The development of advanced driver assistance systems (ADASs) will be a crucial element in the construction of future intelligent transportation systems with the objective of reducing the number of traffic accidents and their subsequent fatalities. Specifically, driving behaviors could be monitored online to determine the crash risk and provide warning information to the driver via their ADAS. In this paper, we focus on aggressiveness as one of the potential causes of traffic accidents. We demonstrate that aggressiveness can be detected by monitoring external driving signals such as lateral and longitudinal accelerations and speed. We model aggressiveness as a linear filter operating on these signals, thus scaling their probability distribution functions and modifying their mean value, standard deviation, and dynamic range. Next, we proceed to validate this model via an experiment, conducted under real driving conditions, involving ten different drivers, traveling a route with five different types of road sections, subject to both smooth and aggressive behaviors. The obtained results confirm the validity of the model of aggressiveness. In addition, they show the generality of this model and its applicability to specific driving signals (speed, longitudinal, and lateral accelerations), every single driver, and every road type. Finally, we build a classifier capable of detecting aggressive behavior from the driving signal. This classifier achieves a success rate up to 92%.

In this paper, a biometric technique based on gesture recognition is proposed to improve the security of operations requiring authentication in mobile phones. Users are authenticated by making a gesture invented by them holding a mobile phone embedding an accelerometer on their hand. An analysis method based on sequence alignment is proposed and evaluated in different experiments. Firstly, a test of distinctiveness of gestures has been proposed obtaining an equal error rate (EER) of 4.98% with a database of 30 users and four repetitions. With the same database, a second experiment representing the unicity of accessing attempts has resulted in an EER value of 1.92%. Finally, a third experiment to evaluate the robustness of the technique has examined a database of 40 users with eight repetitions and real falsification attempts, performed by three impostors from the study of recordings of the carrying out of the original gestures, resulting in an EER of 2.5%.

Due to the intensive use of mobile phones for different purposes, these devices usually contain confidential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner's gait. Nowadays, gait identification in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identification problem, they show difficulties when dealing with gaits at different speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at different velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at different motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

Elliptic Curve Cryptography (ECC) can be used as a tool for encrypting data, creating digital signatures or performing key exchanges. In relation to encryption, the Elliptic Curve Integrated Encryption Scheme (ECIES) is the best known scheme based on ECC, and as such it has been included in several cryptographic standards. In the present work, we provide an extensive review and comparison of the versions of ECIES included in documents from ANSI, IEEE, ISO/IEC, and SECG, highlighting the main differences between them that can prevent implementations of ECIES from being fully interoperable.

A work in defining and implementing a biometric system based on hand geometry identification is presented here. Hand features are extracted from a color photograph taken when the user has placed his hand on a platform designed for such a task. Different pattern recognition techniques have been tested to be used in classification and/or verification from Euclidean distance to neural networks. Experimental results, up to a 97 percent rate of success in classification, will show the possibility of using this system in medium/high security environments with full acceptance from all users.