University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Mapping and organization of highly-repeated DNA sequences by means of simultaneous and sequential FISH and C-banding in 6×-triticale

Abstract: Three families of highly repeated sequences from rye and the rRNA multigenes (NOR and 5S) have been mapped by FISH and C-banding, in chromosomes of triticale. The pSc119.2 probe showed interstitial hybridization in chromosome arms 1RS, 1RL, 4RL, 5RL, 6RS, 6RL, 7RS and 7RL, and is very effective for chromosome identification of rye chromosomes in triticale. This sequence also hybridizes to the 4A, 5A and the seven B-genome wheat chromosomes. Simultaneous hybridization with the pSc119.2 and pTa794 (5S rRNA) is very useful to distinguish the metacentric chromosomes 2R and 3R. The pSc74 probe appears at interstitial sites in the long arm of the most heterobrachial chromosomes (5R and 6R). The three repetitive sequences of 120 bp, 480 bp, and 610 bp hybridize to telomeric regions in rye chromosomes. Different arrangements and complex organizations consisting of arrays of three or more family sequences were found. The results demonstrate a great variation in the relative arrangement of the repetitive sequences in the telomeres of the rye chromosomes. There were quantitative differences in each cytological marker between triticale lines in bothin situ labelling and C-banding, probably as the result of differences in the number and/or kind of repeat sequence.

Fog detection system based on computer vision techniques

Abstract: In this document, a real-time fog detection system using an on-board low cost b&w camera, for a driving application, is presented. This system is based on two clues: estimation of the visibility distance, which is calculated from the camera projection equations and the blurring due to the fog. Because of the water particles floating in the air, sky light gets diffuse and, focus on the road zone, which is one of the darkest zones on the image. The apparent effect is that some part of the sky introduces in the road. Also in foggy scenes, the border strength is reduced in the upper part of the image. These two sources of information are used to make this system more robust. The final purpose of this system is to develop an automatic vision-based diagnostic system for warning ADAS of possible wrong working conditions. Some experimental results and the conclusions about this work are presented.

Efficient ConvNet for real-time semantic segmentation

Abstract: Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. ConvNets excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at the pixel level. However, current approaches normally involve complex architectures that are expensive in terms of computational resources and are not feasible for ITS applications. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our ConvNet is a novel layer that uses residual connections and factorized convolutions in order to remain highly efficient while still retaining remarkable performance. Our network is able to run at 83 FPS in a single Titan X, and at more than 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments demonstrates that our system, trained from scratch on the challenging Cityscapes dataset, achieves a classification performance that is among the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. This makes our model an ideal approach for scene understanding in intelligent vehicles applications.