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Blur trajectory estimation is the core problem in motion blurring restoring from a single image. Normalized blind deconvolution and Shock filter were employed to estimate the blur trajectory more accurately by image details enhancement and steady state solution of non-monolithic local minimum. To reduce disturbance of noises and patch textures, the bilateral grid filter was utilized. During the iteration...
One of the complicated issue in compliance control for rehabilitation and assistive robots is to predict right human's motion intention. In this paper, we have proposed an algorithm to estimate Desired Motion Intention (DMI) so that better compliance could be provided by the rehabilitation and assitive robots. Proposed algorithms is based on Extreme Learning Machine (ELM) and takes inputs from different...
This paper is an attempt to study torque estimation problem of robot manipulators. Introducing bounded nonlinear functions to model torque dynamics, estimation of the robots states via a Lunenburg estimator is investigated. As compared with existing approaches in the literature, the main contribution of this paper is to guarantee the asymptotic stability of the estimator while the torque dynamics...
In this paper, in order to estimate the operational parameters of synchronous generator model suitable for unbalanced conditions, an approach which uses Trajectory Sensitivity Functions (TSF) and is based on Unscented Kalman Filter (UKF) is developed. In this framework, iniatilly, TSF are used to identify the parameters which most affect the behavior of the system. After the ordination of the parameters...
Aiming at the problem how to express relevant relationship between multiple targets, we propose an approach based on the tracking-by-detection (TBD) strategy, where detections from the HOG classifier are regarded as image evidence. Focusing on the issue of localization uncertainty, data association based on greedy heuristics is executed iteratively to retrieve from the erroneous candidate locations...
This article studies human-guided iterative learning control for output-trajectory tracking tasks when the controller does not have direct access to the intended output and needs to infer it from human demonstrations. Human intent (e.g., the desired trajectory) can be inferred by inverting known human-response models. However a challenge is that such human-response models are only available for limited...
This paper addresses the estimation of a time-varying parameter in a network. A group of agents sequentially receive noisy signals about the parameter (or moving target), which does not follow any particular dynamics. The parameter is not observable to an individual agent, but it is globally identifiable for the whole network. Viewing the problem with an online optimization lens, we aim to provide...
This paper considers the closed-loop navigation of a hypothetical ocean-sampling vehicle in the presence of an idealized ocean-eddy pair. This problem embodies many of the challenges of spatiotemporal ocean sampling using minimally actuated Lagrangian sensors. We extend our existing guidance strategy known as the Boundary-Touring Algorithm (BTA) to steer a self-propelled vehicle to a unique streamline...
A probabilistic framework for online motion planning of vehicles in dynamic environments is proposed. We develop a sampling-based motion planner that incorporates prediction of obstacle motion. A key feature is the introduction of task specifications as artificial measurements. This allows us to cast the exploration phase in the planner as a nonlinear, possibly multimodal, estimation problem, which...
In this study, human activity identification is approached as a record, analyze and model from the video sequence as you observe the scene in time methodology. The computational approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streak-lines...
We present an approach for unsupervised object segmentation in unconstrained videos. Driven by the latest progress in this field, we argue that segmentation performance can be largely improved by aggregating the results generated by state-of-the-art algorithms. Initially, objects in individual frames are estimated through a per-frame aggregation procedure using majority voting. While this can predict...
We propose a method to estimate people travel modes and trajectories from control signal records (CSRs) generated at cellular base stations (BSs) when mobile phones send BSs any signal such as handover and data transmission. User locations can be roughly identified by BS IDs in the records as well as the locations of BSs. However, the location accuracy depends on BS cell size, which is not enough...
This paper studies the event-triggered control problem for continuous-time systems subject to external disturbances. In particular, we consider continuous-time systems with bounded external disturbances. To overcome infinitely fast sampling caused by the disturbance, a combination of time-based trigger and event-based trigger is proposed, and an estimation of the magnitude of the external disturbance...
The ultimate boundedness of a chaotic system is very important for the study of the qualitative behavior of a chaotic system. At present, explicit ultimate bound sets can be analytically obtained only for some special chaotic systems, and few results are known for hyperchaotic ones. In this paper, we investigate the ultimate boundedness for the new hyperchaotic Lorenz system by mathematical proof,...
We consider the task of real-time pulse rate estimation from the facial video of a subject recorded in a natural setting in an office environment. For estimating the pulse rate, we exploit the fact that the pulse rate does not vary drastically from one analysis window to the next. For this purpose we sparsify the spectra of windowed traces obtained by independent component analysis of the average...
The paper deals with the sensorless control of salient-pole Interior-Permanent-Magnet-Synchronous-Machines (IPMSMs) adressed to electric vehicle applications, using a high frequency voltage injection technique. This technique is effective at zero and low motor speed, and takes advantage of the peculiarity of the rotor geometry. Indeed, the expected estimation performances heavily depend on the rotor...
This paper presents a novel method for wind characterization and mapping by using an Unmanned Aircraft System (UAS). The generation of a wind map is vital in energy-efficient trajectory planning of efficient trajectories and dynamic soaring applications to detect the shear layer. Firstly, two methods to estimate the parameters that define an unknown wind field (wind speed and direction) by using a...
In video surveillance to count the number of people walking through a corridor or door people counting is used. Counting persons passing through supervised area is important issue of this domain. System of people counting is important for security application (such as in the situation of an evacuation, it is vital to know how many people are present inside the surveillance area at any given time),...
In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.
In urban environments, a multipath propagation and the Doppler effect are dominant phenomena that negatively affect the quality of service in wireless communications and realization of location methods. This paper presents the localization method of emission source in urban terrain for NLOS conditions based on the Doppler effect. To this aim, authors use the signal Doppler frequency (SDF) method....
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