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检索条件"机构=Center of Excellence: Control and Intelligent Processing"

共 675 条记录，以下是61-70 订阅

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Design of a nonlinear adaptive natural oscillator: Towards natural dynamics exploitation in cyclic tasks

Design of a nonlinear adaptive natural oscillator: Towards n...

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Rezvan Nasiri Mahdi Khoramshahi Majid Nili Ahmadabadi Cognitive Robotics Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9781509037636

In this paper, we present the dynamical equations of a nonlinear adaptive natural oscillator (NANO) in order to exploit the natural dynamics in robotic systems. The presented oscillator tries to minimize an energy-based cost function by adapting the shape and frequency of the reference trajectory. Stability, convergence, and optimality of this oscillator are guaranteed analytically. Moreover, the performance of this oscillator is investigated by applying it to three different types of robotic models;i.e., the pendulum, the adaptive-toy, and the hopper-leg.

关键词： Trajectory Learning Energy Efficiency Cyclic Tasks Natural Dynamics

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FMRI brain decoding of facial expressions based on multi-voxel pattern analysis 2

FMRI brain decoding of facial expressions based on multi-vox...

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2nd International Conference on Knowledge-Based Engineering and Innovation, KBEI 2015

作者： Rafiei, Farshad Hossein-Zadeh, Gholam-Ali CIPCE Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781467365062

In a brain decoding study, using the functional magnetic resonance imaging (fMRI) data we determined the facial expression of the visual stimulus that the subject perceived. fMRI data acquired from a healthy right-handed adult volunteer who participated in three separate sessions. Participant viewed blocks of emotionally expressive faces alternating with blocks of neutral faces and scrambled images. Multi-voxel pattern analyses are then used to decode different expressions using the activity pattern of most active parts of brain. We used multi-class support vector machine (SVM) to distinct five brain states corresponding to neutral, happy, sad, angry and surprised. Results show that these facial expressions can be classified from fMRI data with the average sensitivity of 90 percent. © 2015 IEEE.

关键词： Support vector machines

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Robust Vehicle Localization with GPS Dropouts

Robust Vehicle Localization with GPS Dropouts

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International Conference on intelligent and Advanced Systems

作者： Salman Ali Shaukat Muhammad Arif Umar Iqbal Bhatti Khalid Munawar Aamer Iqbal Bhatti Ubaid M. Al-Saggaf Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Department of Computer Science Umm-Alqura University Department of Aeronautics and Astronautics Institute of Space Technology Control and Signal Processing Research Group Capital University of Science and Technology

ISBN: (纸本)9781509008469

Vehicle navigation systems rely on the correct positioning information and GPS is the best solution. However, in situations of GPS outage, like in tunnels and dense woods, the vehicle can lose track quickly. Modern vehicle navigation systems are mostly based on the vehicle dynamics;proprietary algorithms keep navigating for quite some time. But these solutions are expensive and add much to a vehicle's cost. This paper proposes a Kalman filter based dead-reckoning algorithm that fuses GPS information with the orientation information from a cheap IMU/INS, and the vehicle's speed accessed from its ECU. This low-cost system uses GPS and IMU/INS in a loosely coupled manner along with vehicle's speed and keeps supplying a quite accurate position information with GPS outage for significantly long intervals. With proper tuning and initialization, the proposed scheme has a potential to keep working in a completely GPS-denied situation.

关键词： Navigation GPS IMU/INS Dead-reckoning

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Design and modeling of a compact rotational nonlinear spring

Design and modeling of a compact rotational nonlinear spring

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Hamed Jalaly Bidgoly Majid Nili Ahmadabadi Mohammad Reza Zakerzadeh Cognitive Robotics Laboratory The Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Dynamic and Vibration Laboratory School of Mechanical Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9781509037636

In this paper, we propose a new method for implementing a rotational nonlinear spring with user defined profile, based on the combination of a linear spring with a nonlinear transmission mechanism. The proposed structure consists of a non-circular cam, a roller which moves along outer circumference of the cam, and a stretched translational linear spring which is connected between center of the cam and center of the roller. We obtain a set of differential equations to design shape of the cam for any given torque-angle profile. Also, it will be shown that profiles with both positive and negative values can be implemented by the proposed method. At last, the cam of some popular nonlinear springs are designed, including constant, cubic, hyperbolic tangent and sinusoidal springs.

关键词： Computer aided manufacturing Camelopardalis Hookean spring springs Device Spring TRANSFER MECHANISM Modeling nonlinearity Rotational SCROLL Differential equations Spring season

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Measures of the brain functional network that correlate with Alzheimer's neuropsychological test scores: An fMRI and graph analysis study

Measures of the brain functional network that correlate with...

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

作者： Soroosh Golbabaei Amin Dadashi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Radiology Medical Image Analysis Lab. Detroit Michigan USA

Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.

关键词： Correlation Alzheimer's disease Psychology Olfactory Brain Atmospheric measurements

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Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Discovering true association between multimodal data sets us...

