain pseudo-sensor

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