The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform
Philippe Bidaud - One of the best experts on this subject based on the ideXlab platform.
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closed form solutions for inverse kinematics approximation of general 6r manipulators
Mechanism and Machine Theory, 2004Co-Authors: Frederic Chapelle, Philippe BidaudAbstract:This paper presents an original use of Evolutionary Algorithms in order to approximate by a closed form the inverse kinematic model (IKM) of analytical, non-analytical and general (i.e. with an arbitrary geometry) manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated as in design processes of manipulators. A mathematical function is evolved through Genetic Programming according to the known direct kinematic model to determine an analytical expression which approximates the Joint Variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators.
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a closed form for inverse kinematics approximation of general 6r manipulators using genetic programming
International Conference on Robotics and Automation, 2001Co-Authors: Frederic Chapelle, Philippe BidaudAbstract:This paper presents an original use of evolutionary algorithms in order to approximate by a closed form the inverse kinematic model of analytical (non-analytical) and general manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated in the design processes of manipulators. A mathematical function is evolved through genetic programming according to the known direct kinematic model to determine an analytical expression which approximates the Joint Variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and GMF Arc Mate are approximated before applying the algorithm to general 6R manipulators.
Frederic Chapelle - One of the best experts on this subject based on the ideXlab platform.
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closed form solutions for inverse kinematics approximation of general 6r manipulators
Mechanism and Machine Theory, 2004Co-Authors: Frederic Chapelle, Philippe BidaudAbstract:This paper presents an original use of Evolutionary Algorithms in order to approximate by a closed form the inverse kinematic model (IKM) of analytical, non-analytical and general (i.e. with an arbitrary geometry) manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated as in design processes of manipulators. A mathematical function is evolved through Genetic Programming according to the known direct kinematic model to determine an analytical expression which approximates the Joint Variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators.
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a closed form for inverse kinematics approximation of general 6r manipulators using genetic programming
International Conference on Robotics and Automation, 2001Co-Authors: Frederic Chapelle, Philippe BidaudAbstract:This paper presents an original use of evolutionary algorithms in order to approximate by a closed form the inverse kinematic model of analytical (non-analytical) and general manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated in the design processes of manipulators. A mathematical function is evolved through genetic programming according to the known direct kinematic model to determine an analytical expression which approximates the Joint Variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic models of the PUMA and GMF Arc Mate are approximated before applying the algorithm to general 6R manipulators.
Marten Wegkamp - One of the best experts on this subject based on the ideXlab platform.
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Joint Variable and rank selection for parsimonious estimation of high dimensional matrices
Annals of Statistics, 2012Co-Authors: Florentina Bunea, Yiyuan She, Marten WegkampAbstract:We propose dimension reduction methods for sparse, high-dimensional multivariate response regression models. Both the number of responses and that of the predictors may exceed the sample size. Sometimes viewed as complementary, predictor selection and rank reduction are the most popular strategies for obtaining lower-dimensional approximations of the parameter matrix in such models. We show in this article that important gains in prediction accuracy can be obtained by considering them Jointly. We motivate a new class of sparse multivariate regression models, in which the coefficient matrix has low rank and zero rows or can be well approximated by such a matrix. Next, we introduce estimators that are based on penalized least squares, with novel penalties that impose simultaneous row and rank restrictions on the coefficient matrix. We prove that these estimators indeed adapt to the unknown matrix sparsity and have fast rates of convergence. We support our theoretical results with an extensive simulation study and two data analyses.
Moeness G. Amin - One of the best experts on this subject based on the ideXlab platform.
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Radar classification of human gait abnormality based on sum-of-harmonics analysis
2018 IEEE Radar Conference (RadarConf18), 2018Co-Authors: Ann-kathrin Seifert, Abdelhak M. Zoubir, Moeness G. AminAbstract:Radar technology for monitoring of human gait has recently gained interest in the fields of home security, medical diagnosis, assisted living and rehabilitation. Due to its remote, reliable and privacy-preserving sensing, radar is promising to become an effective tool for medical gait analysis. We show the influences of gait abnormalities and assistive walking devices on the Joint-Variable representations of the back-scattered radar signals. Using both, parametric and non-parametric techniques, we extract gait features to classify normal, abnormal and cane-assisted gait. In particular, the fundamental frequency of the time-frequency behavior is estimated using sum-of-harmonics modeling in order to characterize different gaits. Results obtained using experimental K-band radar data are presented for the person-specific and person-generic case.