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IEEE International Symposium on Biomedical Imaging

作者： Ali-Reza Mohammadi-Nejad Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Henry Ford Health System Medical Image Analysis Lab. Detroit Michigan USA

ISBN: (纸本)9781479923519

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.

关键词： Correlation Imaging Laplace equations Data integration Data models Standards Tuning

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Context transfer and Q-Transferable tasks 29

Context transfer and Q-Transferable tasks

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29th AAAI Conference on Artificial Intelligence, AAAI 2015

作者： Mousavi, Amin Araabi, Babak Nadjar Ahmadabadi, Majid Nili Robotics and AI Lab Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran School of Cognitive Science Tehran Iran

ISBN: (纸本)9781577357209

This article discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this article, implies knowledge transfer between tasks that share the same environment's dynamics and reward function, but have different state and action spaces. For example, we have a working mobile robot in an environment. At some point, we decide to upgrade its sensors and/or actuators. Any change in these modules will result in a different description of the agent- environment model, and the trained knowledge is no longer applicable. We consider the tasks of the old and new robots, as the source and target tasks, respectively. The Markov decision process (MDP) of these tasks, under certain conditions, are called Q-transferable tasks, and the problem of knowledge transfer between them is called context transfer. We investigate the relation of the MDPs of these tasks. Copyright © 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Markov processes

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Application of MEG coherence in lateralization of mTLE

Application of MEG coherence in lateralization of mTLE

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

作者： Mohammad R. Nazem-Zadeh Susan M. Bowyer John E. Moran Esmaeil Davoodi-Bojd Andrew Zillgitt Hassan Bagher-Ebadian Fariborz Mahmoudi Kost V. Elisevich Hamid Soltanian-Zadeh Departments of Research Administration and Radiology Henry Ford Health System Detroit MI USA Departments of Neurology Henry Ford Health System Detroit MI USA Department of Clinical Neurosciences Spectrum Health Medical Group Grand Rapids MI USA Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. This study investigates the cerebral functional abnormalities quantified by MEG coherence laterality in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p

关键词： Coherence Epilepsy Brain modeling Imaging Extraterrestrial measurements Analysis of variance Surgery

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The Analysis and Simulation of a Tethered Segway

The Analysis and Simulation of a Tethered Segway

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RSI/ISM International Conference on Robotics and Mechatronics

作者： Mohammad Hossein Salehpour Hadi Moradi Control and Intelligent Processing Center of Excellence School of Electrical Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical Engineering University of Tehran Tehran Iran Intelligent Systems Research Institute SKKU South Korea

ISBN: (纸本)9781467372350

In this paper we present the design and analysis of a tethered Segway designed for moving over steep surfaces such as dome shaped structures. This robotic platform, inspired from human climbers who use a cable to keep themselves stable on steep surfaces, consists of a simple Segway and a mechanism used to tether the robot to the top of the surface. The mathematical model of the robot is derived using Lagrangian approach and the control is developed based on a state feedback gain. The model consists of a two wheeled inverted pendulum driven by two DC motors. Also there is a DC motor used to control the length of the cable tethering the robot to the top. The control inputs are the voltages applied to each DC motor. The system has been simulated and the results show that the controller can move the robot around while keeping its stability on steep surfaces up to 75 degrees.

关键词： tethered robot robotic platform stabilization model of Segway Lagrangian approach state feedback

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Analysis of Backscattered Ultrasound RF Echoes From Adjacent Scan Lines for Surface Roughness Characterization: A Phantom Study

Analysis of Backscattered Ultrasound RF Echoes From Adjacent...

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Iranian Conference on Biomedical Engineering

作者： Amoon Jamzad Faranak Akbarifar Seyed Kamaledin Setarehdan Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran

ISBN: (纸本)9781467393522

Ultrasonography, as a noninvasive and inexpensive routine, can be the modality of choice in roughness characterization of internal body stones for managing treatment methods. In this study, we have investigated the possibility of differentiating roughness levels utilizing backscattered RF data which presumably contain more information than the B-mode images. For this purpose, we modified a conventional medical ultrasound device and recorded RF data from a roughness phantom consisting of 4 standard sandpaper strips. We proposed that the difference of two echoes from adjacent scan lines contains information about the roughness of the imaging surface. Hence, we calculated the Euclidean distance of temporal and spectral features extracted from two adjacent echoes. Then, 3 classifiers of Bayesian, linear, and 1-Nearest Neighbor (NN) were employed for roughness differentiation. The results show that spectral features and 1-NN classifier had the best performance among others. The highest average performance of 99.17%, obtained using all features along with the 1-NN classifier, proves the feasibility of roughness discrimination by acquiring and comparing adjacent echoes.

关键词： Ultrasound echo analysis RF data acquisition Adjacent echoes Roughness characterization Feature extraction Biomedical signal processing

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