DISCUS : end-to-end network design for ubiquitous high speed broadband services

Fibre-to-the-premises (FTTP) has been long sought as the ultimate solution to satisfy the demand for broadband access in the foreseeable future, and offer distance-independent data rate within access network reach. However, currently deployed FTTP networks have in most cases only replaced the transmission medium, without improving the overall architecture, resulting in deployments that are only cost efficient in densely populated areas (effectively increasing the digital divide). In addition, the large potential increase in access capacity cannot be matched by a similar increase in core capacity at competitive cost, effectively moving the bottleneck from access to core. DISCUS is a European Integrated Project that, building on optical-centric solutions such as Long-Reach Passive Optical access and flat optical core, aims to deliver a cost-effective architecture for ubiquitous broadband services. One of the key features of the project is the end-to-end approach, which promises to deliver a complete network design and a conclusive analysis of its economic viability. © 2013 IEEE.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Distributed optimization in transportation and logistics networks

Many important problems in communication networks, transportation networks, and logistics networks are solved by the minimization of cost functions. In general, these can be complex optimization problems involving many variables. However, physicists noted that in a network, a node variable (such as the amount of resources of the nodes) is connected to a set of link variables (such as the flow connecting the node), and similarly each link variable is connected to a number of (usually two) node variables. This enables one to break the problem into local components, often arriving at distributive algorithms to solve the problems. Compared with centralized algorithms, distributed algorithms have the advantages of lower computational complexity, and lower communication overhead. Since they have a faster response to local changes of the environment, they are especially useful for networks with evolving conditions. This review will cover message-passing algorithms in applications such as resource allocation, transportation networks, facility location, traffic routing, and stability of power grids.