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IEEE Sensors Journal 2018年第3期18卷 1336-1337页

作者： Ghanbari, Milad Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Control and Intelligent Processing Center of Excellence Advanced Control Systems Laboratory College of Engineering School of Electrical and Computer Engineering University of Tehran Tehran*** Iran

The experimental results of the paper titled 'Delay compensation of tilt sensors based on mems accelerometer using data fusion technique' was commented by Bodnicki and Luczak. In this note, we address the raised comments by the mentioned authors. Moreover, appropriate explanations are provided in order to make the procedure of the experimental section more clear. © 2001-2012 IEEE.

关键词： Kalman filters

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Evaluation and Diagnosis of Brain Death: a Non-Invasive Pilot Study Using Functional Near-Infrared Spectroscopy (fNIRS)

Evaluation and Diagnosis of Brain Death: a Non-Invasive Pilo...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Abolfazl Arabkoohi Seyed Kamaledin Setarehdan Farahnaz Sadegh Beigee Alireza Jahangirifard Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Organ Procurement Unit Lung Transplantation Research Center National Research Institute of Tuberculosis and Lung Diseases Shahid Beheshti University of Medical Sciences Tehran Iran Lung Transplantation Research Center National Research Institute of Tuberculosis and Lung Diseases Shahid Beheshti University of Medical Sciences Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

Brain death is an irreversible event for the brain that the brain and brainstem functions are destroyed. Conventional methods of diagnosing and evaluating brain death are time-consuming, costly and in many cases dangerous. Alternative monitoring systems also have several limitations. The functional near-infrared spectroscopy (fNIRS) can track oxyhemoglobin and deoxyhemoglobin changes of the tissue using scattering and light absorption properties of the brain and blood components. The spatially resolved spectroscopy (SRS) method can additionally measure tissue oxygen index (TOI). We have collected data from several brain death patients using our non-invasive fNIRS portable system. Initially, we have observed that in brain dead patients, oxyhemoglobin and deoxyhemoglobin changes are quasiperiodic. Moreover, their TOI change has a very low standard deviation and was quasi-periodic. These results can be biomarkers for diagnosis, evaluation, and validation of brain death.

关键词： Spectroscopy Spatial resolution Standards Monitoring Indexes Electroencephalography Detectors

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Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment

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Informatics in Medicine Unlocked 2020年 18卷

作者： Mirbagheri, Mahya Hakimi, Naser Ebrahimzadeh, Elias Setarehdan, S. Kamaledin Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Seaman Family MR Research Centre University of Calgary Calgary Alberta Canada Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Alberta Canada

Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from the EHR, we hypothesized that the EHR includes physiological information beyond HR, which is the functional response of the brain. Method: To evaluate this hypothesis, the mental states of 10 healthy subjects were assessed under a stressful condition induced using the Montreal Imaging Stress Task (MIST). EHR and Reference HR (RHR) signals were then extracted from fNIRS and ECG, respectively. Next, a method based on Independent Component Analysis (ICA) was applied in order to discriminate the independent sources in the EHR signal. Thereafter, the least correlated components with RHR were reconstructed, which is the so-called target signal of EHR¯. Results: Similarity measurement of EHR¯ with fNIRS signals revealed a significant correlation between them. To show the effectiveness of the existing information in EHR¯ signals, they were employed for mental stress assessment by using the SVM classifier, leading to an accuracy of 97.3 ± 0.9%, which was 13%, 30.8% and 1.8% greater than those of the fNIRS, RHR, and EHR signals, respectively. Conclusions: We showed not only that the EHR¯ signal is a brain-related response, but also that it contains useful information for mental stress level measurement. Moreover, it is concluded that the EHR signal includes information from both brain and heart, which is hence useful for applications in which brain and heart functions are altered. © 2019

关键词： Brain hemodynamic response Functional near infrared spectroscopy Heart rate Independent component analysis Stress assessment

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A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy

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Informatics in Medicine Unlocked 2018年 11卷 44-50页

