This paper introduces an improvement to the 6D motion estimation solution by combining the Photonic Mixer Device (PMD) and Stereo Camera. The main feature of the stereo camera is the higher resolution of the 2D image ...
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This paper introduces an improvement to the 6D motion estimation solution by combining the Photonic Mixer Device (PMD) and Stereo Camera. The main feature of the stereo camera is the higher resolution of the 2D image compared to the one from the PMD camera. Whereas the depth information derived from a PMD is usually far superior to the result from a stereo camera. This work hence proposes a combination scheme for the PMD and stereo camera in order to improve the results of the motion estimation. The combined setup was placed on a mobile robot and carried on the 6D motion estimation task using a provided artificial landmark. The robot was moved around while the combined setup simultaneously captures 3D images of the landmark from each new position. The motion was estimated based on the matching of the captured 3D images between two successive positions. The classical singular value decomposition (SVD) algorithm was used to solve the matching problem. The referent points for the SVD algorithm were extracted from the landmark using a robust corner detection algorithm. The experiments were fulfilled using 1) stereo 2) PMD and 3) combination of stereo and PMD camera. The results of these three arrangements are compared and the outcome of the comparing are presented.
Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and co...
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Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data to account for individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subjectpsilas cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subjectpsilas changing level of alertness, indexed by subjectpsilas behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity...
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The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple heterogeneous ant colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, controlengineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm.
This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume fini...
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This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume finite deformation of the brain after opening the skull. We use a test sphere as a model of the brain, tetrahedral finite element mesh, and function optimization that optimizes the modelspsila parameters by minimizing the distance between the resulting deformation and the supposed deformation. Based on the final value of the objective function, we conclude that the accuracy of the solid mechanic model is higher than that of the elastic model. Applications of the methods to the MR images of the brain confirm this finding.
Hard computing based optimization algorithms usually require a lot of computational resources and generally do not have the ability to arrive at the global optimum solution. Soft computing algorithms on the other hand...
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Hard computing based optimization algorithms usually require a lot of computational resources and generally do not have the ability to arrive at the global optimum solution. Soft computing algorithms on the other hand negate these deficiencies, by allowing for reduced computational loads and the ability to find global optimal solutions, even for complex cost surfaces. This paper presents two numerical case studies where a particle swarm optimization (PSO) algorithm is applied to biomedical problems. In particular, the problem of identifying the rupture force for leukocyte adhesion molecules and the problem of finding the correct control parameters of a robotic hand, are addressed. Simulation results indicate that PSO is a feasible alternative to the computational expensive hard computing algorithms.
In this paper a Fuzzy Tuned extended H ∞ (FTH ∞ ) estimator is designed and implemented in an Attitude Heading Reference System (AHRS), which is specialized to vehicular applications. In AHRS, 3-axis accelerometers ...
In this paper a Fuzzy Tuned extended H ∞ (FTH ∞ ) estimator is designed and implemented in an Attitude Heading Reference System (AHRS), which is specialized to vehicular applications. In AHRS, 3-axis accelerometers are allotted to measure the earth's gravity field vector and then to update the roll and pitch angles obtained from gyros’ dynamic. Therefore, the AHRS on an accelerated vehicle will be affected by large disturbances. Additionally, in ground vehicles, the measurements of 3-axis magnetometers are corrupted by both soft and hard iron time varying disturbances. The FTH ∞ estimator is an extended H ∞ *** filter in which only noise and attenuation bounds are tuned based on fuzzy linguistic if-then rules. The mentioned features make the estimator more reliable and suitable for hardware implementation. FTH ∞ estimator relies on a consistent fuzzy combination of two change detection tests; namely, likelihood ratio and averaged norm error. Real-time implementation of new attitude-heading estimator is performed on a TMS320VC5416 Digital Signal Processor (DSP). Incorporating this powerful and small size DSP with micro electro mechanical inertial and resistive magnetic sensors leads to a low cost, small size and low power consumption AHRS. Performance of the AHRS was evaluated in Monte Carlo simulations of vehicle's attitude-headings. The FTH ∞ estimator results in a superior performance compared to that of the extended H ∞ estimator in simulation and real tests.
