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Dive into the research topics where Reza Hoseinnezhad is active.

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Featured researches published by Reza Hoseinnezhad.


IEEE Transactions on Industrial Electronics | 2007

Calibration of Resolver Sensors in Electromechanical Braking Systems: A Modified Recursive Weighted Least-Squares Approach

Reza Hoseinnezhad; Alireza Bab-Hadiashar; Peter Harding

Resolver sensors are utilized as absolute position transducers to control the position and speed of actuators in many industrial applications. The accuracy and convergence of the position and speed measurements provided by resolvers in electromechanical braking system (EMB) designs directly contribute to the braking performance and vehicle safety. In practice, the dc drifts, amplitudes, and phase shift of the resolver signals vary with aging and temperature, and adaptive techniques are required for the calibration of these parameters of resolvers. Existing classical adaptive techniques such as recursive least squares are unable to track the parameters during resting (low-speed actuation or stationary) periods and also a transient period after them. This paper proposes a new approach for real-time tracking of resolver parameters specially developed for actuator-control applications with varying speed and long resting periods. We formulate the algebraic relationship between the resolver parameters and the parameters of resolver characteristic ellipse, which is the ellipse formed by plotting the resolver signals versus each other. Having known the characteristic ellipse parameters, the resolver parameters are calculated using the formulated algebraic relation. Then, a new recursive and adaptive estimator is proposed to track the parameters of characteristic ellipse. The low computational complexity of the proposed method makes it desirable for real-time applications like the EMBs, where limited computational power and memory are available. Experimental results show that the proposed technique is able to track the resolver parameters and the accurate actuator position with a small error in real-time, while other adaptive estimators are unable to track the resolver parameters during and after resting periods


Information Fusion | 2002

Pseudo information measure: a new concept for extension of Bayesian fusion in robotic map building

Behzad Moshiri; Mohammad Reza Asharif; Reza Hoseinnezhad

Abstract A new concept named pseudo information measure is introduced. By this measure, Bayesian fusion of independent sources of information is extended to a wide range of possible formulations and some new fusion formulas are calculated. The coincidence between the performance of the proposed method of fusion with the expected results and output sensitivity of the fusion process are discussed. Also, we have discussed the resulting flexibility for map building applications. Map building by using the proposed fusion formulas has been simulated for a cylindrical robot with eight ultrasonic sensors and implemented on Khepera robot. The resulting maps have been fed to an improved version of A* path planning for comparative purposes. For the resulting routes, two factors have been considered and calculated: length and the least distance to obstacles. The results show that the maps of the environment that are generated by using the proposed fusion formulas are more informative. In addition, more appropriate routes are achieved. Based on the selected function, there is a trade-off between the length of the resulting routes and their safety. This flexibility lets us choose the right fusion function for different map building applications.


IEEE Transactions on Vehicular Technology | 2005

Missing data compensation for safety-critical components in a drive-by-wire system

Reza Hoseinnezhad; Alireza Bab-Hadiashar

In this article, a new multistep ahead predictive filtering scheme is introduced. The proposed technique is essential for proper operation of safety-critical components in a drive-by-wire car. Because limited computational time and memory are available in drive-by-wire systems, the main advantage of our scheme is that it only requires one set of finite impulse response (FIR) filter weights to be tuned while it can be used for different numbers of steps ahead predictions. To verify and compare the proposed filter, our model and four competing methods were applied to predict up to four missing samples of displacement sensor data from a brake pedal in a brake-by-wire system. Experimental results show that prediction performance of our proposed FIR filter is higher than, or at least comparable to, other filters with the same memory requirements and the computational overhead of data missing handling by our proposed method is considerably lower than other comparable methods. Hence, the proposed filter has a superior performance in missing data compensation for drive-by-wire systems.


conference on decision and control | 2005

A Novel Hybrid Angle Tracking Observer for Resolver to Digital Conversion

Reza Hoseinnezhad; Peter Harding

Resolvers are absolute angle transducers that are usually used for position and speed measurement in permanent magnet motors. An observer that uses the sinusoidal signals of the resolver for this measurement is called an Angle Tracking Observer (ATO). Current designs for such observers are not stable in high acceleration and high-speed applications. This paper introduces a novel hybrid scheme for ATO design, in which a closed-loop LTI observer is combined with a quadrature encoder. Finite gain stability of the proposed design is proven based on the circle theorem in input-output stability theory. Simulation results show that the proposed ATO design is stable in two cases where an LTI observer and an extended Kalman filter are unstable due to high speed and acceleration,. In addition, the tracking accuracy of our hybrid scheme is substantially higher than a single quadrature encoder.


