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Auto-associative neural network for sparse representation based face identification 4

Auto-associative neural network for sparse representation ba...

引用

4th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Patnaik, Suprava Xavier Inst Engn Dept Elect & Telecommun Mumbai Maharashtra India

ISBN: (纸本)9781509045594

Sparse representation is a novel methodology that has off late received substantial attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse recognition. Recursive least square based auto-associative neural network model has been used for principal component extraction. Suggested network structure supports data compression along with principal component extraction. The trained principal components are used as the atoms of the dictionary. Proposed approach performs the recognition task at a low dimension space, hence involves less computation. In the recognition phase, a given image is sparsely represented as a linear combination of neuronal weights, which corresponds to underlying principal components. Our extensive experimentation on publicly available face databases (lfw and GT) endorses that this approach is promising for real time applications and can further be applied for face recognition tasks.

关键词： Sparse Representation Classification Principal Component rls algorithm Auto-associative Neural Network

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Adaptive estimation of vehicle dynamics through rls and Kalman filter approaches

Adaptive estimation of vehicle dynamics through RLS and Kalm...

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International IEEE Conference on Intelligent Transportation Systems

作者： Kun Jiang Alessandro Corrêa Victorino Ali Charara Lab. Heudiasyc Univ. de Technol. de Compiegne Compiegne France

ISBN: (纸本)9781467365970

This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental set-up, which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursive least squares (rls) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.

关键词： Kalman filters adaptive estimation least squares approximations observers parameter estimation road safety road vehicles vehicle dynamics Kalman filter rls algorithm low-cost sensors parameter identification pitch dynamics recursive least squares algorithm roll dynamics state observers vehicle dynamics adaptive estimation vehicle vertical tire force estimation Estimation Load modeling Mathematical model Suspensions Tires Vehicle dynamics Vehicles

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Performance Analysis of Adaptive Widely Linear Beamformers 30

Performance Analysis of Adaptive Widely Linear Beamformers

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30th Annual IEEE Canadian Conference on Electrical and Computer Engineering (IEEE CCECE)

作者： Orozco-Tupacyupanqui, W. Peralta-Sevilla, A. Perez-Meana, H. Nakano-Miyatake, M. Univ Politecn Salesiana Cuenca Ecuador Inst Politecn Nacl Mexico City DF Mexico

ISBN: (纸本)9781509055388

In this paper, we consider the problem of enhancing the filtering capacity of single standard beamformers for real base-band signals. Unfortunately, the standard smart arrays are not able to effectively minimize the effects of the complex noise in the array. In order to solve this problem, we analyze a parallel adaptive beamforming system based on complex nonlinear adaptive filters. In this design, the smart array is used as a widely linear (WL) system in which the second order statistical information of the signals is exploited by using the covariance and pseudo-covariance matrices. The simulations results show this architecture has a lead filtering capability as well as an outstanding generation of the radiation patterns. Furthermore, this parallel hybrid array showed to have a better filtering capacity than the standard arrays LMS, NLMS and rls. General equations for the optimal weight vector are derived for this augmented complex (AC) smart array or widely linear beamforming.

关键词： Augmented complex beamformer widely linear beamformer LMS algorithm rls algorithm NLMS algorithm

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Adaptive Digital PID Controller Implemented on 32-bit ARM7 Microcontroller

Adaptive Digital PID Controller Implemented on 32-bit ARM7 M...

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1st International Conference on Computational Intelligence and Information Technology (CIIT 2011)

作者： Hasanzade, Ali Mohammadi, S. M. A. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9783642257339

The variations of the process parameters is a strong reason to use adaptive controller. In this paper, the implementation of adaptive digital proportional-integral-derivative (PID) controller on 32-bit ARM7 microcontroller which is intended for general purpose applications is described. The controlled process is identified using recursive least squares algorithm, and the parameters of the PID controller are designed by the pole placement method on the basis of estimated plant coefficients. The system identification and controller designing are completely done in microcontroller. For to survey the effects of uncertainty on the process, a normalized model of a motor by MATLAB software is used. The connection between MATLAB and microcontroller is compatible with RS232 standard. The extensive studies carried out clearly proves that the proposed approach by the ARM microcontroller,, is an excellent choice for control problems including adaptive control systems due to its high efficiency, speed and robustness.

关键词： ARM microcontroller Diophantine equation Pole placement method Reference model rls algorithm

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A new approach to tracking time-variant channels 5

A new approach to tracking time-variant channels

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5th International Symposium on Wireless Personal Multimedia Communications

作者： Gligorevic, S Sorger, U TH Darmstadt Inst Telecommun Darmstadt Germany

ISBN: (纸本)0780374428

This paper presents a method for joint channel and data esti mation for transmission over frequency-selective and timevariant channels. Hereby, fast time-variant channels can be tracked, which is important for mobile communications at high velocities. We approach the problem by estimating the scattering function, which depends on delay and Doppler frequency. At the transmitter, a short training sequence is inserted at the beginning of the data block. This sequence is used for initializing a recursive estimation filter which carries out the joint channel and data estimation adaptively. After the training sequence hypothetical data symbols are considered to extend the persecuted paths in the data tree.

