Computer Architecture, Automation and Robotics

The logarithmic image processing model (LIP) is a robust
mathematical framework, which, among other benefits, behaves invariantly to illumination changes. This paper presents, for the first time, two general formulations of the 2-D convolution of separable kernels under the LIP paradigm. Although both formulations are mathematically equivalent, one of them has been designed avoiding the operations which are computationally expensive in current computers. Therefore, this fast LIP convolution method allows to obtain significant speedups and is more adequate for real-time processing. In order to support these statements, some experimental results are shown in Section V.

En el presente trabajo, se presenta una nueva técnica que unifica el conocido método de Canny para la obtención de bordes con un paradigma de procesamiento de imágenes conocido como LIP, que tiene un comportamiento logarítmico parecido al del ojo humano. Se ha observado que el método de Canny no detecta bien los bordes en zonas de baja iluminación y que el paradigma LIP permite trabajar en
zonas de iluminación pobre. Esta nueva técnica (LIP–Canny) se compara con Canny, mostrando que LIP–Canny es capaz de detectar bordes en zonas de baja iluminación. También
se compara con otra técnica, en la que se realiza un filtrado homomórfico previo al método de Canny, obteniéndose unos resultados visuales similares, pero LIP–Canny obtiene dichos
resultados más rápidamente y con un ajuste de umbral menos sensible y, por tanto, mucho más sencillo.

Smart Cities are called to change the daily life of human beings. This concept permits improving the efficiency of our cities in several areas such as the use of water, energy consumption, waste treatment, and mobility both for people as well as vehicles throughout the city. This represents an interconnected scenario in which thousands of embedded devices need to work in a collaborative way both for sensing and modifying the environment properly. Under this scenario, the majority of devices will use wireless protocols for communicating among them, representing a challenge for optimizing the use of the electromagnetic spectrum. When the density of deployed nodes increases, the competition for using the physical medium becomes harder and, in consequence, traffic collisions will be higher, affecting data-rates in the communication process. This work presents mDARAL, a multi-radio routing algorithm based on the Dynamic and Adaptive Radio Algorithm (DARAL), which has the capability of isolating groups of nodes into sub-networks. The nodes of each sub-network will communicate among them using a dedicated radio frequency, thus isolating the use of the radio channel to a reduced number of nodes. Each sub-network will have a master node with two physical radios, one for communicating with its neighbours and the other for being the contact point among its group and other sub-networks. The communication among sub-networks is done through master nodes in a dedicated radio frequency. The algorithm works to maximize the overall performance of the network through the distribution of the traffic messages into unoccupied frequencies. The obtained results show that mDARAL achieves great improvement in terms of the number of control messages necessary to connect a node to the network, convergence time and energy consumption during the connection phase compared to DARAL.