Intelligent tires can be employed for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading as well as tread wear monitoring. In this paper we develop a measurement system for intelligent tires equipped with a 3-dimensional piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.

Abstract. In modern medical science evaluation of electrocardiogram (ECG) has proven to be an important task for doctors. These signals contain valuable information on the patients' condition; however analysis of them has encountered numerous challenges, such as storage of long-term recordings, filtering, and segmentation of signals. Resolving these problems is important to ensure a high quality diagnosis. In this paper we propose an ECG analysis method which provides adequate solutions to all of these challenges. The proposed method is based upon the approximation theory in Hilbert spaces. Namely, using the affine transforms of orthonormal Hermite systems, the approach optimizes two free parameters. This is done in order to achieve the best approximation of the ECG signal using a fixed number of Fourier coefficients. The process of optimization is done using Particle Swarm Optimization (PSO), NelderMead (NM) simplex method, and Monte Carlo (MC) algorithm which are embedded into a matching pursuit framework. The former procedure guarantees both good compression ratio and high accuracy, while the latter segments the heartbeats. As it is shown by experiments, the proposed method achieves better results than previously known approaches.

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