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multiple Joint Variable domains recognition of human motion
IEEE Radar Conference, 2017Co-Authors: Branka Jokanovic, Moeness G. Amin, Baris ErolAbstract:Radar has been successfully employed for classifying human motions in defense, security and civilian applications, and has emerged to potentially become a technology of choice in the healthcare industry, specifically in what pertains to assisted living. Due to the relationship between Doppler frequency and motion kinematics, the time-frequency domain has been traditionally used to analyze radar signals of human gross-motor activities. Towards improving motion classification, this paper incorporates three domains, namely, time-frequency, time-range, and range-Doppler domains. Features from each domain are extracted using deep neural network that is based on stacked auto-encoders. Final decision is made by combining the classification outcomes. Experimental results demonstrate that certain domains are more favorable than others in recognizing specific motion articulations, thus reinforcing the merits of multi-domain motion classifications.
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multi window time frequency signature reconstruction from undersampled continuous wave radar measurements for fall detection
Iet Radar Sonar and Navigation, 2015Co-Authors: Branka Jokanovic, Moeness G. Amin, Yimin D Zhang, Fauzia AhmadAbstract:Fall detection is an area of increasing interest in independence-assisting remote monitoring technologies for the elderly population. Immediate assistance following a fall can lower the risk of medical complications, thus saving lives and reducing the associated health care costs. Therefore it is important to detect a fall as it happens and promptly mobilise first responders for proper care and attendance to possible injury. Radar offers privacy and non-intrusive monitoring capabilities. Micro-Doppler signatures are typically employed for radar-based human motion detections and classifications. Proper time–frequency signal representation is, therefore, required from which important features can be extracted. Missing or noise/interference corrupted data can compromise the integrity of micro-Doppler signatures and subsequently confuse the classifier. In this study, the authors restore the time–frequency signatures associated with human motor activities, such as falling, bending over, sitting and standing, by using a hybrid approach of compressive sensing and multi-window analysis based on Slepian or Hermite functions. Because time–frequency representations of many human gross-motor activities are sparse and share common support in Joint-Variable domains, the multiple measurement vector approach can be effectively applied for fall classification in both cases of full data or compressed observations.
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robust time frequency analysis based on the l estimation and compressive sensing
IEEE Signal Processing Letters, 2013Co-Authors: Ljubisa Stankovic, Srdjan Stankovic, Irena Orovic, Moeness G. AminAbstract:The L-estimate transforms and time-frequency representations are presented within the framework of compressive sensing. The goal is to recover signal or local auto-correlation function samples corrupted by impulse noise. The signal is assumed to be sparse in a transform domain or in a Joint-Variable representation. Unlike the standard L-statistics approach, which suffers from degraded spectral characteristics due to the omission of samples, the compressive sensing in combination with the L-estimate permits signal reconstruction that closely approximates the noise free signal representation.
Florentina Bunea - One of the best experts on this subject based on the ideXlab platform.
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Joint Variable and rank selection for parsimonious estimation of high dimensional matrices
Annals of Statistics, 2012Co-Authors: Florentina Bunea, Yiyuan She, Marten WegkampAbstract:We propose dimension reduction methods for sparse, high-dimensional multivariate response regression models. Both the number of responses and that of the predictors may exceed the sample size. Sometimes viewed as complementary, predictor selection and rank reduction are the most popular strategies for obtaining lower-dimensional approximations of the parameter matrix in such models. We show in this article that important gains in prediction accuracy can be obtained by considering them Jointly. We motivate a new class of sparse multivariate regression models, in which the coefficient matrix has low rank and zero rows or can be well approximated by such a matrix. Next, we introduce estimators that are based on penalized least squares, with novel penalties that impose simultaneous row and rank restrictions on the coefficient matrix. We prove that these estimators indeed adapt to the unknown matrix sparsity and have fast rates of convergence. We support our theoretical results with an extensive simulation study and two data analyses.