作者： Rahimpour, Ali Noubari, Hosein Ahmadi Kazemian, Mohammad Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Neonatal Health Research Center Research Institute for Children Health Shahid Beheshti University of Medical Sciences Tehran Iran Electrical and Computer Engineering University of British Colombia Canada

This paper reports the results of cerebral hemodynamic data analysis gathered from infant foreheads in selected case-studies. These studies were utilized for extracting relevant features aimed for discriminatory classification of infants into healthy and unhealthy cases. The principal objective of the study was to examine the effectiveness and accuracy of using near-infrared spectroscopy for measuring cerebral blood flow in infants and for health monitoring purposes. A total of 41 infants from several age groups varying from 2 h to several days since birth were participated for experimental data recordings. Both healthy and unhealthy infants of similar age groups were selected for the study. Selection was made without consideration as to the type of disorder in unhealthy infants, or any consideration as to being under any particular monitoring action. Data were collected during the rest state with no external stimulus. Several data analysis approaches were applied including temporal and time-frequency analyses, which were used for feature extraction of hemodynamic data and for identifying selective features to be used during data classification. We utilized SVM for pattern recognition and feature extraction aimed at discriminatory classification of healthy infants from unhealthy cases. This was followed by the application of the t-test for statistical analysis and accuracy evaluation. The results show a 94% accuracy in classification. A clear relationship was also found between oxy- and deoxy-hemoglobin concentration data belonging to healthy infants as shown in 2D data clustering illustration that can be used for infant classification. Similar correlation results were also observed with other physiological data. © 2018

关键词： Brain hemodynamic response Functional near-infrared spectroscopy Oxy- and deoxy hemoglobin concentration Support vector machine T-test

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Fixel-Based Analysis of White Matter in Temporal Lobe Epilepsy

Fixel-Based Analysis of White Matter in Temporal Lobe Epilep...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Mohamad Feshki Elahe Parham Hamid Soltanin-Zadeh Control and Intelligent Processing Center of Excellence (CIPE) University of Tehran Tehran Iran CIPE University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

Temporal lobe epilepsy (TLE) has been investigated by many researchers in the last two decades. Many efforts have been done based on magnetic resonance imaging (MRI) data such as identifying the TLE type, identifying the missing links, and automatically classifying epileptic subjects. Investigating TLE patients has paved the way for introducing, comparing, and developing new algorithms on diffusion weighted imaging (DWI) data. Fixel study is one of the newest methods for DWI analysis. Superiority of this method over other methods such as diffusion tensor imaging (DTI) is in correct recording of cross-over fibers. Furthermore, combination of fiber density and fiber cross-section measures has created a suitable method for group studies. Using fixel-based analysis, we have depicted the most important alterations in the fiber density of TLE patients. Statistically significant alteration in the ipsilateral hemisphere has been demonstrated for both right and left TLE. Superiority of fiber density and cross-section (FDC) over fiber density (FD) for TLE studies is illustrated.

关键词： Epilepsy White matter Diffusion tensor imaging Biomedical engineering Temporal lobe Deconvolution

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DeepLink: A novel link prediction framework based on deep learning

arXiv

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arXiv 2018年

作者： Keikha, Mohammad Mehdi Rahgozar, Maseud Asadpour, Masoud Database Research Group Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran University of Sistan and Baluchestan Zahedan Iran

Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Sub-pixel X-marker detection by Hough transform

Sub-pixel X-marker detection by Hough transform

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Ali Yazdani Hojjat Aalizadeh Farshid Karimi Saeed Solouki Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence(CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

High precision center detection of X-markers is required in many applications such as navigation surgery systems and camera calibration. Hough transform is a preferable tool for extracting intersecting lines in an image, which leads to center detection. In this paper, we detect X-marker centers by the sub-pixel precision, using Hough transform. Switching to Hough space helps us to apply processes like thresholding, filtering and weighted averaging on coordinates. The algorithm involves two parameters `Hough Size' and `Filter Size' required to be adjusted for best performance of the algorithm. A dataset of 900 images is used and best performance is achieved by values of 180 and 23 for the above parameters, respectively. Using this setting, 90.8% of the centers are detected successfully by the sub-pixel precision. The average distance between detected centers and reference centers is 0.51 pixels. This suggests that the proposed algorithm has the potential to be utilized for sub-pixel marker detection.

