Dr. Alessandro Ortis Profile

Dr. Alessandro Ortis

at Univ degli Studi di Catania

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 16 January 2024 Paper

Deep learning algorithm for advanced level-3 inverse-modeling of silicon-carbide power MOSFET devices

Massimo Orazio Spata, Sebastiano Battiato, Alessandro Ortis, Francesco Rundo, Michele Calabretta, Carmelo Pino, Angelo Messina

Proceedings Volume 12973, 1297309 (2024) https://doi.org/10.1117/12.3016130

Read Abstract +

Inverse modelling with deep learning algorithms involves training deep architecture to predict device’s parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an inverse modelling method. In this work the authors propose a deep learning method trained for retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET (SiC Power MOS). The SiC devices are used in applications where classical silicon devices failed due to high-temperature or high switching capability. The key application of SiC power devices is in the automotive field (i.e. in the field of electrical vehicles). Due to physiological degradation or high-stressing environment, SiC Power MOS shows a significant drift of physical parameters which can be monitored by using inverse modelling. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power MOSFET. Preliminary results based on the retrieving of channel length of the device are reported. Channel length of power MOSFET is a key parameter involved in the static and dynamic behaviour of the device. The experimental results reported in this work confirmed the effectiveness of a multi-layer perceptron designed to retrieve this parameter.

Proceedings Article | 17 March 2015 Paper

A new fast matching method for adaptive compression of stereoscopic images

A. Ortis, S. Battiato

Proceedings Volume 9393, 93930K (2015) https://doi.org/10.1117/12.2086372

KEYWORDS: Image processing, Image compression, Image quality, Bismuth, Image segmentation, Algorithm development, Lithium, Computer programming, 3D image processing, Stereoscopy

Read Abstract +

In the last few years, due to the growing use of stereoscopic images, much effort has been spent by the scientific community to develop algorithms for stereoscopic image compression. Stereo images represent the same scene from two different views, and therefore they typically contain a high degree of redundancy. It is then possible to implement some compression strategies devoted to exploit the intrinsic characteristics of the two involved images that are typically embedded in a MPO (Multi Picture Object) data format. MPO files represents a stereoscopic image by building a list of JPEG images. Our previous work introduced a simple block-matching approach to compute local residual useful to reconstruct during the decoding phase, stereoscopic images that maintain high perceptual quality; this allows to the encoder to force high level of compression at least for one of the two involved images. On the other hand the matching approach, based only on the similarity of the blocks, results rather inefficient. Starting from this point, the main contribution of this paper focuses on the improvement of both matching step effectiveness and its computational cost. Such alternative approach aims to greatly enhance matching step by exploiting the geometric properties of a pair of stereoscopic images. In this way we significantly reduce the complexity of the method without affecting results in terms of quality.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years