IEEE Transactions on Vehicular Technology | 2008

Real-Time Clamp Force Measurement in Electromechanical Brake Calipers

Reza Hoseinnezhad; Alireza Bab-Hadiashar; Tony Rocco

In brake-by-wire systems, central controllers require accurate information about the clamp force between the brake pad and the disc as a function of pad displacement, which is usually denoted as the characteristic curve of the caliper. Due to aging, temperature, and other environmental variations, caliper characteristic curves vary with time. Therefore, automatic caliper calibration in real-time is vital for high-performance braking action and vehicle safety. Due to memory and processing-power limitations, the calibration technique should be memory efficient and of low computational complexity. In a typical electromechanical-braking-system design, clamp force measurement variations with actuator displacement is hysteretic. This paper introduces a simple and memory-efficient real-time calibration technique in which a clamp-force model is fitted to the data samples around each hysteresis cycle. The model includes a Maxwell-slip model for the hysteresis caused by friction. Experimental results from the data recorded in various temperatures show that the proposed technique results in clamp force measurements with less than 0.7% error over the range of clamp-force variations. It is also shown that, by using these measurements, the characteristic curve can be accurately calibrated in real-time.


international conference on acoustics, speech, and signal processing | 2010

Multi-object filtering from image sequence without detection

Reza Hoseinnezhad; Ba-Ngu Vo; David Suter; Ba-Tuong Vo

Almost every single-view visual multi-target tracking method presented in the literature includes a detection routine that maps the image data to point measurements relevant to the target states. These measurements are commonly further processed by a filter to estimate the number of targets and their states. This paper presents a novel visual tracking technique based on a multi-object filtering algorithm that operates directly on the image observations without the need for any detection. Experimental results on tracking sport players show that our proposed method can automatically track numerous interacting targets and quickly finds players entering or leaving the scene.


IEEE Transactions on Vehicular Technology | 2008

Clamp-Force Estimation for a Brake-by-Wire System: A Sensor-Fusion Approach

Stephen Saric; Alireza Bab-Hadiashar; Reza Hoseinnezhad

The elimination of a clamp-force sensor from brake-by-wire system designs is strongly demanded due to implementation difficulties and cost issues. In this paper, a new method is presented to estimate the clamp force based on other sensory information. This estimator fuses the outputs of two models to optimize the root-mean-square error (RMSE) of estimation. Experimental results show that the estimator can accurately track the true clamp force for high-speed cases as demanded by the antilock braking system controls. A training strategy has been used to ensure that the estimator can successfully adapt to parameter variations associated with wear. This paper is concluded with a discussion on the reliability of the developed clamp-force estimator.


Pattern Recognition | 2007

Consistency of robust estimators in multi-structural visual data segmentation

Reza Hoseinnezhad; Alireza Bab-Hadiashar

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated.


Journal of Mathematical Imaging and Vision | 2010

Finite Sample Bias of Robust Estimators in Segmentation of Closely Spaced Structures: A Comparative Study

Reza Hoseinnezhad; Alireza Bab-Hadiashar; David Suter

This paper presents the design and implementation of a new comparative analytical framework for studying the usability of modern high breakdown robust estimators. The emphasis is on finding the intrinsic limits, in terms of size and relative spatial accuracy, of such techniques in solving the emerging challenges of the segmentation of fine structures. A minimum threshold for the distance between separable structures is shown to depend mainly on the scale estimation error. A scale invariant performance measure is introduced to quantify the finite sample bias of the scale estimate of a robust estimator and the measure is evaluated for some state-of-the-art high breakdown robust estimators using datasets containing at least two close but distinct structures with varying distances and inlier ratios. The results show that the new generation of density-based robust estimators (such as pbM-estimator and TSSE) have a poorer performance in problems with datasets containing only a small number of samples in each structure compared with ones based on direct processing of the residuals (such as MSSE). An important message of this paper is that an estimator that performs best in some circumstances, may not be competitive in others: particularly performance on data structures that are relatively large and/or well-separated vs closely spaced fine structures.


ieee sensors | 2002

Sensor fusion for ultrasonic and laser arrays in mobile robotics: a comparative study of fuzzy, Dempster and Bayesian approaches

Reza Hoseinnezhad; Behzad Moshiri; Mohammad Reza Asharif

In any autonomous mobile robot, if not the most, one important issue to be designed and implemented on the robot, is environment perception and its role in autonomous navigation. There are many grid-based and topological methods for environment mapping. Among the grid-based methods the main difference is about the method of data integration that is applied to mapping. In this paper, three different approaches are formulated to perform sensor data integration in the process of generation of a generalized version of occupancy grids map of the environment. The methods are formulated based on Bayesian, Fuzzy and Dempster-Shafer approaches to data fusion/integration. Although, they are famous for data fusion applications, in this research work they have been applied, formulated and simulated to solve a unique problem: map building for the same mobile robot, equipped with 8 Polaroid ultrasonic range finder sensors and operating in the same environment. The simulation results are applied for comparative study of the merits of the methods and their applicability in the map building and environment perception for autonomous mobile robots. They show that the Bayesian approach gives more appropriate maps, by which, A* path planning algorithm leads to shorter and safer routes for the mobile robot to navigate.

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Alireza Bab-Hadiashar

Swinburne University of Technology

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Shafriza Nisha Basah

Swinburne University of Technology

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David Suter

University of Adelaide

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Stephen Saric

Swinburne University of Technology

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Alireza Bab-Hadiashar

Swinburne University of Technology

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