Aria Alasty

Prof. Aria Alasty


Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 July 2007 in International Journal of Solids and Structures
Verified
Journal Article
Closed access
ARAsghar Ramezani
Asghar Ramezani
AAAria Alasty
Aria Alasty
Javad AkbariJavad Akbari
Javad Akbari

1 February 2009 in Communications in Nonlinear Science and Numerical Simulation
Verified
Journal Article
Closed access
HSHassan Salarieh
Hassan Salarieh
AAAria Alasty
Aria Alasty

24 April 2007 in Microsystem Technologies
Verified
Journal Article
Closed access
PDF file
ARAsghar Ramezani
Asghar Ramezani
AAAria Alasty
Aria Alasty
Javad AkbariJavad Akbari
Javad Akbari

8 July 2020 in IEEE Sensors Journal
Verified
Journal Article
Closed access
AKAli Karmozdi
Ali Karmozdi
MHMojtaba Hashemi
Mojtaba Hashemi
HSHassan Salarieh
Hassan Salarieh
AAAria Alasty
Aria Alasty
INS-DVL integration is a common method for underwater navigation. However, inherent errors of sensors, especially in MEMS IMUs, lead to inaccuracies in estimating the position and attitude. In this paper, dynamic motion model of AUV is used to improve MEMS INS-DVL navigation. In this method, which is called model-aided or model-based navigation, the information of the kinetic model of the vehicle (obtained from Newton–Euler equations) is used to improve the navigation performance. Previous model-aided navigation studies about AUVs have been focused on the translational dynamic model of vehicles. As the best of our knowledge, this paper is the first one which suggests using a rotational motion model of AUVs to improve the navigation performance and at the same time, reports successful implementation of method through field-testing. The utilized dynamic model, describes the rotational motion of AUV in a 3DOF form. Information extracted from the rotational dynamic model and the outputs of gyroscopes, will be fused in a secondary Kalman filter. So, before the fusion with auxiliary sensors in the main Kalman filter, the gyroscopes outputs have been improved by the rotational motion model. Therefore, the overall output will be better than the outputs of gyroscopes by itself. The main Kalman filter is a perturbation-based Kalman filter which is employed for data fusion. This algorithm is implemented on real data collected from field-test. The results show that this inexpensive method which requires no additional equipment was truly effective in improving navigation performance. Using this model, the accuracy of navigation was improved 5 to 10 times.

14 June 2016 in Biosensors
Verified
Journal Article
Open access
PDF file
MPMaysam Zamani Pedram
Maysam Zamani Pedram
ASAmir Shamloo
Amir Shamloo
AAAria Alasty
Aria Alasty
Ebrahim Ghafar-ZadehEbrahim Ghafar-Zadeh
Ebrahim Ghafar-Zadeh
This paper scrutinizes the magnetic field effect to deliver the superparamagnetic nanoparticles (SPMNs) through the Blood Brain Barrier (BBB). Herein we study the interaction between the nanoparticle (NP) and BBB membrane using Molecular Dynamic (MD) techniques. The MD model is used to enhance our understanding of the dynamic behavior of SPMNs crossing the endothelial cells in the presence of a gradient magnetic field. Actuation of NPs under weak magnetic field offers the great advantage of a non-invasive drug delivery without the risk of causing injury to the brain. Furthermore, a weak magnetic portable stimulator can be developed using low complexity prototyping techniques. Based on MD simulation results in this paper, SPMNs can cross the cell membrane while experiencing very weak mechanical forces in the range of pN. This study also derives guidelines for the design of the SPMNs dedicated to crossing the BBB using external magnetic fields.

1 August 2008 in Chaos, Solitons & Fractals
Verified
Journal Article
Closed access
HLHamed Layeghi
Hamed Layeghi
MAMehdi Tabe Arjmand
Mehdi Tabe Arjmand
HSHassan Salarieh
Hassan Salarieh
AAAria Alasty
Aria Alasty