关键词： channel estimation equalization scattering function rls algorithm

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SET-MEMBERSHIP RECURSIVE LEAST-SQUARES ADAPTIVE FILTERING algorithm

SET-MEMBERSHIP RECURSIVE LEAST-SQUARES ADAPTIVE FILTERING AL...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Arablouei, Reza Dogancay, Kutluyil Univ S Australia Inst Telecommun Res Mawson Lakes SA 5095 Australia

ISBN: (纸本)9781467300469

A new set-membership adaptive filtering algorithm is developed based on the exponentially-weighted rls algorithm with a time-varying forgetting factor that is optimized at each iteration by imposing a bounded-magnitude constraint on the a posteriori filter output error. The new algorithm is designed to improve the numerical behavior of the previously proposed BEACON algorithm while delivering the same convergence and tracking performance as the BEACON algorithm. Simulation results for a flat-fading MIMO channel estimation application demonstrate the superiority of the new algorithm over the BEACON algorithm in terms of numerical stability.

关键词： set-membership adaptive filtering rls algorithm BEACON algorithm numerical stability MIMO channel estimation

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Camera Motion Estimation for 3-D Structure Reconstruction of Moving Objects

Camera Motion Estimation for 3-D Structure Reconstruction of...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Chwa, Dongkyoung Dani, Ashwin Kim, Hakjae Dixon, Warren Ajou Univ Dept Elect & Comp Engn Suwon 443749 South Korea Natl Geospatial Intelligence Agcy Virginia MD USA Univ Florida Dept Mech & Aerosp Engn Gainesville FL USA

ISBN: (纸本)9781467317146

The method for camera motion estimation is proposed for the moving objects. Whereas the estimation of the structure and motion (SaM) of the moving objects usually involves the constraints on the motion of the camera and the object, the moving camera velocities can be estimated in our work using the images of the moving object from the single camera without any constraint on the camera and object motion. To this end, the dynamics of the partially measurable state are arranged in such a way that the recursive least-squares (rls) algorithm can be employed for stationary objects and then the nonlinear observer based on RISE (robust integral signed error) method for dynamic objects sequentially. The proposed method has advantages in that when the proposed method and the previously developed SaM algorithms are combined together, we can reconstruct the 3-D structure of the moving objects from 2-D images from a single camera. Simulation results under time-varying velocities of both camera and object are presented to verify the proposed method.

关键词： Camera motion estimation moving objects moving camera rls algorithm nonlinear observer based on RISE method

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Analysis of Recursive Least Moduli algorithm for Adaptive Filters in Impulsive Noise Environments

Analysis of Recursive Least Moduli Algorithm for Adaptive Fi...

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International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Koike, Shin'ichi Tokyo Japan

ISBN: (纸本)9781479961207

This paper first reviews an adaptation algorithm named Recursive Least Moduli (RLM) algorithm for complex-domain adaptive filters. The RLM algorithm achieves significant improvement in the filter convergence speed when the filter input is strongly correlated. Stochastic models are presented for two types of impulse noise found in adaptive filtering systems: one in observation noise and another at filter input. Then, analysis of the RLM algorithm in the presence of both types of impulse noise is fully developed for calculating the transient and steady-state behavior of filter convergence. Through experiments with simulations and theoretical calculations of filter convergence for the RLM algorithm, we demonstrate its effectiveness in making adaptive filters fast convergent and robust against not only the impulsive observation noise but also the impulse noise at filter input. We observe good agreement between simulated and theoretical convergence behavior that validates the analysis.

关键词： adaptive filter impulse noise robust filtering fast convergence rls algorithm

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Adaptive Tracking of Ambient System Oscillations by Nonstationary rls Techniques

Adaptive Tracking of Ambient System Oscillations by Nonstati...

引用

IEEE Power and Energy Society General Meeting

作者： I. Moreno A. R. Messina Department of Electrical Engineering Cinvestav

ISBN: (纸本)9781457710001

Measured ambient data in power system are known to exhibit noisy, nonstationarity fluctuations resulting primarily from small magnitude, random changes in load. Accounting for stochastic and time-varying features can provide a better description of the data and result in improved estimation algorithms. In this paper, a new hybrid algorithm combining a recursive least-square (rls) algorithm and a Kalman filter described by a random walk correlation model is proposed to characterize the time evolution of ambient system oscillations. Extensions and generalizations to current rls algorithms to deal with nonstationarity are discussed and the relationship between Kalman filter parameters and rls algorithms is analyzed. Examples of the developed procedures to track the evolving dynamics of critical system modes in both simulated and measured data are presented. Comparisons with well-established approaches such as the exponentially-weighted rls algorithm, rls algorithms with adaptive memory, least-mean squares (LMS) algorithms and normalized LMS algorithms demonstrate the accuracy of the proposed procedure.

关键词： Ambient power system data Kalman filtering LMS algorithm rls algorithm Least mean square algorithm Restless Legs Syndrome Kalman filtering Adaptive tracking Kalman filters algorithms

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On-line Learning System for Gait Assistance with Wearable Robot 56

On-line Learning System for Gait Assistance with Wearable Ro...

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56th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Kagawa, Takahiro Takahashi, Fumiya Uno, Yoji Aichi Inst Technol Dept Mech Engn Toyota Aichi Japan Nagoya Univ Dept Mech Engn & Sci Nagoya Aichi Japan

ISBN: (纸本)9784907764579

In this study, we propose an on-line learning system of gait pattern for a wearable robot to assist walking. In our learning system, the cost function is defined by the sum of the measured arm loading force of a user and the power consumption of the motors during walking. We confirmed that the arm loading force decreased with little change of power consumption by using the proposed algorithm.

关键词： Learning control rls algorithm Wearable robot

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