Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.This work has been funded by FEDER funds and the Ministerio de Ciencia e Innovación of the Spanish Government under Project No. TIN2012-32039

The protection of processor-based systems to mitigate the harmful effect of transient faults (soft errors) is gaining importance as technology shrinks. At the same time, for large segments of embedded markets, parameters like cost and performance continue to be as important as reliability. This paper presents a compiler-based methodology for facilitating the design of fault-tolerant embedded systems. The methodology is supported by an infrastructure that permits to easily combine hardware/software soft errors mitigation techniques in order to best satisfy both usual design constraints and dependability requirements. It is based on a generic microprocessor architecture that facilitates the implementation of software-based techniques, providing a uniform isolated-from-target hardening core that allows the automatic generation of protected source code (hardened code). Two case studies are presented. In the first one, several software-based mitigation techniques are implemented and evaluated showing the flexibility of the infrastructure. In the second one, a customized fault tolerant embedded system is designed by combining selective protection on both hardware and software. Several trade-offs among performance, code size, reliability, and hardware costs have been explored. Results show the applicability of the approach. Among the developed software-based mitigation techniques, a novel selective version of the well known SWIFT-R is presented.

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.

There is an increasing concern about the mitigation of radiation effects in embedded systems. This fact is demanding new flexible design methodologies and tools that allow dealing with design constraints and dependability requirements at the same time. This paper presents a novel proposal to design radiation-tolerant embedded systems combining hardware and software mitigation techniques. A hardening infrastructure, which facilitates the design space exploration and the trade-offs analyses, has been developed to support this fault tolerance co-design approach. The advantages of our proposal are illustrated by means of a case study.