关键词： Image edge detection Transforms Feature extraction Cameras Biomedical engineering Calibration Filtering algorithms

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Robust on-line ADP-based solution of a class of hierarchical nonlinear differential game

arXiv

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arXiv 2019年

作者： Satouri, Mohammad Reza Kebriaei, Hamed Razminia, Abolhassan Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Research Lab. Department of Electrical Engineering School of Engineering Persian Gulf University P.O. Box 75169 Boushehr Iran School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Control & Intelligent Processing Center of Excellence

In this paper a hierarchical one-leader-multi-followers game for a class of continuous-time nonlinear systems with disturbance is investigated by a novel policy iteration reinforcement learning technique in which, the game model consists both of the zero-sum and nonzero-sum games, simultaneously. An adaptive dynamic programming (ADP) method is developed to achieve optimal control strategy under worst case of disturbance. This algorithm reduces the number of neural networks which are used for estimation for about thirty percent. Proposed algorithm uses neural networks to estimate value functions, control policies and disturbances. Convergence analysis of the estimations is investigated using Lyapunov theory and exploiting properties of the Nemytskii operator. Finally the simulation results will show effectiveness of developed ADP *** Codes 91A65, 49N70, 68T05, Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear systems

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Optimal Dynamic Lyapunov Function and The Largest Estimation of Domain of Attraction

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IFAC-PapersOnLine 2017年第1期50卷 2645-2650页

作者： Monfared M.N. Yazdanpanah M.J. Department of Electrical Engineering Faculty of Electrical Biomedical and Mechatronic Qazvin Branch Islamic Azad University Qazvin Iran Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Estimation of the domain of attraction is one of the major difficulties in the analysis of any nonlinear control system. Exact expression of a candidate Lyapunov function is indispensable for the estimation. The optimal Lyapunov function which helps to estimate the domain of attraction exactly is acquired by solving a partial differential equation that is not solvable easily for all of the systems. In this paper, an algebraic equation is proposed instead of the partial differential equation to acquire optimal Lyapunov function. The key tool for this purpose is dynamic Lyapunov function which makes it possible to have an analytic expression of a family of candidate Lyapunov functions that are parametric and functional. The parameters and functions would be selected such that the optimal Lyapunov function is achieved. As the second contribution of this paper, the problem of optimality in the sense of the largest elliptical estimation of region of attraction would be followed up. Using a linear matrix inequality technique and a special criterion it is shown that dynamic Lyapunov function can leads to a larger elliptical estimation than that of the conventional Lyapunov function. © 2017

关键词： Domain of Attraction Lyapunov function Nonlinear System

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A robust adaptive sliding mode controller for robot manipulators 7

A robust adaptive sliding mode controller for robot manipula...

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7th Conference on Artificial Intelligence and Robotics, IRANOPEN 2017

作者： Gorji, Shaghayegh Yazdanpanah, Mohammad Javad Department of Electrical Biomedical and Mechatronics Qazvin Branch Islamic Azad University Qazvin Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538628621

In this paper, a novel adaptive sliding mode control for rigid robot manipulators is proposed. In the considered system, since there may exist explicit unknown system parameters and also perturbations, a lyapunov based approach is presented to increase system robustness, even in presence of arbitrarily large (but not infinite) discontinuous perturbations. The design contains two different phases of adaptation. The first one is related to the robot parameters and the other is accounted for perturbation estimating. A continuous controller is presented from the first step of design procedure. We try to prove the stability in the sense of lyapunov and derive adaptive laws and uniform ultimate boundedness in the worst condition. Simulation results for two degrees of freedom rigid robot manipulator effectively demonstrate the capability of mentioned approach. © 2017 IEEE.

关键词： Sliding mode control

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