Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Hoang. M. Le"'
Publikováno v:
2022 30th European Signal Processing Conference (EUSIPCO).
Deep Learning to Robustify a Geometric Interpretation of Trilateration for 3D RSS-based Localization
Publikováno v:
2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON).
2D DoA-based Positioning with Phase Jump Corrections and An Approximate Maximum Likelihood Estimator
Publikováno v:
2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM).
Publikováno v:
2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT).
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 38:1359-1372
Formal verification of high-level SystemC designs is an important and challenging problem. One has to deal with the full complexity of C++ to extract a suitable formal model (front-end problem) and then, with large cyclic state spaces defined by symb
Publikováno v:
European Journal of Organic Chemistry. 2019:2362-2367
Publikováno v:
International Journal on Software Tools for Technology Transfer. 21:545-565
Sequentialization has been shown to be an effective symbolic verification technique for safety properties in multi-threaded C programs using POSIX threads. The tool Lazy-CSeq, which applies a lazy sequentialization scheme, demonstrated its efficiency
Publikováno v:
EUSIPCO
Trilateration is a popular approach in localization. Many related geometric approaches have been proposed for 2D scenarios. In general, each approach has a standard case in which the main solution is applied, and many specific cases. Each specific ca
Autor:
Hoang M. Le
Publikováno v:
Fundamental Approaches to Software Engineering ISBN: 9783030452339
FASE
FASE
LibKluzzer is a novel implementation of hybrid fuzzing, which combines the strengths of coverage-guided fuzzing and dynamic symbolic execution (a.k.a. whitebox fuzzing). While coverage-guided fuzzing can discover new execution paths at nearly native
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::810f0c86745ef95c588a966baa5091fb
https://doi.org/10.1007/978-3-030-45234-6_29
https://doi.org/10.1007/978-3-030-45234-6_29