Alireza Pourafzal

Norwegian University of Science and Technology | NTNU · Faculty of Engineering

A novel low-cost microwave sensor system is proposed for accurate sensing of the real relative permittivity of materials under test (MUT). The proposed solution eliminates the need for using advanced measurement devices such as the vector network analyzer (VNA) for sensor characterization. The proposed sensor system is built on a software-defined r...

A deep learning method is developed for chaotic time series classification. We investigate the chaotic state of a dynamical system, based on the output of the system. One of the main obstacles in time series classification is mapping a high-dimensional vector into a scalar value. To reduce the dimensions, it is common to use an average pooling laye...

In this work, a hybrid radio frequency (RF)- and acoustic-based activity recognition system was developed to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living. We used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a c...

In this paper, the problem of entropy-based classification of time-series into stochastic, chaotic, and periodic is addressed, followed by proposing an alternative joint-entropy approach to time series classification. These data-driven methods describe the behavior of a signal, using the association of the entropy of a time-series with emergence an...

In this paper, a more accurate diagonal approximation of the covariance matrix in the frequency domain is investigated. For this purpose, First, the frequency snapshot model approximation is revised and the imposed error between the approximated and true value is formulated. Then, by using Taylor series, the problem of inverse matrix approximation...

Chaotic behavior may be observed in many natural and human-made time series, thus one of the first things to acquire is the knowledge on their chaotic behavior. Several approaches including model-based and data-driven classifications are utilized to address this issue; however, the computational burden arisen with the higher performance seems to be...