This paper proposes a single inductor multiple output (SIMO) driver architecture for RGB LEDs with hybrid dimming technique, for use in large installation, with minimal loss of luminous flux and light flickering. Among other benefits, the proposed dimming technique provides improved accuracy in the color control, while maintaining low current ripple. Such advantages are easily attained, even when resorting to a simple SIMO LED driver. To confirm the effectiveness of the proposed hardware and software architecture, a comprehensive set of scenarios is established and tested resorting to a simulation model of the LED system.

Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that can be collected in SDNs and how information can be learned from that data. In this work we describe a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol. We present the data-sets that can be obtained and show how several ML techniques can be applied to it for traffic classification. The results indicate that high accuracy classification can be obtained with the data-sets using supervised learning.

This paper describes the VIP3D machine, developed under contract for an industrial bakery that supplies pre-cooked, frozen bread to supermarkets, hotels and other commercial outlets throughout Europe. The machine, developed and built entirely by INETI's Optoelectronics Department, is designed to make rapid and reliable measurements of bread loaves for the purpose of production quality control. This paper focuses on the problems encountered and the machine vision solutions developed, to obtain accurate 3-D measurements of the different types and sizes of loaves produced.