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20 results on '"Bastani, Vahid"'

1. Incremental learning of environment interactive structures from trajectories of individuals

Author

Campo, Damian, Bastani, Vahid, Marcenaro, Lucio, and Regazzoni, Carlo

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

This work proposes a novel method for estimating the influence that unknown static objects might have over mobile agents. Since the motion of agents can be affected by the presence of fixed objects, it is possible use the information about trajectories deviations to infer the presence of obstacles and estimate the forces involved in a scene. Artificial neural networks are used to estimate a non-parametric function related to the velocity field influencing moving agents. The proposed method is able to incrementally learn the velocity fields due to external static objects within the monitored environment. It determines whether an object has a repulsive or an attractive influence and provides an estimation of its position and size. As stationarity is assumed, i.e., time-invariance of force fields, learned observation models can be used as prior knowledge for estimating hierarchically the properties of new objects in a scene.

Published
2019

2. Online Pedestrian Group Walking Event Detection Using Spectral Analysis of Motion Similarity Graph

Author

Bastani, Vahid, Campo, Damian, Marcenaro, Lucio, and Regazzoni, Carlo S.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

A method for online identification of group of moving objects in the video is proposed in this paper. This method at each frame identifies group of tracked objects with similar local instantaneous motion pattern using spectral clustering on motion similarity graph. Then, the output of the algorithm is used to detect the event of more than two object moving together as required by PETS2015 challenge. The performance of the algorithm is evaluated on the PETS2015 dataset., Comment: Published in: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Published
2019
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3. Incremental Nonlinear System Identification and Adaptive Particle Filtering Using Gaussian Process

Author

Bastani, Vahid, Marcenaro, Lucio, and Regazzoni, Carlo

Subjects
Statistics - Machine Learning
Abstract

An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample rate using stochastic gradient descent based optimization implemented in the state estimation filtering loop. The performance of the proposed method is compared with state-of-the-art Gaussian process based batch learning methods. Finally, it is shown that the state estimation performance significantly improves due to the online learning of state dynamics., Comment: submitted to IEEE Signal Processing Letters

Published
2016

6. A novel patterning control strategy based on real-time fingerprint recognition and adaptive wafer level scanner optimization

Author

Masazumi Matsunobu, Henk Niesing, Chenxi Lin, Jeff Johnson, Alexander Ypma, Cyrus E. Tabery, Venky Subramony, Yi Zou, Linmiao Zhang, William Susanto, Dag Sonntag, Ravin Somasundaram, Bastani Vahid, Hakki Ergun Cekli, Zakir Ullah, and Jelle Nije

Subjects
Scanner, business.industry, Computer science, Integrated circuit, Overlay, Fingerprint recognition, law.invention, Metrology, law, Process control, Wafer, Process optimization, business, Computer hardware
Abstract

In addition to lithography process and equipment induced variations, processes like etching, annealing, film deposition and planarization exhibit variations, each having their own intrinsic characteristics and leaving an effect, a ‘fingerprint’, on the wafers. With ever tighter requirements for CD and overlay, controlling these process induced variations is both increasingly important and increasingly challenging in advanced integrated circuit (IC) manufacturing. For example, the on-product overlay (OPO) requirement for future nodes is approaching

Published
2018
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7. A novel patterning control strategy based on real-time fingerprint recognition and adaptive wafer level scanner optimization

Author

Cekli, Hakki Ergun, primary, Nije, Jelle, primary, Ypma, Alexander, primary, Bastani, Vahid, primary, Sonntag, Dag, primary, Niesing, Henk, primary, Zhang, Linmiao, primary, Ullah, Zakir, primary, Subramony, Venky, primary, Somasundaram, Ravin, primary, Susanto, William, primary, Matsunobu, Masazumi, primary, Johnson, Jeff, primary, Tabery, Cyrus, primary, Lin, Chenxi, primary, and Zou, Yi, primary

Published
2018
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19. Online Bayesian Learning and Classification of Ship-to-Ship Interactions for Port Safety

Author

Carlo S. Regazzoni, Lucio Marcenaro, Francesco Castaldo, Francesco Palmieri, Vahid Bastani, IEEE Signal Processing Society, IEEE Computer Society, Castaldo, Francesco, Palmieri, Francesco A. N., Bastani, Vahid, Marcenaro, Lucio, and Regazzoni, Carlo

Subjects
Computer science, business.industry, Bayesian probability, Probabilistic logic, Inference, Bayesian inference, computer.software_genre, Machine learning, Port (computer networking), Variable-order Bayesian network, Computer Networks and Communication, Signal Processing, Bayesian programming, Data mining, Artificial intelligence, business, computer, Dynamic Bayesian network
Abstract

Interaction analysis of ships mooring and maneuvering in harbors is pursued in this paper by using Bayesian probabilistic models. A number of ship-to-ship interactions are deduced from the navigation rules in port areas, and then used to train different Event-based Dynamic Bayesian Networks (E-DBNs). When data of two interacting ships are injected into the network, inference is performed in order to verify if the interaction between the vessels is known or not, and in the latter case actions to preserve the port safety can be taken. Results are drawn in the final part of the paper by including into the networks data provided by a simulator of realistic trajectories relative to an existing port.

Published
2014

20. Abnormal vessel behavior detection in port areas based on Dynamic Bayesian Networks

Author

Castaldo, F., Palmieri, F. A. N., Bastani, V., Marcenaro, L., Carlo Regazzoni, Castaldo, Francesco, Palmieri, Francesco A. N., Bastani, Vahid, Marcenaro, Lucio, and Regazzoni, Carlo

Subjects
Information Systems
Abstract

Automatic recognition of abnormal situations in harbor environments is approached in this paper with a system based on Dynamic Bayesian Networks. The area under surveillance is partitioned in zones of different sizes and shapes by means of an Instantaneous Topological Map, on which events are detected and inference is carried out. The model is trained with synthetic normal trajectories of ships and vessels mooring in the port, and each time a new trajectory is presented to the system, comparisons with the normal behaviors stored in the network are performed. If no match is found, an abnormal situation is declared and countermeasures can be taken. The algorithm has been tested in a real port with simulated data in order to evaluate the false alarm rate and the abnormal detection capabilities of the proposed approach.

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