PSD Profiles for Dynamic and Durability Tests of Military Off-Road Vehicle Racks

Autor: Mauro Velardocchia, Elvio Bonisoli, Antonio Tota, Domenico Lisitano, Genny Paciullo, Marco Trevisi

Publikováno v: SAE Technical Paper Series.

In a military off-road vehicle, generally designed to operate in an aggressive operating environment, the typical comfort requirements for trucks and passenger cars are revised for robustness, safety and security. An example is the cabin space optimi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::634f2829447773281634fda63ed904d4
https://doi.org/10.4271/2023-01-0107

Compressive Imaging using RIP-compliant CMOS Imager Architecture and Landweber Reconstruction

Autor: Ricardo Carmona-Galan, Ali Akbari, Maria Trocan, Marco Trevisi, Ángel Rodríguez-Vázquez

Publikováno v: IEEE Transactions on Circuits and Systems for Video Technology
Digital.CSIC. Repositorio Institucional del CSIC
instname
idUS. Depósito de Investigación de la Universidad de Sevilla

In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved tha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7938a818571694325947b1363cfe693b
https://surrey.eprints-hosting.org/852474/

Concurrent focal-plane generation of compressed samples from time-encoded pixel values

Autor: Marco Trevisi, H. C. Bandala, Jorge Fernández-Berni, Ángel Rodríguez-Vázquez, Ricardo Carmona-Galan

Publikováno v: DATE
Digital.CSIC. Repositorio Institucional del CSIC
instname
idUS. Depósito de Investigación de la Universidad de Sevilla

Compressive sampling allows wrapping the relevant content of an image in a reduced set of data. It exploits the sparsity of natural images. This principle can be employed to deliver images over a network under a restricted data rate and still receive

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c37ef201f8c5fdad51b8a1daed8ff489
https://doi.org/10.23919/date.2018.8342217

A compressive domain saliency-based adaptive measurement method for image recovery

Autor: Carmona-G. Ricardo, Honggui Li, Maria Trocan, Marco Trevisi

Publikováno v: ICECS

This paper proposes an image compressed sensing method by adaptive measurement matrix based on saliency detection in the compressive domain. The saliency mapping algorithm is simply the difference between adjacent compressive measurements of neighbor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::534affd0871ecd26abe4d051cd0a3620
https://doi.org/10.1109/icecs.2016.7841225

Hardware-oriented feature extraction based on compressive sensing

Autor: Marco Trevisi, Ricardo Carmona-Galan, Ángel Rodríguez-Vázquez

Publikováno v: ICDSC

Feature extraction is used to reduce the amount of resources required to describe a large set of data. A given feature can be represented by a matrix having the same size as the original image but having relevant values only in some specific points.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ec03ea5198eb84fe6c1cbc30a9ccfd90
https://doi.org/10.1145/2789116.2802657