Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstractlevelofriskassessment. Inaddition,wepresentastudyofimplementedusecasestoprovide a practical understanding on the main uses of the BOF and its taxonomy.

In this paper we present a method for autofocusing images of sputum smears taken from a microscope which combines the finding of the optimal focus distance with an algorithm for extending the depth of field (EDoF). Our multifocus fusion method produces an unique image where all the relevant objects of the analyzed scene are well focused, independently to their distance to the sensor. This process is computationally expensive which makes unfeasible its automation using traditional embedded processors. For this purpose a low-cost optimized implementation is proposed using limited resources embedded GPU integrated on cutting-edge NVIDIA system on chip. The extensive tests performed on different sputum smear image sets show the real-time capabilities of our implementation maintaining the quality of the output image.

This work analyzes several drift compensation mechanisms in wireless sensor networks (WSN). Temperature is an environmental factor that greatly affects oscillators shipped in every WSN mote. This behavior creates the need of improving drift compensation mechanisms in synchronization protocols. Using the Flooding Time Synchronization Protocol (FTSP), this work demonstrates that crystal oscillators are affected by temperature variations. Thus, the influence of temperature provokes a low performance of FTSP in changing conditions of temperature. This article proposes an innovative correction factor that minimizes the impact of temperature in the clock skew. By means of this factor, two new mechanisms are proposed in this paper: the Adjusted Temperature (AT) and the Advanced Adjusted Temperature (A2T). These mechanisms have been combined with FTSP to produce AT-FTSP and A2T-FTSP. Both have been tested in a network of TelosB motes running TinyOS. Results show that both AT-FTSP and A2T-FTSP improve the average synchronization errors compared to FTSP and other temperature-compensated protocols (Environment-Aware Clock Skew Estimation and Synchronization for WSN (EACS) and Temperature Compensated Time Synchronization (TCTS)).

To exploit distributed computation in wireless sensor networks (WSNs) time synchronisation protocols are required. These protocols support advanced distributed and synchronous tasks between several nodes. Temperature changes have a strong influence in oscillators and may vary the clock of the nodes. The flooding time synchronisation protocol (FTSP) is a common time‐sync protocol in WSNs. It cannot deal with rapid temperature changes and needs several long periods to readjust the clock drift. Proposed is an innovative correction factor, implemented in a module called advanced adjustment temperature (A2T), that minimises the impact of temperature in the clock skew. This module may be placed on top of any clock‐skew based time synchronisation protocol with minimum coding modifications. For this work, the module has been linked to the FTSP, producing A2T‐FTSP. Experiments show that A2T‐FTSP reduces noticeably the average synchronisation errors compared to FTSP for the same varying temperature conditions.

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.

Wireless sensor networks are used to sample the environment in a distributed way. Therefore, it is mandatory for all of the measurements to be tightly synchronized in order to guarantee that every sensor is sampling the environment at the exact same instant of time. The synchronization drift gets bigger in environments suffering from temperature variations. Thus, this work is focused on improving time synchronization under deployments with temperature variations. The working hypothesis demonstrated in this work is that the clock skew of two nodes (the ratio of the real frequencies of the oscillators) is composed of a multiplicative combination of two main components: the clock skew due to the variations between the cut of the crystal of each oscillator and the clock skew due to the different temperatures affecting the nodes. By applying a nonlinear filtering, the homomorphic filtering, both components are separated in an effective way. A correction factor based on temperature, which can be applied to any synchronization protocol, is proposed. For testing it, an improvement of the FTSP synchronization protocol has been developed and physically tested under temperature variation scenarios using TelosB motes flashed with the IEEE 802.15.4 implementation supplied by TinyOS.