Orange S.A.

About: Orange S.A. is a company organization based out in Paris, France. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 6735 authors who have published 9190 publications receiving 156440 citations. The organization is also known as: Orange SA & France Télécom.

Multichannel optical connection method for optical fibers

Abstract: A multichannel optical connection process starts with making a wafer having a plurality of optical outlets disposed on a common face. The wafer is then disposed on a support having mechanical positioning arrangements, after which the assembly comprising the connecting wafer and the support is aligned with a reference termination having a base with mechanical positioning arrangements and a plate with at least two optical outlets disposed in the same configuration as the connecting plate. The two outlets of the wafer are then dynamically aligned with the outlets of the plate by three independent relative displacements of the wafer and the support. The wafer is fixed to the support when the alignment is achieved.

BPSK Bit Error Outage over Nakagami-m Fading Channels in Lognormal Shadowing Environments

Abstract: In this letter, we address the problem of finding a tractable expression for the bit-error outage (BEO) defined as the probability to observe a given average bit error rate (BER) over a fading channel in a shadowing environment. Our contribution is two-fold: (1) a simple yet tight approximation of the bit error probability (BEP) for binary phase shift keying (BPSK) over a frequency-flat Nakagami-m fading channel is derived, which (2) facilitates the derivation of a tight lower bound of the BEO in presence of lognormal shadowing in closed form. Theoretical results are corroborated by means of simulation results, confirming the tightness of the bounds.

Deep learning and recurrent connectionist-based approaches for Arabic text recognition in videos

Abstract: This paper focuses on recognizing Arabic embedded text in videos. The proposed methods proceed without applying any prior pre-processing operations or character segmentation. Difficulties related to the video or text properties are faced using a learned robust representation of the input text image. This is performed using Convolutional Neural Networks and Deep Auto-Encoders. Features are computed using a multi-scale sliding window scheme. A connectionist recurrent approach is then used. It is trained to predict correct transcriptions of the input image from the associated sequence of features. Proposed methods are extensively evaluated on a large video database recorded from several Arabic TV channels.

Design of survivable IP-over-optical networks

Abstract: In the past years, telecommunications networks have seen an important evolution with the advances in optical technologies and the explosive growth of the Internet. Several optical systems allow a very large transport capacity, and data traffic has dramatically increased. Telecommunications networks are now moving towards a model of high-speed routers interconnected by intelligent optical core networks. Moreover, there is a general consensus that the control plan of the optical networks should utilize IP-based protocols for dynamic provisioning and restoration of lightpaths. The interaction of the IP routers with the optical core networks permits to achieve end-to-end connections, and the lightpaths of the optical networks define the topology of the IP network. This new infrastructure has to be sufficiently survivable, so that network services can be restored in the event of a catastrophic failure. In this paper we consider a multilayer survivable network design problem that may be of practical interest for IP-over-optical neworks. We give an integer programming formulation for this problem and discuss the associated polytope. We describe some valid inequalities and study when these are facet defining. We discuss separation algorithms for these inequalities and introduce some reduction operations. We develop a Branch-and-Cut algorithm based on these results and present extensive computational results.