| Home | << 1 2 >> |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic | ||||
| Title | Sensor Fault Detection and Diagnosis for autonomous vehicles | Type | Conference Article | ||
| Year | 2015 | Publication  |
2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 | Abbreviated Journal | |
| Volume | 30 | Issue | MATEC Web of Conferences | Pages | 1-6 |
| Keywords | |||||
| Abstract | In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | EDP Sciences | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 42 | ||
| Permanent link to this record | |||||
| Author | Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla | ||||
| Title | An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System | Type | Conference Article | ||
| Year | 2017 | Publication |
2017 International Conference on Deep Learning Technologies (ICDLT 2017) | Abbreviated Journal | |
| Volume | Part F128535 | Issue | Pages | 60-65 | |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 56 | ||
| Permanent link to this record | |||||
| Author | Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado | ||||
| Title | Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. | Type | Conference Article | ||
| Year | 2021 | Publication |
19th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | processing, software engineering, deep learning, intelligent vision systems, cloud computing. | ||||
| Abstract | Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems. Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 154 | ||
| Permanent link to this record | |||||