Biblio

Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.

Physical-layer authentication techniques exploit the unique properties of the wireless medium to enhance traditional higher-level authentication procedures. We propose to reduce the higher-level authentication overhead by using a state-of-the-art multi-target tracking technique based on Gaussian processes. The proposed technique has the additional advantage that it is capable of automatically learning the dynamics of the trusted user's channel response and the time-frequency fingerprint of intruders. Numerical simulations show very low intrusion rates, and an experimental validation using a wireless test bed with programmable radios demonstrates the technique's effectiveness.

Physical-layer authentication techniques exploit the unique properties of the wireless medium to enhance traditional higher-level authentication procedures. We propose to reduce the higher-level authentication overhead by using a state-of-the-art multi-target tracking technique based on Gaussian processes. The proposed technique has the additional advantage that it is capable of automatically learning the dynamics of the trusted user's channel response and the time-frequency fingerprint of intruders. Numerical simulations show very low intrusion rates, and an experimental validation using a wireless test bed with programmable radios demonstrates the technique's effectiveness.