Pole placement is a well-established design method for linear control systems. Note however that with an output feedback controller of low-order such as PID one cannot achieve arbitrary pole placement for a high-order...
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Pole placement is a well-established design method for linear control systems. Note however that with an output feedback controller of low-order such as PID one cannot achieve arbitrary pole placement for a high-order or delay system, and then partially or hopefully, dominant pole placement becomes the only choice. To the best of the authors’ knowledge, no method is available in the literature to guarantee dominance of the assigned poles in the above case. This paper proposes two simple and easy methods which can guarantee the dominance of the assigned two poles for PID control systems. They are based on Root-Locus and Nyquist plot, respectively. If a solution exists, the parametrization of all the solutions is explicitly given. Examples are provided for illustration.
In this paper, the feasibility of using active noise control inside personal computers is demonstrated by applying an adaptive filter to Hewlett-Packard's Blackbird 002 Gaming PC. Conditions relating the control s...
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In this paper, the feasibility of using active noise control inside personal computers is demonstrated by applying an adaptive filter to Hewlett-Packard's Blackbird 002 Gaming PC. Conditions relating the control signals and the sound heard by an external listener are presented as a means of evaluating the hardware design before applying the adaptive filter. A multichannel stochastic adaptive filter is derived by vectorizing the equations and applying gradient descent and Newton's method. For implementation purposes, data-based approximations to the gradient and hessian are provided. It is shown that a 6dB reduction in sound pressure level is obtainable by adding reference microphones, error microphones, a speaker, and some acoustical foam into the system even though the length of the noise cancelation system is small, approx. 6”, compared to the fundamental wavelength of the noise, approx 23”.
In the absence of plant parameter uncertainties, inversion-based feedforward techniques have been known to deliver accurate tracking performance. Due to changes in operating conditions like ambient temperature, humidi...
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ISBN:
(纸本)9781424424948
In the absence of plant parameter uncertainties, inversion-based feedforward techniques have been known to deliver accurate tracking performance. Due to changes in operating conditions like ambient temperature, humidity and loading, piezoelectric-stack actuated nanopositioning platforms can undergo significant changes in their system parameters. Nonlinear effects of hysteresis, an inherent property of a piezoelectric actuator, are also present; charge actuation is applied to reduce the effects of hysteresis. In this work, a suitable feedback controller that reduces the effects of parameter uncertainties is integrated with the inversion-based feedforward technique to deliver accurate nanopositioning over a large bandwidth. It is shown experimentally that by integrating closed-loop damping, inversion-based feedforward and charge actuation, the tracking bandwidth of the platform from can be increased significantly from 310 Hz to 1320 Hz.
Globalization is making both developed and developing countries think about effective and efficient strategies that will advance their economies and social development. Throughout the history of civilization, engineer...
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Globalization is making both developed and developing countries think about effective and efficient strategies that will advance their economies and social development. Throughout the history of civilization, engineering has played a critical role in economic development. Engineers are key not only in solving local problems but also in knowledge creation and knowledge transfer. Thus, it is imperative that technical know-how be supplemented with professional skills to develop an 'adaptive leader' who is capable of addressing the multiple challenges of an ever changing world. The key-question posed by the 21st century global economy to engineering educators and stake-holders is this: "How can education in science and technology help to reduce poverty, boost socio-economic development, and take the right decisions for sustainable and environmental compatible development?" To answer these questions, a global approach is needed: and this can only be accomplished by a "team" which has its roots in all regions of the world, i.e. which is enabled to think globally and then act locally. There's a need to establish effective engineering education processes of high quality around the world to assure a global supply of well-prepared engineering graduates. This paper describes the reasons for the creation in October 2006 of IFEES - the International Federation of engineering Education Societies. It will describe the vision, mission, and key strategies of IFEES. It will also describe some of the initiatives currently underway and how the various engineering education stakeholders can leverage and benefit from engaging with IFEES, thus strengthening the organizational capacity of engineering education societies throughout the world. The paper will share plans to help members learn from each other's best practices and even failures and how those organizations that have been in existence for quite some time (ASEE was founded in the late 19th century) can contribute to the institutio
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