System Level Simulation and Radio Resource Management for Distributed Antenna Systems with Cognitive Radio and Multi-Cell Cooperation

4th International Conference on Future Generation Communication Technologies (FGCT 2015), Luton, United Kingdom.

Profibus protocol extensions for enabling inter-cell mobility in bridge-based hybrid wired/wireless networks

Future industrial control/multimedia applications will increasingly impose or benefit from wireless and mobile communications. Therefore, there is an enormous eagerness for extending currently available industrial communications networks with wireless and mobility capabilities. The RFieldbus European project is just one example, where a PROFIBUS-based hybrid (wired/wireless) architecture was specified and implemented. In the RFieldbus architecture, interoperability between wired and wireless components is achieved by the use specific intermediate networking systems operating at the physical layer level, i.e. operating as repeaters. Instead, in this paper we will focus on a bridge-based approach, which presents several advantages. This concept was introduced in (Ferreira, et al., 2002), where a bridge-based approach was briefly outlined. Then, a specific Inter-Domain Protocol (IDP) was proposed to handle the Inter-Domain transactions in such a bridge-based approach (Ferreira, et al., 2003a). The major contribution of this paper is in extending these previous works by describing the protocol extensions to support inter-cell mobility in such a bridge-based hybrid wired/wireless PROFIBUS networks.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.